A data process management system and method based on blockchain technology

Abstract

The present invention discloses a data process management system and method based on blockchain technology, which relates to the technical field of big data analysis. Through a multi-dimensional dynamic analysis and prediction mechanism, the present invention uses technologies such as time series analysis and neural networks to accurately grasp the change laws of the data reading request volume, packet loss rate, and storage capacity of each block based on historical data, breaks through traditional limitations, and improves the foresight and accuracy of data processing. At the same time, a comprehensive risk assessment and intelligent storage strategy system is constructed, the risk value is calculated by combining multiple factors, the data backup sharding storage layout is optimized, the storage blocks are dynamically monitored and intelligently replaced, the data loss risk is reduced, and the data security and availability are enhanced. From data prediction to dynamic migration, each link is closely coordinated. Through transmission encryption and real-time index update, data security and consistency are ensured, the management process is continuously optimized, and a complete, efficient, and intelligent solution is provided for blockchain data process management.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and specifically to a data process management system and method based on blockchain technology. Background Art

[0002] In the blockchain data sharding backup storage, the existing technology generally adopts a static storage strategy, without considering problems such as fluctuations in the data read request volume of different blocks, differences in packet loss rates, and changes in the storage capacity consumption rate. This method easily causes the difference in the long-term fixed storage of data backup shards to change from low-risk blocks to high-risk blocks, not only leading to a decline in data read performance and low utilization rate of storage resources, but also possibly causing a reduction in data transmission reliability and insufficient backup recovery efficiency, and it is difficult to meet the requirements of the blockchain system for high efficiency and security of data storage. Summary of the Invention

[0003] The purpose of the present invention is to provide a data process management system and method based on blockchain technology to solve the problems raised in the existing technology.

[0004] To achieve the above purpose, the present invention provides the following technical solution: A data process management method based on blockchain technology, the data process management method includes the following steps:

[0005] Step S1, obtain the data read request volume of the block to construct a data throughput analysis set, and predict the change trend of the data read request volume of the block according to the data throughput analysis set;

[0006] Step S1-1, select any block in the blockchain as the research object, obtain the data read request volume of the block through node log records, centrally store the data read request volume in the block and the corresponding timestamp data, and then construct a data throughput analysis set;

[0007] Step S1-2, by traversing the data throughput analysis set, use the time series analysis method to predict the change trend of the data read request volume of the block within a unit time.

[0008] The time series analysis method prediction calculation uses the following formula:

[0009] ;

[0010] In the formula, Q t represents the predicted value of the data read request volume of the block at the prediction time t; a represents the average request volume reference value; n represents the number of time intervals of the data read request volume in the data throughput analysis set; Y i represents the weight coefficient, which is used to measure the importance of the data of the i-th past time interval; Q t-idenoted as the actual data read request volume at time t-i; m denotes the number of time intervals of packet loss rate data in the data throughput analysis set; R j denoted as the weight coefficient for weighted summation; F t-j denoted as the packet loss rate at time t-j in addition to the data read request volume; C t denoted as the error value at time t;

[0011] Through time series analysis method, combined with historical data read request volume and related influencing factors, the change trend of block data read request volume can be accurately predicted. It constructs an analysis set based on actual log records, comprehensively considers the weight coefficient and external indicators, not only effectively utilizes the law of historical data, but also can dynamically adapt to the changes in the network environment.

[0012] Step S2: Collect the historical data packet loss rate and data read request volume of the block for analysis, calculate the relationship between the data packet loss rate and the data read request volume on the block, construct a request volume-packet loss correlation model, and calculate the predicted data packet loss rate of the block according to the predicted data read request volume combined with the request volume-packet loss correlation model;

[0013] Step S2-1: Collect the historical data packet loss rate of the block through a network protocol analysis tool, and remove abnormal data from the collected packet loss rate, denoted as standard packet loss rate data. The abnormal data includes abnormal high packet loss rate and abnormal high data read request volume data caused by network congestion, network attack, hardware failure, software problem or configuration error;

[0014] Step S2-2: Use a neural network model to perform a correlation analysis on the standard packet loss rate data and the data read request volume. Input multiple groups of standard packet loss rate data and data read request volume with the same timestamp in the block into the neural network model for training to obtain the mapping relationship between the data packet loss rate and the data read request volume, and then construct a request volume-packet loss correlation model;

[0015] Step S2-3: Input the predicted data read request volume into the request volume-packet loss correlation model to perform mapping calculation to obtain the predicted data packet loss rate of the block.

[0016] By collecting historical data, removing outliers, and using a neural network to construct a packet loss rate-request volume correlation model, the complex non-linear relationship between the two can be accurately mined. It can effectively avoid network anomaly interference, based on standardized data, make the model more suitable for the actual business scenario, and quickly obtain the corresponding packet loss rate by predicting the request volume.

[0017] Step S3: Obtain the data storage capacity consumption rate of the block, calculate the remaining value of the data storage capacity of the block based on the time for predicting the change trend of the data read request volume of the block, and denote it as the storage capacity prediction value; calculate the risk value of the block according to the predicted data packet loss rate and the storage capacity prediction value;

[0018] Step S3-1: Obtain the data storage capacity of the block through a blockchain browser, and calculate the data storage capacity consumption rate of the block according to the change of the data storage capacity per unit time. Specifically, it is calculated by dividing the difference between the data storage capacity at the initial time of the block and the current data storage capacity by the time interval between the initial time and the current time;

[0019] Step S3-2: Use the product of the time for predicting the change trend of the data read request volume of the block and the data storage capacity consumption rate to obtain the consumption amount of the data storage capacity of the block; calculate the remaining value of the data storage capacity of the block by subtracting the consumption amount from the current data storage capacity of the block, and denote it as the storage capacity prediction value;

[0020] Step S3-3: Calculate the risk value of the block through weighted fusion using the predicted data packet loss rate and the storage capacity prediction value.

[0021] The risk value of the block is calculated using the following formula:

[0022] ;

[0023] In the formula, X represents the risk value of the block; y i represents the predicted data packet loss rate; q1 represents the weight coefficient of the predicted data packet loss rate; Y represents the storage capacity prediction value; q2 represents the weight coefficient of the storage capacity prediction value;

[0024] By quantifying the data storage capacity consumption and the data packet loss risk, comprehensively evaluate the running status of the block. Based on actual data, accurately calculate the data storage capacity consumption rate and the remaining value, and combine the change trend of the data read request volume to prospectively predict the change of storage resources; at the same time, take the predicted data packet loss rate into consideration, and construct a risk value model through weighted fusion to comprehensively reflect the potential risks of the block in data transmission and storage.

[0025] Step S4: Sort according to the risk values of each block in the blockchain, store the data backup shards according to the size of the block risk value, mark the block storing the data backup shard as the target block, and monitor the data read request volume of the target block;

[0026] Step S4-1: Create a data backup shard index set, which is used to store the indexes of each data backup shard. After the data to be backed up is sharded, store the indexes of each data backup shard in the data backup shard index set, and synchronously update the data in the data backup shard index set to each block of the blockchain.

[0027] Step S4-2: Sort the risk values of each blockchain in the blockchain. According to the risk value sorting result, store the data backup shards according to the size of the block risk value, and mark the blocks storing the data backup shards as target blocks. Since there are multiple data backup shards in the blockchain, there are multiple target blocks in the blockchain. Select any one of the target blocks as the research object, and use a network protocol analysis tool to monitor the data read request volume of the target block.

[0028] By creating an index set, efficient management and network-wide synchronization of data shards are achieved, ensuring the traceability and consistency of data backup. According to the risk value sorting, the data backup shards are preferentially stored in low-risk blocks, significantly reducing the risks of data loss and transmission failures, and improving data security and availability. At the same time, key monitoring is implemented for the target blocks storing the backup data, and the data read dynamics are mastered in real time, facilitating the timely discovery of anomalies and the adoption of countermeasures, effectively optimizing the data management and operation and maintenance efficiency of the blockchain system, and enhancing the overall risk resistance ability.

[0029] Step S5: Obtain the analysis of the historical data read request volume of the target block, set a threshold for the data read request volume, and judge the replacement condition of the target block through the threshold.

[0030] Calculate the average value and standard deviation of the historical data read request volume of the target block. Determine the fluctuation coefficient range of the data read request volume through the three-sigma principle, and use the average value of the historical data read request volume of the target block plus the product of the fluctuation coefficient and the standard deviation to obtain the threshold of the target block, denoted as the target block request volume threshold. The specific process of judging the replacement condition of the target block through the threshold is as follows:

[0031] When the data read request volume obtained by the target block through the node log record does not exceed the target block request volume threshold, it is judged that the replacement condition of the target block is not met.

[0032] When the data read request volume obtained by the target block through the node log record exceeds the target block request volume threshold, it is judged that the replacement condition of the target block is met.

[0033] By calculating the mean and standard deviation, the three-sigma principle is used to accurately define the normal fluctuation range of the data reading request volume, so as to construct a dynamic and reasonable request volume threshold standard. When the actual request volume of the target block exceeds the threshold, potential risks and performance bottlenecks are identified in a timely manner, triggering the block replacement mechanism, effectively avoiding problems such as abnormal data transmission or excessive storage pressure caused by request overload, and realizing intelligent allocation and dynamic optimization of the resources of the blockchain system.

[0034] Step S6: Perform data backup shard migration on the target block that meets the replacement conditions, screen according to the risk values of each block in the blockchain, and select the block with the lowest block risk value and no stored data backup shard as the migration block for migration.

[0035] Step S6-1: Through the blockchain network communication protocol, encrypt and transmit the data backup shards of the target block from the original storage location to the selected migration block. The migration block compares the size values through the risk values of each block in the blockchain, and selects the block with the lowest block risk value and no stored data backup shard as the migration block;

[0036] Step S6-2: After the data backup shard migration is completed, synchronously update the data backup shard index set, mark the migrated block as the target block, and remove the mark of the data backup shard on the original storage block to complete the data backup shard migration.

[0037] By screening the block with the lowest risk value and no stored data backup shard as the migration target, it effectively avoids high-risk storage environments, reduces the probability of data loss and transmission failures, and improves data storage security; data encryption transmission and real-time update of the index set not only ensure the confidentiality and integrity of the data migration process, but also ensure data traceability and consistency; update the marks and indexes in a timely manner after migration, seamlessly connect the data management process, realize the continuous optimization and efficient utilization of the data storage resources of the blockchain system, and enhance the system's anti-risk ability and long-term stability.

[0038] Furthermore, a data process management system based on blockchain technology, the data process management system includes a data reading request volume prediction module, a packet loss rate correlation modeling module, a storage capacity risk calculation module, a data backup shard storage module, a target block threshold setting module, and a data backup shard migration module;

[0039] The data read request volume prediction module is used to obtain the block data read request volume, construct an analysis set, and predict the change trend; the packet loss rate correlation modeling module is used to collect and analyze the historical data packet loss rate and request volume, construct an association model between the two, and calculate the predicted packet loss rate; the storage capacity risk calculation module is used to obtain the block data storage capacity consumption rate, and calculate the predicted value and risk value of the storage capacity in combination with the request volume trend; the data backup shard storage module is used to manage the data backup shard index set, store shards according to the block risk value, and mark the target block; the target block threshold setting module is used to statistically analyze the historical request volume of the target block, set a threshold, and judge the target block replacement condition; the data backup shard migration module is used to select the migration block and complete the data backup shard migration.

[0040] The output end of the data read request volume prediction module is electrically connected to the input end of the packet loss rate correlation modeling module; the input end of the packet loss rate correlation modeling module is electrically connected to the input end of the storage capacity risk calculation module; the output end of the storage capacity risk calculation module is electrically connected to the input end of the data backup shard storage module; the output end of the data backup shard storage module is electrically connected to the input end of the target block threshold setting module; the output end of the target block threshold setting module is electrically connected to the input end of the data backup shard migration module.

[0041] The data read request volume prediction module includes a request volume set construction unit and a request volume trend prediction unit; the request volume set construction unit is used to select a block, obtain the request volume through node log records, and store it in the time stamp dataset to construct an analysis set; the request volume trend prediction unit is used to traverse the analysis set and use the time series analysis method to predict the change trend of the request volume per unit time.

[0042] The packet loss rate correlation modeling module includes a packet loss rate data processing unit and an association model construction unit; the packet loss rate data processing unit is used to collect the historical packet loss rate through a network protocol analysis tool and remove abnormal data to obtain standard data; the association model construction unit is used to train the standard packet loss rate data and request volume using a neural network model to obtain the mapping relationship between the two and construct an association model.

[0043] The storage capacity risk calculation module includes a capacity rate calculation unit and a risk value calculation unit; the capacity rate calculation unit is used to obtain the data storage capacity through a blockchain browser and calculate the capacity consumption rate per unit time; the risk value calculation unit is used to calculate the predicted value of the storage capacity according to the predicted request volume trend time and capacity consumption rate, and calculate the risk value by weighted fusion with the predicted packet loss rate.

[0044] The data backup shard storage module includes an index set management unit and a shard storage marking unit; the index set management unit is used to create and manage a data backup shard index set, and synchronously update the index data to each block; the shard storage marking unit is used to sort the risk values of each block in the blockchain, store shards according to the risk level, and mark the target block.

[0045] The target block threshold setting module includes a historical data statistics unit and a threshold judgment unit; the historical data statistics unit is used to calculate the average value and standard deviation of the historical request volume of the target block; the threshold judgment unit is used to determine the fluctuation coefficient range according to the three-sigma principle, set the request volume threshold, and judge the target block replacement condition.

[0046] The data backup shard migration module includes a migration block selection unit and a shard migration update unit; the migration block selection unit is used to select the block with the lowest risk and no stored shards as the migration block by comparing the block risk values; the shard migration update unit is used to encrypt and transmit the shard data to the migration block, update the index set, and re-mark the target block.

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0048] 1. Through the multi-dimensional dynamic analysis and prediction mechanism, the present invention accurately grasps the change rules of the data reading request volume, packet loss rate, and storage capacity of each block in the blockchain. Based on historical data, an analysis model is constructed, and technologies such as time series analysis and neural networks are comprehensively used to break through the limitations of traditional static storage strategies, effectively predict the data change trend, provide a scientific basis for data management pre-judgment, and significantly improve the forward-looking and accuracy of data processing.

[0049] 2. Through a comprehensive risk assessment and intelligent storage strategy system. The block risk value is calculated by combining various factors such as data reading, transmission, and storage, and the data backup shard storage layout is optimized according to the risk level. The data is preferentially stored in low-risk blocks, and at the same time, the storage blocks are dynamically monitored and intelligently replaced. This method greatly reduces the risk of data loss, improves the utilization rate of storage resources, ensures the high efficiency and stability of data storage and reading, and significantly enhances the data security and availability of the blockchain system.

[0050] 3. From data prediction, risk assessment to storage layout adjustment, and then to dynamic migration based on threshold judgment, each link in the present invention is closely connected and operates in coordination. Through transmission migration and real-time index update, the security and consistency of data during the migration process are ensured, the data management process is continuously optimized, the overall performance of the blockchain system is effectively improved, and a complete, efficient, and intelligent solution is provided for the blockchain data process management. Brief Description of the Drawings

[0051] Figure 1 Schematic diagram of the process of a data process management method based on blockchain technology according to the present invention;

[0052] Figure 2 Schematic diagram of the structure of a data process management system based on blockchain technology according to the present invention. Specific embodiments

[0053] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0054] Embodiment 1: As Figure 1 shown, the present invention provides a technical solution, a data process management method based on blockchain technology, and the data process management method includes the following steps:

[0055] Step S1, obtain the data read request volume of the block to construct a data throughput analysis set, and predict the change trend of the data read request volume of the block according to the data throughput analysis set;

[0056] Step S1-1, select any block in the blockchain as the research object, obtain the data read request volume of the block through node log records, centrally store the data read request volume in the block and the corresponding timestamp data, and then construct a data throughput analysis set;

[0057] Step S1-2, by traversing the data throughput analysis set, use the time series analysis method to predict the change trend of the block data read request volume within a unit time.

[0058] In specific implementation, taking the scenario of Internet of Things device data being uploaded to the blockchain as an example, select a blockchain block that collects environmental monitoring data such as temperature, humidity, and air quality data, record the number of environmental data read requests for this block by node logs every minute, including operations such as real-time data query and historical data retrieval, and organize and store the read request volume and the corresponding timestamp in the database to construct a data throughput analysis set. The principle is to use the time series analysis method to mine the change pattern of the data read request volume over time from historical data, and then predict the future trend. It should be noted during implementation that the recording frequency of node logs should be adapted to the data change frequency to avoid redundant data caused by too high a frequency or missing key changes caused by too low a frequency; at the same time, the integrity of data collection should be ensured to prevent data loss due to network fluctuations or equipment failures, which will affect the prediction accuracy.

[0059] For example, the number of data read requests in the data throughput analysis set is 3. The historical request volume data sorted in ascending order of time is Q1 = 110 times per hour, Q2 = 90 times per hour, and Q3 = 120 times per hour;

[0060] The reference value a is calculated as the average value from the historical request volume data, and a = 106.67; the weight coefficient Y i = [0.5, 0.3, 0.2]; n is the number of time intervals for the data read requests in the data throughput analysis set, n = 3; m is the number of time intervals for the packet loss rate data in the data throughput analysis set, m = 2; the weight coefficient R for weighted summation j = [0.4, 0.1]; the historical packet loss rate F t-1 = 0.08, F t-2 = 0.05; the error term takes the standard deviation of historical fluctuations as 10, and a random error term C is generated t = 2;

[0061] Centered around the reference value, the historical request volume fluctuation term is calculated as follows:

[0062] 0.5×(120 - 106.67) + 0.3×(90 - 106.67) + 0.2×(110 - 106.67)

[0063] = 6.67 - 5.0 + 0.67 = 2.34

[0064] The calculation process of the packet loss rate impact term is as follows:

[0065] 0.4×0.08 + 0.1×0.05 = 0.032 + 0.005 = 0.037;

[0066] The calculation process of predicting the request volume of block data read requests within the next hour by the time series analysis method is as follows:

[0067] Q4 = 106.67 + 2.34 + 0.037 + 2 = 110.05;

[0068] According to the number of times being an integer, through the rounding principle, Q4 = 110.05 ≈ 110 times per hour;

[0069] Step S2: Collect the historical data of the block's packet loss rate and data read requests for analysis, calculate the relationship between the packet loss rate and data read requests on the block, construct a request volume - packet loss association model, and calculate the predicted packet loss rate of the block based on the predicted data read requests combined with the request volume - packet loss association model;

[0070] Step S2-1, collect the historical data packet loss rate of the block through a network protocol analysis tool, and remove abnormal data from the collected packet loss rate to record it as standard packet loss rate data. The abnormal data includes abnormally high packet loss rate and abnormally high data read request volume data caused by network congestion, network attack, hardware failure, software problem or configuration error;

[0071] Step S2-2: Use a neural network model to perform correlation analysis on the standard packet loss rate data and the data read request volume, input multiple groups of standard packet loss rate data and data read request volumes with the same timestamp in the block into the neural network model for training, obtain a mapping relationship between the data packet loss rate and the data read request volume, and then construct a request volume packet loss correlation model;

[0072] Step S2-3: input the predicted data read request volume into the request volume packet loss association model, and perform mapping calculation to obtain the predicted data packet loss rate of the block.

[0073] In the specific implementation, for example, in the blockchain system where the data of IoT devices is uploaded to the chain, network protocol analysis tools such as Wireshark are used to collect the packet loss rate of each block data transmission process within one month, remove abnormal data caused by sudden network failures and malicious attacks, and retain the standard packet loss rate data under stable operation. These data and the data reading request volume at the corresponding time are input into the built multi-layer perceptron neural network model for training to obtain the mapping relationship between the two. The principle is to use the powerful nonlinear fitting ability of the neural network to capture the complex relationship between the data packet loss rate and the request volume. During implementation, attention should be paid to the rationality of abnormal data removal to avoid mistakenly deleting normal fluctuating data. At the same time, the hyperparameter settings of the neural network, such as the learning rate and the number of hidden layer neurons, will affect the model training effect and prediction accuracy.

[0074] For example, through the mapping relationship obtained by the request volume packet loss correlation model, the quadratic function simulates the nonlinear packet loss calculation formula when the load increases as follows:

[0075] y=0.00005×Q 2 +0.003×Q+0.01;

[0076] Substituting the number of requests in the next hour, Q4=110, predicted by step S1, into the function yields:

[0077] y=0.00005×110 2 +0.003×110+0.01=6.05+0.33+0.01=0.0639;

[0078] The percentage indicates the packet loss rate is 6.39%;

[0079] Step S3: Obtain the data storage capacity consumption rate of the block. Based on the time for predicting the change trend of the data read request volume of the block, calculate the remaining value of the data storage capacity of the block, denoted as the storage capacity prediction value; calculate the risk value of the block according to the predicted data packet loss rate and the storage capacity prediction value.

[0080] Step S3-1: Obtain the data storage capacity of the block through the blockchain browser. Calculate the data storage capacity consumption rate of the block according to the change of the data storage capacity per unit time. Specifically, it is calculated by dividing the difference between the data storage capacity at the initial time of the block and the current data storage capacity by the time interval between the initial time and the current time.

[0081] Step S3-2: Use the product of the time for predicting the change trend of the data read request volume of the block and the data storage capacity consumption rate to obtain the consumption amount of the data storage capacity of the block; calculate the remaining value of the data storage capacity of the block by subtracting the consumption amount from the current data storage capacity of the block, denoted as the storage capacity prediction value.

[0082] Step S3-3: Calculate the risk value of the block through weighted fusion using the predicted data packet loss rate and the storage capacity prediction value.

[0083] In specific implementation, taking the file storage blockchain as an example, obtain the storage capacity of the block storing files such as pictures and documents through the blockchain browser, such as counting the used storage capacity of the block once a day. Divide the difference between the initial capacity and the daily capacity by the number of days to obtain the storage capacity consumption rate. Combine the change trend of the data read request volume predicted in Step S1 for the next week, calculate the storage capacity consumption amount and the remaining value, and then calculate the risk value by weighted calculation with the predicted data packet loss rate. The principle is to comprehensively consider the data storage and transmission risks and quantitatively evaluate the block status.

[0084] For example, according to the log analysis result, the basic consumption rate is 50 GB / hour; according to the prediction in Step S1, the data request volume Q4 for the next hour is 110; the predicted data packet loss rate calculated in Step S2 is 0.0639; the initial storage capacity is 1000 GB; the measured data of the current remaining capacity is 800 GB, and the prediction time window is 2 hours; the weight coefficient q1 of the packet loss rate is 0.7; q2 = 1 - 0.7 = 0.3; when the request volume is high, write / cache increases, set as 0.1 × the predicted data request volume Q4; calculate the storage consumption rate as:

[0085] Storage consumption rate = 50 + 0.1×110 = 61 GB / hour;

[0086] Predict the capacity consumed in the next two hours, obtained by multiplying the prediction time by the storage consumption rate:

[0087] Storage consumption capacity = 2×61 = 122 GB;

[0088] Obtained by subtracting the predicted storage consumption capacity from the remaining storage capacity:

[0089] Y = 800 - 122 = 688GB;

[0090] The normalized calculation of the predicted value of the storage capacity is obtained:

[0091] 668÷1000 = 0.688;

[0092] Set the upper limit of the tolerable packet loss rate of the network to 20%, and perform the normalized calculation according to the calculated predicted packet loss rate:

[0093] 0.0639÷20% = 0.3195;

[0094] Set the weight coefficient of the predicted data packet loss rate as q1 = 0.8; the weight coefficient of the predicted value of the storage capacity as q2 = 0.2;

[0095] According to the risk value calculation formula of the block, it can be obtained:

[0096] X = 0.3195×0.8 + 0.688×0.2 = 0.2556 + 0.1376 = 0.3932;

[0097] The risk value of the block is represented as 39.32% by percentage;

[0098] Step S4. Sort according to the risk values of each block in the blockchain, store the data backup shards according to the size of the block risk values, and mark the blocks storing the data backup shards as target blocks, and monitor the data read request volume of the target blocks;

[0099] Step S4-1. Create a data backup shard index set, which is used to store the indexes of each data backup shard; after data sharding the data to be backed up, store the indexes of each data backup shard into the data backup shard index set, and synchronously update the data of the data backup shard index set to each block of the blockchain;

[0100] Step S4-2. Sort the risk values of each blockchain in the blockchain. According to the risk value sorting result, store the data backup shards according to the size of the block risk values, and mark the blocks storing the data backup shards as target blocks; there are multiple data backup shards in the blockchain, so there are multiple target blocks in the blockchain; select any one of the target blocks as the research object, and use the network protocol analysis tool to monitor the data read request volume of the target block.

[0101] In specific implementation, in the medical data blockchain, data such as patient diagnosis and treatment records is sharded and backed up, an index set is created to store information such as the hash value and storage location of each shard, and it is synchronized to each block. According to the calculated risk values of each block, the data backup shards are preferentially stored in the blocks with low risk, marked as target blocks, and a network protocol analysis tool is used to monitor the data read request volume. The principle is to optimize the data storage layout based on risk assessment to ensure data security.

[0102] Step S5: Obtain the data analysis of the historical data read request volume of the target block, set a threshold for the data read request volume, and judge the replacement condition of the target block through the threshold.

[0103] Calculate the average value and standard deviation of the historical data read request volume of the target block, determine the fluctuation coefficient range of the data read request volume through the three-sigma principle, and use the average value of the historical data read request volume of the target block plus the product of the fluctuation coefficient and the standard deviation to obtain the threshold of the target block, denoted as the target block request volume threshold; the specific process of judging the replacement condition of the target block through the threshold is as follows:

[0104] When the data read request volume obtained by the target block through the node log record does not exceed the target block request volume threshold, it is judged that the replacement condition of the target block is not satisfied;

[0105] When the data read request volume obtained by the target block through the node log record exceeds the target block request volume threshold, it is judged as the replacement condition of the target block.

[0106] In specific implementation, in the social network data blockchain, select the target block storing user dynamic information, count the data read request volume per hour in the past 30 days, calculate the average value and standard deviation, determine the fluctuation range according to the three-sigma principle, and set the request volume threshold. When the actual request volume at a certain moment exceeds the threshold, the replacement condition is triggered.

[0107] Step S6: Migrate the data backup shards of the target block that meet the replacement conditions, screen according to the risk values of each block in the blockchain, and select the block with the lowest block risk value and no stored data backup shards as the migration block for migration.

[0108] Step S6-1: Through the blockchain network communication protocol, encrypt and transmit the data backup shards of the target block from the original storage location to the selected migration block. The migration block compares the size values through the risk values of each block in the blockchain, and selects the block with the lowest block risk value and no stored data backup shards as the migration block;

[0109] Step S6-2: After the data backup shard migration is completed, synchronously update the data backup shard index set, mark the migrated block as the target block, and remove the mark of the data backup shard on the original storage block to complete the data backup shard migration.

[0110] In specific implementation, in the supply chain blockchain, when the target block storing the data backup shard of the goods transportation record meets the replacement condition, select the block with the lowest risk value and not storing this type of data backup shard from all blocks as the migration block, encrypt and transmit the data shard through the blockchain network, and update the index set and block mark after completion. The principle is to dynamically migrate data to reduce storage risks and ensure data security.

[0111] Embodiment 2, as Figure 2 shown, the present invention provides a data process management system based on blockchain technology. The data process management system includes a data read request volume prediction module, a packet loss rate correlation modeling module, a storage capacity risk calculation module, a data backup shard storage module, a target block threshold setting module, and a data backup shard migration module;

[0112] The data read request volume prediction module is used to obtain the data read request volume of the block, construct an analysis set, and predict the change trend; the packet loss rate correlation modeling module is used to collect and analyze the historical data packet loss rate and request volume, construct the correlation model between the two, and calculate the predicted packet loss rate; the storage capacity risk calculation module is used to obtain the storage capacity consumption rate of the block data, and calculate the predicted value and risk value of the storage capacity in combination with the request volume trend; the data backup shard storage module is used to manage the data backup shard index set, store the shard according to the block risk value, and mark the target block; the target block threshold setting module is used to statistically analyze the historical request volume of the target block, set the threshold, and judge the target block replacement condition; the data backup shard migration module is used to select the migration block and complete the data backup shard migration;

[0113] The output end of the data read request volume prediction module is electrically connected to the input end of the packet loss rate correlation modeling module; the input end of the packet loss rate correlation modeling module is electrically connected to the input end of the storage capacity risk calculation module; the output end of the storage capacity risk calculation module is electrically connected to the input end of the data backup shard storage module; the output end of the data backup shard storage module is electrically connected to the input end of the target block threshold setting module; the output end of the target block threshold setting module is electrically connected to the input end of the data backup shard migration module;

[0114] The data read request volume prediction module includes a request volume set construction unit and a request volume trend prediction unit; the request volume set construction unit is used to select blocks, obtain the request volume through node log records, and store and construct an analysis set with the timestamp dataset; the request volume trend prediction unit is used to traverse the analysis set and use the time series analysis method to predict the change trend of the request volume per unit time;

[0115] The packet loss rate correlation modeling module includes a packet loss rate data processing unit and a correlation model construction unit; the packet loss rate data processing unit is used to collect historical packet loss rates through network protocol analysis tools and remove abnormal data to obtain standard data; the correlation model construction unit is used to train the standard packet loss rate data and the request volume using a neural network model to obtain the mapping relationship between the two and construct a correlation model;

[0116] The storage capacity risk calculation module includes a capacity rate calculation unit and a risk value calculation unit; the capacity rate calculation unit is used to obtain the data storage capacity through a blockchain browser and calculate the capacity consumption rate per unit time; the risk value calculation unit is used to calculate the predicted storage capacity value based on the predicted request volume trend time and the capacity consumption rate, and calculate the risk value by weighted fusion with the predicted packet loss rate;

[0117] The data backup shard storage module includes an index set management unit and a shard storage marking unit; the index set management unit is used to create and manage the data backup shard index set and synchronously update the index data to each block; the shard storage marking unit is used to sort the risk values of each block in the blockchain, store the shards according to the risk level, and mark the target block;

[0118] The target block threshold setting module includes a historical data statistics unit and a threshold judgment unit; the historical data statistics unit is used to calculate the average value and standard deviation of the historical request volume of the target block; the threshold judgment unit is used to determine the fluctuation coefficient range according to the three-sigma principle, set the request volume threshold, and judge the replacement condition of the target block;

[0119] The data backup shard migration module includes a migration block selection unit and a shard migration update unit; the migration block selection unit is used to select the block with the lowest risk and no stored shards as the migration block by comparing the block risk values; the shard migration update unit is used to encrypt and transmit the shard data to the migration block, update the index set, and re-mark the target block.

[0120] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A data process management method based on blockchain technology, characterized in that: The data flow management method includes the following steps: Step S1: Obtain the data read request volume of the block to construct a data throughput analysis set, and predict the change trend of the data read request volume of the block based on the data throughput analysis set; Step S2: Collect the historical data packet loss rate of the block and analyze it with the data read request volume, calculate the relationship between the data packet loss rate and the data read request volume on the block, construct a request volume-packet loss correlation model, and calculate the predicted data packet loss rate of the block by combining the predicted data read request volume with the request volume-packet loss correlation model; Step S3: Obtain the data storage capacity consumption rate of the block, calculate the remaining value of the data storage capacity of the block according to the time of predicting the change trend of the data read request volume of the block, and record it as the storage capacity prediction value; calculate the risk value of the block according to the predicted data packet loss rate and the storage capacity prediction value; Step S4: Sort the risk values of each block in the blockchain, store the data backup shards according to the size of the block risk value, mark the block storing the data backup shards as the target block, and monitor the data read request volume of the target block; Step S5: Obtain the analysis of the historical data read request volume data of the target block, set a threshold for the data read request volume, and judge the replacement condition of the target block through the threshold; Step S6: Migrate the data backup shards of the target block that meet the replacement conditions, screen according to the risk values of each block in the blockchain, and select the block with the lowest block risk value and no stored data backup shards as the migration block for migration.

2. The data flow management method based on blockchain technology according to claim 1, wherein: The specific steps of step S1 are as follows: Step S1-1: Select any block in the blockchain as the research object, obtain the data read request volume of the block through node log records, centrally store the data read request volume and the corresponding timestamp data in the block, and then construct a data throughput analysis set; Step S1-2: By traversing the data throughput analysis set, use the time series analysis method to predict the change trend of the data read request volume of the block per unit time.

3. A data flow management method based on blockchain technology according to claim 2, characterized in that: The specific steps of step S2 are as follows: Step S2-1: Collect the historical data packet loss rate of the block through a network protocol analysis tool, and remove abnormal data from the collected packet loss rate, which is recorded as the standard packet loss rate data. The abnormal data includes abnormal high packet loss rates and abnormal high data read request volume data caused by network congestion, network attacks, hardware failures, software problems, or configuration errors; Step S2-2: Use a neural network model to perform correlation analysis on the standard packet loss rate data and the data read request volume, input multiple groups of standard packet loss rate data and data read request volume with the same timestamp in the block into the neural network model for training, obtain the mapping relationship between the data packet loss rate and the data read request volume, and then construct a request volume-packet loss correlation model; Step S2-3: Input the predicted data read request volume into the request volume-packet loss correlation model, and perform mapping calculation to obtain the predicted data packet loss rate of the block.

4. A data flow management method based on blockchain technology according to claim 3, characterized in that: The specific steps of step S3 are as follows: Step S3-1: Obtain the data storage capacity of the block through a blockchain browser, and calculate the data storage capacity consumption rate of the block according to the change in data storage capacity per unit time. Specifically, it is calculated by dividing the difference between the data storage capacity at the initial time of the block and the current data storage capacity by the time interval between the initial time and the current time. Step S3-2: Multiply the time for predicting the change trend of the data read request volume of the block by the data storage capacity consumption rate to obtain the consumption amount of the block data storage capacity; calculate the remaining value of the block data storage capacity by subtracting the consumption amount from the current data storage capacity of the block, and record it as the storage capacity prediction value. Step S3-3: Calculate the risk value of the block through weighted fusion using the predicted data packet loss rate and the storage capacity prediction value.

5. A data process management method based on blockchain technology according to claim 4, characterized in that: The specific steps of step S4 are as follows: Step S4-1: Create a data backup shard index set, which is used to store the indexes of each data backup shard; after data sharding of the data to be backed up, store the indexes of each data backup shard in the data backup shard index set, and synchronously update the data in the data backup shard index set to each block of the blockchain. Step S4-2: Sort the risk values of each block in the blockchain. According to the risk value sorting result, store the data backup shards according to the size of the block risk value, and mark the blocks storing the data backup shards as target blocks; since there are multiple data backup shards in the blockchain, there are multiple target blocks in the blockchain. Select any one of the target blocks as the research object, and monitor the data read request volume of the target block using a network protocol analysis tool.

6. The data flow management method based on blockchain technology according to claim 5, characterized in that: In step S5, calculate the average value and standard deviation of the historical data read request volume of the target block, determine the fluctuation coefficient range of the data read request volume through the three-sigma principle, and use the average value of the historical data read request volume of the target block plus the product of the fluctuation coefficient and the standard deviation to obtain the threshold of the target block, denoted as the target block request volume threshold; the specific process of judging the replacement condition of the target block through the threshold is as follows: When the data read request volume obtained by the target block through the node log record does not exceed the target block request volume threshold, it is judged that the replacement condition of the target block is not met. When the data read request volume obtained by the target block through the node log record exceeds the target block request volume threshold, it is judged as the replacement condition of the target block.

7. A data flow management method based on blockchain technology according to claim 6, characterized in that: The specific steps of step S6 are as follows: Step S6-1: Through the blockchain network communication protocol, encrypt and transfer the data backup shards of the target block from the original storage location to the selected migration block. The migration block compares the size values of the risk values of each block in the blockchain, and selects the block with the lowest block risk value and no stored data backup shards as the migration block. Step S6-2: After the data backup shard migration is completed, synchronously update the data backup shard index set, mark the migrated block as the target block, and remove the mark of the data backup shard on the original storage block to complete the data backup shard migration.

8. A data process management system based on blockchain technology, which is applied to a data process management method based on blockchain technology according to any one of claims 1-7, characterized in that: The data flow management system includes a data reading request volume prediction module, a packet loss rate correlation modeling module, a storage capacity risk calculation module, a data backup shard storage module, a target block threshold setting module, and a data backup shard migration module; The data reading request volume prediction module is used to obtain the block data reading request volume, construct an analysis set, and predict the change trend; The packet loss rate correlation modeling module is used to collect and analyze the historical data packet loss rate and request volume, construct an association model between the two, and calculate the predicted packet loss rate; The storage capacity risk calculation module is used to obtain the block data storage capacity consumption rate, and calculate the predicted value and risk value of the storage capacity in combination with the request volume trend; The data backup shard storage module is used to manage the data backup shard index set, store the shards according to the block risk value, and mark the target block; The target block threshold setting module is used to statistically analyze the historical request volume of the target block, set a threshold, and judge the target block replacement condition; The data backup shard migration module is used to select the migration block and complete the data backup shard migration; The output end of the data reading request volume prediction module is electrically connected to the input end of the packet loss rate correlation modeling module; The input end of the packet loss rate correlation modeling module is electrically connected to the input end of the storage capacity risk calculation module; the output end of the storage capacity risk calculation module is electrically connected to the input end of the data backup shard storage module; the output end of the data backup shard storage module is electrically connected to the input end of the target block threshold setting module; the output end of the target block threshold setting module is electrically connected to the input end of the data backup shard migration module.

9. The data flow management system based on blockchain technology according to claim 8, characterized in that: The data reading request volume prediction module includes a request volume set construction unit and a request volume trend prediction unit; the request volume set construction unit is used to select a block, obtain the request volume through node log records, and store it in the timestamp dataset to construct an analysis set; The request volume trend prediction unit is used to traverse the analysis set and use the time series analysis method to predict the change trend of the request volume per unit time; The packet loss rate correlation modeling module includes a packet loss rate data processing unit and an association model construction unit; the packet loss rate data processing unit is used to collect the historical packet loss rate through a network protocol analysis tool and remove abnormal data to obtain standard data; The association model construction unit is used to train the standard packet loss rate data and request volume using a neural network model, obtain the mapping relationship between the two, and construct an association model; The storage capacity risk calculation module includes a capacity rate calculation unit and a risk value calculation unit; the capacity rate calculation unit is used to obtain the data storage capacity through a blockchain browser and calculate the capacity consumption rate per unit time; The risk value calculation unit is used to calculate the predicted value of the storage capacity according to the predicted request volume trend time and capacity consumption rate, and calculate the risk value by weighted fusion with the predicted packet loss rate.

10. The data flow management system based on blockchain technology according to claim 8, characterized in that: The data backup shard storage module includes an index set management unit and a shard storage marking unit; the index set management unit is used to create and manage a data backup shard index set, and synchronously update the index data to each block; the shard storage marking unit is used to sort the risk values of each block in the blockchain, store shards according to the risk level, and mark the target block. The target block threshold setting module includes a historical data statistics unit and a threshold judgment unit; the historical data statistics unit is used to calculate the average value and standard deviation of the historical request volume of the target block; the threshold judgment unit is used to determine the fluctuation coefficient range according to the three-sigma principle, set the request volume threshold, and judge the replacement condition of the target block. The data backup shard migration module includes a migration block selection unit and a shard migration update unit; the migration block selection unit is used to select the block with the lowest risk and without stored shards as the migration block by comparing the block risk values. The shard migration update unit is used to encrypt and transmit the shard data to the migration block, update the index set, and re-mark the target block.

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