A Data Processing Method for a Blockchain-Based Private Platform

Abstract

This invention relates to the field of data processing, and more particularly to a data processing method for a private platform based on blockchain. The method includes: determining a data verification index based on a monitoring continuity index and an auxiliary monitoring index, and determining the data verification method for each platform verification data based on the data verification index; during aggregated verification analysis, determining the verification analysis method for aggregated verification data based on auxiliary aggregated parameters and a data anomaly index; during radiation verification analysis, determining the data analysis strategy for the radiation verification data based on a data change index and a reference difference index; under verification completion conditions, determining the block allocation method for each platform verification data that has completed verification analysis based on auxiliary aggregated parameters; and under block setting conditions, co-uploading the platform verification data determined by the target upload block and its corresponding data reliability coefficient. This invention improves the data analysis efficiency of a private platform in analyzing production process issues.

Description

Technical Field

[0001] This invention relates to the field of data processing, and more particularly to a data processing method for a private platform based on blockchain. Technical Background

[0002] Blockchain technology can be used to store relevant operational data on private platforms, effectively ensuring data access security and immutability. In the industrial production field, there is a large amount of real-time data collected during the manufacturing process and equipment operation that needs to be stored on the blockchain. However, due to interference factors during the collection process, the reliability of the stored data cannot be guaranteed. Therefore, how to analyze the reliability of the data content before storing it on the blockchain and adjust the block selection process for blockchain storage to ensure the efficiency of data analysis during subsequent access is a problem that urgently needs to be solved by those skilled in the art.

[0003] Chinese Patent Publication No. CN118677700A discloses a blockchain-based method for encrypted storage of industrial IoT data, relating to the field of IoT data processing. The method includes: S1: Segmenting data in the industrial IoT to obtain data segmentation information, and then digitizing this information to obtain digital information; S2: Dynamically constructing network routes using a node management algorithm to achieve peer-to-peer communication between nodes, improving data traceability and reliability; S3: Designing a data synchronization control algorithm and a data partitioning storage algorithm to optimize data storage and retrieval; S4: When a user uploads data, verification and storage are performed through client software or by calling a data upload interface, and the newly added data is monitored in real time, sending data synchronization requests to neighboring nodes. However, the above solution has the following problems: During the data verification and storage process, it fails to determine targeted data verification and storage strategies based on the actual situation of different types of industrial data, resulting in low reliability and analysis efficiency of the data stored on the blockchain, which in turn leads to poor data analysis efficiency in subsequent access processes. Summary of the Invention

[0004] To address this, the present invention provides a blockchain-based private platform data processing method to overcome the problem in the prior art that the failure to determine targeted data verification and storage strategies based on the actual situation of different types of industrial data leads to low reliability and analysis efficiency of the data stored on the blockchain, which in turn results in poor data analysis efficiency in subsequent access processes.

[0005] To achieve the above objectives, the present invention provides a blockchain-based private platform data processing method, comprising:

[0006] The data verification index is determined based on the monitoring continuity index and the auxiliary monitoring index. The data verification method for each platform verification data is determined based on the data verification index, which is either aggregate verification analysis or radiation verification analysis of the platform verification data.

[0007] During the aggregation verification analysis, the verification analysis method of the aggregation verification data is determined based on the auxiliary aggregation parameters and the data anomaly index. The verification analysis method is to determine the data reliability coefficient based on the verification difference degree and the reference weight coefficient, or to determine the data reliability coefficient based on the anomaly correlation degree at each anomaly collection time, or to determine the data reliability coefficient based on the data anomaly index and the reference anomaly independence index.

[0008] During radiation verification analysis, the data analysis strategy for determining radiation verification data based on the data change index and the reference difference index is to determine the data reliability coefficient based on the stage matching coefficient of each assessment reference data, or to determine the data reliability coefficient based on the data stability index of each assessment reference data.

[0009] Under the condition that the verification is completed, the block allocation method for the verification data of each platform that has completed the verification analysis is determined according to the auxiliary aggregation parameters. The target upload block is determined according to the link auxiliary ratio and the reference reliability coefficient, or the target upload block is determined according to the reference auxiliary index.

[0010] Under the block setting conditions, the platform verification data that completes the determination of the target upload block and its corresponding data reliability coefficient will be uploaded in tandem. The block setting conditions are that there is a platform verification data to complete the determination of the target upload block.

[0011] Furthermore, under the condition of data verification, the data verification index of the verification data of each platform is determined based on the monitoring continuity index and the auxiliary monitoring index;

[0012] The monitoring continuity index is determined based on the data collection interval index of the platform verification data.

[0013] The auxiliary monitoring index is determined based on the data coverage of the verification data from each platform.

[0014] The data verification index is negatively correlated with both the monitoring continuity index and the auxiliary monitoring index.

[0015] The data verification condition is that the target management platform has platform verification data that needs to be stored on the blockchain.

[0016] Furthermore, for individual platform verification data, if the data verification index of that platform verification data is greater than the preset data verification index, then aggregated verification analysis is performed on that platform verification data, including:

[0017] The platform verification data is recorded as aggregated verification data. The verification analysis method of the aggregated verification data is determined based on the auxiliary aggregated parameters and the data anomaly index, so as to determine the data reliability coefficient of the aggregated verification data.

[0018] The data anomaly index is determined based on the proportion of abnormal time series and the missing monitoring index.

[0019] Furthermore, if the auxiliary aggregation parameter of the aggregated verification data is greater than the preset auxiliary aggregation parameter and the data anomaly index is greater than the preset data anomaly index, the data reliability coefficient of the aggregated verification data is determined based on the verification difference of each reference auxiliary data and the reference weight coefficient.

[0020] The data reliability coefficient and the verification difference are negatively correlated.

[0021] Furthermore, if the auxiliary aggregation parameter of the aggregation verification data is greater than the preset auxiliary aggregation parameter and the data anomaly index is less than or equal to the preset data anomaly index, the data reliability coefficient of the aggregation verification data is determined based on the anomaly correlation degree of each anomaly collection time of the aggregation verification data.

[0022] The data reliability coefficient is positively correlated with the reference anomaly correlation.

[0023] Furthermore, if the auxiliary aggregation parameter of the aggregation verification data is less than or equal to the preset auxiliary aggregation parameter, the data reliability coefficient of the aggregation verification data is determined according to the data anomaly index and the reference anomaly independence index, and it is determined whether to perform defect compensation for each anomaly collection time according to the anomaly independence index.

[0024] For a single abnormal acquisition moment, if the abnormality independence index of that abnormal acquisition moment is greater than the preset abnormality independence index, then it is determined that the value of the aggregated verification data acquired at that abnormal acquisition moment will be subject to defect compensation.

[0025] Furthermore, for individual platform verification data, if the data verification index of that platform verification data is less than or equal to the preset data verification index, then a radiation verification analysis is performed on that platform verification data, including:

[0026] The platform verification data is designated as radiation verification data. Based on the data change index and the reference difference index, the data analysis strategy for this radiation verification data is determined to determine the data reliability coefficient of this radiation verification data.

[0027] Furthermore, if the data change index of a radiation verification data is greater than the preset data change index or the reference difference index is greater than the preset reference difference index, the data reliability coefficient of the radiation verification data is determined according to the stage matching coefficient of each evaluation reference data.

[0028] The data reliability coefficient and the reference stage matching coefficient are positively correlated.

[0029] Furthermore, if the data change index of a radiation verification data is less than or equal to the preset data change index and the reference difference index is less than or equal to the preset reference difference index, the data reliability coefficient of the radiation verification data is determined based on the data stability index of each evaluation reference data.

[0030] The data reliability coefficient is positively correlated with the reference data stability index, which is the average of the data stability indices of various evaluation reference data for this radiation verification data.

[0031] Furthermore, under the condition that the verification is completed, the block allocation method of the verification data of each platform that has completed the verification analysis is determined according to the auxiliary aggregation parameters;

[0032] For platform verification data that has completed verification analysis on a single item,

[0033] If the auxiliary aggregation parameter of the platform verification data is greater than the preset auxiliary aggregation parameter, the target upload block of the platform verification data is determined according to the link auxiliary ratio and the reference reliability coefficient.

[0034] If the auxiliary aggregation parameter of the platform verification data is less than or equal to the preset auxiliary aggregation parameter, the target upload block of the platform verification data is determined according to the reference auxiliary index.

[0035] The verification completion condition is the existence of platform verification data and completion of verification analysis.

[0036] Compared with existing technologies, the beneficial effects of this invention are as follows: the technical solution of this invention determines the data verification index of each platform verification data based on the monitoring continuity index and the auxiliary monitoring index, and determines the data verification method of each platform verification data based on the data verification index, ensuring that the data verification method of the platform verification data is more in line with the actual working scenario, thereby improving the accuracy of the determined data reliability coefficient of the platform verification data. When storing the data on the blockchain later, it is uploaded in conjunction with the data reliability coefficient and provides a reference for the selection of the target upload block. This invention improves the data analysis efficiency of the private platform in the process of analyzing production process problems.

[0037] Furthermore, in this invention, the data verification index of each platform verification data is determined based on the monitoring continuity index and the auxiliary monitoring index. The data verification index characterizes the frequency of data collection and the range of data variation of the platform verification data, thereby characterizing the reference data situation of the platform verification data. Based on this, the data verification method is selected to better reflect the actual data situation. This invention improves the accuracy of the determined data reliability coefficient of the platform verification data.

[0038] Furthermore, in this invention, for platform verification data with a large data verification index, a targeted verification analysis method is determined based on the auxiliary aggregation parameter and the data anomaly index. This further ensures the accuracy of the determined data reliability coefficient of the platform verification data. When both the auxiliary aggregation parameter and the data anomaly index are large, the data reliability coefficient is determined by referring to the fit between the changes in auxiliary data and the time stages corresponding to data anomalies. When the auxiliary aggregation parameter is large but the data anomaly index is small, there is no long data anomaly stage. Therefore, the data reliability coefficient is determined based on whether the occurrence of anomalies at each anomaly collection time is reasonable, based on the analysis of the auxiliary data. When the auxiliary aggregation parameter is small, effective auxiliary verification cannot be performed using other data. Therefore, the data reliability coefficient is determined based on the data anomaly index and the reference anomaly independence index to represent the effectiveness for verification of other data. This invention improves the accuracy of the determined data reliability coefficient of the platform verification data.

[0039] Furthermore, in this invention, for platform verification data with a small data verification index, the data analysis strategy for this radiation verification data is determined based on the data change index and the reference difference index. The change range of such platform verification data is large and the change situation is relatively obvious. Therefore, it is more reasonable to analyze the reliability of its abnormal data based on the change situation, and to select a targeted data analysis strategy to ensure the accuracy of the determined data reliability coefficient of the platform verification data.

[0040] Furthermore, in this invention, the block allocation method for each platform verification data that has completed verification analysis is determined based on auxiliary aggregation parameters, so that the determined target upload block is more consistent with the correlation between the corresponding platform verification data and other data, providing convenience for the subsequent data access process. At the same time, the data is uploaded in conjunction with the data reliability coefficient during the on-chain storage process, thereby providing a reference for the subsequent data access process. This invention improves the data analysis efficiency of the private platform in the process of analyzing production process problems. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the data processing method for a blockchain-based private platform according to the present invention;

[0042] Figure 2 This is a flowchart illustrating the data verification method for determining various platform verification data based on the data verification index, as described in this invention.

[0043] Figure 3 A flowchart illustrating the data analysis strategy for determining the radiation verification data based on the data change index and the reference difference index;

[0044] Figure 4This is a flowchart illustrating how the present invention determines the block allocation method of verification data from each platform that has completed verification analysis based on auxiliary aggregation parameters. Detailed Implementation

[0045] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0046] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0047] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0048] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0049] Please see Figures 1 to 4 As shown, this invention provides a data processing method for a blockchain-based private platform, comprising:

[0050] The data verification index is determined based on the monitoring continuity index and the auxiliary monitoring index. The data verification method for each platform verification data is determined based on the data verification index, which is either aggregate verification analysis or radiation verification analysis of the platform verification data.

[0051] During the aggregation verification analysis, the verification analysis method of the aggregation verification data is determined based on the auxiliary aggregation parameters and the data anomaly index. The verification analysis method is to determine the data reliability coefficient based on the verification difference degree and the reference weight coefficient, or to determine the data reliability coefficient based on the anomaly correlation degree at each anomaly collection time, or to determine the data reliability coefficient based on the data anomaly index and the reference anomaly independence index.

[0052] During radiation verification analysis, the data analysis strategy for determining radiation verification data based on the data change index and the reference difference index is to determine the data reliability coefficient based on the stage matching coefficient of each assessment reference data, or to determine the data reliability coefficient based on the data stability index of each assessment reference data.

[0053] Under the condition that the verification is completed, the block allocation method for the verification data of each platform that has completed the verification analysis is determined according to the auxiliary aggregation parameters. The target upload block is determined according to the link auxiliary ratio and the reference reliability coefficient, or the target upload block is determined according to the reference auxiliary index.

[0054] Under the block setting conditions, the platform verification data that completes the determination of the target upload block and its corresponding data reliability coefficient will be uploaded in tandem. The block setting conditions are that there is a platform verification data to complete the determination of the target upload block.

[0055] This invention is used for data verification and storage planning before storing various platform verification data in a target management platform on the blockchain. The target management platform is a blockchain-based platform for storing and managing access to various monitoring data during the production and manufacturing process in an industrial park. The monitoring data that needs to be uploaded to the blockchain is recorded as platform verification data. Each platform verification data has a corresponding upload verification cycle and data collection cycle. For a single platform verification data, the value of the platform verification data is obtained at the end of the data collection cycle. At the end of the upload verification cycle, the set of values ​​of the platform verification data obtained in each upload verification cycle is recorded as the data verification set of the platform verification data. Verification analysis is performed on the data verification set. The duration of the upload verification cycle and data collection cycle for each platform verification data can be set by the user according to the actual working scenario. How to set the duration of the upload verification cycle and data collection cycle for each platform verification data is easy for those skilled in the art to understand and will not be elaborated here. The categories of platform verification data in this invention include, but are not limited to: equipment operation data, production quality data, equipment performance data, and quality inspection data.

[0056] This invention utilizes several verification analysis records. Each verification analysis record documents at least one verification analysis of various platform verification data for the target management platform, including data verification index, anomaly correlation index, auxiliary aggregation parameters, data anomaly index, anomaly independence index, fitting reference coefficient, evaluation reference coefficient, data change index, reference difference index, change stage overlap, auxiliary aggregation parameters, and the proportion of relevant monitoring data. Each verification analysis record also has a corresponding pass / fail marker. The pass / fail marker records whether the efficiency and accuracy of the data processing in the on-chain storage process meet the user's requirements. It can be understood that the user can determine whether the efficiency and accuracy of the data processing in the on-chain storage process meet the requirements based on self-defined indicators. For example, self-defined indicators can be, but are not limited to, the on-chain execution coefficient, which is the number of times a warning about the existence of data confidence in the block occurs while meeting the data upload efficiency.

[0057] Specifically, under the data verification condition, the data verification index of the verification data of each platform is determined based on the monitoring continuity index and the auxiliary monitoring index;

[0058] The monitoring continuity index is determined based on the data collection interval index of the platform verification data.

[0059] The auxiliary monitoring index is determined based on the data coverage of the verification data from each platform.

[0060] The data verification index is negatively correlated with both the monitoring continuity index and the auxiliary monitoring index.

[0061] The data verification condition is that the target management platform contains platform verification data.

[0062] Specifically, for single-platform verification data, the data verification index = 1 / (monitoring continuity index + auxiliary monitoring index), where the monitoring continuity index = the duration of the data collection period used to collect data within the current upload verification period / the duration of the upload verification period. The auxiliary monitoring index = (maximum value of the data coverage range of the platform verification data - minimum value of the data coverage range of the platform verification data) / maximum value of the data coverage range of the platform verification data. The maximum value of the data coverage range is the maximum value allowed for the platform verification data during production and manufacturing on the target management platform, and the minimum value of the data coverage range is the minimum value allowed for the platform verification data during production and manufacturing on the target management platform. How to set the data coverage range of each platform verification data is a content easily understood by those skilled in the art.

[0063] Specifically, for a single platform verification data item, if the data verification index of that platform verification data item is greater than the preset data verification index, then a clustered verification analysis is performed on that platform verification data item, including:

[0064] The platform verification data is recorded as aggregated verification data. The verification analysis method of the aggregated verification data is determined based on the auxiliary aggregated parameters and the data anomaly index, so as to determine the data reliability coefficient of the aggregated verification data.

[0065] The data anomaly index is determined based on the proportion of abnormal time series and the missing monitoring index.

[0066] The value of the preset data verification index can be determined by the user based on the actual work scenario. For example, the user can set it based on the verification analysis records. The higher the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process, the smaller the value of the preset data verification index. A method for determining the value of the preset data verification index is provided, in which the verification analysis records that aggregate and verify the platform verification data are recorded as the analysis reference records, and the average value of the data verification indices of each platform verification data in the analysis reference records that meet the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process is recorded as the preset data verification index.

[0067] For a single platform verification data item, the auxiliary aggregation parameter is the number of auxiliary verification data items for that platform verification data item. The auxiliary verification data items are platform verification data items whose anomaly correlation index with the platform verification data item is greater than a preset anomaly correlation index. The anomaly correlation index between any platform verification data item other than the platform verification data item and the platform verification data item is calculated as: the number of times the data anomaly indices of the two platform verification data items simultaneously exceed the preset data anomaly index during the relevant evaluation phase / the total number of times the data anomaly indices of the two platform verification data items exceed the preset data anomaly index during the relevant evaluation phase. The end time of the relevant evaluation phase is the time when it is determined whether the platform verification data item will undergo aggregated verification analysis. The duration can be determined by the user based on the actual work scenario. A value for the duration of the relevant evaluation stage is provided, which is 20 times the upload verification cycle of the aggregated verification data. The value of the preset anomaly correlation index can also be determined by the user based on the actual work scenario. For example, the user can set it based on the verification analysis record. The higher the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process, the larger the value of the preset anomaly correlation index. A method for determining the value of the preset anomaly correlation index is provided, which is the average value of the anomaly correlation index of each auxiliary verification data in the verification analysis record that meets the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process.

[0068] For single platform verification data, the data anomaly index is the sum of the anomaly time series ratio and the missing monitoring index. The anomaly time series ratio = the number of abnormal collection moments of the platform verification data included in the current upload verification period / the number of times the value of the platform verification data is obtained in the current upload verification period. For a single collection moment of the platform verification data, if the value obtained at that collection moment is not within the data coverage range of the platform verification data, then the collection moment is determined to be an abnormal collection moment. If the value of the platform verification data is not obtained at that collection moment, then the collection moment is determined to be a missing collection moment. The missing monitoring index = (the standard collection index of the current upload verification period - the number of times the value of the platform verification data is obtained in the current upload verification period) / the standard collection index of the current upload verification period. The standard collection index = the duration of the current upload verification period / the monitoring continuity index of the platform verification data.

[0069] Specifically, if the auxiliary aggregation parameter of the aggregation verification data is greater than the preset auxiliary aggregation parameter and the data anomaly index is greater than the preset data anomaly index, the data reliability coefficient of the aggregation verification data is determined based on the verification difference of each reference auxiliary data and the reference weight coefficient.

[0070] The data reliability coefficient and the verification difference are negatively correlated.

[0071] The values ​​of the preset auxiliary aggregation parameters and the preset data anomaly index can be determined by the user according to the actual working scenario. For example, the user can set them according to the verification analysis records. The higher the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process, the larger the value of the preset auxiliary aggregation parameters and the smaller the value of the preset data anomaly index. A method for determining the value of the preset auxiliary aggregation parameters is provided, in which the verification analysis records that determine the data reliability coefficient of the aggregated verification data based on the verification difference degree and reference weight coefficient of each reference auxiliary data are recorded as aggregated verification records. The minimum value of the auxiliary aggregation parameters of the aggregated verification data in the aggregated verification records that meet the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process is recorded as the preset auxiliary aggregation parameters. A method for determining the value of the preset data anomaly index is provided, in which the maximum value of the data anomaly index of the aggregated verification data in the aggregated verification records that meet the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process is recorded as the preset data anomaly index.

[0072] For single-item aggregated verification data, if the auxiliary aggregated parameter is greater than the preset auxiliary aggregated parameter and the data anomaly index is greater than the preset data anomaly index, the data reliability coefficient is the sum of the products of the verification difference of each reference auxiliary data and the reference weight coefficient corresponding to each reference auxiliary data. The reference auxiliary data are auxiliary verification data whose data anomaly index is less than the preset reference anomaly index. The data verification set of the reference auxiliary data within the current upload verification cycle is obtained. For a single reference auxiliary data item, the verification difference is the absolute value of the difference between the proportion of change phases of that reference auxiliary data item and the proportion of abnormal moments of that aggregated verification data item. The proportion of abnormal moments = (the number of abnormal collection moments of that aggregated verification data item within the current verification analysis cycle) / that aggregated verification data item. The number of data collections within the current verification and analysis period, the proportion of the change phase = the interval between the first and last change collection times of the reference auxiliary data / the duration of the current verification and analysis period. For the end time of any data collection period within the current verification and analysis period, if the data change degree is greater than the preset data change degree, then the end time of the data collection period is recorded as the change collection time. The data change degree = the absolute value of the difference between the end time of the current data collection period and the end time of the previous data collection period / the value obtained at the end time of the previous data collection period. The reference weight coefficient = 1 / (the data anomaly index of the auxiliary aggregation parameter + the anomaly correlation index of the auxiliary aggregation parameter).

[0073] The values ​​of the preset data change degree and the preset reference anomaly index can be determined by the user according to the actual working scenario. For example, the user can set them based on the verification analysis records. The higher the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process, the smaller the value of the preset data change degree and the smaller the preset reference anomaly index. A method for determining the value of the preset data change degree is provided, with a preset data change degree value of 0.05. A method for determining the value of the preset reference anomaly index is provided, where the average value of the data anomaly index of the auxiliary verification data that meets the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process is recorded as the preset reference anomaly index.

[0074] Specifically, if the auxiliary aggregation parameter of the aggregation verification data is greater than the preset auxiliary aggregation parameter and the data anomaly index is less than or equal to the preset data anomaly index, the data reliability coefficient of the aggregation verification data is determined based on the anomaly correlation at each anomaly collection time of the aggregation verification data.

[0075] The data reliability coefficient is positively correlated with the reference anomaly correlation.

[0076] Specifically, for single-item aggregated verification data, if the auxiliary aggregated parameter is greater than the preset auxiliary aggregated parameter and the data anomaly index is less than or equal to the preset data anomaly index, the anomaly correlation degree of each anomaly collection time of the aggregated verification data is detected. The reference anomaly correlation degree is the average of the anomaly correlation degrees of each anomaly collection time. For a single anomaly collection time, the anomaly correlation degree = 1 / the average anomaly correlation duration of each reference auxiliary data of the aggregated verification data. For a single reference auxiliary data, the anomaly correlation duration is the interval between the anomaly collection time and its reference time. The reference time is the change collection time of the reference auxiliary data that is closest to the anomaly collection time in the current verification analysis cycle.

[0077] Specifically, if the auxiliary aggregation parameter of the aggregation verification data is less than or equal to the preset auxiliary aggregation parameter, the data reliability coefficient of the aggregation verification data is determined according to the data anomaly index and the reference anomaly independence index, and whether to perform defect compensation for each anomaly collection time is determined according to the anomaly independence index.

[0078] For a single abnormal acquisition moment, if the abnormality independence index of that abnormal acquisition moment is greater than the preset abnormality independence index, then it is determined that the value of the aggregated verification data acquired at that abnormal acquisition moment will be subject to defect compensation.

[0079] Specifically, for single-item aggregated verification data, when the auxiliary aggregated parameter is less than or equal to the preset auxiliary aggregated parameter, the data reliability coefficient and the anomaly reference index are negatively correlated. The anomaly reference index = data anomaly index of the aggregated verification data / reference anomaly independence index. The reference anomaly independence index is the average of the anomaly independence indices of each anomaly collection time of the aggregated verification data. For a single anomaly collection time, the anomaly independence index = 1 / (anomaly correlation index + anomaly correlation duration index). The anomaly correlation parameter = number of anomaly collection times within the correlation analysis range / number of collection times within the correlation analysis range. The anomaly correlation duration index = minimum interval between the anomaly collection time and each anomaly collection time within the current verification analysis cycle / duration of the verification analysis cycle. The middle time of the correlation analysis range is the anomaly collection time. The duration of the correlation analysis range can be determined by the user according to the actual working scenario. A correlation analysis range duration is provided, which is 5% of the duration of the verification analysis cycle.

[0080] For a single abnormal acquisition moment, if the abnormal independence index is greater than the preset abnormal independence index, then defect compensation is performed on the value of the aggregated verification data acquired at that abnormal acquisition moment. The defect compensation process is as follows: obtain fitting reference data within the relevant analysis range for that abnormal acquisition moment; for the value acquired in a single acquisition, if the fitting reference coefficient at that acquisition moment is greater than the preset fitting reference coefficient, then the value acquired in that acquisition is recorded as the fitting reference data. The fitting reference coefficient = 1 / the minimum interval between that acquisition moment and each missing acquisition moment in the verification analysis period; determine the fitting curve based on the determined fitting reference data, and then determine the value of the aggregated verification data acquired at that abnormal acquisition moment. How to predict the value acquired at that abnormal acquisition moment based on the determined fitting reference data is a content that is easy for those skilled in the art to understand, and will not be elaborated here.

[0081] The values ​​of the preset anomaly independence index and the preset fitting reference coefficient can be determined by the user according to the actual working scenario. For example, the user can set them based on the verification analysis records. The higher the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process, the larger the value of the preset anomaly independence index. A method for determining the preset anomaly independence index is provided, in which the verification analysis record for defect compensation of the aggregated verification data obtained at the time of anomaly collection is recorded as the independent reference record, and the minimum value of the anomaly independence index at each anomaly collection time in the independent reference record that meets the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process is recorded as the preset anomaly independence index. A method for determining the preset fitting reference coefficient is provided, in which the average value of the fitting reference coefficients of each fitting reference data in the independent reference record that meets the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process is recorded as the preset fitting reference coefficient.

[0082] Specifically, for a single platform verification data item, if the data verification index of that platform verification data is less than or equal to a preset data verification index, then a radiation verification analysis is performed on that platform verification data, including:

[0083] The platform verification data is designated as radiation verification data. Based on the data change index and the reference difference index, the data analysis strategy for this radiation verification data is determined to determine the data reliability coefficient of this radiation verification data.

[0084] Specifically, if the data change index of a radiation verification data is greater than the preset data change index or the reference difference index is greater than the preset reference difference index, the data reliability coefficient of the radiation verification data shall be determined according to the stage matching coefficient of each evaluation reference data.

[0085] The data reliability coefficient and the reference stage matching coefficient are positively correlated.

[0086] Specifically, for a single radiation verification data item, the data change index is calculated as: |Maximum value of the radiation verification data item acquired in the current upload verification cycle - Maximum value of the radiation verification data item acquired in the previous upload verification cycle| / Maximum value of the radiation verification data item acquired in the previous upload verification cycle. The reference difference index is the average of the data difference indices for each collection order. It is calculated for the absolute value of the difference between the values ​​acquired in the same collection order in the current upload verification cycle and the previous upload verification cycle, and recorded as the data difference index for the corresponding collection order. The collection order is the order in which the radiation verification data item is collected within its respective upload verification cycle. The evaluation reference data is other platform verification data with an evaluation reference coefficient greater than the preset evaluation reference coefficient. For a single platform verification data item, the evaluation reference coefficient is the product of the data anomaly index and the reference evaluation index for that platform verification data item. The reference evaluation index is the number of times that platform verification data item is used in the process of determining the data reliability coefficient of that radiation verification data item.

[0087] The values ​​of the preset evaluation reference coefficient, preset data change index, and preset reference difference index can be determined by the user according to the actual working scenario. For example, the user can set them according to the verification analysis record. The higher the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process, the larger the value of the preset evaluation reference coefficient, the smaller the value of the preset data change index, and the smaller the value of the preset reference difference index. A method for determining the value of the preset evaluation reference coefficient is provided, in which the average value of the evaluation reference coefficients of each evaluation reference data in the radiation reference record that meets the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process is recorded as the preset evaluation reference coefficient. A method for determining the value of the preset data change index is provided, in which the verification analysis record that determines the data reliability coefficient of the radiation verification data based on the stage matching coefficient of each evaluation reference data is recorded as the radiation reference record, and the average value of the data change index of each radiation verification data in the radiation reference record that meets the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process is recorded as the preset data change index. A method for determining the value of the preset reference difference index is provided, in which the average value of the reference difference index of each radiation verification data in the radiation reference record that meets the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process is recorded as the preset reference difference index.

[0088] For a single radiation verification data item, when the data change index is greater than the preset data change index or the reference difference index is greater than the preset reference difference index, the time range corresponding to the current upload verification cycle is divided into several verification stages of equal duration. The number of verification stages obtained can be set by the user according to the actual work scenario. The reference stage matching coefficient is the average of the stage matching coefficients of each verification stage. For a single verification stage, the values ​​obtained from each evaluation reference data of the radiation verification data collected each time within the time range corresponding to the verification stage are obtained. The stage matching coefficient is the number of change overlapping data in the verification stage. The change overlapping data is the evaluation reference data with a change stage overlap degree greater than the preset change stage overlap degree. For a single evaluation reference data item, the change stage overlap degree = change association duration / verification stage duration. The shortest duration between the abnormal collection time of the evaluation reference data item and the abnormal collection time of the radiation verification data item within the verification stage is recorded as the change association duration.

[0089] The value of the preset change stage overlap can be determined by the user based on the actual work scenario. For example, the user can set it based on the verification analysis records. The higher the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process, the larger the value of the preset change stage overlap. A method for determining the value of the preset change stage overlap is provided, which records the average value of the change stage overlap of each change overlapping data in the verification analysis records that meet the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process as the preset change stage overlap.

[0090] Specifically, if the data change index of a radiation verification data is less than or equal to the preset data change index and the reference difference index is less than or equal to the preset reference difference index, the data reliability coefficient of the radiation verification data shall be determined according to the data stability index of each evaluation reference data.

[0091] The data reliability coefficient is positively correlated with the reference data stability index, which is the average of the data stability indices of various evaluation reference data for this radiation verification data.

[0092] Specifically, for individual radiation verification data, if the data change index is less than or equal to the preset data change index or the reference difference index is less than or equal to the preset reference difference index, the data stability index of each evaluation reference data is tested. For individual evaluation reference data, the data stability index is calculated as: maximum value of the evaluation reference data obtained within the current upload verification period / (maximum value of the evaluation reference data obtained within the current upload verification period - minimum value of the evaluation reference data obtained within the current upload verification period). If the maximum value and the minimum value of the evaluation reference data obtained within the current upload verification period are the same, the data stability index is recorded as 1.

[0093] Specifically, under the condition that the verification is completed, the block allocation method of the verification data of each platform that has completed the verification analysis is determined according to the auxiliary aggregation parameters;

[0094] For platform verification data that has completed verification analysis on a single item,

[0095] If the auxiliary aggregation parameter of the platform verification data is greater than the preset auxiliary aggregation parameter, the target upload block of the platform verification data is determined according to the link auxiliary ratio and the reference reliability coefficient.

[0096] If the auxiliary aggregation parameter of the platform verification data is less than or equal to the preset auxiliary aggregation parameter, the target upload block of the platform verification data is determined according to the reference auxiliary index.

[0097] The verification is completed when platform verification data is available and verification analysis is performed.

[0098] Specifically, for a single platform verification data point that has completed verification analysis, the auxiliary aggregation parameter is the number of other platform verification data points involved in determining the data reliability coefficient of that platform verification data point. If the auxiliary aggregation parameter of the platform verification data point that has completed verification analysis is greater than the preset auxiliary aggregation parameter, the allocation priority coefficient of each block to be matched is determined based on the linking assistance ratio and the reference reliability coefficient. For a single block to be matched, the allocation priority coefficient is the product of the linking assistance ratio and the reference reliability coefficient. The linking assistance ratio = the number of linking assistance blocks of the block to be matched / the number of linking blocks of the block to be matched. The linking assistance block is a linking block that stores any platform verification data point involved in the verification analysis of that platform verification data point. The linking block is a block that has a linking relationship with the block to be matched. The reference reliability coefficient is the average of the data reliability coefficients of the platform verification data stored in each linking assistance block. The block with the highest allocation priority coefficient is designated as the target upload block for the platform verification data. If the auxiliary aggregation parameter of the platform verification data that has completed verification analysis is less than or equal to the preset auxiliary aggregation parameter, the allocation priority coefficient of each block to be matched is determined according to the reference auxiliary index. The block with the highest allocation priority coefficient is designated as the target upload block for the platform verification data. For a single block to be matched, the allocation priority coefficient = 1 / reference auxiliary index. The reference auxiliary index is the average value of the auxiliary aggregation parameters of the platform verification data stored in each linked block of the block to be matched. The block to be matched is a block that can be used to complete the upload of the platform verification data. For a single platform verification data, after determining the target upload block, the verification dataset and data reliability coefficient of the platform verification data in the current verification analysis cycle are uploaded together. How to store the data on the blockchain is a content that is easy for those skilled in the art to understand and will not be elaborated here.

[0099] The value of the preset auxiliary aggregation parameter can be determined by the user according to the actual working scenario. For example, the user can set it according to the verification analysis record. A method for determining the value of the preset auxiliary aggregation parameter is provided, in which the verification analysis record of the target upload block of the platform verification data is determined according to the link assistance ratio and the reference reliability coefficient is recorded as the allocation reference record, and the minimum value of the auxiliary aggregation parameter of the platform verification data in the allocation reference record that meets the user's requirements for the efficiency and accuracy of data processing in the on-chain storage process is recorded as the preset auxiliary aggregation parameter.

[0100] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0101] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A data processing method for a blockchain-based private platform, characterized in that, include: The data verification index is determined based on the monitoring continuity index and the auxiliary monitoring index. The data verification method for each platform verification data is determined based on the data verification index, which is either aggregate verification analysis or radiation verification analysis of the platform verification data. During the aggregation verification analysis, the verification analysis method for the aggregation verification data is determined based on the auxiliary aggregation parameters and the data anomaly index. The verification analysis method is as follows: For single-item aggregated verification data, If the auxiliary aggregation parameter of the aggregated verification data is greater than the preset auxiliary aggregation parameter and the data anomaly index is greater than the preset data anomaly index, the data reliability coefficient of the aggregated verification data is determined based on the verification difference of each reference auxiliary data and the reference weight coefficient, or... If the auxiliary aggregation parameter of the aggregation verification data is greater than the preset auxiliary aggregation parameter and the data anomaly index is less than or equal to the preset data anomaly index, the data reliability coefficient of the aggregation verification data is determined based on the anomaly correlation at each anomaly collection time of the aggregation verification data, or... If the auxiliary aggregation parameter of the aggregation verification data is less than or equal to the preset auxiliary aggregation parameter, the data reliability coefficient of the aggregation verification data is determined based on the data anomaly index and the reference anomaly independence index. During radiation verification analysis, the data analysis strategy for determining radiation verification data based on the data change index and the reference difference index is to determine the data reliability coefficient based on the stage matching coefficient of each assessment reference data, or to determine the data reliability coefficient based on the data stability index of each assessment reference data. Under the condition that the verification is completed, the block allocation method for the verification data of each platform that has completed the verification analysis is determined according to the auxiliary aggregation parameters. The target upload block is determined according to the link auxiliary ratio and the reference reliability coefficient, or the target upload block is determined according to the reference auxiliary index. Under the block setting condition, the platform verification data that completes the target upload block determination and its corresponding data reliability coefficient will be uploaded in tandem. The block setting condition is that there is a platform verification data that completes the determination of the target upload block. For single platform verification data, the data verification index = 1 / (monitoring continuity index + auxiliary monitoring index), where the monitoring continuity index = the duration of the data collection period used to collect data for this platform verification data within the current upload verification period / the duration of the upload verification period; the auxiliary monitoring index = (maximum value of the data coverage range of this platform verification data - minimum value of the data coverage range of this platform verification data) / maximum value of the data coverage range of this platform verification data; the auxiliary aggregation parameter is the number of auxiliary verification data for this platform verification data; the data anomaly index is the sum of the anomaly time series ratio and the missing monitoring index; where the anomaly time series ratio = the number of abnormal collection moments of this platform verification data included in the current upload verification period / the number of times the value of this platform verification data is obtained within the current upload verification period; and the missing monitoring index = (standard collection index of the current upload verification period - the number of times the value of this platform verification data is obtained within the current upload verification period) / standard collection index of the current upload verification period; where the standard collection index = the duration of the current upload verification period / the monitoring continuity index of this platform verification data. For a single reference auxiliary data, the verification difference is the absolute value of the difference between the proportion of change stages of the reference auxiliary data and the proportion of abnormal moments of the aggregated verification data, and the reference weight coefficient = 1 / (the data abnormality index of the reference auxiliary data + the abnormality correlation index of the reference auxiliary data). For a single abnormal collection time, the abnormal correlation degree = 1 / the average abnormal correlation duration of each reference auxiliary data of the aggregated verification data. For a single reference auxiliary data, the abnormal correlation duration is the interval between the abnormal collection time and its reference time. For a single radiation verification data, the data change index = |the maximum value of the radiation verification data obtained in the current upload verification cycle - the maximum value of the radiation verification data obtained in the previous upload verification cycle| / the maximum value of the radiation verification data obtained in the previous upload verification cycle. The reference difference index is the average value of the data difference index of each collection sequence. For a single verification phase, the phase matching coefficient is the number of overlapping change data in that verification phase. The overlapping change data is the evaluation reference data whose overlap degree in the change phase is greater than the preset overlap degree in the change phase. For a single evaluation reference data, the overlap degree in the change phase = change association duration / verification phase duration. The shortest duration between the abnormal collection time of the evaluation reference data and the abnormal collection time of the radiation verification data within the verification phase is recorded as the change association duration. For a single evaluation reference data, the data stability index = the maximum value of the evaluation reference data obtained within the current upload verification cycle / (the maximum value of the evaluation reference data obtained within the current upload verification cycle - the minimum value of the evaluation reference data obtained within the current upload verification cycle). For a single block to be matched, the link assistance ratio = the number of link assistance blocks for the block to be matched / the number of link blocks for the block to be matched, the reference reliability coefficient is the average of the data reliability coefficients of the platform verification data stored in each link assistance block, and the reference assistance index is the average of the auxiliary aggregation parameters of the platform verification data stored in each link block for the block to be matched.

2. The data processing method for a blockchain-based private platform according to claim 1, characterized in that, Under data verification conditions, the data verification index of each platform's verification data is determined based on the monitoring continuity index and the auxiliary monitoring index; The monitoring continuity index is determined based on the data collection interval index of the platform verification data. The auxiliary monitoring index is determined based on the data coverage of the verification data from each platform. The data verification index is negatively correlated with both the monitoring continuity index and the auxiliary monitoring index. The data verification condition is that the target management platform has platform verification data that needs to be stored on the blockchain.

3. The data processing method for a blockchain-based private platform according to claim 2, characterized in that, For individual platform verification data, if the data verification index of that platform verification data is greater than the preset data verification index, then aggregated verification analysis will be performed on that platform verification data, including: The platform verification data is denoted as aggregated verification data. The verification analysis method of the aggregated verification data is determined based on the auxiliary aggregated parameters and the data anomaly index, so as to determine the data reliability coefficient of the aggregated verification data.

4. The data processing method for a blockchain-based private platform according to claim 3, characterized in that, If the auxiliary aggregation parameter for the aggregated verification data is greater than the preset auxiliary aggregation parameter and the data anomaly index is greater than the preset data anomaly index, the data reliability coefficient and the verification difference are negatively correlated.

5. The data processing method for a blockchain-based private platform according to claim 4, characterized in that, If the auxiliary aggregation parameter of the aggregated verification data is greater than the preset auxiliary aggregation parameter and the data anomaly index is less than or equal to the preset data anomaly index, the data reliability coefficient and the reference anomaly correlation degree are positively correlated. The reference anomaly correlation degree is the average of the anomaly correlation degrees at each anomaly collection time.

6. The data processing method for a blockchain-based private platform according to claim 5, characterized in that, If the auxiliary aggregation parameter of the aggregation verification data is less than or equal to the preset auxiliary aggregation parameter, the defect compensation for each abnormal collection time shall be determined according to the abnormal independence index. For a single abnormal acquisition moment, if the abnormality independence index of that abnormal acquisition moment is greater than the preset abnormality independence index, then it is determined that the value of the aggregated verification data acquired at that abnormal acquisition moment will be subject to defect compensation.

7. The data processing method for a blockchain-based private platform according to claim 6, characterized in that, For single platform verification data, if the data verification index of that platform verification data is less than or equal to the preset data verification index, then a radiation verification analysis is performed on that platform verification data, including: The platform verification data is designated as radiation verification data. Based on the data change index and the reference difference index, the data analysis strategy for this radiation verification data is determined to determine the data reliability coefficient of this radiation verification data.

8. The data processing method for a blockchain-based private platform according to claim 7, characterized in that, If the data change index of a radiation verification data is greater than the preset data change index or the reference difference index is greater than the preset reference difference index, the data reliability coefficient of the radiation verification data shall be determined according to the stage matching coefficient of each evaluation reference data. The data reliability coefficient and the reference stage matching coefficient are positively correlated.

9. The data processing method for a blockchain-based private platform according to claim 8, characterized in that, If the data change index of a radiation verification data is less than or equal to the preset data change index and the reference difference index is less than or equal to the preset reference difference index, the data reliability coefficient of the radiation verification data shall be determined according to the data stability index of each evaluation reference data. The data reliability coefficient is positively correlated with the reference data stability index, which is the average of the data stability indices of various evaluation reference data for this radiation verification data.

10. The data processing method for a blockchain-based private platform according to claim 9, characterized in that, Once verification is complete, the allocation method for the verification data blocks of each platform that has completed verification analysis is determined based on the auxiliary aggregation parameters. For platform verification data that has completed verification analysis on a single item, If the auxiliary aggregation parameter of the platform verification data is greater than the preset auxiliary aggregation parameter, the target upload block of the platform verification data is determined according to the link auxiliary ratio and the reference reliability coefficient. If the auxiliary aggregation parameter of the platform verification data is less than or equal to the preset auxiliary aggregation parameter, the target upload block of the platform verification data is determined according to the reference auxiliary index. The verification is completed when platform verification data is available and verification analysis is performed.

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