A Blockchain-Based Distributed Data Identifier Activity Consensus Method

Abstract

This application discloses a blockchain-based distributed data identifier activity consensus method, comprising: when a storage node is determined to be the rotating management storage node in the first round of the cycle, collecting the usage information of each data identifier stored on each storage node in the distributed storage network; determining the data identifier prefix activity level of each data identifier based on all collected usage information, and determining the target data identifier to be reclaimed based on the determined data identifier prefix activity level of each data identifier; reclaiming the target data identifier from the storage node where each target data identifier is located, and generating distributed ledger data of data identifiers corresponding to the first round of the cycle for storage in the blockchain network. This application reduces the redundancy burden of the data storage system, optimizes the long-term operational stability of the distributed system, and solves the problem of reduced data validity caused by the accumulation of zombie identifiers.

Description

Technical Field

[0001] This application belongs to the field of distributed identifier management, specifically relating to a blockchain-based distributed data identifier activity consensus method, system, device, equipment, and storage medium. Background Technology

[0002] In the development of distributed storage networks, data identification management has gradually become a key factor affecting system efficiency and resource utilization. Data identification not only undertakes the basic function of data addressing but also affects the security and verifiability of data circulation. However, with the expansion of data storage scale, identification management faces problems such as long-term occupation, resource waste, and lack of activity evaluation. These challenges directly affect the long-term stability and sustainability of distributed systems.

[0003] Existing data identifier management technologies primarily rely on centralized allocation mechanisms or hierarchical resolution schemes. Centralized management systems provide stable resolution capabilities, while hierarchical identifier resolution schemes can improve the structural nature of identifier management. In addition, decentralized identifier technologies exist, enabling the self-declaration and decentralized storage of data identifiers.

[0004] However, existing data identification management technologies generally lack reasonable recycling mechanisms, leading to long-term occupation of identifications and turning a large number of data identifications into "zombie identifications," which affects the recycling of identification resources. Summary of the Invention

[0005] This application aims to provide a blockchain-based distributed data identification activity consensus method, system, device, equipment, and storage medium, which at least solves the problem of reduced data validity caused by the accumulation of zombie identifiers.

[0006] In a first aspect, embodiments of this application disclose a blockchain-based distributed data identifier activity consensus method, applied to storage nodes in a distributed storage network, comprising:

[0007] When the storage node is determined to be the rotating management storage node in the first round of the cycle, the usage of each data identifier stored by each storage node in the distributed storage network is collected;

[0008] Based on all the collected usage data, determine the data identifier prefix activity level for each data identifier, and based on the determined data identifier prefix activity level for each data identifier, determine the target data identifier to be reclaimed;

[0009] The target data identifier is retrieved from the storage node where each target data identifier is located, and distributed ledger data corresponding to the first rotation period is generated for storage in the blockchain network; the distributed ledger data contains record information of the process of retrieving the target data identifier in the first rotation period.

[0010] Secondly, embodiments of this application also disclose a blockchain-based distributed data identification activity consensus system, comprising:

[0011] Multiple storage nodes used to form a distributed storage network, and a blockchain network;

[0012] The storage node is used to collect usage information of each data identifier stored on each storage node in the distributed storage network when the storage node is determined to be the rotating management storage node in the first rotation period. Based on all the collected usage information, it determines the data identifier prefix activity level of each data identifier, and based on the determined data identifier prefix activity level of each data identifier, it determines the target data identifier to be reclaimed. It then reclaims the target data identifier from the storage node where each target data identifier is located, and generates distributed ledger data for the data identifier corresponding to the first rotation period. The distributed ledger data for the data identifier contains record information about the process of reclaiming the target data identifier in the first rotation period.

[0013] The blockchain network is used to store the distributed ledger data.

[0014] Thirdly, embodiments of this application also disclose a blockchain-based distributed data identifier activity consensus device, applied to storage nodes in a distributed storage network, comprising:

[0015] The data collection module is used to collect the usage information of each data identifier stored by each storage node in the distributed storage network when the storage node is determined to be the rotating management storage node in the first rotation cycle;

[0016] The activity rating module is used to determine the activity level of the data identifier prefix for each data identifier based on all the collected usage data, and to determine the target data identifier to be recycled based on the determined activity level of the data identifier prefix for each data identifier.

[0017] The recycling record module is used to recycle the target data identifier from the storage node where each target data identifier is located, and generate distributed ledger data of the data identifier corresponding to the first rotation period for storage in the blockchain network; the distributed ledger data of the data identifier records the record information of the process of recycling the target data identifier in the first rotation period.

[0018] Fourthly, embodiments of this application also disclose an electronic device, including a processor and a memory, wherein the memory stores a program or instructions executable on the processor, and the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0019] Fifthly, embodiments of this application also disclose a readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the method described in the first aspect.

[0020] In summary, in this embodiment, by setting up rotating management of storage nodes, the usage status of identifiers for each storage node is collected periodically, giving the storage nodes dynamic monitoring capabilities. This allows for continuous tracking of the usage status of data identifiers, ensuring that the existence status of data identifiers is not in a state of uncertainty for a long period, thereby avoiding inefficient occupation of identifier resources. Furthermore, an activity evaluation mechanism is used to comprehensively analyze the access frequency of data identifiers, and target data identifiers to be recycled are accurately screened based on the activity level of the data identifier prefix, ensuring that low-activity identifiers can be identified and adjusted in a timely manner. A blockchain ledger mechanism is established in the data identifier recycling and storage process, making the identifier recycling process verifiable. By storing distributed ledger data corresponding to the recycling cycle in the blockchain network, the network-wide status information of the identifier lifecycle is obtained in real time, and the rationality of identifier recycling is verified. This not only improves the transparency of data storage but also prevents malicious nodes from manipulating identifier activity records, strengthening the fairness of the consensus mechanism. Therefore, based on the method of this embodiment, dynamic optimization of data identifier resources is achieved, ensuring that the data circulation system can continuously eliminate zombie identifiers and release idle resources. By leveraging the distributed storage characteristics of blockchain, data identifier management overcomes the drawbacks of traditional centralized control. While ensuring the right to autonomously allocate identifiers, it makes the activity evaluation and identifier recycling processes more intelligent and reliable. This ensures improved efficiency in data identifier management, reduces the redundancy burden on the data storage system, optimizes the long-term operational stability of the distributed system, and solves the problem of reduced data validity caused by the accumulation of zombie identifiers. Attached Figure Description

[0021] In the attached diagram:

[0022] Figure 1This is a flowchart illustrating the steps of a blockchain-based distributed data identifier activity consensus method provided in an embodiment of this application.

[0023] Figure 2 This is a flowchart illustrating the steps of another blockchain-based distributed data identifier activity consensus method provided in this application embodiment;

[0024] Figure 3 This is the process of determining an activity evaluation range according to the embodiments of this application;

[0025] Figure 4 This is a schematic diagram of the structure of a distributed ledger data according to an embodiment of this application;

[0026] Figure 5 This is an architecture diagram of a blockchain-based distributed data identification activity consensus system provided in an embodiment of this application;

[0027] Figure 6 This is a data flow diagram under the embodiments of this application;

[0028] Figure 7 This is a complete data information management cycle under the embodiments of this application;

[0029] Figure 8 This is a block diagram of a blockchain-based distributed data identification activity consensus device provided in an embodiment of this application;

[0030] Figure 9 This is a block diagram of an electronic device provided in one embodiment of this application. Detailed Implementation

[0031] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0032] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0033] Based on the above process, such as Figure 1 The image shows a blockchain-based distributed data identifier activity consensus method provided in this application embodiment, which is applied to storage nodes in a distributed storage network.

[0034] The method may include the following steps:

[0035] Step 101: If a storage node is determined to be the rotating management storage node in the first round of the cycle, collect the usage information of each data identifier stored by each storage node in the distributed storage network.

[0036] In some embodiments of this application, to ensure that the usage status of data identifiers can be accurately tracked, it is necessary to periodically collect the usage information of each data identifier stored on each storage node in the distributed storage network. Specifically, the rotating management storage node is responsible for counting the usage frequency and interaction records of data identifiers across all storage nodes during the first rotation period. This process enables the storage system to build a complete view of data availability, allowing subsequent processes to accurately identify inactive identifiers and make appropriate adjustments. Through periodic monitoring of storage nodes, the status of data identifiers can be dynamically updated, preventing identifiers from remaining idle for extended periods or failing to accurately assess their activity, thereby improving the efficient utilization of storage resources.

[0037] In a specific example, during the actual operation of a distributed storage network, a rotating management storage node receives data identifier usage information reported by other storage nodes. This information includes the access records of the data identifiers, the number of parsing requests, and the number of interacting nodes involved. The system can further analyze which data identifiers are in stable use and which are in a low-activity state, providing a reasonable basis for the subsequent identifier reclamation mechanism.

[0038] Step 102: Based on all the collected usage data, determine the activity level of the data identifier prefix for each data identifier, and based on the determined activity level of the data identifier prefix for each data identifier, determine the target data identifier to be recycled.

[0039] In some embodiments of this application, to effectively manage data identifier resources, it is necessary to assess the activity of each data identifier based on collected usage data, and determine the target data identifiers to be reclaimed accordingly. Specifically, the system analyzes the access data reported by storage nodes, summarizes the interaction frequency, number of parsing requests, and long-term access trends of each data identifier, and establishes an activity assessment model. This model calculates the activity score of the data identifier and divides it into different activity levels to accurately identify identifiers that have been inactive or even idle for a long time. Finally, based on the activity level of the data identifier, the system filters the target data identifiers to be reclaimed, enabling the distributed storage network to periodically release idle resources and maintain a reasonable data storage structure.

[0040] In a specific example, during a cycle of a distributed storage network, the rotating management storage node receives access logs from each node regarding data identifiers. These logs may include, for example, the number of parsing requests and the number of interacting nodes involved. Based on this data, the management node calculates an activity score for each data identifier, categorizes it into activity levels according to a preset scoring threshold, and marks low-activity data identifiers as objects to be reclaimed. This mechanism ensures that the storage system can continuously optimize the use of data identifiers, improve resource utilization efficiency, and maintain the stable operation of the distributed storage network.

[0041] Step 103: Retrieve the target data identifier from the storage node where each target data identifier is located, and generate distributed ledger data of the data identifier corresponding to the first round of value for storage in the blockchain network.

[0042] Among them, the distributed ledger data for data identification records information about the process of recovering the target data identification in the first value cycle.

[0043] In some embodiments of this application, to ensure that data identifier resources are managed reasonably and effectively reclaimed, the system needs to reclaim inactive identifiers from storage nodes and record detailed information about the reclamation process in the blockchain network. Specifically, the system identifies data identifiers to be reclaimed based on activity assessment results and instructs storage nodes to perform reclamation operations, releasing inactive data identifiers from the storage system. This process ensures that inactive identifiers do not occupy storage resources for extended periods and avoids data storage redundancy caused by the accumulation of zombie identifiers. To ensure the transparency and verifiability of the entire reclamation process, the system generates distributed ledger data for data identifiers while performing the reclamation operation and stores this data in the blockchain network, ensuring that all nodes can query and verify the entire process of identifier reclamation. The distributed ledger data for data identifiers includes the timestamp of identifier reclamation, the storage nodes involved, the target data identifier being reclaimed, and related operation records, ensuring the auditability of the data management process and preventing malicious manipulation.

[0044] In a specific example, during a rotation cycle of a distributed storage network, the rotating management storage node identifies multiple data identifiers with long-term low activity based on an activity evaluation model and notifies the corresponding storage nodes to perform identifier reclamation. Each storage node removes these identifiers as instructed and generates identifier reclamation records, which include the identifier's unique identifier, reclamation time, and storage location change information. These records are then packaged and stored in the blockchain network's distributed ledger, allowing all storage nodes to query and verify the reclamation process. In subsequent rotation cycles, the system can adjust the data identifier allocation strategy based on this storage data to ensure the rational use of data storage resources and further optimize storage efficiency in the decentralized environment.

[0045] In summary, in this embodiment, by setting up rotating management of storage nodes, the usage status of identifiers for each storage node is collected periodically, giving the storage nodes dynamic monitoring capabilities. This allows for continuous tracking of the usage status of data identifiers, ensuring that the existence status of data identifiers is not in a state of uncertainty for a long period, thereby avoiding inefficient occupation of identifier resources. Furthermore, an activity evaluation mechanism is used to comprehensively analyze the access frequency of data identifiers, and target data identifiers to be recycled are accurately screened based on the activity level of the data identifier prefix, ensuring that low-activity identifiers can be identified and adjusted in a timely manner. A blockchain ledger mechanism is established in the data identifier recycling and storage process, making the identifier recycling process verifiable. By storing distributed ledger data corresponding to the recycling cycle in the blockchain network, the network-wide status information of the identifier lifecycle is obtained in real time, and the rationality of identifier recycling is verified. This not only improves the transparency of data storage but also prevents malicious nodes from manipulating identifier activity records, strengthening the fairness of the consensus mechanism. Therefore, based on the method of this embodiment, dynamic optimization of data identifier resources is achieved, ensuring that the data circulation system can continuously eliminate zombie identifiers and release idle resources. By leveraging the distributed storage characteristics of blockchain, data identifier management overcomes the drawbacks of traditional centralized control. While ensuring the right to autonomously allocate identifiers, it makes the activity evaluation and identifier recycling processes more intelligent and reliable. This ensures improved efficiency in data identifier management, reduces the redundancy burden on the data storage system, optimizes the long-term operational stability of the distributed system, and solves the problem of reduced data validity caused by the accumulation of zombie identifiers.

[0046] Figure 2 This application provides another blockchain-based distributed data identifier activity consensus method, which is applied to storage nodes in a distributed storage network.

[0047] The method may include the following steps:

[0048] Step 201: If a storage node is determined to be the rotating management storage node in the first round of the cycle, collect the usage information of each data identifier stored by each storage node in the distributed storage network.

[0049] The method shown in this step has been explained in step 101 and will not be repeated here.

[0050] Step 202: Based on all the collected usage data, determine the activity level of the data identifier prefix for each data identifier, and based on the determined activity level of the data identifier prefix for each data identifier, determine the target data identifiers to be recycled.

[0051] The method shown in this step has been explained in step 102 and will not be repeated here.

[0052] Optionally, in order to determine the data identifier prefix activity level for each data identifier based on all collected usage data, step 202 includes the following sub-steps:

[0053] Sub-step 2021: Determine the activity evaluation value for each data identifier based on at least one of the following: access frequency, access node diversity, time domain distribution, and abnormal access situation during the first round of value cycle.

[0054] In some embodiments of this application, to accurately assess the activity of data identifiers, the system needs to calculate the activity evaluation value of the data identifiers based on access characteristics across multiple dimensions. Specifically, the system comprehensively calculates the activity evaluation value for each data identifier based on its access frequency, access node diversity, temporal distribution, and abnormal access patterns within the first round of evaluation, ensuring accurate quantification of data identifier usage. Access frequency represents the number of times a data identifier is accessed within a period, reflecting its usage frequency; access node diversity measures the number of different nodes accessing the identifier, indicating the dispersion and sharing degree of the data; temporal distribution is used to analyze the stability of access behavior, avoiding scoring distortion caused by short-term surges in access; abnormal access patterns are used to detect potential malicious access behavior and adjust the scores of data with high abnormal access rates. The calculation results of these evaluation dimensions collectively constitute the activity evaluation value of the data identifiers, enabling the system to objectively judge the usage of data identifiers and providing data basis for subsequent activity level classification and identifier recycling decisions.

[0055] In a specific example, during the operation of a distributed storage network, a storage node manages multiple data identifiers and periodically records their access activity. In the first monitoring cycle, this node counted 200 accesses for a particular data identifier, involving 50 different nodes. The access times were relatively evenly distributed, and no abnormal access behavior was detected. The system calculates its activity rating based on the access characteristics of this data identifier and compares it with the ratings of other identifiers to determine its activity level. In subsequent identifier management, this activity rating will be used to divide the activity range, providing a basis for deciding whether to reclaim the identifier. This calculation process ensures that the system can reasonably assess the usage of data identifiers and improves the scientific nature and transparency of identifier management.

[0056] Optionally, in a further embodiment, the activity evaluation value for each data identifier is determined based on the access frequency, access node diversity, temporal distribution, and abnormal access status of each data identifier within the first round of value cycle. In this case, sub-step 2021 includes the following sub-steps:

[0057] Sub-step 20211 determines the access frequency evaluation value used to evaluate the access frequency of the data identifier, the diversity evaluation value used to evaluate the diversity of the access nodes of the data identifier, the stability evaluation value used to evaluate the temporal distribution stability of the access process of the data identifier in the first round of value period, and the abnormal access weight used to evaluate the abnormal access of the data identifier.

[0058] In some embodiments of this application, to accurately assess the activity of data identifiers, the system needs to calculate the usage of data identifiers from multiple dimensions to ensure that the assessment results comprehensively reflect the actual activity status of the data. Specifically, the system determines several key evaluation indicators for measuring activity, including access frequency evaluation value, diversity evaluation value, stability evaluation value, and abnormal access weight. The access frequency evaluation value quantifies the number of times a data identifier is accessed within a specific period to reflect its basic usage; the diversity evaluation value measures the number of storage nodes accessing the data identifier, ensuring the breadth of data distribution; the stability evaluation value focuses on the temporal distribution of access behavior to avoid scoring anomalies caused by short-term surges in access; and the abnormal access weight is used to detect whether a data identifier has suffered abnormal access and adjusts the weights of other evaluation values ​​according to the degree of abnormality. These evaluation indicators ensure that the system can establish an accurate activity model, making activity analysis more comprehensive and improving the rationality of data identifier management.

[0059] In a specific example, during the operation of a distributed storage network, the system statistically analyzes the usage of a certain data identifier during the first value cycle, and:

[0060] According to the formula Calculate the access frequency evaluation value ,

[0061] in It is the number of times the data identifier is accessed within the first round of the cycle;

[0062] According to the formula Calculate the diversity evaluation value ,

[0063] in It is the number of nodes accessing the data identifier during the first round of the evaluation period;

[0064] According to the formula Calculate stability evaluation value ,

[0065] in, This represents the average number of visits across all time intervals within this period. This represents the standard deviation of the number of visits in each time interval within this period;

[0066] According to the formula Calculate the weight of abnormal access Ultimately, these calculated evaluation values ​​can be used to divide activity ranges, ensuring that the data storage system can reasonably classify and identify data, thus providing support for subsequent resource management decisions.

[0067] In sub-step 20212, the activity evaluation value is determined by the weighted result of the access frequency evaluation value, diversity evaluation value, and stability evaluation value after the abnormal access weight is adjusted.

[0068] In some embodiments of this application, to ensure that the activity evaluation of data identifiers accurately reflects their actual usage, the system needs to integrate multiple evaluation values ​​and adjust their weights to generate a final activity evaluation value. Specifically, the system first adjusts the access frequency evaluation value, diversity evaluation value, and stability evaluation value according to the abnormal access weight to reduce the impact of abnormal access behavior on the evaluation results. Subsequently, the system performs a weighted calculation on these adjusted evaluation values ​​according to preset weights to ensure that the impact of each evaluation indicator on the activity evaluation conforms to the actual data usage patterns. Finally, the system determines the activity evaluation value for each data identifier based on the weighted calculation results, so that the evaluation value can comprehensively reflect the access status of the data identifier and ensure that the subsequent activity level classification is more reasonable. Through this calculation method, the system effectively reduces the impact of a single indicator on the evaluation results, ensures that the activity evaluation of data identifiers can adapt to complex access behavior patterns, and improves the accuracy of data storage management.

[0069] In a specific example, during an activity assessment in a distributed storage network, the system calculates access frequency, diversity, and stability scores for a given data identifier, and also determines an abnormal access weight adjustment coefficient. This can be done using the following formula:

[0070] Calculate activity rating ,in, , , These are the preset weights. This activity evaluation value will be used in subsequent steps for interval division and will affect the prefix activity level of data identifiers, ensuring that the storage system can accurately manage data identifier resources, improve storage efficiency, and optimize identifier lifecycle management strategies.

[0071] Sub-step 2022: Divide the total activity ratings into multiple consecutive activity rating intervals based on the highest activity rating among all the determined activity ratings.

[0072] Each activity evaluation interval has a corresponding prefix activity level; the order of the prefix activity levels is negatively correlated with the order of the corresponding activity evaluation intervals arranged from smallest to largest.

[0073] In some embodiments of this application, to effectively distinguish the activity levels of data identifiers, the system needs to divide the data identifier status into multiple consecutive activity evaluation intervals based on a determined activity evaluation value, enabling reasonable classification of the data identifier status. Specifically, the system first calculates the activity evaluation value of all data identifiers and determines the maximum activity evaluation value as the benchmark for interval division. Subsequently, the system constructs multiple consecutive intervals based on this maximum activity evaluation value, with each interval representing a different activity level, arranged sequentially from the lowest to the highest activity level. This division method ensures that different data identifiers can be assigned to the corresponding level according to their actual activity, making subsequent identifier management more accurate and dynamic adjustments more reasonable. The division of activity evaluation intervals directly affects the management strategy of data identifier prefixes, ensuring that high-activity identifiers are given priority for retention, while low-activity identifiers can be reasonably reclaimed, thereby improving the utilization efficiency of storage resources.

[0074] like Figure 3 As shown, in a specific example, in a distributed storage network, the system collects activity evaluation values ​​for multiple data identifiers within a period and determines the highest evaluation value. Based on this value, the system divides the system into multiple activity ranges, for example from... arrive The range is divided into intervals 0 to 4. Based on this distribution, the system assigns different activity levels to each data identifier, categorizing low-activity data identifiers into lower levels and high-activity data identifiers into higher levels. In subsequent identifier management, this classification will be used to determine whether data identifiers need to be reclaimed or continued to be stored, thereby optimizing the data management strategy of the storage network. Through this method, the system can dynamically adjust the lifecycle of data identifiers, achieve rational resource allocation, and ensure the stability of data flow.

[0075] Sub-step 2023: Based on the activity evaluation range into which each activity evaluation value falls, determine the data identifier prefix activity level corresponding to each activity evaluation value.

[0076] In some embodiments of this application, to accurately determine the activity status of data identifiers, the system needs to assign corresponding prefix activity levels based on the interval in which the activity evaluation value falls. Specifically, the system first categorizes the activity evaluation values ​​of all data identifiers and checks the activity interval in which each evaluation value falls. Then, according to the hierarchical order of the activity intervals, the system assigns corresponding prefix activity levels to the data identifiers, enabling the identifier management system to optimize storage strategies in a targeted manner. The prefix activity level is an important classification indicator for data identifiers, and it has a negative correlation with the ranking of the activity evaluation intervals, resulting in lower-level identifiers with low activity and higher-level identifiers with high activity. This process ensures more refined management of data identifiers and provides a clear basis for subsequent identifier recycling or retention decisions.

[0077] In a specific example, during one cycle of a distributed storage network, the system calculates the activity evaluation values ​​of multiple data identifiers and matches each evaluation value with a corresponding prefix activity level based on previously defined activity intervals. For instance, if a data identifier's evaluation value falls within interval θ2, it is assigned a prefix activity level of 3, while another identifier's evaluation value falls within interval θ4 and is classified as prefix activity level 1. In subsequent data storage management, this classification result will be used for optimized allocation of identifier resources, enabling the system to make more rational decisions about which identifiers need to be retained and which need to be reclaimed, thereby improving the operational efficiency of the data storage network.

[0078] Considering that the activity assessment of data identifiers requires long-term trend analysis capabilities to avoid short-term fluctuations affecting the assessment results, and to ensure the consistency and traceability of the recovery strategy, the following sub-steps are included prior to sub-step 2022:

[0079] Sub-step 2024: Obtain distributed historical data to record information about the process of recovering target data identifiers in the third round of value cycle.

[0080] The third round of duty is the round of duty preceding the first round of duty.

[0081] In some embodiments of this application, to improve the accuracy of data identifier activity assessment, the system needs to refer to historical data to ensure that the assessment process is not only based on the data of the current period but also optimized by combining data trends from previous periods. Specifically, the system obtains distributed historical data from the blockchain network to record the detailed process of reclaiming target data identifiers in the third round of evaluation. This distributed historical data includes identifier reclamation records, activity scores, and related access logs from previous periods, enabling the system to establish long-term activity trend analysis of data identifiers. This acquisition process provides a reliable reference for subsequent activity evaluations, allowing the system to optimize the identifier evaluation results of the current period based on historical activity data, thereby improving the rationality of the activity evaluation and ensuring the accuracy and consistency of identifier reclamation decisions.

[0082] In a specific example, during the operation of a distributed storage network, before evaluating the activity of data identifiers in the first cycle, the system extracts activity data from the historical records of the blockchain storage for the third cycle. This data includes information such as reclaimed identifiers, their activity evaluation process, access counts, and storage node interactions. The system uses this data to compare with the identifier activity data for the current cycle to determine whether the current activity score conforms to the long-term trend. For example, if a data identifier has a high activity score in the third cycle but its score drops sharply in the current cycle, the system can further analyze whether there are abnormal access patterns, rather than directly classifying the identifier as a low-activity identifier. This process ensures that the activity evaluation is more robust and avoids unreasonable reclamation decisions caused by short-term abnormal fluctuations.

[0083] Sub-step 2025: Adjust each determined activity evaluation value based on distributed historical data.

[0084] In some embodiments of this application, to improve the accuracy of data identifier activity evaluation and avoid the impact of short-term fluctuations on the evaluation results, the system needs to optimize and adjust the current activity evaluation value by combining historical data. Specifically, the system compares the current activity evaluation value of each data identifier with distributed historical data and adjusts the evaluation results in conjunction with the data trends of previous rotation periods. Historical data includes identifier access records, parsing times, storage node interaction information, etc., from previous periods, enabling the system to identify long-term activity trends and avoid misclassifying short-term declining data identifiers as low-activity. Simultaneously, the system can also detect abnormal fluctuations through historical data to ensure that the activity evaluation results are more stable. Ultimately, this adjustment process makes the activity evaluation more reliable, optimizes data identifier management decisions, and improves the rationality of identifier recycling.

[0085] In a specific example, during an activity assessment of a distributed storage network, the system calculated a significantly lower activity rating for a certain data identifier compared to the previous period. The system queried historical distributed data and found that this data identifier had maintained a high access frequency for several past periods, only experiencing a brief decrease in the current period. Based on this historical data, the system appropriately adjusted the activity rating for this data identifier to maintain a high activity level, rather than immediately classifying it as low-activity and adding it to the recycling list. This adjustment avoided the erroneous recycling of data identifiers due to short-term access fluctuations, ensuring that the system could more accurately identify the true activity level and improve the stability and resource utilization efficiency of data storage management.

[0086] Optional, such as Figure 4 As shown, in a further embodiment, distributed ledger data (i.e., blocks in the blockchain) is generated as follows: To ensure the transparency of the data identifier recycling and storage process, the system uses a blockchain structure to record identifier activity evaluation and recycling decision information. The block header records the hash value of the previous block, the information of the rotating management storage node, and the Merkle root attribute to ensure data integrity and chain traceability. The block body includes target storage node information, data prefix activity score, storage node evaluation value, and access log statistics uploaded by each storage node. Simultaneously, the system uses a Merkle tree structure to organize the hash data of storage node evaluation values, making data verification more efficient and preventing data tampering. Through blockchain storage, the system can record the entire data identifier recycling process and provide verifiable storage credentials, allowing all participating nodes to share this data and ensuring the fairness and auditability of data identifier management.

[0087] Inspired by the aforementioned blockchain storage model, the distributed historical data records the historical activity evaluation value of each data identifier in the third value cycle. Sub-step 2025 then includes the following sub-steps:

[0088] Sub-step 20251 involves weighting the activity evaluation value and historical activity evaluation value of the data identifier that coexist in the first and third value cycles according to a preset smoothing weight.

[0089] In some embodiments of this application, to improve the stability of data identifier activity evaluation and ensure that short-term fluctuations do not affect the evaluation results, the system needs to smooth the current activity evaluation value by combining historical data. Specifically, the system weights the activity evaluation values ​​of data identifiers that coexist in the first and third evaluation periods with historical activity evaluation values ​​to synthesize recent and long-term activity trends. This weighting calculation is based on preset smoothing weight parameters, enabling evaluation values ​​from different time periods to be merged according to set weights, ensuring that the evaluation results reflect both the current activity status and long-term trends. The smoothing weight setting is used to control the degree of influence of historical data on the current evaluation, making the activity of data identifiers change more smoothly over time and avoiding misjudgments caused by short-term anomalies.

[0090] In a specific example, during an activity assessment in a distributed storage network, the system calculates an activity evaluation value of 0.70 for a certain data identifier in the current period. Simultaneously, it queries historical data from the blockchain storage and finds that the historical activity evaluation value for this identifier in the third evaluation period was 0.85. Based on a preset smoothing weight, the system sets the weight of the current evaluation value to 0.6 and the weight of the historical evaluation value to 0.4, and performs a weighted calculation, obtaining a weighted activity evaluation value of 0.76. This result is used for subsequent activity interval division, enabling the data storage system to reasonably classify data identifiers and ensuring that short-term access anomalies do not affect the long-term stability of identifier management. This mechanism optimizes the resource allocation strategy of the data storage network and improves the rationality of identifier lifecycle management.

[0091] Sub-step 20252 determines the updated value of the activity evaluation value of the data identifier by weighting the result according to the smoothing weight.

[0092] In some embodiments of this application, to ensure that the activity evaluation of data identifiers accurately reflects long-term trends while reducing the impact of short-term fluctuations on the evaluation results, the system needs to update the activity evaluation value of the data identifiers based on the weighted calculation results. Specifically, the system determines the final activity evaluation value of the data identifier based on the previously calculated weighted result, and uses it for subsequent activity level classification and identifier reclamation decisions. This update process ensures that the activity evaluation can be adjusted to the current evaluation results in conjunction with historical data, making the identifier management of the storage system more refined and improving the ability to rationally allocate storage resources. In addition, this method also avoids erroneous reclamation decisions caused by short-term abnormal access, enabling the system to more accurately identify and manage the lifecycle of data identifiers.

[0093] In a specific example, during an evaluation of a distributed storage network, the system calculates a weighted activity rating of 0.76 for a data identifier and updates its final evaluation result accordingly. This updated value will be used in subsequent steps to define activity ranges and determine the data identifier's continued existence. For instance, if the identifier's activity rating remains high, the system retains the identifier; however, if the value falls below a set threshold, the identifier may be reclaimed. In this way, the system can adjust its identifier management strategy based on reasonable data trends, improving the stability of the distributed storage network and optimizing the sustainability of data storage.

[0094] The above process can be followed The formula is used for calculation, where This is the adjusted activity rating. It is distributed historical data. It is an adjustment coefficient used to smooth the evaluation value in a statistical sense.

[0095] Step 203: Retrieve the target data identifier from the storage node where each target data identifier is located, and generate distributed ledger data of the data identifier corresponding to the first round of value for storage in the blockchain network.

[0096] Among them, the distributed ledger data for data identification records information about the process of recovering the target data identification in the first value cycle.

[0097] The method shown in this step has been explained in step 103 and will not be repeated here.

[0098] Step 204: Based on all the collected usage data, determine the node activity evaluation value of each storage node, and based on the determined node activity evaluation value of each storage node, determine multiple target storage nodes from the storage nodes as candidate storage nodes for the second round of rotation management storage nodes, and randomly select the second round of rotation management storage nodes from the multiple target storage nodes.

[0099] The second round of duty is the next round of duty after the first round of duty.

[0100] In some embodiments of this application, to ensure the dynamic updating of the rotating management storage node and to allow different nodes to participate in the task of managing storage identifiers in different periods, the system needs to determine the node activity evaluation value of each storage node based on all collected usage data. Based on the determined node activity evaluation value, the system identifies multiple target storage nodes from the storage nodes as candidate storage nodes for the rotating management storage node in the second round. Finally, the rotating management storage node for the second round is randomly selected from these target storage nodes. Specifically, the system filters multiple target storage nodes based on their node activity evaluation values. Subsequently, the system randomly selects one of the selected target storage nodes as the rotating management storage node for the second round, ensuring the fairness and decentralization of node election. This process prevents the rotating management storage node from being occupied by a fixed node for a long period, thereby reducing the risk of centralization in system operation and enabling different storage nodes to dynamically participate in the identifier management task. By reasonably adjusting the election rules for the rotating management storage node, the balance of the distributed storage network can be improved, while optimizing the lifecycle management of data identifiers.

[0101] In a specific example, after one cycle of the distributed storage network, the system first analyzes the data identifier activity of each storage node and selects the storage nodes managing highly active data identifiers as target storage nodes. Within this set of target storage nodes, the system can use verifiable random function (VRF) technology to generate random numbers to determine the rotating management storage node for the next cycle. For example, the system can first generate an asymmetric key pair as a verification key using the elliptic curve secp256r1 (in the form y² = x³ + ax + b), containing a public key and a private key. Then, based on the data identifier prefix activity level determined in the previous steps, five target storage nodes are selected. The system then calculates the difference between each node and its node number using preset random adaptation functions VRF_Output (to obtain random numbers) and VRF_Proof (to prove the validity of random numbers), and selects the storage node with the smallest difference as the rotating management storage node for the next cycle. Subsequently, this selected node assumes management tasks in the new cycle and is responsible for collecting the usage information of data identifiers across the entire network. In this way, the system can ensure the fairness of the election of rotating management storage nodes, while avoiding fixed nodes bearing management tasks for a long time, thus improving the sustainable operation capability of the distributed storage network.

[0102] Optionally, step 204 includes the following sub-steps:

[0103] Sub-step 2041: Determine the node activity evaluation value for each storage node based on at least one of the following: the access frequency of each storage node during the first round of evaluation, the diversity of accessing nodes, and the distribution of user access.

[0104] In some embodiments of this application, to reasonably assess the activity of storage nodes and ensure that the election of rotating storage nodes accurately reflects their long-term contributions, the system needs to calculate the node activity evaluation value based on multiple key factors. Specifically, the system comprehensively calculates the node activity evaluation value based on the access frequency, access node diversity, and user access distribution of each storage node during the first rotation period. Access frequency quantifies the number of times a node processes data identifiers during this period to reflect the node's basic data interaction level; access node diversity measures the different storage sources accessing the node to ensure the balance of data flow; user access distribution is used to analyze the information entropy of access behavior to avoid excessively high or low access concentration affecting the rationality of the score. The calculation results of these evaluation indicators together constitute the storage node activity evaluation value, enabling the system to accurately determine the usage of each node and provide a basis for the subsequent election of rotating storage nodes.

[0105] In a specific example, within one cycle of a distributed storage network, the system collects access behavior data for each storage node. Furthermore:

[0106] According to the formula Calculate the access frequency evaluation value ,

[0107] in It is the number of times the storage node is accessed during the first round of evaluation;

[0108] According to the formula Calculate the diversity evaluation value ,

[0109] in This is the number of access nodes counted for the storage nodes during the first round of evaluation.

[0110] According to the formula Calculate the user request information entropy, which characterizes the distribution of user access to storage nodes. ,

[0111] in This represents the proportion of storage node accesses in the total accesses. Then, it can be calculated using the formula... The node activity evaluation value of each storage node is obtained. Ultimately, these calculated evaluation values ​​can be used to assess the activity level of each node, ensuring that the data storage system can rationally select target storage nodes and providing support for subsequent voting and election decisions.

[0112] Sub-step 2042: Based on the determined node activity evaluation value for each storage node, and according to the preset threshold for the number of candidate nodes, determine multiple target storage nodes from all storage nodes.

[0113] In some embodiments of this application, in order to reasonably select candidate storage nodes and ensure that the election process of the rotating management storage nodes meets the system's operational requirements, the system needs to filter target storage nodes based on node activity evaluation values. Specifically, the system selects a group of target storage nodes from all storage nodes according to the determined node activity evaluation value of each storage node and a preset threshold for the number of candidate nodes. This filtering rule ensures that candidate nodes are selected only from storage nodes with high activity, so as to ensure that the management node in the new rotation cycle can effectively perform data identification collection, activity assessment, and identification recycling tasks. The preset threshold for the number of candidate nodes is used to limit the number of target storage nodes, ensuring that there is no over-screening or insufficient options during the election process, thereby making the election process more stable and fair.

[0114] In a specific example, within a cycle of a distributed storage network, the system filters storage nodes based on their activity scores, with a threshold set for the top 10 highest-scoring nodes as the candidate nodes. After analyzing the access frequency, diversity score, and stability score of each node, the system selects 10 highly active storage nodes as target storage nodes. These nodes will participate in a random election process in subsequent steps. Through this selection strategy, the system ensures that management tasks are undertaken by highly active nodes, while improving the sustainability of data storage and optimizing the rational allocation of storage resources.

[0115] Sub-step 2043: Randomly select one of the target storage nodes as the rotation management storage node for the second rotation cycle.

[0116] In some embodiments of this application, to ensure the fairness and decentralization of the election process for rotating management storage nodes, the system needs to randomly select one node from the selected target storage nodes as the management node for the next round. Specifically, the system first confirms the candidate qualifications of all target storage nodes and uses a verifiable random function (VRF) to calculate a random number for each node to ensure that the election result is unpredictable and fair. The system then compares the random numbers of each storage node, selects the storage node that meets the set election rules, and determines it as the rotating management storage node for the next round. This random election mechanism can prevent fixed nodes from occupying management tasks for a long time, while enhancing the rotation of storage nodes, making the management of the storage network more decentralized, and improving the overall fairness and reliability of the operation.

[0117] In a specific example, during a node election in a distributed storage network, the system selects 10 highly active storage nodes as target storage nodes. The system then uses each storage node's public key and current timestamp to calculate a VRF random number and compares the random number results from all candidate nodes. Ultimately, the system selects the node whose random number conforms to the optimal distribution rule as the new rotating management storage node, enabling that node to assume management responsibilities in the next cycle. Through this election method, the system ensures the fairness of storage node elections, avoids human manipulation, and improves the stability and transparency of storage management.

[0118] Optionally, similar to substep 2025, the node activity evaluation value can be adjusted before determining the target storage node:

[0119] Sub-step 2044: Adjust the activity evaluation value of each node based on the distributed historical data.

[0120] In some embodiments of this application, to improve the accuracy of node activity evaluation and avoid the impact of short-term fluctuations on the evaluation results, the system needs to optimize and adjust the current node activity evaluation value by combining historical data. Specifically, the system compares the current node activity evaluation value of each storage node with distributed historical data and adjusts the evaluation results by combining the data trends of previous rotation cycles. This enables the system to identify long-term activity trends and avoids misclassifying short-term declines in data as low activity. Simultaneously, the system can also detect abnormal fluctuations through historical data to ensure that the node activity evaluation results are more stable. Ultimately, this adjustment process makes the activity evaluation more reliable, optimizes data labeling management decisions, and improves the rationality of label reclamation.

[0121] In a specific example, during an activity assessment of a distributed storage network, the system calculated a significantly lower activity rating for a particular storage node compared to the previous period. The system then queried historical distributed data and found that this storage node had maintained a high access frequency for several previous periods, experiencing only a brief decrease in the current period. Based on this historical data, the system appropriately adjusted the activity rating of this storage node to maintain a high activity rating, rather than immediately disqualifying it from being a candidate node. This adjustment avoided the unintended loss of candidate status due to short-term access fluctuations, ensuring that the system could more accurately identify the true activity level and improve the stability and resource utilization efficiency of data storage management.

[0122] The above process can be followed The formula is used for calculation, where This is the adjusted node activity evaluation value. It refers to the distributed historical data of the nodes. It is an adjustment coefficient used to smooth the evaluation value in a statistical sense.

[0123] In summary, in this embodiment, by setting up rotating management of storage nodes, the usage status of identifiers for each storage node is collected periodically, giving the storage nodes dynamic monitoring capabilities. This allows for continuous tracking of the usage status of data identifiers, ensuring that the existence status of data identifiers is not in a state of uncertainty for a long period, thereby avoiding inefficient occupation of identifier resources. Furthermore, an activity evaluation mechanism is used to comprehensively analyze the access frequency of data identifiers, and target data identifiers to be recycled are accurately screened based on the activity level of the data identifier prefix, ensuring that low-activity identifiers can be identified and adjusted in a timely manner. A blockchain ledger mechanism is established in the data identifier recycling and storage process, making the identifier recycling process verifiable. By storing distributed ledger data corresponding to the recycling cycle in the blockchain network, the network-wide status information of the identifier lifecycle is obtained in real time, and the rationality of identifier recycling is verified. This not only improves the transparency of data storage but also prevents malicious nodes from manipulating identifier activity records, strengthening the fairness of the consensus mechanism. Therefore, based on the method of this embodiment, dynamic optimization of data identifier resources is achieved, ensuring that the data circulation system can continuously eliminate zombie identifiers and release idle resources. By leveraging the distributed storage characteristics of blockchain, data identifier management overcomes the drawbacks of traditional centralized control. While ensuring the right to autonomously allocate identifiers, it makes the activity evaluation and identifier recycling processes more intelligent and reliable. This ensures improved efficiency in data identifier management, reduces the redundancy burden on the data storage system, optimizes the long-term operational stability of the distributed system, and solves the problem of reduced data validity caused by the accumulation of zombie identifiers.

[0124] refer to Figure 5 It illustrates a blockchain-based distributed data identification activity consensus system 30 provided in an embodiment of this application, comprising:

[0125] Multiple storage nodes A used to form the distributed storage network 301 i (i=0,…,n), and blockchain network 302;

[0126] Storage Node A i Used in storage node A i If A0 is identified as the rotating management storage node in the first rotation cycle, collect data from each storage node A in the distributed storage network 301. i The usage status of each stored data identifier is determined, and based on the collected usage data, the activity level of the data identifier prefix for each data identifier is determined. Then, based on the determined activity level of the data identifier prefix for each data identifier, the target data identifiers to be reclaimed are identified, and data is retrieved from storage node A where each target data identifier resides. iThe target data identifier is recovered, and distributed ledger data X0 corresponding to the first value cycle is generated; the distributed ledger data contains record information about the process of recovering the target data identifier in the first value cycle;

[0127] Blockchain network 302 is used to store distributed ledger data X0.

[0128] Based on the above system architecture, refer to Figure 6 This is a data flow process under the embodiments of this application:

[0129] R1, Data Collection and Activity Assessment: In a distributed storage network, each storage node A i (i=0,…,n) stores multiple data identifiers. When a certain storage node A... i When a storage node A0 is identified as the rotating management node for the current rotation period, this node is responsible for collecting the identifier usage information of all storage nodes in the entire network. The system calculates the activity evaluation value of each data identifier based on factors such as access frequency, diversity of accessing nodes, stability of temporal distribution, and abnormal access, and classifies them into different activity levels to facilitate the execution of subsequent identifier reclamation strategies.

[0130] R2. Data Identifier Reclamation and Distributed Ledger Data Generation: The rotating management storage node A0 filters low-activity identifiers based on their assessed activity levels and instructs other storage nodes to reclaim these target data identifiers. After reclamation, the system generates corresponding distributed ledger data X0, recording the data identifier's lifecycle information, including reclamation time, involved storage nodes, and access behavior statistics. This data is used for identifier resource management and historical trend analysis to optimize storage strategies for future rotation cycles.

[0131] R3. Data is stored on a blockchain network: The generated distributed ledger data X0 is stored in the blockchain network, ensuring that all storage nodes can query and verify the data identifier management process. The blockchain network records the identifier storage and retrieval status for each rotation cycle, making the data identifier lifecycle transparent and auditable, thereby enhancing the credibility of data management and providing an efficient and fair mechanism for collaboration among storage nodes.

[0132] refer to Figure 7 This refers to a complete data information management cycle under the embodiments of this application:

[0133] S1. In a distributed storage network, the usage of data identifiers needs to be tracked periodically to ensure that the system can dynamically adjust storage strategies: Each storage node records its own access log, including the number of queries for data identifiers, the access source node, and the time distribution. This data is used to evaluate the activity of the nodes and provide a basis for global storage status analysis. The rotating management storage node collects access log data from the entire network and integrates the access records of all storage nodes to form a complete data interaction view. This statistical data is not only used for activity evaluation but also for subsequent storage resource optimization decisions.

[0134] S2. To identify low-activity identifiers and determine whether to reclaim them, the system needs to calculate the activity scores of data identifiers and storage nodes: Each storage node calculates its activity score based on its access records, considering access frequency, access node diversity, and stability to ensure that the node's usage of storage resources can be accurately quantified; the prefix activity score of a data identifier is calculated based on the comprehensive access status of all data identifiers under that prefix, used to determine the overall activity level of the prefix, in order to distinguish between frequently used data identifiers and long-term idle identifiers; the system divides different activity levels according to the prefix score to ensure that low-activity identifiers can be accurately identified, and the activity level is set into multiple intervals to make identifier management more flexible; for data identifiers assessed as low-activity, the system adds them to the list to be reclaimed, and releases storage resources after the storage node performs the reclamation operation, while generating distributed ledger data to ensure that the management process is traceable.

[0135] S3. To maintain decentralization and ensure that different nodes have the opportunity to participate in storage management, the system needs to elect new rotating management storage nodes: The system selects several high-scoring storage nodes from the storage node rating data of the previous cycle as a candidate set. These nodes have high activity and are capable of performing management tasks. The system uses a verifiable random function to elect the candidate storage nodes and determine the rotating management storage node for the next cycle. This election method ensures that the selection of storage nodes is fair and decentralized, avoids fixed nodes occupying management tasks for a long time, and ensures the long-term stability of the storage network.

[0136] refer to Figure 8 This application illustrates a blockchain-based distributed data identifier activity consensus device 40, applied to storage nodes in a distributed storage network, comprising:

[0137] Data collection module 401 is used to collect the usage of each data identifier stored by each storage node in the distributed storage network when the storage node is determined to be the rotating management storage node in the first round of the rotation period;

[0138] The activity rating module 402 is used to determine the activity level of the data identifier prefix for each data identifier based on all the collected usage data, and to determine the target data identifier to be recycled based on the determined activity level of the data identifier prefix for each data identifier.

[0139] The recycling record module 403 is used to recycle the target data identifier from the storage node where each target data identifier is located, and generate distributed ledger data of the data identifier corresponding to the first round of value for storage in the blockchain network; the distributed ledger data of the data identifier records the record information of the process of recycling the target data identifier in the first round of value.

[0140] Optionally, the blockchain-based distributed data identifier activity consensus device 40 also includes:

[0141] The node election module is used to determine the node activity evaluation value of each storage node based on all collected usage data, and to determine multiple target storage nodes from the storage nodes as candidate storage nodes for the second round of rotation management based on the determined node activity evaluation value of each storage node, and to randomly select the rotation management storage node for the second round of rotation from the multiple target storage nodes; the second round of rotation is the next round of rotation after the first round of rotation.

[0142] Optional, the activity rating module 402 includes:

[0143] The evaluation value submodule is used to determine the activity evaluation value for each data identifier based on at least one of the following factors: access frequency, access node diversity, time domain distribution, and abnormal access situation during the first round of value cycle.

[0144] The interval division submodule is used to divide multiple consecutive activity evaluation intervals based on the maximum activity evaluation value among all determined activity evaluation values; each activity evaluation interval has a corresponding prefix activity level; the order of the prefix activity level is negatively correlated with the order of the corresponding activity evaluation intervals arranged from smallest to largest;

[0145] The rating submodule is used to determine the data identifier prefix activity level of each activity rating value based on the activity rating range into which each activity rating value falls.

[0146] Optionally, the evaluation value submodule includes:

[0147] The component calculation unit is used to determine the access frequency evaluation value for evaluating the access frequency of the data identifier, the diversity evaluation value for evaluating the diversity of the access nodes of the data identifier, the stability evaluation value for evaluating the temporal distribution stability of the access process of the data identifier in the first round of value period, and the abnormal access weight for evaluating the abnormal access of the data identifier.

[0148] The integrated calculation unit is used to determine the activity evaluation value by weighting the access frequency evaluation value, diversity evaluation value, and stability evaluation value according to the abnormal access weight.

[0149] Optionally, the activity rating module 402 may also include:

[0150] The historical data submodule is used to acquire distributed historical data to record information about the process of recovering target data identifiers in the third round of the duty cycle; the third round of the duty cycle is the previous round of the first round of the duty cycle.

[0151] The evaluation adjustment submodule is used to adjust each determined activity evaluation value based on distributed historical data.

[0152] Optionally, the distributed historical data records the historical activity evaluation value of each data identifier in the third value cycle. The evaluation adjustment submodule includes:

[0153] The smoothing weighting unit is used to weight the activity evaluation value and historical activity evaluation value of the data identifier that coexist in the first and third value cycles according to the preset smoothing weight.

[0154] The evaluation adjustment unit is used to determine the updated value of the activity evaluation value of the data identifier after weighting according to the smoothing weight.

[0155] Optionally, the node election module includes:

[0156] The node evaluation submodule is used to determine the node activity evaluation value for each storage node based on at least one of the following: the access frequency of each storage node during the first round of evaluation, the diversity of accessing nodes, and the distribution of user access.

[0157] The target node submodule is used to determine multiple target storage nodes from all storage nodes based on the determined node activity evaluation value for each storage node and according to a preset threshold for the number of candidate nodes.

[0158] The node election submodule is used to randomly select one of the target storage nodes as the rotation management storage node for the second round of rotation.

[0159] In summary, in this embodiment, by setting up rotating management of storage nodes, the usage status of identifiers for each storage node is collected periodically, giving the storage nodes dynamic monitoring capabilities. This allows for continuous tracking of the usage status of data identifiers, ensuring that the existence status of data identifiers is not in a state of uncertainty for a long period, thereby avoiding inefficient occupation of identifier resources. Furthermore, an activity evaluation mechanism is used to comprehensively analyze the access frequency of data identifiers, and target data identifiers to be recycled are accurately screened based on the activity level of the data identifier prefix, ensuring that low-activity identifiers can be identified and adjusted in a timely manner. A blockchain ledger mechanism is established in the data identifier recycling and storage process, making the identifier recycling process verifiable. By storing distributed ledger data corresponding to the recycling cycle in the blockchain network, the network-wide status information of the identifier lifecycle is obtained in real time, and the rationality of identifier recycling is verified. This not only improves the transparency of data storage but also prevents malicious nodes from manipulating identifier activity records, strengthening the fairness of the consensus mechanism. Therefore, based on the method of this embodiment, dynamic optimization of data identifier resources is achieved, ensuring that the data circulation system can continuously eliminate zombie identifiers and release idle resources. By leveraging the distributed storage characteristics of blockchain, data identifier management overcomes the drawbacks of traditional centralized control. While ensuring the right to autonomously allocate identifiers, it makes the activity evaluation and identifier recycling processes more intelligent and reliable. This ensures improved efficiency in data identifier management, reduces the redundancy burden on the data storage system, optimizes the long-term operational stability of the distributed system, and solves the problem of reduced data validity caused by the accumulation of zombie identifiers.

[0160] Reference Figure 9 The electronic device 500 may include one or more of the following components: processing component 502, memory 504, power supply component 506, multimedia component 508, audio component 510, input / output (I / O) interface 512, sensor component 514, and communication component 516.

[0161] Processing component 502 typically controls the overall operation of electronic device 500, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 502 may include one or more processors 520 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 502 may include one or more modules to facilitate interaction between processing component 502 and other components. For example, processing component 502 may include a multimedia module to facilitate interaction between multimedia component 508 and processing component 502.

[0162] Memory 504 is used to store various types of data to support the operation of electronic device 500. Examples of this data include instructions for any application or method operating on electronic device 500, contact data, phonebook data, messages, pictures, multimedia, etc. Memory 504 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0163] Power supply component 506 provides power to various components of electronic device 500. Power supply component 506 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 500.

[0164] Multimedia component 508 includes an interface that provides an output interface between electronic device 500 and user. In some embodiments, the interface may include a liquid crystal display (LCD) and a touch panel (TP). If the interface includes a touch panel, the interface may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may not only sense the boundaries of touch or swipe actions but also detect the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 508 includes a front-facing camera and / or a rear-facing camera. When electronic device 500 is in an operating mode, such as shooting mode or multimedia mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0165] Audio component 510 is used to output and / or input audio signals. For example, audio component 510 includes a microphone (MIC) used to receive external audio signals when electronic device 500 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 504 or transmitted via communication component 516. In some embodiments, audio component 510 also includes a speaker for outputting audio signals.

[0166] Input / output (I / O) interface 512 provides an interface between processing component 502 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0167] Sensor assembly 514 includes one or more sensors for providing state assessments of various aspects of electronic device 500. For example, sensor assembly 514 may detect the on / off state of electronic device 500, the relative positioning of components such as the display and keypad of electronic device 500, changes in position of electronic device 500 or a component of electronic device 500, the presence or absence of user contact with electronic device 500, orientation or acceleration / deceleration of electronic device 500, and temperature changes of electronic device 500. Sensor assembly 514 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 514 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 514 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0168] Communication component 516 facilitates wired or wireless communication between electronic device 500 and other devices. Electronic device 500 can access wireless networks based on communication standards, such as WiFi, carrier networks (such as 2G, 3G, 4G, or 5G), or combinations thereof. In one exemplary embodiment, communication component 516 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 516 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0169] In an exemplary embodiment, the electronic device 500 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to implement the methods provided in the embodiments of this application.

[0170] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 504 including instructions, which can be executed by a processor 520 of an electronic device 500 to perform the above-described method. For example, the non-transitory storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0171] In an exemplary embodiment, the electronic device 500 may further include a power supply component 506 configured to perform power management of the electronic device 500, a wired or wireless communication component 516 configured to connect the electronic device 500 to a network, and an input / output (I / O) interface 512. The electronic device 500 may operate on an operating system stored in memory 504, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0172] It should be noted that, for the sake of simplicity, the method embodiments of this application are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential to the embodiments of this application.

[0173] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0174] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A blockchain-based distributed data identifier activity consensus method, characterized in that, Storage nodes used in distributed storage networks include: When the storage node is determined to be the rotating management storage node in the first round of the cycle, the usage of each data identifier stored by each storage node in the distributed storage network is collected; Based on all the collected usage data, the activity level of the data identifier prefix for each data identifier is determined, including: determining an activity evaluation value for each data identifier based on at least one of the following factors during the first round of evaluation: access frequency, access node diversity, temporal distribution, and abnormal access; dividing the data identifier into multiple consecutive activity evaluation intervals based on the highest activity evaluation value among all the determined activity evaluation values; each activity evaluation interval has a corresponding prefix activity level; the order of the prefix activity levels is negatively correlated with the order of the corresponding activity evaluation intervals arranged from smallest to largest; determining the data identifier prefix activity level of the data identifier corresponding to each activity evaluation value based on the activity evaluation interval to which each activity evaluation value falls; and determining the target data identifier to be reclaimed based on the determined data identifier prefix activity level of each data identifier. The target data identifier is retrieved from the storage node where each target data identifier is located, and distributed ledger data corresponding to the first rotation period is generated for storage in the blockchain network; the distributed ledger data contains record information of the process of retrieving the target data identifier in the first rotation period.

2. The blockchain-based distributed data identifier activity consensus method as described in claim 1, characterized in that, The blockchain-based distributed data identifier activity consensus method also includes: Based on all the collected usage data, a node activity evaluation value is determined for each storage node. Based on the determined node activity evaluation value for each storage node, multiple target storage nodes are determined from the storage nodes as candidate storage nodes for the second round of rotation management storage nodes. The second round of rotation management storage nodes is randomly selected from the multiple target storage nodes. The second round of rotation is the next round of rotation after the first round of rotation.

3. The blockchain-based distributed data identifier activity consensus method as described in claim 1, characterized in that, The step of determining an activity evaluation value for each data identifier based on at least one of the following factors within the first round of evaluation: access frequency, access node diversity, temporal distribution, and abnormal access patterns, includes: The system determines an access frequency evaluation value for evaluating the frequency of access to the data identifier, a diversity evaluation value for evaluating the diversity of access nodes to the data identifier, a stability evaluation value for evaluating the temporal distribution stability of the access process to the data identifier within the first round period, and an abnormal access weight for evaluating abnormal access to the data identifier. The activity evaluation value is determined by weighting the access frequency evaluation value, the diversity evaluation value, and the stability evaluation value according to the abnormal access weight.

4. The blockchain-based distributed data identifier activity consensus method as described in claim 1, characterized in that, Before dividing multiple consecutive activity evaluation intervals based on the maximum activity evaluation value among all the determined activity evaluation values, the blockchain-based distributed data identifier activity consensus method further includes: Acquire distributed historical data for recording information of the process of retrieving the target data identifier in the third round of the cycle; the third round of the cycle is the previous round of the first round of the cycle. Based on the distributed historical data, each of the determined activity evaluation values ​​is adjusted.

5. The blockchain-based distributed data identifier activity consensus method as described in claim 4, characterized in that, The distributed historical data records the historical activity evaluation value of each data identifier in the third round of evaluation. The step of adjusting each determined activity evaluation value based on the distributed historical data includes: The activity evaluation value and historical activity evaluation value of the data identifiers that coexist in the first and third rounds are weighted according to a preset smoothing weight; The result obtained after weighting according to the smoothing weights is determined as the updated value of the activity evaluation value of the data identifier.

6. A blockchain-based distributed data identification activity consensus system, characterized in that, include: Multiple storage nodes used to form a distributed storage network, and a blockchain network; The storage node is used to collect usage information of each data identifier stored by each storage node in the distributed storage network when the storage node is determined as the rotating management storage node in the first rotation period, and to determine the data identifier prefix activity level of each data identifier based on all the collected usage information, including: determining an activity evaluation value for each data identifier based on at least one of the following: access frequency, access node diversity, time-domain distribution, and abnormal access situations of each data identifier in the first rotation period; dividing the data identifier into multiple consecutive activity evaluation intervals based on the maximum activity evaluation value among all the determined activity evaluation values; each activity evaluation interval has a corresponding prefix activity level. The activity level is determined by the following criteria: The order of the prefix activity levels is negatively correlated with the order of the corresponding activity evaluation intervals arranged from smallest to largest. Based on the activity evaluation interval into which each activity evaluation value falls, the data identifier prefix activity level of each activity evaluation value is determined. Based on the determined data identifier prefix activity level of each data identifier, the target data identifier to be reclaimed is determined, and the target data identifier is reclaimed from the storage node where each target data identifier is located, and distributed ledger data corresponding to the first cycle is generated. The distributed ledger data contains record information about the process of reclaiming the target data identifier in the first cycle. The blockchain network is used to store the distributed ledger data.

7. A blockchain-based distributed data identifier activity consensus device, characterized in that, Storage nodes used in distributed storage networks include: The data collection module is used to collect the usage information of each data identifier stored by each storage node in the distributed storage network when the storage node is determined to be the rotating management storage node in the first rotation cycle; The activity rating module is used to determine the data identifier prefix activity level of each data identifier based on all the collected usage data, including: determining an activity evaluation value for each data identifier based on at least one of the following: access frequency, access node diversity, time-domain distribution, and abnormal access situations during the first round period; dividing multiple consecutive activity evaluation intervals based on the maximum activity evaluation value among all the determined activity evaluation values; each activity evaluation interval has a corresponding prefix activity level; the order of the prefix activity levels is negatively correlated with the order of the corresponding activity evaluation intervals arranged from smallest to largest; determining the data identifier prefix activity level of the data identifier corresponding to each activity evaluation value based on the activity evaluation interval to which each activity evaluation value falls; and determining the target data identifier to be reclaimed based on the determined data identifier prefix activity level of each data identifier. The recycling record module is used to recycle the target data identifier from the storage node where each target data identifier is located, and generate distributed ledger data of the data identifier corresponding to the first rotation period for storage in the blockchain network; the distributed ledger data of the data identifier records the record information of the process of recycling the target data identifier in the first rotation period.

8. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements the blockchain-based distributed data identifier activity consensus method as described in any one of claims 1 to 5.

9. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the blockchain-based distributed data identifier activity consensus method as described in any one of claims 1 to 5.

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