Distributed data identification activeness consensus method based on block chain

Abstract

The invention discloses a distributed data identifier activeness consensus method based on a block chain, and the method comprises the steps: collecting the use condition of each data identifier stored in each storage node in a distributed storage network under the condition that a storage node is determined to be a round-valued management storage node in a first round-valued period; determining a data identifier prefix activeness level of each data identifier according to all the collected use conditions, and determining a to-be-recycled target data identifier according to the determined data identifier prefix activeness level of each data identifier; and recovering the target data identifier from the storage node where each target data identifier is located, and generating data identifier distributed bookkeeping data which is stored in the block chain network and corresponds to the first round value period. According to the method, the redundant burden of a data storage system is reduced, the long-term operation stability of a distributed system is optimized, and the problem that the data validity is reduced due to zombie identifier accumulation is solved.

Description

Technical Field

[0001] This application belongs to the field of distributed identification management, and specifically relates to a distributed data identification activity consensus method, system, device, equipment and storage medium based on blockchain. Background Art

[0002] In the development of distributed storage networks, data identification management has become a key factor affecting system efficiency and resource utilization. Data identification not only provides fundamental data addressing but also impacts the security and verifiability of data circulation. However, as data storage scales, identification management faces challenges such as long-term data occupancy, resource waste, and a lack of activity assessment. These challenges directly impact the long-term stability and sustainability of distributed systems.

[0003] Existing data identification management technologies primarily rely on centralized allocation mechanisms or hierarchical resolution schemes. Centralized management systems provide stable resolution capabilities, while hierarchical identification resolution schemes can improve the structure of identification management. Furthermore, decentralized identification technologies exist, enabling autonomous declaration and decentralized storage of data identification.

[0004] However, existing data identification management technologies generally lack a reasonable recycling mechanism, which leads to long-term occupation of identifications, causing a large number of data identifications to become "zombie identifications", affecting the recycling of identification resources. Summary of the Invention

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

[0006] In a first aspect, embodiments of the present application disclose a distributed data identification activity consensus method based on blockchain, which is applied to storage nodes in a distributed storage network, including: In a case where the storage node is determined to be a rotation management storage node in a first rotation period, collecting usage information of each data identifier stored by each storage node in the distributed storage network; Determining a data identifier prefix activity level for each data identifier based on all the collected usage conditions, and determining a target data identifier to be recycled based on the determined data identifier prefix activity level for each data identifier; The target data identifier is recovered from each storage node where the target data identifier is located, and data identifier distributed accounting data corresponding to the first rotation period is generated for storage in the blockchain network; the data identifier distributed accounting data records information about the process of recovering the target data identifier in the first rotation period.

[0007] In a second aspect, the present application also discloses a distributed data identification activity consensus system based on blockchain, including: Multiple storage nodes and blockchain networks that form a distributed storage network; The storage node is configured to, when the storage node is determined to be a rotation management storage node within a first rotation period, collect usage information of each data identifier stored by each storage node in the distributed storage network, determine a data identifier prefix activity level of each data identifier based on all the collected usage information, determine a target data identifier to be recycled based on the determined data identifier prefix activity level of each data identifier, recycle the target data identifier from each storage node where the target data identifier is located, and generate data identifier distributed accounting data corresponding to the first rotation period; the data identifier distributed accounting data records information about a process of recycling the target data identifier in the first rotation period; The blockchain network is used to store the distributed accounting data.

[0008] In a third aspect, the present application also discloses a blockchain-based distributed data identification activity consensus device, which is applied to storage nodes in a distributed storage network, including: a data collection module, configured to collect usage information of each data identifier stored by each storage node in the distributed storage network when the storage node is determined to be a rotation management storage node in a first rotation period; an activity rating module, configured to determine an activity level of a data identifier prefix of each data identifier based on all the collected usage conditions, and determine a target data identifier to be recycled based on the determined activity level of the data identifier prefix of each data identifier; A recovery record module is used to recover the target data identifier from each storage node where the target data identifier is located, and generate data identifier distributed accounting data corresponding to the first rotation period for storage in the blockchain network; the data identifier distributed accounting data records information about the process of recovering the target data identifier in the first rotation period.

[0009] In a fourth aspect, an embodiment of the present application further discloses an electronic device comprising a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the method described in the first aspect are implemented.

[0010] In a fifth aspect, an embodiment of the present application further discloses a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0011] In summary, in the embodiment of the present application, by periodically collecting the identification usage of each storage node under the setting of rotating management storage nodes, giving the storage node dynamic monitoring capabilities, the usage status of the data identification can be continuously tracked, ensuring that the survival status of the data identification will not be in uncertainty for a long time, thereby avoiding the inefficient occupation of identification resources; then, the access frequency of the data identification is comprehensively analyzed through the activity evaluation mechanism, and the target data identification to be recycled is accurately screened according to the activity level of the data identification prefix, ensuring that low-activity identification can be identified and adjusted in time; and a blockchain accounting mechanism is established in the data identification recycling and storage link, so that the identification recycling process has verifiability, by storing distributed accounting data corresponding to the recycling cycle in the blockchain network, obtaining the full network status information of the identification life cycle in real time, and verifying whether the identification recycling is reasonable, not only improving the transparency of data storage, but also preventing malicious nodes from manipulating the identification activity record, and strengthening the fairness of the consensus mechanism. Therefore, based on the method of the embodiment of the present application, dynamic optimization of data identification resources is achieved, ensuring that the data circulation system can continuously eliminate zombie identifications and release idle resources. Combined with the distributed storage characteristics of blockchain, data identification management breaks away from the drawbacks of traditional centralized control. While ensuring the right to autonomously allocate identification, it also makes the activity evaluation and identification recycling processes more intelligent and reliable. This ensures that while improving data identification management efficiency, it also reduces the redundant 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 identifications. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In the attached figure: Figure 1 This is a flowchart of a distributed data identification activity consensus method based on blockchain provided by an embodiment of the present application; Figure 2 This is a flowchart of another blockchain-based distributed data identification activity consensus method provided by an embodiment of the present application; Figure 3 This is a process for determining an activity evaluation interval provided in an embodiment of the present application; Figure 4 This is a schematic diagram of the structure of distributed accounting data provided according to an embodiment of the present application; Figure 5 This is an architectural diagram of a distributed data identification activity consensus system based on blockchain provided in an embodiment of the present application; Figure 6 It is a data flow diagram under an embodiment of the present application; Figure 7 It is a complete data information management cycle under the embodiment of the present application; Figure 8 This is a block diagram of a distributed data identification activity consensus device based on blockchain provided by an embodiment of the present application; Figure 9 This is a block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0014] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0015] Based on the above process, Figure 1 As shown, a distributed data identification activity consensus method based on blockchain is provided in an embodiment of the present application, which is applied to storage nodes in a distributed storage network.

[0016] The method may include the following steps: Step 101: When a storage node is determined to be a rotation management storage node in a first rotation period, the usage of each data identifier stored by each storage node in the distributed storage network is collected.

[0017] In some embodiments of the present application, in order to ensure that the usage status of data identifiers can be accurately tracked, it is necessary to periodically collect the usage status of each data identifier stored by each storage node in the distributed storage network. Specifically, the rotation management storage node is responsible for counting the usage frequency and interaction records of data identifiers of all storage nodes during the first rotation period. This process enables the storage system to build a complete view of the data survival status, so that subsequent links can accurately identify low-activity identifiers and make reasonable adjustments. Through periodic monitoring of storage nodes, the status of data identifiers can be dynamically updated to avoid long-term idleness of identifiers or the inability to accurately assess activity, thereby improving the reasonable utilization of storage resources.

[0018] In a specific example, during the actual operation of a distributed storage network, a rotating management storage node receives data identifier usage reports from other storage nodes, including access records, resolution request counts, and the number of 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 subsequent identifier recycling mechanisms.

[0019] Step 102 : determining the data identifier prefix activity level of each data identifier based on all collected usage conditions, and determining the target data identifier to be recycled based on the determined data identifier prefix activity level of each data identifier.

[0020] In some embodiments of the present application, in order to effectively manage data identification resources, it is necessary to evaluate the activity of each data identification based on the collected usage, and determine the target data identification to be recycled accordingly. Specifically, the system will analyze the access data reported by the storage node, summarize the interaction frequency, number of resolution requests and long-term access trends of each data identification, and establish an activity evaluation model. The model calculates the activity score of the data identification and divides it into different activity levels in order to accurately identify identifications that are of low activity or even idle for a long time. Finally, based on the activity level of the data identification, the system screens the target data identification to be recycled, so that the distributed storage network can regularly release idle resources and maintain a reasonable data storage structure.

[0021] In a specific example, during a distributed storage network cycle, the rotating management storage node receives access logs for data identifiers from each node. This log may include, for example, the number of resolution requests and the number of interacting nodes involved. Based on this data, the management node calculates an activity score for the data identifier, classifies the activity level according to a preset score threshold, and marks low-activity data identifiers for recycling. 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.

[0022] Step 103: Recover the target data identifier from the storage node where each target data identifier is located, and generate data identifier distributed accounting data corresponding to the first round period for storage in the blockchain network.

[0023] The data identifier distributed accounting data records information about the process of recovering the target data identifier in the first round of value cycle.

[0024] In some embodiments of the present application, in order to ensure that data identification resources can be reasonably managed and effectively recycled, the system needs to recycle low-activity identifiers from storage nodes and record detailed information about the recycling process in the blockchain network. Specifically, the system will identify the data identifiers to be recycled based on the activity evaluation results, and instruct the storage nodes to perform the recycling operation to release the no longer active data identifiers from the storage system. This process ensures that low-activity identifiers do not occupy storage resources for a long time, while avoiding data storage redundancy caused by the accumulation of zombie identifiers. In order to ensure the transparency and verifiability of the entire recycling process, the system generates data identification distributed accounting data while performing the recycling operation, and stores the data in the blockchain network to ensure that all nodes can query and verify the entire process of identifier recycling. The data identification distributed accounting data includes the timestamp of the identifier recycling, the storage nodes involved, the target data identifier to be recycled, and related operation records, to ensure that the data management process is auditable and to avoid malicious manipulation.

[0025] In a specific example, during a rotation cycle in 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 reclaim these identifiers. Each storage node removes these identifiers as instructed and generates a record of the identifier's reclaim, which contains information such as the identifier's unique identifier, reclaim time, and storage location change. These records are then packaged and stored in the blockchain network's distributed ledger, allowing all storage nodes to query and verify the reclaim process. In subsequent rotation cycles, the system can adjust the data identifier allocation strategy based on this stored data, ensuring the rational use of data storage resources and further optimizing storage efficiency in a decentralized environment.

[0026] In summary, in the embodiment of the present application, by periodically collecting the identification usage of each storage node under the setting of rotating management storage nodes, giving the storage node dynamic monitoring capabilities, the usage status of the data identification can be continuously tracked, ensuring that the survival status of the data identification will not be in uncertainty for a long time, thereby avoiding the inefficient occupation of identification resources; then, the access frequency of the data identification is comprehensively analyzed through the activity evaluation mechanism, and the target data identification to be recycled is accurately screened according to the activity level of the data identification prefix, ensuring that low-activity identification can be identified and adjusted in time; and a blockchain accounting mechanism is established in the data identification recycling and storage link, so that the identification recycling process has verifiability, by storing distributed accounting data corresponding to the recycling cycle in the blockchain network, obtaining the full network status information of the identification life cycle in real time, and verifying whether the identification recycling is reasonable, not only improving the transparency of data storage, but also preventing malicious nodes from manipulating the identification activity record, and strengthening the fairness of the consensus mechanism. Therefore, based on the method of the embodiment of the present application, dynamic optimization of data identification resources is achieved, ensuring that the data circulation system can continuously eliminate zombie identifications and release idle resources. Combined with the distributed storage characteristics of blockchain, data identification management breaks away from the drawbacks of traditional centralized control. While ensuring the right to autonomously allocate identification, it also makes the activity evaluation and identification recycling processes more intelligent and reliable. This ensures that while improving data identification management efficiency, it also reduces the redundant 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 identifications.

[0027] Figure 2 This is another blockchain-based distributed data identification activity consensus method provided in an embodiment of the present application, which is applied to storage nodes in a distributed storage network.

[0028] The method may include the following steps: Step 201: When a storage node is determined to be a rotation management storage node in a first rotation period, the usage of each data identifier stored by each storage node in the distributed storage network is collected.

[0029] The method shown in this step has been described in step 101 and will not be repeated here.

[0030] Step 202 : determining the data identifier prefix activity level of each data identifier based on all collected usage conditions, and determining the target data identifier to be recycled based on the determined data identifier prefix activity level of each data identifier.

[0031] The method shown in this step has been described in step 102 and will not be repeated here.

[0032] Optionally, in order to determine the data identifier prefix activity level of each data identifier based on all collected usage conditions, step 202 includes the following sub-steps: Sub-step 2021: determining an activity evaluation value for each data identifier based on at least one of the access frequency, access node diversity, time domain distribution, and abnormal access situation of each data identifier in the first rotation period.

[0033] In some embodiments of the present application, in order to accurately evaluate the activity of a data identifier, the system needs to calculate the activity evaluation value of the data identifier based on the access characteristics of multiple dimensions. Specifically, the system comprehensively calculates the activity evaluation value based on the access frequency, access node diversity, time domain distribution and abnormal access of each data identifier in the first round of value cycle to ensure accurate quantification of the usage of the data identifier. The access frequency indicates the number of times the data identifier is accessed within the cycle, reflecting its frequency of use; the access node diversity measures the number of different nodes that access the identifier, indicating the dispersion and sharing of the data; the time domain distribution is used to analyze the stability of the access behavior and avoid scoring distortion caused by short-term access surges; abnormal access is used to detect potential malicious access behavior and adjust the score of highly abnormal access data. The calculation results of these evaluation dimensions together constitute the activity evaluation value of the data identifier, which enables the system to objectively judge the usage of the data identifier and provide a data basis for subsequent activity level classification and identifier recycling decisions.

[0034] In a specific example, during the operation of a distributed storage network, a storage node is responsible for managing multiple data identifiers and regularly recording their access status. During the first rotation period, the node counted the access frequency of a certain data identifier as 200 times, involving 50 different nodes accessed, the access time distribution was relatively even, and no abnormal access behavior was detected. The system calculates its activity evaluation value based on the access characteristics of the data identifier, and compares it with the evaluation values ​​of other identifiers to determine its activity level. In the subsequent identifier management process, the activity evaluation value of the data identifier will be used to divide the activity interval and provide a basis for deciding whether to recycle the identifier. This calculation process ensures that the system can reasonably evaluate the usage of data identifiers and improve the scientific nature and transparency of identifier management.

[0035] Optionally, in a further embodiment, the activity evaluation value of each data identifier is determined based on the access frequency, access node diversity, time domain distribution, and abnormal access of each data identifier in the first rotation period. In this case, sub-step 2021 includes the following sub-steps: 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 to the data identifier, the stability evaluation value used to evaluate the time domain distribution stability of the access process to the data identifier within the first round of value cycle, and the abnormal access weight used to evaluate the abnormal access to the data identifier.

[0036] In some embodiments of the present application, in order to accurately evaluate the activity of data identifiers, the system needs to calculate the usage of data identifiers from multiple dimensions to ensure that the evaluation results can fully reflect the actual activity status of the data. Specifically, the system determines a number of key evaluation indicators for measuring activity, including access frequency evaluation value, diversity evaluation value, stability evaluation value and abnormal access weight. Among them, the access frequency evaluation value is used to quantify the number of times a data identifier is accessed within a specific period to reflect its basic usage; the diversity evaluation value is used to measure the number of storage nodes that access the data identifier to ensure the breadth of data distribution; the stability evaluation value focuses on the distribution of access behavior over time to avoid scoring anomalies caused by short-term access surges; the abnormal access weight is used to detect whether the data identifier has suffered abnormal access, and adjust 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, make activity analysis more comprehensive, and improve the rationality of data identifier management.

[0037] In a specific example, during the operation of a distributed storage network, the system collects statistics on the usage of a data identifier in the first rotation period and: According to the formula Calculate visit frequency evaluation value , in is the number of accesses to the data identifier during the first round of value cycle; According to the formula Calculate diversity evaluation value , in It is the number of nodes accessing the data identifier counted in the first round of value cycle; According to the formula Calculate stability evaluation value , in, is the average number of visits in each time interval within this cycle, is the standard deviation of the number of visits at each time interval within this cycle; According to the formula Calculating abnormal access weights Ultimately, these calculated evaluation values ​​can be used to divide activity intervals, ensuring that the data storage system can reasonably classify data identifiers and provide support for subsequent resource management decisions.

[0038] In sub-step 20212, the weighted result of the access frequency evaluation value, the diversity evaluation value, and the stability evaluation value adjusted according to the abnormal access weight is determined as the activity evaluation value.

[0039] In some embodiments of the present application, in order to ensure that the activity evaluation of a data identifier can accurately reflect its 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 weighted calculations 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 rules. Finally, the system determines the activity evaluation value of each data identifier based on the weighted calculation results, so that the evaluation value can fully reflect the access situation of the data identifier and ensure that the subsequent activity level division 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 the data identifier can adapt to complex access behavior patterns, and improves the accuracy of data storage management.

[0040] In a specific example, during an activity evaluation process on a distributed storage network, the system calculates the access frequency evaluation value, diversity evaluation value, and stability evaluation value for a certain data identifier, and also determines the abnormal access weight adjustment coefficient. Then, the formula can be used: Calculate activity evaluation value ,in, 、 、 The activity evaluation value will be used for interval division in subsequent steps and will affect the prefix activity level of data identifiers to ensure that the storage system can accurately manage data identifier resources, improve storage efficiency, and optimize the identifier lifecycle management strategy.

[0041] Sub-step 2022 , dividing the activity evaluation intervals into a plurality of continuous activity evaluation intervals according to the maximum activity evaluation value among all the determined activity evaluation values.

[0042] 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 interval when arranged from small to large.

[0043] In some embodiments of the present application, in order to effectively distinguish the activity levels of data identifiers, the system needs to divide the data identifiers into multiple continuous activity evaluation intervals based on the determined activity evaluation values, so that the status of the data identifiers can be reasonably classified. Specifically, the system first calculates the activity evaluation values ​​of all data identifiers, and determines the maximum activity evaluation value as the basis for interval division. Subsequently, the system constructs multiple continuous intervals based on the maximum activity evaluation value, each interval representing a different activity level, arranged in order from the lowest activity to the highest activity. This division method ensures that different data identifiers can be assigned to corresponding levels according to the actual activity situation, 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 retained first, and low-activity identifiers can be reasonably recycled, thereby improving the utilization efficiency of storage resources.

[0044] like Figure 3 As shown in a specific example, in a distributed storage network, the system collects activity evaluation values ​​of multiple data identifiers in a cycle and determines the highest evaluation value as Based on this value, the system divides multiple activity intervals, for example arrive The system then assigns different activity levels to each data identifier based on the distribution of these intervals, classifying low-activity data identifiers into lower levels and high-activity data identifiers into higher levels. This classification is used in subsequent identifier management to determine whether data identifiers should be recycled or stored, thereby optimizing the data management strategy of the storage network. This approach allows the system to dynamically adjust the lifecycle of data identifiers, achieve optimal resource allocation, and ensure the stability of data circulation.

[0045] Sub-step 2023 , determining the data identifier prefix activity level of the data identifier corresponding to each activity evaluation value according to the activity evaluation interval into which each activity evaluation value falls.

[0046] In some embodiments of the present application, in order to accurately determine the activity status of a data identifier, the system needs to assign a corresponding prefix activity level based on the interval in which the activity evaluation value is located. Specifically, the system first classifies the activity evaluation values ​​of all data identifiers, and checks the activity interval into which each evaluation value falls. Subsequently, the data identifier is assigned a corresponding prefix activity level based on the hierarchical order of the activity intervals, so that the identifier management system can optimize the storage strategy in a targeted manner. The prefix activity level is an important classification indicator for data identifiers, which is negatively correlated with the ranking of the activity evaluation intervals, so that low-activity identifiers obtain lower levels, while high-activity identifiers are classified into higher levels. This process ensures that the management of data identifiers is more refined, and provides a clear basis for subsequent identifier recovery or retention decisions.

[0047] In a specific example, in a cycle of a distributed storage network, the system calculates the activity evaluation values ​​of multiple data identifiers and matches the prefix activity level corresponding to each evaluation value based on the previously divided activity intervals. For example, if the evaluation value of a data identifier is in interval θ2, the identifier is assigned a prefix activity level of 3, while the evaluation value of another identifier falls in interval θ4, it is classified as prefix activity level 1. In the subsequent data storage management process, the classification results will be used to optimize the allocation of identification resources, enabling the system to more reasonably decide which identifiers need to be retained and which identifiers need to be recycled, thereby improving the operating efficiency of the data storage network.

[0048] Considering that the activity assessment of data identifiers requires the ability to analyze long-term trends to avoid short-term fluctuations affecting the assessment results and to ensure the consistency and traceability of the recycling strategy, the following sub-steps are included before sub-step 2022: Sub-step 2024: obtaining distributed historical data for recording information on the process of recycling the target data identifier in the third rotation period.

[0049] The third rotation period is the rotation period before the first rotation period.

[0050] In some embodiments of the present application, in order to improve the accuracy of the data identification activity evaluation, the system needs to refer to historical data to ensure that the evaluation process is not only based on the data of the current cycle, but can also be optimized in combination with the data trends of the previous cycle. Specifically, the system obtains distributed historical data from the blockchain network to record the detailed process of recovering the target data identification in the third round of value. The distributed historical data contains the identification recovery records, activity scores and related access logs of the previous cycle, enabling the system to establish a long-term activity trend analysis of the data identification. This acquisition process provides a reliable reference for subsequent activity evaluations, enabling the system to optimize the identification evaluation results of the current cycle based on historical activity data, thereby improving the rationality of the activity evaluation while ensuring the accuracy and consistency of the identification recovery decision.

[0051] In a specific example, during the operation of a distributed storage network, before evaluating data identifier activity in the first rotation, the system extracts activity data for the third rotation from the historical records stored on the blockchain. This data includes information such as recovered identifiers, their activity evaluation process, access counts, and storage node interactions. The system compares this data with the identifier activity data for the current rotation to determine whether the current activity score is consistent with long-term trends. For example, if a data identifier had a high activity score in the third rotation but experiences a sudden drop in the current rotation, the system can further analyze whether the access pattern is abnormal, rather than simply classifying the identifier as low-activity. This process ensures more robust activity evaluation and avoids unreasonable recycling decisions caused by short-term fluctuations.

[0052] Sub-step 2025 , adjusting each determined activity evaluation value according to the distributed historical data.

[0053] In some embodiments of the present application, in order 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 in combination with historical data. Specifically, the system compares the current activity evaluation value of each data identifier based on distributed historical data, and adjusts the evaluation result in combination with the data trend of the previous rotation cycle. Historical data contains identifier access records, parsing times, storage node interaction information, etc. of the previous cycle, enabling the system to identify long-term activity trends and avoid mistakenly classifying data identifiers that have declined in the short term as low activity. At the same time, 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 activity evaluation more reliable, optimizes management decisions for data identifiers, and improves the rationality of identifier recovery.

[0054] In a specific example, during an activity evaluation process in a distributed storage network, the activity evaluation value calculated by the system for a certain data identifier was significantly lower than that of the previous cycle. The system queried the distributed historical data and found that the data identifier had maintained a high access frequency in the past multiple cycles, with only a brief decline in the current cycle. Based on this historical data, the system made appropriate adjustments to the activity evaluation value of the data identifier so that it still maintained a high activity level, rather than immediately classifying it as low activity and entering the recycling list. This adjustment avoids the incorrect recycling of data identifiers due to short-term access fluctuations, ensuring that the system can more accurately identify the actual activity situation and improve the stability of data storage management and resource utilization efficiency.

[0055] Optional, such as Figure 4 As shown, in a further embodiment, distributed ledger data (i.e., blocks in a blockchain) is generated in the following manner: To ensure transparency in the data identifier recovery and storage process, the system uses a blockchain structure to record identifier activity evaluations and recovery decision information. The block header records the hash value of the previous block, information about the rotating management storage node, and the Merkle root properties to ensure data integrity and chain traceability. The block body includes target storage node information, data prefix activity scores, storage node evaluation values, and access log statistics uploaded by each storage node. Furthermore, the system uses a Merkle tree structure to organize the hash data of the storage node evaluation values, making data verification more efficient and preventing data tampering. Through blockchain storage, the system records the entire data identifier recovery process and provides verifiable storage credentials, allowing all participating nodes to share this data, ensuring the fairness and auditability of data identifier management.

[0056] Based on the inspiration of the above blockchain storage model, the distributed historical data records the historical activity evaluation value of each data identifier in the third round of value cycle. In this case, sub-step 2025 includes the following sub-steps: Sub-step 20251: weighting the activity evaluation values ​​and activity historical evaluation values ​​of the data identifiers that exist in both the first rotation period and the third rotation period according to a preset smoothing weight.

[0057] In some embodiments of the present application, in order to improve the stability of the data identification activity evaluation and ensure that short-term fluctuations do not affect the evaluation results, the system needs to smooth the current activity evaluation value in combination with historical data. Specifically, the system weights the activity evaluation value of the data identification that coexists in the first rotation period and the third rotation period with the historical activity evaluation value to integrate the recent and long-term activity trends. This weighted calculation is based on preset smoothing weight parameters, so that the evaluation values ​​of different time periods can be fused according to the set weights, ensuring that the evaluation results can reflect the current activity status and take into account long-term trends. The smoothing weight setting is used to control the degree of influence of historical data on the current evaluation, so that the activity of the data identification changes more smoothly over time, and avoid misjudgments due to short-term anomalies.

[0058] In a specific example, during an activity evaluation on a distributed storage network, the system calculated the activity of a data identifier in the current cycle as 0.70. Simultaneously, a query of historical data stored on the blockchain revealed that the identifier's historical activity evaluation in the third cycle was 0.85. Based on preset smoothing weights, the system set a weight of 0.6 for the current evaluation value and a weight of 0.4 for the historical evaluation value, and performed a weighted calculation to obtain a weighted activity evaluation value of 0.76. This result is used to divide activity intervals in subsequent steps, enabling the data storage system to properly classify data identifiers and ensure 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.

[0059] In sub-step 20252, the result obtained by weighting according to the smoothing weight is determined as the updated value of the activity evaluation value of the data identifier.

[0060] In some embodiments of the present application, in order to ensure that the activity evaluation of the data identifier can accurately reflect the long-term trend and reduce the impact of short-term fluctuations on the evaluation results, the system needs to update the activity evaluation value of the data identifier based on the weighted calculation result. Specifically, the system determines the weighted result calculated previously as the final activity evaluation value of the data identifier, and uses it for subsequent activity level classification and identifier recycling decisions. This update process ensures that the activity evaluation can adjust the current evaluation results in combination with historical data, making the identification management of the storage system more refined and improving the reasonable allocation of storage resources. In addition, this method also avoids erroneous recycling decisions caused by short-term abnormal access, enabling the system to more accurately identify and manage the life cycle of data identifiers.

[0061] In a specific example, during an evaluation of a distributed storage network, the system calculates the weighted activity evaluation value of a data identifier as 0.76, and updates its final evaluation result based on this value. This updated value will be used in subsequent steps to divide the activity interval to determine the survival status of the data identifier. For example, if the activity evaluation value of the identifier is still at a high level, the system retains the identifier, but if the value is lower than the set threshold, the identifier may enter the recycling process. In this way, the system can adjust the identifier management strategy based on reasonable data trends, improve the stability of the distributed storage network, and optimize the sustainability of data storage.

[0062] The above process can be followed The formula is calculated, where is the adjusted activity evaluation value, is distributed historical data, is an adjustment coefficient used to smooth the evaluation value in a statistical sense.

[0063] Step 203: Recover the target data identifier from each storage node where the target data identifier is located, and generate data identifier distributed accounting data corresponding to the first round period for storage in the blockchain network.

[0064] The data identifier distributed accounting data records information about the process of recovering the target data identifier in the first round of value cycle.

[0065] The method shown in this step has been described in step 103 and will not be repeated here.

[0066] Step 204: Determine the node activity evaluation value of each storage node based on all collected usage information, and determine multiple target storage nodes as alternative storage nodes for the rotating management storage nodes of the second rotation period from the storage nodes based on the determined node activity evaluation value of each storage node, and randomly select the rotating management storage nodes of the second rotation period from the multiple target storage nodes.

[0067] The second rotation period is the next rotation period after the first rotation period.

[0068] In some embodiments of the present application, in order to ensure the dynamic update of the rotating management storage nodes and enable different nodes to have the opportunity to participate in the task of managing storage identifiers in different cycles, the system needs to determine the node activity evaluation value of each storage node based on the collected usage data, 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 rotating management storage nodes in the second rotation cycle, and finally randomly select the rotating management storage node for the second rotation cycle from the multiple target storage nodes. Specifically, the system will screen multiple target storage nodes from the storage nodes, and these storage nodes are selected based on their node activity evaluation values. Subsequently, the system randomly selects one of the multiple screened target storage nodes as the rotating management storage node for the second rotation cycle, ensuring the fairness and decentralization of the node election. This process prevents the rotating management storage nodes from being occupied by fixed nodes for a long time, thereby reducing the risk of centralization in system operation and enabling different storage nodes to dynamically participate in the identification management task. By reasonably adjusting the election rules for the rotating management storage nodes, the balance of the distributed storage network can be improved, while optimizing the lifecycle management of data identifiers.

[0069] In a specific example, after a distributed storage network cycle ends, the system first analyzes the data identifier activity of each storage node and selects storage nodes that manage highly active data identifiers as target storage nodes. Within this set of target storage nodes, the system can use verifiable random functions (VRFs) to generate random numbers to determine the rotating management storage node for the next rotation cycle. For example, the system can first generate an asymmetric key pair consisting of a public key and a private key using the elliptic curve secp256r1 (in the form y² = x³ + ax + b) as verification keys. Five target storage nodes are then selected based on the data identifier prefix activity level determined in the previous step. The difference between each node's node number and its own node number is calculated using the preset random adaptation functions VRF_Output (to obtain the random number) and VRF_Proof (to prove the validity of the random number). The storage node with the smallest difference is selected as the rotating management storage node for the next rotation cycle. This selected node then assumes management responsibilities for the new cycle and is responsible for collecting network-wide data identifier usage information. In this way, the system can ensure the fairness of the election of rotating management storage nodes, while avoiding fixed nodes from taking on long-term management tasks, and improving the sustainable operation capabilities of the distributed storage network.

[0070] Optionally, step 204 includes the following sub-steps: Sub-step 2041 : determining a node activity evaluation value for each storage node according to at least one of the access frequency, access node diversity, and user access distribution of each storage node in the first rotation period.

[0071] In some embodiments of the present application, in order to reasonably evaluate the activity of storage nodes and ensure that the election of storage nodes for rotation management can accurately reflect the long-term contribution of the nodes, the system needs to calculate the node activity evaluation value of the storage nodes 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 in the first rotation cycle. The access frequency is used to quantify the number of times the node processes data identifiers in the cycle to reflect the basic data interaction level of the node; the access node diversity measures the different storage sources accessing the node to ensure the balance of data flow; the user access distribution is used to analyze the information entropy of the access behavior to avoid the influence of too high or too low access concentration on the rationality of the score. The calculation results of these evaluation indicators together constitute the activity evaluation value of the storage node, so that the system can accurately judge the usage of each node and provide a basis for the subsequent election of storage nodes for rotation management.

[0072] In a specific example, during a cycle of the distributed storage network, the system collects access behavior data for each storage node and: According to the formula Calculate visit frequency evaluation value , in is the number of visits to the storage node during the first round of duty cycle; According to the formula Calculate diversity evaluation value , in It is the number of access nodes to the storage node in the first round of duty cycle; According to the formula Calculate the user request information entropy used to characterize the distribution of user access to storage nodes , in It represents the proportion of the access volume of the storage node in the total access volume. Then, we can use the formula Obtain the node activity evaluation value of the storage node. Ultimately, these calculated evaluation values ​​can be used to evaluate the activity level of each node, ensuring that the data storage system can reasonably select the target storage node and provide support for subsequent voting and election decisions.

[0073] Sub-step 2042 , based on the determined node activity evaluation value of each storage node and according to a preset candidate node quantity threshold, a plurality of target storage nodes are determined from all storage nodes.

[0074] In some embodiments of the present application, in order to reasonably select alternative storage nodes and ensure that the election process of the rotating management storage nodes meets the system operation requirements, the system needs to screen the target storage nodes based on the node activity evaluation value. Specifically, the system screens out a group of target storage nodes from all storage nodes based on the node activity evaluation value determined for each storage node and according to the preset threshold value for the number of alternative nodes. This screening rule ensures that alternative nodes are selected only from storage nodes with higher activity, so as to ensure that the management nodes of the new rotation cycle can effectively perform data identification collection, activity evaluation and identification recovery tasks. The preset threshold value for the number of alternative nodes is used to limit the number of target storage nodes to ensure that there will be no over-screening or insufficient options during the election process, thereby making the election process more stable and fair.

[0075] In a specific example, during a distributed storage network cycle, the system selects storage nodes based on their activity scores, with the threshold for candidate nodes set at the top 10 highest-scoring storage nodes. After analyzing each node's access frequency, diversity score, and stability score, the system selects 10 highly active storage nodes as target storage nodes. These nodes will participate in the random election process in subsequent steps. This selection strategy ensures that management tasks are handled by highly active nodes, while improving the sustainability of data storage and optimizing the allocation of storage resources.

[0076] Sub-step 2043: randomly determine a storage node from all target storage nodes as the rotation management storage node for the second rotation period.

[0077] In some embodiments of the present application, in order to ensure that the election process of the rotating management storage node is fair and decentralized, the system needs to randomly select a node from the screened target storage nodes as the management node for the next rotation period. Specifically, the system first confirms the candidacy of all target storage nodes and uses a verifiable random function (VRF) to calculate the random number of each node to ensure that the election results are 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 cycle. 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 fairness and reliability of the overall operation.

[0078] 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 for all candidate nodes. Ultimately, the system selects the node whose random number matches the optimal distribution rule as the new rotating management storage node, assigning it management responsibilities for the next cycle. This election method ensures the fairness of storage node elections, prevents manipulation, and improves the stability and transparency of storage management.

[0079] Optionally, before determining the target storage node, similar to sub-step 2025, the node activity evaluation value may also be adjusted: Sub-step 2044 , adjusting the activity evaluation value of each node determined based on the distributed historical data.

[0080] In some embodiments of the present application, in order 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 in combination with historical data. Specifically, the system compares the current node activity evaluation value of each storage node based on distributed historical data, and adjusts the evaluation results in combination with the data trend of the previous rotation cycle. This enables the system to identify long-term activity trends and avoid mistakenly classifying data identifiers that have declined in the short term as low activity. At the same time, 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 activity evaluation more reliable, optimizes management decisions for data identifiers, and improves the rationality of identifier recovery.

[0081] In a specific example, during an activity evaluation process in a distributed storage network, the node activity evaluation value calculated by the system for a certain storage node was significantly lower than that of the previous cycle. The system queried the distributed historical data and found that the storage node had maintained a high access frequency in the past multiple cycles, with only a brief drop in the current cycle. Based on this historical data, the system made appropriate adjustments to the activity evaluation value of the storage node so that it still maintained a high activity evaluation value rather than immediately losing its candidate qualification. This adjustment avoids the mistaken loss of candidate qualification due to short-term access fluctuations, ensures that the system can more accurately identify the actual activity situation, and improve the stability of data storage management and resource utilization efficiency.

[0082] The above process can be followed The formula is calculated, where is the adjusted node activity evaluation value, It is the distributed historical data of the node. is an adjustment coefficient used to smooth the evaluation value in a statistical sense.

[0083] In summary, in the embodiment of the present application, by periodically collecting the identification usage of each storage node under the setting of rotating management storage nodes, giving the storage node dynamic monitoring capabilities, the usage status of the data identification can be continuously tracked, ensuring that the survival status of the data identification will not be in uncertainty for a long time, thereby avoiding the inefficient occupation of identification resources; then, the access frequency of the data identification is comprehensively analyzed through the activity evaluation mechanism, and the target data identification to be recycled is accurately screened according to the activity level of the data identification prefix, ensuring that low-activity identification can be identified and adjusted in time; and a blockchain accounting mechanism is established in the data identification recycling and storage link, so that the identification recycling process has verifiability, by storing distributed accounting data corresponding to the recycling cycle in the blockchain network, obtaining the full network status information of the identification life cycle in real time, and verifying whether the identification recycling is reasonable, not only improving the transparency of data storage, but also preventing malicious nodes from manipulating the identification activity record, and strengthening the fairness of the consensus mechanism. Therefore, based on the method of the embodiment of the present application, dynamic optimization of data identification resources is achieved, ensuring that the data circulation system can continuously eliminate zombie identifications and release idle resources. Combined with the distributed storage characteristics of blockchain, data identification management breaks away from the drawbacks of traditional centralized control. While ensuring the right to autonomously allocate identification, it also makes the activity evaluation and identification recycling processes more intelligent and reliable. This ensures that while improving data identification management efficiency, it also reduces the redundant 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 identifications.

[0084] refer to Figure 5 , which shows a distributed data identification activity consensus system 30 based on blockchain provided by an embodiment of the present application, including: Multiple storage nodes A used to constitute the distributed storage network 301 i (i=0,…,n), and blockchain network 302; Storage Node A i For storage node A i When the storage node A0 is determined to be the rotation management storage node in the first rotation period, the storage nodes A in the distributed storage network 301 are collected. i The usage of each stored data identifier is determined, and the data identifier prefix activity level of each data identifier is determined based on the collected usage information. The target data identifier to be recycled is determined based on the determined data identifier prefix activity level of each data identifier, and the target data identifier to be recycled is determined from the storage node A where each target data identifier is located. iReclaim the target data identifier and generate data identifier distributed accounting data X0 corresponding to the first rotation period; the data identifier distributed accounting data records information about the process of reclaiming the target data identifier in the first rotation period; The blockchain network 302 is used to store distributed accounting data X0.

[0085] Based on the above system architecture, refer to Figure 6 , is a data flow process under the embodiment of the present application: R1. Data collection and identification activity evaluation: In a distributed storage network, each storage node A i (i=0,…,n) stores multiple data identifiers. When a storage node A i When a node is confirmed as the rotation management storage node A0 for the current rotation cycle, it is responsible for collecting the identifier usage 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, access node diversity, time domain distribution stability, and abnormal access, and divides it into different activity levels to facilitate the execution of subsequent identifier recycling strategies. R2. Data Identifier Recycling and Distributed Ledger Data Generation: The rotating management storage node A0 selects low-activity identifiers based on the assessed activity level and instructs each storage node to recycle these target data identifiers. After recycling, the system generates the corresponding distributed ledger data X0, recording the lifecycle information of the data identifier, including recycling time, storage nodes involved, access behavior statistics, and other information. This data is used for identifier resource management and historical trend analysis to optimize storage strategies for future rotation cycles. R3. Data Storage on the Blockchain Network: The generated distributed ledger data X0 is stored on the blockchain network, ensuring that all storage nodes can query and verify the data identification management process. The blockchain network records the storage and recycling of identifications during each rotation cycle, making the lifecycle of data identifications transparent and auditable, thereby enhancing the credibility of data management and providing an efficient and fair mechanism for collaboration between storage nodes.

[0086] refer to Figure 7 , is a complete data information management cycle under the embodiment of this application: 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 will record its own access log, including the number of queries for data identifiers, the access source nodes, and the time distribution. These data are used to evaluate the activity of the node and provide a basis for global storage status analysis; the storage node on duty 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.

[0087] S2. In order to identify low-activity identifiers and decide whether to recycle them, the system needs to calculate the activity scores of data identifiers and storage nodes: each storage node calculates the activity score based on its access records, taking into account the 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 the data identifier is calculated based on the comprehensive access situation of all data identifiers under the prefix, and is 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 levels are set into multiple intervals to make identifier management more flexible; for data identifiers assessed as low-activity, the system will put them in the list to be recycled, and release the storage resources after the storage node performs the recycling operation, and generate distributed accounting data at the same time to ensure that the management process is traceable.

[0088] S3. To maintain decentralization and give different nodes the opportunity to participate in storage management, the system needs to elect new rotating management storage nodes: the system selects several storage nodes with higher scores from the storage node scoring 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 candidate storage nodes and determine the rotating management storage nodes for the next cycle. This election method ensures fair and decentralized storage node selection, avoids fixed nodes from occupying management tasks for a long time, and ensures the long-term stability of the storage network.

[0089] refer to Figure 8 , which shows a distributed data identification activity consensus device 40 based on blockchain provided by an embodiment of the present application, applied to a storage node in a distributed storage network, including: The data collection module 401 is configured to collect usage information of each data identifier stored by each storage node in the distributed storage network when the storage node is determined to be a rotation management storage node in the first rotation period; The activity rating module 402 is configured to determine the activity level of the data identifier prefix of each data identifier based on all collected usage information, and determine the target data identifier to be recycled based on the determined activity level of the data identifier prefix of each data identifier; The recovery record module 403 is used to recover the target data identifier from the storage node where each target data identifier is located, and generate data identifier distributed accounting data corresponding to the first rotation period for storage in the blockchain network; the data identifier distributed accounting data records the process of recovering the target data identifier in the first rotation period.

[0090] Optionally, the distributed data identification activity consensus device 40 based on blockchain further includes: The node election module is used to determine the node activity evaluation value of each storage node based on all the collected usage information, and to determine multiple target storage nodes from the storage nodes as candidate storage nodes for the rotating management storage nodes in the second rotation period based on the determined node activity evaluation value of each storage node, and to randomly select the rotating management storage nodes for the second rotation period from the multiple target storage nodes; the second rotation period is the rotation period next to the first rotation period.

[0091] Optionally, the activity rating module 402 includes: An evaluation value submodule, configured to determine an activity evaluation value for each data identifier based on at least one of the access frequency, access node diversity, time domain distribution, and abnormal access conditions of each data identifier in a first rotation period; An interval division submodule is configured to divide the activity evaluation intervals into a plurality of continuous activity evaluation intervals according to 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 level is negatively correlated with the order of the corresponding activity evaluation intervals when arranged from small to large; The rating submodule is used to determine the data identifier prefix activity level of the data identifier corresponding to each activity evaluation value according to the activity evaluation interval into which each activity evaluation value falls.

[0092] Optionally, the evaluation value submodule includes: a component calculation unit, configured to determine 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 stability of the time domain distribution of the access process to the data identifier within a first round of value cycles, and an abnormal access weight for evaluating abnormal access to the data identifier; The integrated calculation unit is used to determine the activity evaluation value as the weighted result of the access frequency evaluation value, the diversity evaluation value and the stability evaluation value adjusted according to the abnormal access weight.

[0093] Optionally, the activity rating module 402 further includes: A historical data submodule is used to obtain distributed historical data for recording information about a process of recovering target data identifiers in a third rotation cycle; the third rotation cycle is a rotation cycle preceding the first rotation cycle; The evaluation adjustment submodule is used to adjust each determined activity evaluation value according to distributed historical data.

[0094] Optionally, the distributed historical data records the historical activity evaluation value of each data identifier in the third round of value cycle, and the evaluation adjustment submodule includes: A smoothing weighting unit, configured to weight the activity evaluation values ​​and activity history evaluation values ​​of the data identifiers that coexist in the first rotation period and the third rotation period according to a preset smoothing weight; The evaluation adjustment unit is used to determine the result obtained after weighting according to the smoothing weight as the updated value of the activity evaluation value of the data identifier.

[0095] Optionally, the node election module includes: The node evaluation submodule is used to determine a node activity evaluation value for each storage node based on at least one of the access frequency, access node diversity, and user access distribution of each storage node in the first rotation period.

[0096] The target node submodule is used to determine multiple target storage nodes from all storage nodes according to the determined node activity evaluation value of each storage node and a preset candidate node quantity threshold.

[0097] The node election submodule is used to randomly determine a storage node from all target storage nodes as the rotation management storage node for the second rotation period.

[0098] In summary, in the embodiment of the present application, by periodically collecting the identification usage of each storage node under the setting of rotating management storage nodes, giving the storage node dynamic monitoring capabilities, the usage status of the data identification can be continuously tracked, ensuring that the survival status of the data identification will not be in uncertainty for a long time, thereby avoiding the inefficient occupation of identification resources; then, the access frequency of the data identification is comprehensively analyzed through the activity evaluation mechanism, and the target data identification to be recycled is accurately screened according to the activity level of the data identification prefix, ensuring that low-activity identification can be identified and adjusted in time; and a blockchain accounting mechanism is established in the data identification recycling and storage link, so that the identification recycling process has verifiability, by storing distributed accounting data corresponding to the recycling cycle in the blockchain network, obtaining the full network status information of the identification life cycle in real time, and verifying whether the identification recycling is reasonable, not only improving the transparency of data storage, but also preventing malicious nodes from manipulating the identification activity record, and strengthening the fairness of the consensus mechanism. Therefore, based on the method of the embodiment of the present application, dynamic optimization of data identification resources is achieved, ensuring that the data circulation system can continuously eliminate zombie identifications and release idle resources. Combined with the distributed storage characteristics of blockchain, data identification management breaks away from the drawbacks of traditional centralized control. While ensuring the right to autonomously allocate identification, it also makes the activity evaluation and identification recycling processes more intelligent and reliable. This ensures that while improving data identification management efficiency, it also reduces the redundant 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 identifications.

[0099] Reference Figure 9 , electronic device 500 may include one or more of the following components: a processing component 502 , a memory 504 , a power component 506 , a multimedia component 508 , an audio component 510 , an input / output (I / O) interface 512 , a sensor component 514 , and a communication component 516 .

[0100] The processing component 502 generally controls the overall operation of the electronic device 500, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 502 may include one or more processors 520 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 502 may include one or more modules to facilitate interaction between the processing component 502 and other components. For example, the processing component 502 may include a multimedia module to facilitate interaction between the multimedia component 508 and the processing component 502.

[0101] The memory 504 is used to store various types of data to support operations on the electronic device 500. Examples of such data include instructions for any application or method operating on the electronic device 500, contact data, phone book data, messages, pictures, multimedia, etc. The 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 memory, flash memory, magnetic disk, or optical disk.

[0102] The power supply assembly 506 provides power to the various components of the electronic device 500. The power supply assembly 506 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 500.

[0103] The multimedia component 508 includes an interface that provides an output interface between the electronic device 500 and the 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 touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensors can not only sense the demarcation of a touch or slide action, but also detect the duration and pressure associated with the touch or slide action. In some embodiments, the multimedia component 508 includes a front-facing camera and / or a rear-facing camera. When the electronic device 500 is in an operating mode, such as a capture mode or a multimedia mode, the front-facing camera and / or the rear-facing camera can receive external multimedia data. Each front-facing camera and the rear-facing camera can have a fixed optical lens system or have focal length and optical zoom capabilities.

[0104] The audio component 510 is used to output and / or input audio signals. For example, the audio component 510 includes a microphone (MIC) that receives external audio signals when the electronic device 500 is in an operating mode, such as a call mode, a recording mode, or a voice recognition mode. The received audio signals may be further stored in the memory 504 or transmitted via the communication component 516. In some embodiments, the audio component 510 also includes a speaker for outputting audio signals.

[0105] The input / output I / O interface 512 provides an interface between the processing component 502 and peripheral interface modules, such as a keyboard, a click wheel, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0106] The sensor assembly 514 includes one or more sensors for providing various aspects of status assessment for the electronic device 500. For example, the sensor assembly 514 can detect the open / closed state of the electronic device 500, the relative positioning of components, such as the display and keypad of the electronic device 500. The sensor assembly 514 can also detect changes in the position of the electronic device 500 or a component of the electronic device 500, the presence or absence of user contact with the electronic device 500, the orientation or acceleration / deceleration of the electronic device 500, and temperature changes of the electronic device 500. The sensor assembly 514 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The 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, the sensor assembly 514 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0107] The communication component 516 is used to facilitate wired or wireless communication between the electronic device 500 and other devices. The electronic device 500 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G, or 5G), or a combination thereof. In an exemplary embodiment, the communication component 516 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 516 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0108] 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 the present application.

[0109] 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 the processor 520 of the electronic device 500 to perform the above method. For example, the non-transitory storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0110] 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 based on an operating system stored in the memory 504, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or the like.

[0111] It should be noted that, for the sake of simplicity, the method embodiments of the present application are described as a series of action combinations. However, those skilled in the art should be aware that the embodiments of the present application are not limited by the order of the actions described, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present application.

[0112] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0113] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A distributed data identification activity consensus method based on blockchain, characterized by: Storage nodes used in distributed storage networks include: In a case where the storage node is determined to be a rotation management storage node in a first rotation period, collecting usage information of each data identifier stored by each storage node in the distributed storage network; Determining a data identifier prefix activity level for each data identifier based on all the collected usage conditions, and determining a target data identifier to be recycled based on the determined data identifier prefix activity level for each data identifier; The target data identifier is recovered from each storage node where the target data identifier is located, and data identifier distributed accounting data corresponding to the first rotation period is generated for storage in the blockchain network; the data identifier distributed accounting data records information about the process of recovering the target data identifier in the first rotation period.

2. The distributed data identification activity consensus method based on blockchain as claimed in claim 1 is characterized in that: The distributed data identification activity consensus method based on blockchain also includes: Based on all the collected usage conditions, the node activity evaluation value of each storage node is determined, and based on the determined node activity evaluation value of each storage node, multiple target storage nodes as alternative storage nodes for the rotating management storage nodes in the second rotation period are determined from the storage nodes, and the rotating management storage nodes for the second rotation period are randomly selected from the multiple target storage nodes; the second rotation period is the next rotation period of the first rotation period.

3. The distributed data identification activity consensus method based on blockchain as claimed in claim 1 is characterized in that: Determining the data identifier prefix activity level of each data identifier based on all the collected usage conditions includes: Determine an activity evaluation value for each data identifier according to at least one of an access frequency, access node diversity, time domain distribution, and abnormal access condition of each data identifier during the first rotation period; Dividing a plurality of continuous activity evaluation intervals according to 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 level is negatively correlated with the order of the corresponding activity evaluation intervals when arranged from small to large; According to the activity evaluation interval into which each activity evaluation value falls, a data identifier prefix activity level of the data identifier corresponding to each activity evaluation value is determined.

4. The distributed data identification activity consensus method based on blockchain as claimed in claim 3 is characterized in that: The determining of the activity evaluation value of each data identifier according to at least one of the access frequency, access node diversity, time domain distribution, and abnormal access situation of each data identifier during the first rotation period includes: Determining 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 stability of the time domain distribution of the access process to the data identifier within the first rotation period, and an abnormal access weight for evaluating abnormal access to the data identifier; The activity evaluation value is determined as a weighted result of the access frequency evaluation value, the diversity evaluation value, and the stability evaluation value adjusted according to the abnormal access weight.

5. The distributed data identification activity consensus method based on blockchain as claimed in claim 3 is characterized in that: Before dividing a plurality of continuous activity evaluation intervals according to the maximum activity evaluation value among all the determined activity evaluation values, the blockchain-based distributed data identification activity consensus method further includes: Obtaining distributed historical data for recording information about a process of reclaiming the target data identifier in a third rotation cycle; the third rotation cycle is a rotation cycle preceding the first rotation cycle; Each of the determined activity evaluation values ​​is adjusted according to the distributed historical data.

6. The distributed data identification activity consensus method based on blockchain as claimed in claim 5 is characterized in that: The distributed historical data records the activity historical evaluation value of each data identifier in the third rotation period, and adjusting each of the determined activity evaluation values ​​according to the distributed historical data includes: Weighting the activity evaluation values ​​and activity historical evaluation values ​​of the data identifiers that exist in both the first rotation period and the third rotation period according to a preset smoothing weight; A result obtained by weighting according to the smoothing weight is determined as an updated value of the activity evaluation value of the data identifier.

7. A distributed data identification activity consensus system based on blockchain, characterized by: include: Multiple storage nodes and blockchain networks that form a distributed storage network; The storage node is configured to, when the storage node is determined to be a rotation management storage node within a first rotation period, collect usage information of each data identifier stored by each storage node in the distributed storage network, determine a data identifier prefix activity level of each data identifier based on all the collected usage information, determine a target data identifier to be recycled based on the determined data identifier prefix activity level of each data identifier, recycle the target data identifier from each storage node where the target data identifier is located, and generate data identifier distributed accounting data corresponding to the first rotation period; the data identifier distributed accounting data records information about a process of recycling the target data identifier in the first rotation period; The blockchain network is used to store the distributed accounting data.

8. A distributed data identification activity consensus device based on blockchain, characterized in that: Storage nodes used in distributed storage networks include: a data collection module, configured to collect usage information of each data identifier stored by each storage node in the distributed storage network when the storage node is determined to be a rotation management storage node in a first rotation period; an activity rating module, configured to determine an activity level of a data identifier prefix of each data identifier based on all the collected usage conditions, and determine a target data identifier to be recycled based on the determined activity level of the data identifier prefix of each data identifier; A recovery record module is used to recover the target data identifier from each storage node where the target data identifier is located, and generate data identifier distributed accounting data corresponding to the first rotation period for storage in the blockchain network; the data identifier distributed accounting data records information about the process of recovering the target data identifier in the first rotation period.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the distributed data identification activity consensus method based on blockchain as described in any one of claims 1 to 6.

10. An electronic device, characterized in that: The invention comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the distributed data identification activity consensus method based on blockchain are implemented as described in any one of claims 1 to 6.

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