Blockchain-based trust assessment data storage traceability method and system

Abstract

The application discloses a trust evaluation data storage tracing method and system based on a blockchain technology, and particularly relates to the technical field of data storage management, and comprises the following steps: based on the distributed storage characteristics of the blockchain, trust evaluation data is recorded in the form of blocks, the automation management of data submission, verification, storage and updating is realized by using a smart contract, the data authenticity and non-tamperability are ensured, a time mark is added to the data through a time stamp function, a time-ordered operation chain is formed, a data tracing function is provided, authorized users are allowed to query data sources and historical operation records, based on data access characteristics, the storage area is dynamically adjusted, efficient utilization and optimized management of storage resources are realized; the application can dynamically adjust the storage position of data, hot data is saved in a high-performance storage area, and cold data is migrated to a low-cost storage area, so that the access efficiency of high-frequency data is ensured, and the storage cost is optimized.

Description

Technical Field

[0001] This invention relates to the field of data storage management technology, and more specifically, to a trust assessment data storage traceability method and system based on blockchain technology. Background Technology

[0002] Blockchain technology is a decentralized distributed ledger technology that uses cryptographic methods to ensure the immutability and transparency of data. Blockchain uses hash algorithms to link data blocks; once data is recorded on the chain, it cannot be tampered with, ensuring data authenticity. All data operations are fully recorded and can be queried and traced at any time, making it widely used in finance, logistics, healthcare, and other fields.

[0003] Trust assessment data is crucial for measuring the trust relationship between entities (such as users, devices, and nodes). It typically includes: Basic data: such as user ratings, interaction records, and transaction status; Dynamic data: real-time generated data on changes in trust levels; Historical data: including the generation time and change records of trust scores; and Processing results: trust scores or trust levels generated based on the raw data. Trust assessment data plays a vital role in many fields: for example, in e-commerce, it's used to assess the creditworthiness of buyers and sellers; in financial transactions, it's used to prevent fraud; and in the Internet of Things (IoT), it's used to verify secure connections between device nodes.

[0004] While blockchain technology has important applications in data storage and management, its application in trust assessment data management still faces the following challenges: the distributed storage model of blockchain leads to high data redundancy; and current blockchain storage mechanisms do not fully consider data access frequency and usage characteristics, resulting in unreasonable allocation of storage resources. Therefore, this paper proposes a blockchain-based method and system for tracing and tracking trust assessment data to address these issues. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A trust assessment data storage and traceability method based on blockchain technology includes the following steps:

[0007] By leveraging the distributed storage characteristics of blockchain networks, the raw data and processing results generated during the trust assessment process are recorded in the blockchain in the form of blocks;

[0008] The submission, verification, storage, and updating of trust assessment data are managed through smart contracts to ensure the authenticity and immutability of the data, and the timestamp function of blockchain is used to add time markers to the trust assessment data, forming a time-ordered data chain.

[0009] The data traceability function is provided to allow authorized users to query data sources and historical operation records based on the smart contract;

[0010] The trust evaluation data access information is monitored, and based on the monitored trust evaluation data access information, access characteristic analysis is performed, and the data storage area is adjusted based on the access characteristic analysis result to realize dynamic storage optimization.

[0011] In a preferred embodiment, in the access characteristic analysis, an access pattern irregularity index for measuring the irregularity of the time distribution of data access, a data access activity index for measuring the activity of data access, and a cumulative residence time index for evaluating the migration demand through the distribution and change trend of residence time are generated respectively.

[0012] In a preferred embodiment, the smart contract includes a data verification unit, a data storage unit, and a data query unit;

[0013] The data verification unit is used to verify the integrity, format and source of the submitted data;

[0014] The data storage unit is used to store the verified data in the blockchain after encryption;

[0015] The data query unit is used to provide access rights to the corresponding data according to the user's rights and query conditions.

[0016] In a preferred embodiment, the data traceability function refers to:

[0017] Query the initial generation time and source of the data, query the operation records of the data on the blockchain, including submission, modification and storage location change, and generate a visual traceability chain to show the full life cycle record of the data.

[0018] In a preferred embodiment, the access pattern irregularity index is obtained by:

[0019] In a fixed time window, the time series of access time is obtained, the mean value of all access times and the total number of accesses are calculated according to the i-th access time point, and the randomness of the time interval distribution is measured by the entropy value calculation result of the time interval distribution:

[0020] ; represents the probability distribution of the k-th time interval length in the interval divided by the fixed time window, represents the total number of time intervals divided by the fixed time window, represents the entropy value of the time interval distribution;

[0021] The calculation formula of the access pattern irregularity index is:

[0022] MaxGap represents the maximum interval between two adjacent accesses in the time series, MinGap represents the minimum interval between two adjacent accesses in the time series, represents the mean of all access times, represents the i-th access time point, represents the total number of accesses, represents the access pattern irregularity index.

[0023] In a preferred embodiment, the acquisition logic of the data access activity index is:

[0024] In a fixed time window, all access records are acquired, and the i-th access time point, the i-th access query intensity, and the current time point are extracted. For each access, the time decay weight is calculated according to the difference between the access time and the current time :

[0025] ; represents the current time point, represents the i-th access time point, represents a preset non-zero time decay factor;

[0026] The calculation formula of the data access activity index is:

[0027] ; represents the i-th access query intensity, represents the data access activity index.

[0028] In a preferred embodiment, the acquisition logic of the i-th access query intensity is:

[0029] ; represents the number of query operations of the i-th access, represents the data volume generated by the i-th access, represents the response time of the i-th access, represents the number of threads occupied by the i-th access query, represents a preset non-zero constant.

[0030] In a preferred embodiment, the acquisition logic of the cumulative residence time index is: acquiring the cumulative residence time of data in the j-th type of storage area in the last k adjacent time windows, the access frequency of data in the j-th type of storage area, and the cumulative residence time index calculation formula is:

[0031] ​​ ; represents a preset non-zero adjustment coefficient, represents the access frequency of data in the storage area of the type, represents the total number of types of storage areas, represents the cumulative residence time of data in the storage area of the type, represents the cumulative residence time index.

[0032] In a preferred embodiment, adjusting the data storage area based on the access characteristic analysis result means:

[0033] Taking the data corresponding access mode irregularity index, data access activity index, and cumulative residence time index as the input data of logical reasoning, taking the target storage area type of data migration as the output data of logical reasoning, fuzzifying the input variable, converting the value of the input variable into a fuzzy set, fuzzifying the output variable, converting the output variable into a fuzzy set, formulating fuzzy rules to describe the adaptability of each storage area type under different data category combinations, reasoning the fuzzified input variable through the fuzzy rules to obtain the target storage area type of data migration, and when the target storage area type of data migration is inconsistent with the current storage area type of data, performing a migration operation on the data to realize dynamic storage optimization.

[0034] In a preferred embodiment, the trust evaluation data storage tracing system based on the blockchain technology comprises:

[0035] A data recording module records the original data and processing results generated in the trust evaluation process in the form of blocks in the blockchain by using the distributed storage characteristics of the blockchain network;

[0036] An intelligent contract module manages the submission, verification, storage, and update of trust evaluation data through the built-in intelligent contract, ensures the authenticity and non-tamperability of the data, and adds a time marker to the trust evaluation data by using the timestamp function of the blockchain to form a time-ordered data chain;

[0037] A data tracing module provides a data tracing function and allows authorized users to query the data source and historical operation records based on the intelligent contract;

[0038] A dynamic storage module monitors the trust evaluation data access information, performs access characteristic analysis based on the monitored trust evaluation data access information, adjusts the data storage area based on the access characteristic analysis result, and realizes dynamic storage optimization.

[0039] Technical effects and advantages of the present application:

[0040] The trust evaluation data in the application is stored in multiple nodes, avoiding the risk of data loss and tampering caused by single point failure and centralized storage. After the data is chained, a timestamp is automatically generated to ensure the time sequence integrity of the data. Any operation will be recorded in the chain to form an auditable history track. The integrity and source verification are automatically performed when the data is submitted, eliminating the possibility of false data and illegal operations. The encryption storage mechanism further improves the confidentiality of the data, protecting sensitive data from leakage even in a distributed storage environment.

[0041] The application provides a full life cycle traceability function for data, including generation time, source, storage path and operation record, which facilitates users to review the complete chain of trust evaluation data. The data traceability information is visualized, which intuitively displays the change history and operation details of each piece of data, reducing the complexity of traceability. The data change and operation record is completely open, ensuring the transparency of the data usage process. The traceability function supports supervision and compliance audit, which is particularly important in financial transactions and supply chain management scenarios.

[0042] The application analyzes the data based on access pattern irregularity, data access activity, and cumulative residence time index, and dynamically adjusts the storage location of the data. Hot data is saved in a high-performance storage area, and cold data is migrated to a low-cost storage area, which not only ensures the access efficiency of high-frequency data, but also optimizes the storage cost. Data that has not been accessed for a long time or has a low access frequency is migrated to the cold storage area through cumulative residence time index analysis, avoiding the occupation of high-performance storage devices by low-priority data, and classifying the management of storage areas, making the allocation of storage resources more reasonable.

[0043] Through data access activity index analysis, high-frequency access data is preferentially saved in a fast storage area, significantly improving query response speed. Dynamic analysis of data activity enables the system to adapt to changes in data access demand in real time. For irregularly accessed data, it is transferred to the corresponding storage area to avoid hot data and cold data concentrated in a single storage node, reducing the pressure on the storage system. Through fuzzy analysis of multiple access characteristic indexes, the adaptive storage area of the data is flexibly judged to meet the diversified storage optimization needs. High-frequency data is stored in a fast storage area, while low-frequency data and long-term unused data are migrated to a low-cost area, saving high-performance storage resources. In the context of rapid growth of data volume, hierarchical management and dynamic adjustment of storage location reduce the total storage cost of the system. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to facilitate the understanding of those skilled in the art, the application will be further described below with reference to the accompanying drawings;

[0045] Figure 1 The figure is a schematic diagram of the trust evaluation data storage and traceability method based on blockchain technology in the application.

[0046] Figure 2 The schematic diagram of the trust evaluation data storage and traceability system based on the blockchain technology in the application. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments of the application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the application.

[0048] Reference Figure 1 - Figure 2 The following embodiments are obtained:

[0049] Embodiment 1: With the rapid development of Internet technology and blockchain technology, trust evaluation has become an important tool to ensure data security and improve transaction transparency. However, the traditional trust evaluation method has the following problems: data authenticity is difficult to guarantee: the trust evaluation data in the traditional system is easy to be tampered with, and lacks reliable storage and verification mechanism. Data traceability is insufficient: the traditional system often cannot provide data source, change record and transparent management of the whole life cycle. Data storage efficiency is low: when facing large-scale dynamic data, the traditional storage strategy is difficult to adjust according to the data access mode, resulting in waste of storage resources and low access efficiency. The trust evaluation data storage and traceability method based on the blockchain technology can solve the above problems and provide a safe, efficient and transparent technical solution for trust evaluation.

[0050] The purpose of the application is: to guarantee the authenticity and security of data: by using the non-tamperable and distributed storage characteristics of the blockchain, the authenticity and security of the trust evaluation data are ensured. Realize the traceability of data: through the intelligent contract to record and manage the source, change and operation history of data, provide comprehensive data traceability. Improve the efficiency of data storage: through access characteristic analysis, dynamically adjust the data storage area, optimize the utilization rate of storage resources. Enhance the intelligent level of trust evaluation: combine the blockchain and intelligent contract to improve the automation level of trust evaluation and reduce the management complexity.

[0051] The application provides a trust evaluation data storage and traceability method based on the blockchain technology, which comprises the following steps:

[0052] The original data and processing results generated in the trust evaluation process are recorded in the blockchain in the form of blocks by using the distributed storage characteristics of the blockchain network, ensuring the data's non-tamperability: once the data is uploaded to the chain, it cannot be modified, ensuring the data's authenticity. Distributed storage improves reliability: data is stored on multiple nodes in the blockchain network, avoiding single-point failures. The trust evaluation data is provided with a basic storage architecture, making it highly secure and highly available.

[0053] The submission, verification, storage, and updating of trust evaluation data are managed by smart contracts, ensuring the data's authenticity and non-tamperability, and the trust evaluation data is time-stamped using the blockchain's timestamp function, forming a time-ordered data chain. Smart contracts automate management, enabling automatic data verification, storage, and updating through smart contracts, reducing human intervention. Time-stamped data chains record data generation time and submission order, forming a time-ordered data chain that enhances data auditability; preventing malicious data submission or tampering, and improving data security.

[0054] A data traceability function is provided, allowing authorized users to query data sources and historical operation records based on smart contracts; users can understand data generation, changes, and operation records through the data traceability function, improving transparency, and clear traceability can enhance trust in the system, enhancing trust, supporting data auditing and compliance checks, and being particularly suitable for trust management needs in the financial, e-commerce, and supply chain fields.

[0055] Trust evaluation data access information is monitored, and based on the monitored trust evaluation data access information, access characteristic analysis is performed, and the data storage area is adjusted based on the access characteristic analysis results to achieve dynamic storage optimization. By dynamically adjusting the storage area, storage resource waste is avoided, data storage efficiency is improved, high-frequency access data is migrated to faster access storage areas, query response speed is improved, access efficiency is improved, and an intelligent storage optimization model is constructed based on the access pattern irregularity index, data access activity index, and cumulative residence time index.

[0056] When analyzing access characteristics, an access pattern irregularity index is generated to measure the time distribution irregularity of data access, a data access activity index is generated to measure the activity of data access, and a cumulative residence time index is generated to evaluate the migration demand of data by the distribution and trend of residence time. The access pattern irregularity index measures whether the time distribution of data access is random, helps to identify whether the access pattern has regularity, and is used to evaluate the adjustment demand of the data storage area. The data access activity index measures the recent access activity of data, reflects the importance and frequency of data use, and is used to determine whether the data needs to be migrated to a higher priority storage area. The cumulative residence time index evaluates whether long-term unaccessed data needs to be migrated to a low-level storage area by the residence time and access frequency of data in different storage areas, improves the efficiency of cold data storage, and saves resources.

[0057] The smart contract includes a data verification unit, a data storage unit, and a data query unit; the data verification unit is used to verify the integrity, format and source of submitted data; the data storage unit is used to store the verified data after encryption in the blockchain; the data query unit is used to provide access rights to the corresponding data according to the user's rights and query conditions. More specifically, the design of the smart contract module includes the following three core units, each unit has independent function and practical significance:

[0058] Data verification unit: verify the integrity of the submitted data: ensure that the data has not been tampered with or lost. Verify data format: check if the data meets the preset standards (such as data fields, encoding methods). Verify data source: verify if the source of the submitted data is legal and reliable. Significance: By verifying the integrity, it is ensured that the data has not been tampered with or damaged during transmission. Format verification ensures that data can be correctly stored and recognized by the system. Establish a trust foundation, verify the source to prevent malicious users or untrusted sources from submitting false or incorrect data. Reduce storage resource waste, reject data that does not meet the requirements, and avoid unnecessary storage overhead.

[0059] Data storage unit: receives verified data, encrypts data to ensure data security during storage, and records encrypted data in the form of blocks in the blockchain. Significance: Data security: protected by encryption, prevents data from being leaked during storage and transmission. Even if the data is stored in a public blockchain, it can prevent unauthorized interpretation. Data tamper-proofing, the distributed storage and chain structure of the blockchain ensure that data cannot be changed once stored, providing a trusted storage basis for trust evaluation. Meet the compliance requirements, the encrypted storage of sensitive information meets the requirements of data privacy protection and security.

[0060] Data query unit: receives user query requests. Verifies user permissions and determines whether to grant access to relevant data based on user identity. Retrieves stored data on the blockchain according to query conditions and returns results. Significance: Enhances system transparency, authorized users can clearly understand the source, changes and current status of data, enhancing trust in the system. Safeguards data access security, permission control ensures that only authorized users can access sensitive or important data, preventing illegal misuse of data. Improves query efficiency, optimizes query logic through preset query conditions, making data access more efficient. Supports multiple scenario requirements, provides customized data access services according to different user permissions and needs, suitable for different business scenarios.

[0061] Data traceability function refers to:

[0062] Querying the initial generation time and source of data, querying the operation records of data on the blockchain, including submission, modification and storage location changes, and generating a visual traceability chain to show the full life cycle records of data. By tracing the generation time and source of data, it ensures that the data source is reliable and the content is true. In trust evaluation, traceability can help judge the reliability of data, especially in supply chain management, medical records and other scenarios, users can clearly understand the background of data generation, improving the transparency and credibility of the system. Record data submission, modification and storage location changes and other operations to form a complete life cycle log. Operation records are saved in the blockchain, ensuring that all operations are tracked and any changes are recorded, making it easy to audit and trace back. Especially in the fields of finance, medicine and supply chain, complete operation records can meet the audit and regulatory requirements. By graphically displaying the entire process of data generation, change and storage, users can quickly understand the complete trajectory of data. For non-technical users, visual traceability chain reduces the understanding threshold, making data tracking more convenient. Managers can analyze data flow through the traceability chain to determine the reliability of trust evaluation or the cause of the problem.

[0063] Moreover, it should be noted that: Dynamic adjustment of storage areas will be performed according to the characteristics of data access, such as migrating frequently accessed data to high-efficiency storage areas (such as hot storage areas or super-hot storage areas). Authorized users can retrieve traceability data faster, improving system response speed. After optimizing the layout of storage areas, frequently traced data is always located in priority storage areas, avoiding time overhead caused by inefficient storage. Data traceability operations will call a large number of historical records, and dynamic adjustment of storage areas can move long-term non-accessible cold data to low-level storage areas, freeing up high-efficiency storage space. Benefits: By distinguishing between cold and hot data, avoid high-cost storage resources being occupied by cold data. Keep important traceability data in high-priority storage at all times to improve overall resource utilization.

[0064] The dynamic adjustment of the storage area can timely migrate relevant data according to the data traceability requirements, and ensure that the data is located in a suitable storage area when used. When the traceability requirement appears, the relevant data can be immediately retrieved without waiting for the migration or loading of the data in the cold storage. For users, fast traceability response makes the system more practical and reliable. The data traceability function may increase the load pressure as the data size grows, and through the dynamic adjustment of the storage area, the access pressure of the data can be dispersed. Advantage: transfer low-frequency traceability data to low-level storage, reduce the load of the hot storage area, and after optimizing the storage allocation, the system can handle more traceability tasks while ensuring performance.

[0065] The access mode irregularity index acquisition logic is:

[0066] In a fixed time window, the time sequence of access time is obtained, the mean value of all access times is calculated according to the ith access time point, and the total number of accesses is calculated. The randomness of the time interval distribution is measured by the entropy value calculation result of the time interval distribution:

[0067] ; P(k) represents the probability distribution of the kth time interval length in the fixed time window division, N represents the total number of time intervals divided by the fixed time window, H represents the entropy value of the time interval distribution; the higher the entropy value, the more random the access time interval distribution, and the more unpredictable the access behavior; the lower the entropy value, the more concentrated and regular the access time interval distribution. By calculating the probability distribution of the time interval, the tendency of the access behavior in different time periods can be reflected, and the formula of Shannon entropy is used to ensure accurate quantification of randomness and uncertainty.

[0068] The calculation formula of the access mode irregularity index is:

[0069] ; MaxGap represents the maximum interval between two adjacent accesses in the time sequence, MinGap represents the minimum interval between two adjacent accesses in the time sequence, μ represents the mean value of all access times, ti represents the ith access time point, N represents the total number of accesses, represents the access pattern irregularity index. The ratio of extreme values (maximum interval and minimum interval) in the time interval amplifies the sensitivity of the irregularity index. If the maximum interval is much larger than the minimum interval, the access pattern has higher irregularity, reflecting the dispersion of access behavior in the time interval. The larger the interval range, the more uneven the access distribution. By calculating the standard deviation of the time point, the degree of deviation of the access time point from the mean is quantified. The greater the fluctuation, the more irregular. The standard deviation combines the overall dispersion of the time point, which is complementary to the entropy value, and comprehensively reflects the access regularity. By amplifying the index, the randomness of the time interval distribution is enhanced. When the entropy value is larger, the growth of the exponential term is more significant, highlighting the key impact of distribution randomness on irregularity. The revelation of the storage strategy: high AII value: data access is irregular, suitable for migration to the cold storage area, and reduce the occupation of efficient storage resources. Low AII value: data access is more regular, suitable for saving in hot storage or super-hot storage area to meet the high-frequency access demand.

[0070] The logic for obtaining the data access activity index is as follows:

[0071] In a fixed time window, all access records are obtained, and the time point of the ith access, the query intensity of the ith access, and the current time point are extracted. For each access, the time decay weight is calculated according to the difference between the access time and the current time :

[0072] ; represents the current time point, represents the ith access time point, represents a preset non-zero time decay factor. By setting different λ values, the decay rate of the weight can be flexibly controlled to adapt to different scenarios (for example, the current time window belongs to a high activity scenario, and slower decay is required).

[0073] The calculation formula of the data access activity index is as follows:

[0074] ; represents the query intensity of the ith access, represents the data access activity index. The time decay weight In conjunction with query intensity, the activity level of data access is dynamically assessed. Data with recent high-intensity access is assigned a higher activity index, highlighting the dual impact of time and intensity, and reflecting the current state of the data. A higher data access activity index indicates more recent access to the data, with each access exhibiting high query intensity. Active data should be prioritized for storage in high-performance storage areas (such as hot or ultra-hot storage areas) to improve access efficiency. The higher the recent usage frequency of data, the higher the time decay weight, indicating that the data access behavior occurred within a shorter period. A higher data access activity index indicates greater data importance and may require more resource support. Impact on storage strategy: High DAI: Prioritize allocation of fast storage resources to reduce query latency. Low DAI: Can be moved to lower-priority storage areas (such as cold storage areas).

[0075] The logic for obtaining the query strength of the i-th visit is as follows:

[0076] ; This represents the number of query operations performed on the i-th access, reflecting the complexity of the access. This represents the amount of data generated during the i-th access, which directly affects the query intensity. This represents the response time for the i-th access, measuring the efficiency of the access. This represents the number of threads used in the i-th query, reflecting the system resources consumed during the access. This represents a preset non-zero constant. By combining the number of operations and the amount of data, and introducing the number of threads and response time, the true intensity of a single access can be accurately measured.

[0077] The logic for obtaining the cumulative dwell time index is as follows: obtain adjacent... In the latest time window, the data is in the [number]th [timeframe]. The cumulative dwell time of each type of storage area, data in the [number]th [period]. The formula for calculating the cumulative retention time index for the access frequency of each type of storage region is as follows:

[0078] ; This represents the preset non-zero adjustment coefficient. Indicates the data in the first place. The access frequency of different types of storage areas is used to determine which data is more important and should be prioritized for storage in the fast storage area. Add the following to the denominator: This reduces the contribution of frequently accessed data to the index, causing the index to focus more on less frequently accessed data. Indicates the total number of types in the storage area. Indicates the data in the first place. One type of storage area accumulates the cumulative residence time, indicating that long-term residence of data may occupy storage resources, and long-term unaccessed data needs to be migrated from high-priority storage areas to low-priority areas to save resources. The cumulative residence time index is represented. The influence of access frequency on CRTI is controlled by an exponential function, and high causes the exponential to decay rapidly, reducing the influence of high-frequency data on residence time. Low Data with less access frequency will contribute more to the index, indicating that it needs to be migrated to a low-priority storage area. Normalization is performed on all time windows to smooth short-term fluctuations and ensure that CRTI is more representative in the long term. The cumulative residence time and access frequency of data are dynamic, and taking the average improves the stability and accuracy of the index. High CRTI indicates that the data occupies a large amount of high-performance storage resources, but the actual access demand is low. Data with high CRTI is a migration candidate for dynamic storage optimization, suitable for transferring from fast storage areas (such as hot storage areas) to low-cost storage areas (such as cold storage areas). The cumulative residence time index, as a core parameter, is used together with the access pattern irregularity index and the data access activity index for dynamic storage optimization decisions: high CRTI: preferentially migrate to a low-priority storage area. Low CRTI: remain in the current storage area or migrate to a higher-priority storage area.

[0079] Adjusting the data storage area based on access characteristic analysis results means:

[0080] The access pattern irregularity index, data access activity index, and cumulative residence time index of the data are used together as input data for logical reasoning, and the target storage area type for data migration is used as output data for logical reasoning. The input variables are fuzzified, converting the values of the input variables into fuzzy sets, and the output variables are fuzzified, converting the output variables into fuzzy sets. Fuzzy rules are developed to describe the adaptability of each storage area type under different data category combinations. The fuzzified input variables are reasoned through the fuzzy rules to obtain the target storage area type for data migration. When the target storage area type for data migration is different from the current storage area type of the data, the data is migrated to achieve dynamic storage optimization.

[0081] The method for adjusting the data storage area based on access characteristic analysis results uses fuzzy logic reasoning technology to achieve dynamic storage optimization through comprehensive analysis of data access characteristic indicators. The specific principles are as follows: input variable acquisition: the system analyzes the access behavior of the data and extracts three key indicators: access pattern irregularity, data access activity, and cumulative residence time. These indicators measure the regularity of data access, recent usage frequency, and residence in storage, forming the basis data for storage optimization decisions.

[0082] Fuzzification: Convert the precise access characteristic indicators into fuzzy sets. For example, divide the access activity into "low activity", "medium activity", "high activity", and divide the cumulative residence time into "short time", "medium time", "long time", to make the indicators more adaptive. The specific process of fuzzification can use a triangular membership function or a trapezoidal membership function to correspond the indicator value to the membership degree of different fuzzy language variables.

[0083] Fuzzy rule making: According to the characteristics of the storage area and the impact of different access characteristic combinations on the demand for storage resources, design a set of rules. For example: if the access activity is "high activity" and the residence time is "short time", then store to the high-performance storage area first. If the access pattern irregularity is "high irregularity" and the activity is "low activity", then migrate to the low-priority storage area.

[0084] In the fuzzy reasoning process, multiple indicators need to be processed comprehensively. Common methods include: max-min method: for the membership degree in each rule, take the minimum value of the input variable as the membership degree of the reasoning result. For example, if the membership degree of "high activity" in a rule is 0.8 and the membership degree of "short residence time" is 0.6, then the membership degree of the reasoning result of this rule is 0.6. Weighted average method: when synthesizing the results of multiple fuzzy rules, assign weights according to the membership degrees and calculate the weighted average value as the output result. For example, if the membership degree of rule 1 is 0.7 and the weight is 0.5, and the membership degree of rule 2 is 0.8 and the weight is 0.5, then the comprehensive result is 0.7x0.5+0.8x0.5=0.75.

[0085] Fuzzy reasoning and defuzzification: According to the formulated rules and the membership degrees of the input variables, use fuzzy reasoning technology to calculate the fuzzy set of the target storage area type. Use defuzzification methods (such as the center of gravity method) to convert the fuzzy set into a precise storage area type. The center of gravity method can integrate all membership degrees and output variable values to generate a specific storage target.

[0086] Data migration execution: If the target storage area obtained by reasoning is inconsistent with the current storage area, the system automatically performs data migration operation to move data from the current area to the target storage area determined by reasoning.

[0087] By dynamically adjusting the data storage location, high-frequency access data is saved in high-performance storage areas, and low-frequency access or long-time data is migrated to low-cost storage areas, improving the utilization of storage resources. Migrating long-term non-access or low-activity data to low-cost storage areas can significantly reduce the use cost of high-performance storage devices and optimize the allocation of storage resources. High-activity data is always in fast storage areas, ensuring that the system can quickly respond to user queries and operation requests, improving overall performance. Intelligent reasoning and decision-making are achieved using fuzzy logic, reducing manual intervention and improving the automation level of the system, making storage management more flexible and efficient. Data access behavior may change over time, and the dynamic adjustment mechanism can adapt to such changes in real time, ensuring that system resource allocation is always in the optimal state. This mechanism can be widely applied to cloud storage, distributed file systems, large databases, and data lakes, meeting the needs of different businesses for storage efficiency and performance.

[0088] Embodiment 2: A trust evaluation data storage traceability system based on blockchain technology, comprising:

[0089] A data recording module utilizes the distributed storage characteristics of the blockchain network to record the original data and processing results generated during the trust evaluation process in the form of blocks in the blockchain;

[0090] An intelligent contract module manages the submission, verification, storage, and updating of trust evaluation data through an embedded intelligent contract, ensuring data authenticity and tamper resistance, and using the timestamp function of the blockchain to add a time marker to the trust evaluation data, forming a time-ordered data chain; the intelligent contract includes a data verification unit, a data storage unit, and a data query unit;

[0091] The data verification unit is used to verify the integrity, format, and source of the submitted data;

[0092] The data storage unit is used to store the encrypted data that passes the verification in the blockchain;

[0093] The data query unit provides access to the corresponding data based on user permissions and query conditions.

[0094] A data traceability module provides data traceability functions, allowing authorized users to query data sources and historical operation records based on the intelligent contract;

[0095] A dynamic storage module monitors trust evaluation data access information and performs access characteristic analysis based on the monitored trust evaluation data access information, and adjusts the data storage area based on the access characteristic analysis results to achieve dynamic storage optimization.

[0096] The above formulas are all de-dimensioned to calculate the numerical values, the formulas are obtained by collecting a large amount of data to simulate the most recent real situation, and the preset parameters in the formulas are set by a person skilled in the art according to the actual situation.

[0097] It should be understood that the size of the sequence number of each process described above in various embodiments of the present application does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0098] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0099] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the system, device and unit described above can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0100] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A blockchain-based method for storing and tracing trust evaluation data, characterized in that, The method comprises the following steps: The original data and processing results generated in the trust evaluation process are recorded in the blockchain in the form of blocks by utilizing the distributed storage characteristics of the blockchain network; The submission, verification, storage and update of the trust evaluation data are managed by the smart contract, ensuring the authenticity and non-tamperability of the data, and the time stamp function of the blockchain is used to add time labels to the trust evaluation data, forming a time-ordered data chain; A data traceability function is provided to allow authorized users to query the data source and historical operation records based on the smart contract; The trust evaluation data access information is monitored, and based on the monitored trust evaluation data access information, access characteristic analysis is performed, and the data storage area is adjusted based on the access characteristic analysis results to realize dynamic storage optimization; In the access characteristic analysis, an access pattern irregularity index for measuring the irregularity of the time distribution of data access, a data access activity index for measuring the activity of data access, and a cumulative residence time index for evaluating the migration demand through the distribution and trend of residence time are generated; The calculation formula of the access pattern irregularity index is: MaxGap represents the maximum interval between two adjacent visits in the time series, MinGap represents the minimum interval between two adjacent visits in the time series, represents the entropy value of the time interval distribution, represents the mean value of all visit times, represents the i-th visit time point, represents the total number of visits, represents the visit pattern irregularity index; The calculation formula of the data access activity index is: ; denotes the query strength of the i-th access, denotes the data access activity index, denotes the time decay weight calculated according to the difference between the access time and the current time for each access; The logic for obtaining the cumulative dwell time index is as follows: obtain adjacent... In the latest time window, the data is in the [number]th [timeframe]. The cumulative dwell time of each type of storage area, data in the [number]th [period]. The formula for calculating the cumulative retention time index for the access frequency of each type of storage region is as follows: ; denotes a preset non-zero adjustment coefficient, denotes the access frequency of data in the th type of storage area, denotes the total number of types of storage areas, denotes the cumulative residence time of data in the th type of storage area, denotes the cumulative residence time index. 2.The blockchain-based trust evaluation data storage traceability method of claim 1, wherein, The smart contract comprises a data verification unit, a data storage unit, and a data query unit; The data verification unit is used to verify the integrity, format and source of the submitted data; The data storage unit is used to store the verified data in the blockchain after encryption; The data query unit is used to provide access rights to the corresponding data according to the user's rights and query conditions. 3.The blockchain-based trust evaluation data storage traceability method of claim 2, wherein, The data traceability function refers to: The initial generation time and source of the query data, the operation records of the query data on the blockchain, including submission, modification and storage location change, and the generation of a visual traceability chain for displaying the full life cycle records of the data. 4.The blockchain-based trust evaluation data storage traceability method of claim 3, wherein, The access pattern irregularity index acquisition logic is: In a fixed time window, the time sequence of access time is obtained, the mean value of all access times is calculated according to the i-th access time point, and the total number of accesses is obtained, and the randomness of the time interval distribution is measured by the entropy value calculation result of the time interval distribution: ; denotes the probability distribution of the length of the kth time interval divided by the fixed time window, denotes the total number of time intervals divided by the fixed time window, denotes the entropy value of the time interval distribution. 5.The blockchain-based trust evaluation data storage traceability method of claim 4, wherein, The data access activity index acquisition logic is: In a fixed time window, all access records are obtained, and the time point of the ith access, the query intensity of the ith access, and the current time point are extracted. For each access, the time decay weight is calculated according to the difference between the access time and the current time : ; denotes the current time point, denotes the i-th access time point, denotes a preset non-zero time decay factor. 6.The blockchain-based trust assessment data storage traceability method of claim 5, wherein, The query intensity acquisition logic of the i-th access is: ; represents the number of query operations of the i-th access, represents the data volume generated by the i-th access, represents the response time of the i-th access, represents the number of query threads occupied by the i-th access, represents a preset non-zero constant. 7.The blockchain-based trust assessment data storage traceability method of claim 6, wherein, Based on the access characteristic analysis results, the data storage area is adjusted, which means: The access pattern irregularity index, data access activity index and cumulative residence time index of the data are used as input data for logical reasoning, the target storage area type for data migration is used as output data for logical reasoning, the input variables are fuzzified, the values of the input variables are converted into fuzzy sets, the output variables are fuzzified, the output variables are converted into fuzzy sets, fuzzy rules are developed to describe the adaptation degree of each storage area type under different data category combinations, the fuzzified input variables are reasoned through the fuzzy rules to obtain the target storage area type for data migration, and when the target storage area type for data migration is different from the current storage area type of the data, the data is migrated to realize dynamic storage optimization.

8. A blockchain-based trust evaluation data storage traceability system for implementing the blockchain-based trust evaluation data storage traceability method according to any one of claims 1 to 7, characterized in that, The method comprises: The data recording module records the original data and processing results generated in the trust evaluation process in the form of blocks in the blockchain by using the distributed storage characteristics of the blockchain network. The smart contract module manages the submission, verification, storage and update of trust evaluation data through the built-in smart contract, ensures the authenticity and non-tamperability of the data, and adds a time stamp to the trust evaluation data using the time stamp function of the blockchain to form a time-ordered data chain. The data tracing module provides data tracing function, allowing authorized users to query data sources and historical operation records based on the smart contract. The dynamic storage module monitors trust evaluation data access information and, based on the monitored trust evaluation data access information, performs access characteristic analysis and adjusts the data storage area based on the access characteristic analysis results to achieve dynamic storage optimization.

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