A data flow circulation and traceability method and system based on a smart contract

Abstract

This invention relates to the field of data traceability technology and discloses a data circulation traceability method and system based on smart contracts. This method addresses the problem of changes in access patterns during data circulation traceability. The method includes: obtaining behavior change parameters of the access behavior sequence; calculating a behavior change index based on the behavior change parameters; determining whether the current access behavior in the access behavior sequence has a behavior change that affects the reliability of the data circulation traceability results based on the behavior change index; and if it is determined that the current access behavior in the access behavior sequence has a behavior change that affects the reliability of the data circulation traceability results, adjusting the initial data access control rules based on the behavior change index to obtain adjusted data access control rules. This effectively improves the authenticity of the traceability results in reflecting the real data circulation process and enhances the reliability and effectiveness of the data circulation traceability results.

Description

Technical Field

[0001] This invention relates to the field of data traceability technology, and more specifically to a data circulation traceability method and system based on smart contracts. Background Technology

[0002] With the continuous development of blockchain and smart contract technologies, the demand for data circulation among multiple entities and systems is increasing. Against the backdrop of the ever-increasing value of data elements, the data sources, flow paths, and access records involved in the data circulation process exhibit characteristics such as diverse participating entities, complex flow processes, and dynamically changing access behaviors. In this context, how to effectively record and track the data circulation process, and ensure its traceability and verifiability, has become an important and ongoing technological focus in the fields of data circulation and data management.

[0003] In existing technologies, data circulation traceability methods based on smart contracts have been widely applied in scenarios such as data sharing, data trading, and data supervision. These methods typically generate unique identifiers for data objects and, combined with the immutability of blockchain, record access behavior during data circulation on the blockchain, thus forming a data circulation path record. In practical implementation, the system generally judges data access requests based on preset access control rules, executes data access operations when access conditions are met, and simultaneously writes the relevant access behavior into the blockchain network to achieve recording and traceability of the entire data circulation process.

[0004] In actual data flow, data access behavior exhibits dynamic evolution as the access subjects, access frequency, and access methods constantly change. The order of occurrence of various access behaviors and their impact on the overall data flow process are not entirely the same in different application scenarios and operational stages. However, in existing data flow traceability mechanisms, access control rules are typically set during system deployment and remain relatively fixed during subsequent operation. When data access behavior changes, the system still processes access requests according to the existing access control rules.

[0005] However, the above-mentioned technologies have at least the following technical problems: In actual data circulation, when the access behavior patterns of accessing entities change, if fixed access control rules are still used, some abnormal access behaviors may participate in data circulation without effective constraints and be recorded in the data circulation path. In this case, although the complete data circulation path can be obtained based on blockchain records, the presence of nodes with abnormal access behaviors in the circulation path may affect the reflection of the traceability results on the actual data circulation process, thereby reducing the reliability and effectiveness of the data circulation traceability results. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, the present invention provides a data circulation traceability method and system based on smart contracts to solve the problems existing in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A data circulation traceability method based on smart contracts includes the following steps: Step 1: Obtain the data to be circulated from the smart contract, and perform format standardization processing on the data to form a data object with a unified structure; generate a corresponding data identifier based on the content information of the data object, and use the data identifier as a unique identifier for the data object in the subsequent circulation process; Step 2: Store the data object in a preset data storage medium, and after the data object is stored, obtain the data storage location information corresponding to the data object, and write the data identifier and its corresponding data storage location information into the blockchain network to form an on-chain mapping record; Step 3: Based on the data circulation requirements, deploy the smart contract corresponding to the data identifier. Based on data flow requirements, initial data access control rules are written into the smart contract. These rules include access time interval limits, maximum access frequency per unit time, and maximum data volume that can be accessed in a single instance. Step 4: During data access, the request subject information, access time, and access result corresponding to each access behavior are obtained. These access behaviors are then arranged in chronological order to construct an access behavior sequence corresponding to the data identifier. Step 5: Behavior change parameters of the access behavior sequence are obtained. These parameters include access time interval, access behavior position index, and access interval change direction. A behavior change index is calculated based on these parameters, and a judgment is made based on the behavior change index. Step 6: When a data flow request is received, obtain the request subject information and the target data identifier, and then combine the request subject information with the adjusted data access control rules. If the current access behavior in the access behavior sequence is determined to have a behavioral change affecting the reliability of the data flow traceability results, the initial data access control rules are adjusted according to the behavioral change index to obtain the adjusted data access control rules. If the current access behavior in the access behavior sequence is determined not to have a behavioral change affecting the reliability of the data flow traceability results, the initial data access control rules are determined as the adjusted data access control rules. The rules are matched to determine whether the requesting subject meets the data access conditions; Step 7: If the requesting subject meets the access conditions, the corresponding data object is read from the data storage medium and provided to the requesting subject. At the same time, an access record corresponding to this data access behavior is generated and written to the blockchain network; If the requesting subject does not meet the access conditions, the data access request is rejected and the access rejection record is written to the blockchain network; Step 8: When a traceability query request is received, the on-chain mapping record and access record corresponding to the target data identifier are obtained from the blockchain network. The on-chain mapping record and access record are organized in chronological order to generate the traceability result of the data flow process.

[0008] Preferably, the steps for obtaining the behavior change index are as follows: obtaining historical access behaviors, constructing a target access behavior set, calculating the access time interval between each adjacent access behavior based on the target access behavior set to form an access time interval sequence, and calculating the time interval dispersion coefficient based on the access time interval sequence; obtaining the position index of each historical access behavior to form a historical position sequence, and calculating the sequence position offset coefficient based on the historical position sequence; obtaining the access time interval sequence, forming an access interval change direction sequence based on the access time interval sequence, and calculating the interval continuity coefficient based on the access interval change direction sequence; normalizing the time interval dispersion coefficient, the sequence position offset coefficient, and the interval continuity coefficient, and calculating the behavior change index based on the normalized time interval dispersion coefficient, the sequence position offset coefficient, and the interval continuity coefficient.

[0009] Preferably, the step of obtaining the time interval dispersion coefficient is as follows: extracting historical access behaviors from the access behavior sequence that have the same data identifier and request subject information as the current access behavior, and arranging them in order of access time to form a target access behavior set; if the number of historical access behaviors in the target access behavior set is less than 2, then the time interval dispersion coefficient is determined to be 1; if the number of historical access behaviors in the target access behavior set is not less than 2, adding the current access behavior to the target access behavior set, and calculating the access time interval between each adjacent access behavior based on the access time difference between two adjacent access behaviors in the target access behavior set to form an access time interval sequence; comparing each access time interval in the access time interval sequence, determining the maximum value as the maximum access time interval, and determining the minimum value as the minimum access time interval; rearranging each access time interval in the access time interval sequence in ascending order, and determining the access time interval in the middle position as the median access time interval; when the number of access time intervals is even, taking the arithmetic mean of the two middle access time intervals as the median access time interval; subtracting the minimum access time interval from the maximum access time interval and dividing by the median access time interval to obtain the access time interval dispersion; adding 1 to the access time interval dispersion to obtain the time interval dispersion coefficient.

[0010] Preferably, the step of obtaining the sequence position offset coefficient is as follows: extracting each historical access behavior from the access behavior sequence that has the same data identifier and request subject information as the current access behavior, and obtaining the position index of each historical access behavior in the access behavior sequence to form a historical position sequence; if the number of historical access behaviors in the historical position sequence is less than 2, then the sequence position offset coefficient is determined to be 1; when the number of historical access behaviors in the historical position sequence is not less than 2, appending the position index of the current access behavior in the access behavior sequence to the end of the historical position sequence to form a target position sequence; obtaining the first historical position index and the last historical position index in the historical position sequence, based on... The total number of location indices is obtained from the target location sequence. The historical location advance is calculated based on the first historical location index, the last historical location index, and the total number of location indices. The historical location advance is summed with the last historical location index to obtain the predicted location index. The actual location index of the current access behavior in the access behavior sequence is obtained, and the absolute difference between the actual location index and the predicted location index is calculated to obtain the location offset. The first historical location index in the historical location sequence is subtracted from the last historical location index in the historical location sequence, and then 1 is added to calculate the location offset baseline. The sequence location offset coefficient is calculated by dividing the location offset baseline by the location offset.

[0011] Preferably, the step of obtaining the interval continuity coefficient is as follows: obtaining an access time interval sequence; comparing all two adjacent access time intervals in the access time interval sequence to determine the direction of change of the latter access time interval relative to the former access time interval, and forming an access interval change direction sequence; sequentially scanning the access interval change direction sequence, grouping the change directions with the same value and appearing consecutively into the same continuous segment, and counting the number of continuous segments; counting the total number of access interval change directions in the access interval change direction sequence, and dividing the total number of access interval change directions by the number of continuous segments to obtain the interval continuity coefficient.

[0012] Preferably, the step of obtaining the access interval change direction sequence is as follows: for each access time interval pair in the access time interval sequence, the next access time interval is compared with the previous access time interval; if the next access time interval is greater than the previous access time interval, the change direction of the access time interval pair is recorded as 1; if the next access time interval is less than the previous access time interval, the change direction of the access time interval pair is recorded as -1; if the next access time interval is equal to the previous access time interval, the change direction of the access time interval pair is recorded as 0; the change directions corresponding to each access time interval pair are combined in sequence to form the access interval change direction sequence.

[0013] Preferably, the step of determining whether there is a behavioral change in the current access behavior in the access behavior sequence that affects the reliability of the data flow traceability result based on the behavioral change index is as follows: compare the behavioral change index with the behavioral change threshold; if the behavioral change index is greater than or equal to the change threshold, it is determined that there is a behavioral change in the current access behavior in the access behavior sequence that affects the reliability of the data flow traceability result; if the behavioral change index is less than the change threshold, it is determined that there is no behavioral change in the current access behavior in the access behavior sequence that affects the reliability of the data flow traceability result.

[0014] Preferably, the step of adjusting the initial data access control rules based on the behavior change index to obtain the adjusted data access control rules is as follows: Obtain the access time interval limit, the upper limit of access times per unit time, and the upper limit of data volume accessible in a single access from the initial data access control rules; simultaneously, based on the access behavior sequence, obtain the historical access time interval, historical access times per unit time, and historical data volume accessed in a single access; determine the upper boundary of the time interval adjustment corresponding to the access time interval limit, the lower boundary of the number of access times corresponding to the upper limit of access times per unit time, and the lower boundary of the data volume adjustment corresponding to the upper limit of data volume accessible in a single access, respectively; calculate the ratio of the behavior change index to the behavior change threshold to obtain the overall adjustment coefficient; determine the time interval dispersion coefficient as the time adjustment coefficient, the sequence position offset coefficient as the number of access times adjustment coefficient, and the reciprocal of the interval continuity coefficient as the data volume adjustment coefficient; multiply the access time interval limit by the overall adjustment coefficient and the time adjustment coefficient to obtain the intermediate access time interval limit. The system first sets the access time interval limit value and compares it with the upper boundary of the time interval adjustment, taking the smaller value as the adjusted access time interval limit value. Then, it divides the upper limit value of accesses per unit time by the product of the overall adjustment coefficient and the number of accesses adjustment coefficient to obtain the intermediate upper limit value of accesses per unit time, and compares this intermediate upper limit value with the lower boundary of the number of accesses adjustment, taking the larger value as the adjusted upper limit value of accesses per unit time. Finally, it divides the upper limit value of the amount of data that can be accessed at one time by the product of the overall adjustment coefficient and the data volume adjustment coefficient to obtain the intermediate upper limit value of the amount of data that can be accessed at one time, and compares this intermediate upper limit value with the lower boundary of the data volume adjustment, taking the larger value as the adjusted upper limit value of the amount of data that can be accessed at one time. These adjusted access time interval limits, adjusted upper limits of accesses per unit time, and adjusted upper limits of the amount of data that can be accessed at one time are written into the corresponding rule entries in the initial data access control rules to generate the adjusted data access control rules.

[0015] Preferably, a data circulation traceability system based on smart contracts includes: a data construction module, used to acquire data to be circulated from smart contracts and perform format standardization processing on the data to be circulated to form a data object with a unified structure; generate a corresponding data identifier based on the content information of the data object, and use the data identifier as a unique identifier for the data object in the subsequent circulation process; an on-chain mapping module, used to store the data object in a preset data storage medium, and after the data object is stored, obtain the data storage location information corresponding to the data object, and write the data identifier and its corresponding data storage location information into the blockchain network to form an on-chain mapping record; and a contract configuration module, used to deploy the data identifier and data identifier based on data circulation requirements. The corresponding smart contract is used to write initial data access control rules into the smart contract according to data circulation requirements. These rules include access time interval limits, maximum access frequency per unit time, and maximum data volume that can be accessed in a single instance. A behavior sequence module is used to obtain the request subject information, access time, and access result for each access behavior during data access, and arranges these behaviors in chronological order to construct an access behavior sequence corresponding to the data identifier. A behavior evaluation module is used to obtain behavior change parameters for the access behavior sequence. These parameters include the access time interval, access behavior location index, and access interval change direction. A behavior change index is calculated based on these parameters. The behavior change index determines whether the current access behavior in the access behavior sequence has any behavioral changes that could affect the reliability of the data flow traceability results. If the current access behavior in the access behavior sequence is determined to have behavioral changes that could affect the reliability of the data flow traceability results, the initial data access control rules are adjusted according to the behavior change index to obtain the adjusted data access control rules. If the current access behavior in the access behavior sequence is determined not to have behavioral changes that could affect the reliability of the data flow traceability results, the initial data access control rules are determined as the adjusted data access control rules. The permission determination module is used to obtain the request subject information and the target data identifier when a data flow request is received, and to match the request subject information with the adjusted data access control rules. The system uses matching rules to determine whether the requesting entity meets the data access conditions. The access record module, when determining that the requesting entity meets the access conditions, reads the corresponding data object from the data storage medium and provides it to the requesting entity, while simultaneously generating an access record corresponding to this data access behavior and writing the access record to the blockchain network. When determining that the requesting entity does not meet the access conditions, the system rejects the data access request and writes the access rejection record to the blockchain network. The traceability generation module, upon receiving a traceability query request, obtains the corresponding on-chain mapping record and access record in the blockchain network based on the target data identifier, organizes the on-chain mapping record and access record in chronological order, and generates the traceability result of the data flow process.

[0016] The technical effects and advantages of this invention are as follows: The system acquires behavior change parameters of the access behavior sequence, calculates a behavior change index based on these parameters, and determines whether the current access behavior in the access behavior sequence has any behavior changes that affect the reliability of the data flow traceability results. If it is determined that the current access behavior in the access behavior sequence has behavior changes that affect the reliability of the data flow traceability results, the initial data access control rules are adjusted based on the behavior change index to obtain the adjusted data access control rules. This effectively improves the authenticity of the traceability results in reflecting the real data flow process and enhances the reliability and effectiveness of the data flow traceability results. Attached Figure Description

[0017] Figure 1 A flowchart of a data flow traceability method based on smart contracts is provided for an embodiment of this application.

[0018] Figure 2 This is a structural diagram of a data flow traceability system based on smart contracts, provided as an embodiment of this application. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The data circulation traceability method and system based on smart contracts involved in the present invention are not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] This invention provides a data flow traceability method based on smart contracts, such as... Figure 1 As shown, it includes the following steps: Step 1: Obtain the data to be circulated from the smart contract and standardize the format of the data to form a data object with a unified structure; generate a corresponding data identifier based on the content information of the data object. The content information includes the data content in the data object and its structured attribute information. The data identifier is used as a unique identifier for the data object in the subsequent circulation process to realize the indexing and association of the data object. Step 2: Store the data object in the preset data storage medium, and after the data object storage is completed, obtain the data storage location information corresponding to the data object. Write the data identifier and its corresponding data storage location information into the blockchain network to form an on-chain mapping record, which is used to realize the correspondence between on-chain information and off-chain data objects. Step 3: Based on the data circulation requirements, deploy the smart contract corresponding to the data identifier, and write the initial data access control rules into the smart contract according to the data circulation requirements. The initial data access control rules are used to limit the access permissions and access conditions of different access subjects to the data object. The initial data access control rules include the access time interval limit, the upper limit of the number of accesses per unit time, and the upper limit of the amount of data that can be accessed at one time. It should be noted that, in this embodiment, the initial data access control rule refers to the access control constraints pre-set according to the basic needs of the data circulation scenario before data circulation begins. These constraints are used to limit the access permissions and conditions of different access subjects to data objects. Specifically, the access permission range, access frequency, or access conditions can be configured based on the data object type information, the access subject's identity information, and the basic access requirements during data circulation, thereby forming the initial data access control rule and writing it into the smart contract.

[0021] Step 4: During the data access process, obtain the request subject information, access time and access result corresponding to each access behavior, and arrange each access behavior in order of access time to construct an access behavior sequence corresponding to the data identifier; Step 5: Obtain the behavior change parameters of the access behavior sequence. The behavior change parameters include the access time interval, the access behavior location index, and the direction of the access interval change. Calculate the behavior change index based on the behavior change parameters, and determine whether there are any behavior changes in the current access behavior in the access behavior sequence that affect the reliability of the data flow tracing results based on the behavior change index. If it is determined that the current access behavior in the access behavior sequence has a behavioral change that affects the reliability of the data flow traceability results, the initial data access control rules are adjusted according to the behavioral change index to obtain the adjusted data access control rules. If it is determined that the current access behavior in the access behavior sequence does not have any behavioral changes that affect the reliability of the data flow tracing results, then the initial data access control rule will be determined as the adjusted data access control rule. In this embodiment, it should be specifically explained that the steps for obtaining the behavior change index are as follows: Obtain historical access behavior, construct a target access behavior set, calculate the access time interval between each adjacent access behavior based on the target access behavior set, form an access time interval sequence, and calculate the time interval discrepancy coefficient based on the access time interval sequence. Obtain the location index of each historical access behavior to form a historical location sequence, and calculate the sequence location offset coefficient based on the historical location sequence. Obtain the access time interval sequence, form the access interval change direction sequence based on the access time interval sequence, and calculate the interval continuity coefficient based on the access interval change direction sequence; The time interval dispersion coefficient, sequence position offset coefficient, and interval continuity coefficient are normalized. Specifically, in this embodiment, vector normalization can be used to normalize these coefficients. Specifically, the time interval dispersion coefficient, sequence position offset coefficient, and interval continuity coefficient are combined to form a three-dimensional vector. The vector norm is obtained by calculating the square root of the sum of squares of each component of this three-dimensional vector. Each coefficient is then divided by the vector norm to achieve normalization of different coefficients at a unified scale. This normalization method effectively eliminates the influence of differences in the value range or variation amplitude of the time interval dispersion coefficient, sequence position offset coefficient, and interval continuity coefficient, ensuring that each coefficient is within a comparable dimensional range in the subsequent calculation of the behavior change index, thereby improving the stability and consistency of the behavior change index calculation results. Since the vector normalization method is existing technology, its specific calculation process will not be described in detail in this embodiment. The behavior change index is calculated based on the normalized time interval dispersion coefficient, sequence position offset coefficient, and interval continuity coefficient. The specific steps are as follows: ; In the formula, This is represented as a behavioral change index. The coefficient of variation for the time interval is represented by the normalized time interval dispersion coefficient. The greater the fluctuation in the access time interval, the more significant the instability of the access behavior in the time dimension, and the greater the deviation from the historical access rhythm, thus increasing the behavior change index accordingly. Conversely, when the distribution of access time intervals is more concentrated and the changes are more gradual, the time interval dispersion coefficient is smaller, indicating that the access behavior tends to be stable in the time dimension, and the behavior change index decreases accordingly. This represents the normalized sequence position offset coefficient. The greater the deviation of the current access behavior's actual position in the access behavior sequence from the historical position trend, the higher the degree of disturbance to the access order structure, thus increasing the behavior change index accordingly. Conversely, when the current access behavior's position is basically consistent with the historical access behavior trend, the sequence position offset coefficient is small, indicating that the access order structure remains stable, and the behavior change index decreases accordingly. The interval continuity coefficient is represented by the normalized interval variation coefficient. The more continuous the change in the access interval and the more stable the direction of change, the smoother and more consistent the overall access behavior, thus reducing the behavior variation index. Conversely, when the direction of change in the access interval changes frequently and the change process exhibits sudden fluctuations, the interval continuity coefficient is small, indicating significant instability in the temporal rhythm of the access behavior, and the behavior variation index increases accordingly. , , This represents the weighting coefficients of the normalized time interval discreteness coefficient, the normalized sequence position offset coefficient, and the normalized interval continuity coefficient, and... , , , Obtained through the analytic hierarchy process, for example , , The values ​​can be 0.4, 0.3, or 0.3. The Analytic Hierarchy Process (AHP) is an existing multi-factor decision analysis method. It constructs a hierarchical structure of indicators, compares each evaluation factor pairwise to form a judgment matrix, and obtains the relative weights of each factor through a consistency test, thereby achieving a comprehensive evaluation of multiple indicators. In this embodiment, the weight coefficients are determined based on the AHP, which is a commonly used data processing technique in this field. This embodiment does not provide a detailed explanation of its specific steps.

[0022] In this embodiment, it should be specifically explained that the steps for obtaining the time interval dispersion coefficients are as follows: Extract historical access behaviors from the access behavior sequence that have the same data identifier and request subject information as the current access behavior, and arrange them in order of access time to form a target access behavior set; if the number of historical access behaviors in the target access behavior set is less than 2, then the time interval dispersion coefficient is set to 1. If the number of historical access behaviors in the target access behavior set is not less than 2, the current access behavior is added to the target access behavior set, and the access time interval between each adjacent access behavior is calculated based on the access time difference between two adjacent access behaviors in the target access behavior set, forming an access time interval sequence. The maximum value in the access time interval sequence is determined as the maximum access time interval, and the minimum value is determined as the minimum access time interval, so as to characterize the maximum and minimum span of access behavior in the time distribution. The access time intervals in the access time interval sequence are rearranged in ascending order, and the access time interval in the middle position is determined as the median access time interval; when the number of access time intervals is even, the arithmetic mean of the two middle access time intervals is taken as the median access time interval. Subtracting the minimum access time from the maximum access time and then dividing by the median access time gives the access time dispersion, which characterizes the degree of dispersion of the access time relative to the normal level. By adding 1 to the access time interval dispersion, we obtain the time interval dispersion coefficient. The time interval dispersion coefficient is made to be greater than or equal to 1, so as to facilitate subsequent participation in the calculation of behavior change index and adjustment of data access control rules.

[0023] In this embodiment, it should be specifically explained that the steps for obtaining the sequence position offset coefficient are as follows: Extract historical access behaviors from the access behavior sequence that have the same data identifier and request subject information as the current access behavior, and obtain the position index of each historical access behavior in the access behavior sequence to form a historical position sequence; if the number of historical access behaviors in the historical position sequence is less than 2, then the sequence position offset coefficient is set to 1. It should be noted that the location index refers to the sequential number of each access behavior in the access behavior sequence after the access time is arranged. When the number of historical access behaviors in the historical location sequence is not less than 2, the position index of the current access behavior in the access behavior sequence is appended to the end of the historical location sequence to form the target location sequence. Obtain the first and last historical position indices from the historical position sequence. Calculate the total number of position indices based on the target position sequence. Then, calculate the historical position advancement based on the first historical position index, the last historical position index, and the total number of position indices. The specific steps are as follows: ; In the formula Represented as historical position advance, it characterizes the historical advancement trend of the current requesting subject's access behavior for the same data identifier within the overall access behavior sequence. This is represented as the index of the last historical position in the historical position sequence. This represents the index of the first historical location in the historical location sequence. This represents the total number of location indices; The historical position advance amount is summed with the last historical position index to obtain the predicted position index, which is used to predict the target position index of the current access behavior in the access behavior sequence, so as to characterize the position where the current access behavior should occur when the historical access position advance trend remains stable. Obtain the actual position index of the current access behavior in the access behavior sequence, and calculate the absolute difference between the actual position index and the predicted position index to obtain the position offset, which represents the degree of deviation of the current access behavior from the historical position advancement trend. The larger the position offset, the more the current access behavior deviates from the historical advancement trend in the access behavior sequence. The location offset baseline is calculated by subtracting the first historical location index from the last historical location index in the historical location sequence and then adding 1. This baseline represents the cumulative distribution span of historical access behavior in the overall access behavior sequence. Since the last historical location index is greater than or equal to the first historical location index, the location offset baseline is greater than or equal to 1. Divide the position offset by the position offset base and add 1 to calculate the sequence position offset coefficient. Make the sequence position offset coefficient greater than or equal to 1 so that it can be directly used in the subsequent calculation of behavior change index and adjustment of data access control rules. The larger the sequence position offset coefficient, the more the current access behavior deviates from the historical position trend in the access behavior sequence, and the more obvious the disturbance to the access order structure.

[0024] In this embodiment, it should be specifically explained that the steps for obtaining the interval continuity coefficient are as follows: Obtain the access time interval sequence, compare all adjacent two access time intervals in the access time interval sequence, determine the direction of change of the later access time interval relative to the previous access time interval, and form a sequence of access interval change direction; The sequence of access interval change directions is scanned sequentially, and the change directions with the same value and appearing consecutively are grouped into the same continuous segment. The number of continuous segments is then counted. It should be noted that if the change directions in the access interval change direction sequence are consistent, the number of consecutive segments is small, indicating that the access interval change process is relatively continuous; if the access interval change direction changes frequently, the number of consecutive segments is large, indicating that the access interval change process is relatively fragmented.

[0025] The total number of access interval change directions in the sequence of access interval change directions is counted. The total number of access interval change directions is divided by the number of consecutive segments to obtain the interval continuity coefficient, which characterizes the degree of continuity of access interval change throughout the access process. The larger the interval continuity coefficient, the more continuous and smooth the access interval change direction is; the smaller the interval continuity coefficient, the more fragmented and sudden the access interval change direction is.

[0026] In this embodiment, it should be specifically explained that the steps for obtaining the access interval change direction sequence are as follows: For each access time interval pair in the access time interval sequence, compare the next access time interval with the previous access time interval; If the subsequent access time interval is greater than the previous access time interval, then the direction of change of the access time interval pair is recorded as 1; If the subsequent access time interval is less than the previous access time interval, then the direction of change of the access time interval pair is recorded as -1; If the next access time interval is equal to the previous access time interval, then the direction of change of the access time interval pair is recorded as 0; The corresponding change directions of each access time interval are combined sequentially to form a sequence of access interval change directions.

[0027] In this embodiment, it should be specifically explained that the step of determining whether there is a behavioral change in the current access behavior in the access behavior sequence that affects the reliability of the data flow tracing results based on the behavioral change index is as follows: The behavior change index is compared with the behavior change threshold. If the behavior change index is greater than or equal to the threshold, it is determined that the current access behavior in the access behavior sequence has a behavior change that affects the reliability of the data flow traceability results. If the behavior change index is less than the threshold, it is determined that the current access behavior in the access behavior sequence does not have a behavior change that affects the reliability of the data flow traceability results. It should be noted that the behavior change threshold is obtained through an adaptive thresholding method. The adaptive thresholding method is an existing data processing method that dynamically determines the threshold based on the distribution characteristics or change trends of historical data, so that the threshold can be automatically adjusted as the data status changes. In the specific implementation process, the corresponding threshold can be determined based on the statistical characteristics (such as value range, fluctuation, or distribution pattern) of the historical behavior change index sequence, so that the behavior change threshold can reflect the change level of the current access behavior. This embodiment uses the adaptive thresholding method to determine the behavior change threshold, which is a commonly used technical means in this field. This embodiment does not describe its specific steps in detail.

[0028] In this embodiment, it should be specifically explained that the steps for adjusting the initial data access control rules based on the behavior change index to obtain the adjusted data access control rules are as follows: The system obtains the access time interval limit, the maximum number of accesses per unit time, and the maximum amount of data that can be accessed in a single instance from the initial data access control rules. At the same time, it obtains the historical access time interval, the historical number of accesses per unit time, and the historical amount of data accessed in a single instance based on the access behavior sequence. It then determines the upper boundary of the time interval adjustment corresponding to the access time interval limit, the lower boundary of the number of accesses per unit time corresponding to the maximum number of accesses, and the lower boundary of the data amount adjustment corresponding to the maximum amount of data that can be accessed in a single instance. It should be noted that the historical number of accesses per unit time can be obtained by dividing the access behavior sequence into preset statistical periods and then counting the number of accesses in each statistical period. It should be noted that obtaining historical access time intervals, historical access counts per unit time, and historical single access data volume based on access behavior sequences, and determining the upper boundary of time interval adjustment corresponding to the access time interval limit, the lower boundary of access count adjustment corresponding to the upper limit of access counts per unit time, and the lower boundary of data volume adjustment corresponding to the upper limit of single accessable data volume, can be achieved using existing data statistical analysis methods. Specifically, by dividing historical access behaviors in the access behavior sequence into time windows and performing statistical analysis, the access time intervals between each access behavior, the number of accesses per unit time, and the data volume of a single access can be obtained. Based on the above statistical results, the corresponding extreme values ​​or boundary ranges can be determined, thereby obtaining the adjustment boundary values ​​corresponding to each rule item parameter. Therefore, the method for obtaining the adjustment boundaries described in this embodiment belongs to a commonly used data processing technique in this field.

[0029] The overall adjustment coefficient is obtained by calculating the ratio of the behavior change index to the behavior change threshold. The time interval dispersion coefficient is determined as the time adjustment coefficient, the sequence position offset coefficient is determined as the frequency adjustment coefficient, and the reciprocal of the interval continuity coefficient is determined as the data volume adjustment coefficient. It should be noted that since the time interval dispersion coefficient, sequence position offset coefficient, and the reciprocal of the interval continuity coefficient are all used to characterize the degree of abnormality of the corresponding behavioral change dimension, and the larger their values ​​are, the higher the degree of abnormality of the corresponding dimension, they are used as adjustment coefficients for the corresponding rule terms to ensure that the adjustment range of the corresponding rule terms is consistent with the degree of abnormality of the dimension.

[0030] The intermediate access time interval limit is obtained by multiplying the access time interval limit by the overall adjustment factor and the time adjustment factor. The intermediate access time interval limit is then compared with the upper boundary of the time interval adjustment, and the smaller of the two values ​​is taken as the adjusted access time interval limit. Divide the maximum number of visits per unit time by the product of the overall adjustment coefficient and the number adjustment coefficient to obtain the intermediate maximum number of visits per unit time. Then compare the intermediate maximum number of visits per unit time with the lower boundary of the number adjustment and take the larger value as the adjusted maximum number of visits per unit time. Divide the maximum amount of data that can be accessed at one time by the product of the overall adjustment coefficient and the data volume adjustment coefficient to obtain the intermediate maximum amount of data that can be accessed at one time. Then compare the intermediate maximum amount of data that can be accessed at one time with the lower boundary of the data volume adjustment and take the larger value of the two as the adjusted maximum amount of data that can be accessed at one time. Write the adjusted access time interval limit, the adjusted maximum number of accesses per unit time, and the adjusted maximum amount of data that can be accessed at one time into the corresponding rule item positions in the initial data access control rules to generate the adjusted data access control rules.

[0031] In this embodiment, data access control rules are adjusted only when the current access behavior is determined to have a change that affects the reliability of data flow tracing results. Since this type of access behavior is considered abnormal or risky, a suppressive adjustment strategy is adopted during the rule adjustment process. Specifically, the access time interval limit is used to constrain the minimum time interval between access behaviors; when abnormal access behavior occurs, the access time interval limit is increased to reduce the access frequency. The upper limit for the number of accesses per unit time is used to constrain the number of accesses within a statistical period; when abnormal access behavior occurs, the upper limit for the number of accesses per unit time is compressed to reduce access density. The upper limit for the amount of data that can be accessed at one time is used to constrain the impact range of a single access behavior; when abnormal access behavior occurs, the upper limit for the amount of data that can be accessed at one time is compressed to reduce the impact of a single access on the data flow path. Therefore, in this embodiment, the adjustment direction of each rule item is a tightening adjustment, rather than a reverse relaxation strategy.

[0032] Step 6: When a data flow request is received, obtain the request subject information and the target data identifier, and match the request subject information with the adjusted data access control rules to determine whether the request subject meets the data access conditions. Step 7: When it is determined that the requesting subject meets the access conditions, the corresponding data object is read from the data storage medium and provided to the requesting subject. At the same time, an access record corresponding to this data access behavior is generated and written to the blockchain network, so that the access record forms an immutable on-chain record. When it is determined that the requesting subject does not meet the access conditions, the data access request is rejected, and a corresponding access rejection record is generated and written to the blockchain network. Step 8: Upon receiving a traceability query request, obtain the corresponding on-chain mapping record and access record in the blockchain network based on the target data identifier. Organize the on-chain mapping record and access record in chronological order to generate traceability results of the data flow process, which are used to reflect the source, flow path and access history of the data object.

[0033] In this embodiment, it should be specifically explained that, as Figure 2 As shown, a data circulation traceability system based on smart contracts is provided. The system includes: The data construction module is used to acquire the data to be circulated from the smart contract, and to standardize the format of the data to be circulated to form a data object with a unified structure; based on the content information of the data object, a corresponding data identifier is generated, and the data identifier is used as the unique identifier of the data object in the subsequent circulation process; The on-chain mapping module is used to store data objects in a preset data storage medium, and after the data object is stored, it obtains the data storage location information corresponding to the data object, writes the data identifier and its corresponding data storage location information into the blockchain network, and forms an on-chain mapping record. The contract configuration module is used to deploy smart contracts corresponding to data identifiers based on data circulation needs, and to write initial data access control rules into the smart contracts according to data circulation needs. The initial data access control rules include access time interval limit, maximum number of accesses per unit time, and maximum amount of data that can be accessed at one time. The behavior sequence module is used to obtain the request subject information, access time and access result corresponding to each access behavior during the data access process, and arrange each access behavior in the order of access time to construct an access behavior sequence corresponding to the data identifier. The behavior assessment module is used to obtain behavior change parameters of the access behavior sequence. The behavior change parameters include the access time interval, the access behavior location index, and the direction of the access interval change. The behavior change index is calculated based on the behavior change parameters. The behavior change index is used to determine whether there are any behavior changes in the current access behavior in the access behavior sequence that affect the reliability of the data flow traceability results. If it is determined that the current access behavior in the access behavior sequence has a behavioral change that affects the reliability of the data flow traceability results, the initial data access control rules are adjusted according to the behavioral change index to obtain the adjusted data access control rules. If it is determined that the current access behavior in the access behavior sequence does not have any behavioral changes that affect the reliability of the data flow tracing results, then the initial data access control rule will be determined as the adjusted data access control rule. The permission determination module is used to obtain the request subject information and target data identifier when a data flow request is received, and to match the request subject information with the adjusted data access control rules to determine whether the request subject meets the data access conditions. The access record module is used to read the corresponding data object from the data storage medium and provide it to the requesting subject when it is determined that the requesting subject meets the access conditions. At the same time, it generates an access record corresponding to this data access behavior and writes the access record to the blockchain network. When it is determined that the requesting subject does not meet the access conditions, it rejects this data access request and writes the access rejection record to the blockchain network. The traceability generation module is used to obtain the corresponding on-chain mapping record and access record in the blockchain network based on the target data identifier when a traceability query request is received. The on-chain mapping record and access record are then organized in chronological order to generate traceability results for the data flow process.

[0034] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0035] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A data circulation traceability method based on smart contracts, characterized in that, Includes the following steps: Step 1: Obtain the data to be circulated from the smart contract, and standardize the format of the data to form a data object with a unified structure; generate a corresponding data identifier based on the content information of the data object, and use the data identifier as the unique identifier of the data object in the subsequent circulation process; Step 2: Store the data object in the preset data storage medium, and after the data object is stored, obtain the data storage location information corresponding to the data object, write the data identifier and its corresponding data storage location information into the blockchain network to form an on-chain mapping record; Step 3: Based on the data circulation requirements, deploy the smart contract corresponding to the data identifier, and write the initial data access control rules into the smart contract according to the data circulation requirements. The initial data access control rules include the access time interval limit, the maximum number of accesses per unit time, and the maximum amount of data that can be accessed at one time. Step 4: During the data access process, obtain the request subject information, access time and access result corresponding to each access behavior, and arrange each access behavior in order of access time to construct an access behavior sequence corresponding to the data identifier; Step 5: Obtain the behavior change parameters of the access behavior sequence. The behavior change parameters include the access time interval, the access behavior location index, and the direction of the access interval change. Calculate the behavior change index based on the behavior change parameters, and determine whether there are any behavior changes in the current access behavior in the access behavior sequence that affect the reliability of the data flow tracing results based on the behavior change index. If it is determined that the current access behavior in the access behavior sequence has a behavioral change that affects the reliability of the data flow traceability results, the initial data access control rules are adjusted according to the behavioral change index to obtain the adjusted data access control rules. If it is determined that the current access behavior in the access behavior sequence does not have any behavioral changes that affect the reliability of the data flow tracing results, then the initial data access control rule will be determined as the adjusted data access control rule. Step 6: When a data flow request is received, obtain the request subject information and the target data identifier, and match the request subject information with the adjusted data access control rules to determine whether the request subject meets the data access conditions. Step 7: When it is determined that the requesting subject meets the access conditions, the corresponding data object is read from the data storage medium and provided to the requesting subject. At the same time, an access record corresponding to this data access behavior is generated and written into the blockchain network. If the requesting entity does not meet the access conditions, the data access request will be rejected and the access rejection record will be written to the blockchain network. Step 8: Upon receiving a traceability query request, obtain the corresponding on-chain mapping record and access record in the blockchain network based on the target data identifier, organize the on-chain mapping record and access record in chronological order, and generate the traceability result of the data flow process.

2. The data flow traceability method based on smart contracts according to claim 1, characterized in that: The steps for obtaining the behavior change index are as follows: Obtain historical access behavior, construct a target access behavior set, calculate the access time interval between each adjacent access behavior based on the target access behavior set, form an access time interval sequence, and calculate the time interval discrepancy coefficient based on the access time interval sequence. Obtain the location index of each historical access behavior to form a historical location sequence, and calculate the sequence location offset coefficient based on the historical location sequence. Obtain the access time interval sequence, form the access interval change direction sequence based on the access time interval sequence, and calculate the interval continuity coefficient based on the access interval change direction sequence; The time interval dispersion coefficient, sequence position offset coefficient, and interval continuity coefficient are normalized, and the behavior change index is calculated based on the normalized time interval dispersion coefficient, sequence position offset coefficient, and interval continuity coefficient.

3. The data flow traceability method based on smart contracts according to claim 2, characterized in that, The steps for obtaining the time interval discrete coefficients are as follows: Extract historical access behaviors from the access behavior sequence that have the same data identifier and request subject information as the current access behavior, and arrange them in order of access time to form a target access behavior set; if the number of historical access behaviors in the target access behavior set is less than 2, then the time interval dispersion coefficient is set to 1. If the number of historical access behaviors in the target access behavior set is not less than 2, the current access behavior is added to the target access behavior set, and the access time interval between each adjacent access behavior is calculated based on the access time difference between two adjacent access behaviors in the target access behavior set, forming an access time interval sequence. Compare each access time interval in the access time interval sequence, determine the maximum value as the maximum access time interval, and determine the minimum value as the minimum access time interval. The access time intervals in the access time interval sequence are rearranged in ascending order, and the access time interval in the middle position is determined as the median access time interval; when the number of access time intervals is even, the arithmetic mean of the two middle access time intervals is taken as the median access time interval. The dispersion of access time intervals is obtained by subtracting the minimum access time interval from the maximum access time interval and then dividing by the median access time interval. Adding 1 to the time interval dispersion gives the time interval dispersion coefficient.

4. The data flow traceability method based on smart contracts according to claim 2, characterized in that, The steps for obtaining the sequence position offset coefficient are as follows: Extract historical access behaviors from the access behavior sequence that have the same data identifier and request subject information as the current access behavior, and obtain the position index of each historical access behavior in the access behavior sequence to form a historical position sequence; if the number of historical access behaviors in the historical position sequence is less than 2, then the sequence position offset coefficient is set to 1. When the number of historical access behaviors in the historical location sequence is not less than 2, the position index of the current access behavior in the access behavior sequence is appended to the end of the historical location sequence to form the target location sequence. Obtain the first and last historical position indices in the historical position sequence, obtain the total number of position indices based on the target position sequence, and calculate the historical position advancement based on the first historical position index, the last historical position index, and the total number of position indices. The predicted position index is obtained by summing the historical position advance amount with the last historical position index; Obtain the actual location index of the current access behavior in the access behavior sequence, and calculate the absolute difference between the actual location index and the predicted location index to obtain the location offset; The position offset baseline is calculated by subtracting the first historical position index from the last historical position index in the historical position sequence and then adding 1. The sequence position offset coefficient is calculated by dividing the position offset by the position offset reference and adding 1.

5. The data flow traceability method based on smart contracts according to claim 3, characterized in that: The steps for obtaining the interval continuity coefficient are as follows: Obtain the access time interval sequence, compare all adjacent two access time intervals in the access time interval sequence, determine the direction of change of the later access time interval relative to the previous access time interval, and form a sequence of access interval change direction; The sequence of access interval change directions is scanned sequentially, and the change directions with the same value and appearing consecutively are grouped into the same continuous segment. The number of continuous segments is then counted. The total number of access interval change directions in the sequence is counted, and the interval continuity coefficient is obtained by dividing the total number of access interval change directions by the number of consecutive segments.

6. The data flow traceability method based on smart contracts according to claim 5, characterized in that: The steps for obtaining the access interval change direction sequence are as follows: For each access time interval pair in the access time interval sequence, compare the next access time interval with the previous access time interval; If the subsequent access time interval is greater than the previous access time interval, then the direction of change of the access time interval pair is recorded as 1; If the subsequent access time interval is less than the previous access time interval, then the direction of change of the access time interval pair is recorded as -1; If the next access time interval is equal to the previous access time interval, then the direction of change of the access time interval pair is recorded as 0; The corresponding change directions of each access time interval are combined sequentially to form a sequence of access interval change directions.

7. The data flow traceability method based on smart contracts according to claim 1, characterized in that: The step of determining whether there are any behavioral changes in the current access behavior in the access behavior sequence that affect the reliability of the data flow tracing results based on the behavioral change index is as follows: The behavior change index is compared with the behavior change threshold. If the behavior change index is greater than or equal to the threshold, it is determined that the current access behavior in the access behavior sequence has a behavior change that affects the reliability of the data flow traceability results. If the behavior change index is less than the threshold, it is determined that the current access behavior in the access behavior sequence does not have a behavior change that affects the reliability of the data flow traceability results.

8. A data flow traceability method based on smart contracts according to any one of claims 3-5, characterized in that: The step of adjusting the initial data access control rules based on the behavior change index to obtain the adjusted data access control rules is as follows: Obtain the access time interval limit, the maximum number of accesses per unit time, and the maximum amount of data that can be accessed in a single instance from the initial data access control rules. At the same time, obtain the historical access time interval, the historical number of accesses per unit time, and the historical amount of data accessed in a single instance based on the access behavior sequence. Determine the upper boundary of the time interval adjustment corresponding to the access time interval limit, the lower boundary of the number of accesses per unit time adjustment corresponding to the maximum number of accesses, and the lower boundary of the data amount adjustment corresponding to the maximum amount of data that can be accessed in a single instance. The overall adjustment coefficient is obtained by calculating the ratio of the behavior change index to the behavior change threshold. The time interval dispersion coefficient is determined as the time adjustment coefficient, the sequence position offset coefficient is determined as the frequency adjustment coefficient, and the reciprocal of the interval continuity coefficient is determined as the data volume adjustment coefficient. The intermediate access time interval limit is obtained by multiplying the access time interval limit by the overall adjustment factor and the time adjustment factor. The intermediate access time interval limit is then compared with the upper boundary of the time interval adjustment, and the smaller of the two values ​​is taken as the adjusted access time interval limit. Divide the maximum number of visits per unit time by the product of the overall adjustment coefficient and the number adjustment coefficient to obtain the intermediate maximum number of visits per unit time. Then compare the intermediate maximum number of visits per unit time with the lower boundary of the number adjustment and take the larger value as the adjusted maximum number of visits per unit time. Divide the maximum amount of data that can be accessed at one time by the product of the overall adjustment coefficient and the data volume adjustment coefficient to obtain the intermediate maximum amount of data that can be accessed at one time. Then compare the intermediate maximum amount of data that can be accessed at one time with the lower boundary of the data volume adjustment and take the larger value of the two as the adjusted maximum amount of data that can be accessed at one time. Write the adjusted access time interval limit, the adjusted maximum number of accesses per unit time, and the adjusted maximum amount of data that can be accessed at one time into the corresponding rule item positions in the initial data access control rules to generate the adjusted data access control rules.

9. A data circulation traceability system based on smart contracts, used to implement the data circulation traceability method based on smart contracts as described in any one of claims 1-8, characterized in that: The system includes: The data construction module is used to acquire the data to be circulated from the smart contract, and to standardize the format of the data to be circulated to form a data object with a unified structure; based on the content information of the data object, a corresponding data identifier is generated, and the data identifier is used as the unique identifier of the data object in the subsequent circulation process; The on-chain mapping module is used to store data objects in a preset data storage medium, and after the data object is stored, it obtains the data storage location information corresponding to the data object, writes the data identifier and its corresponding data storage location information into the blockchain network, and forms an on-chain mapping record. The contract configuration module is used to deploy smart contracts corresponding to data identifiers based on data circulation needs, and to write initial data access control rules into the smart contracts according to data circulation needs. The initial data access control rules include access time interval limit, maximum number of accesses per unit time, and maximum amount of data that can be accessed at one time. The behavior sequence module is used to obtain the request subject information, access time and access result corresponding to each access behavior during the data access process, and arrange each access behavior in the order of access time to construct an access behavior sequence corresponding to the data identifier. The behavior assessment module is used to obtain behavior change parameters of the access behavior sequence. The behavior change parameters include the access time interval, the access behavior location index, and the direction of the access interval change. The behavior change index is calculated based on the behavior change parameters. The behavior change index is used to determine whether there are any behavior changes in the current access behavior in the access behavior sequence that affect the reliability of the data flow traceability results. If it is determined that the current access behavior in the access behavior sequence has a behavioral change that affects the reliability of the data flow traceability results, the initial data access control rules are adjusted according to the behavioral change index to obtain the adjusted data access control rules. If it is determined that the current access behavior in the access behavior sequence does not have any behavioral changes that affect the reliability of the data flow tracing results, then the initial data access control rule will be determined as the adjusted data access control rule. The permission determination module is used to obtain the request subject information and target data identifier when a data flow request is received, and to match the request subject information with the adjusted data access control rules to determine whether the request subject meets the data access conditions. The access record module is used to read the corresponding data object from the data storage medium and provide it to the requesting subject when it is determined that the requesting subject meets the access conditions. At the same time, it generates an access record corresponding to this data access behavior and writes the access record to the blockchain network. When it is determined that the requesting subject does not meet the access conditions, it rejects this data access request and writes the access rejection record to the blockchain network. The traceability generation module is used to obtain the corresponding on-chain mapping record and access record in the blockchain network based on the target data identifier when a traceability query request is received. The on-chain mapping record and access record are then organized in chronological order to generate traceability results for the data flow process.

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