Private platform data processing method based on block chain

Abstract

The invention relates to the field of data processing, in particular to a block chain-based private platform data processing method, which comprises the following steps of: determining a data verification index according to a monitoring duration index and an auxiliary monitoring index, and determining a data verification mode of each piece of platform verification data according to the data verification index; during aggregation verification analysis, determining a verification analysis mode of aggregation verification data according to the auxiliary aggregation parameters and the data anomaly index; during radiation verification analysis, determining a data analysis strategy of the radiation verification data according to the data change index and the reference difference index; under the verification completion condition, determining a block distribution mode of the verification data of each platform completing verification analysis according to the auxiliary aggregation parameters; under the block setting condition, the platform verification data determined by the target uploading block and the corresponding data reliability coefficient are uploaded cooperatively, and the data analysis efficiency of the private platform in the process of analyzing production link problems is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a data processing method for a privatized platform based on blockchain. Technical Background

[0002] Preserving relevant operational data of privatized platforms based on blockchain technology can effectively ensure data access security and non-tamperability. In the field of industrial production, a large amount of real-time collected data in both the manufacturing process and the equipment operation process needs to be stored on the chain. However, due to interference factors in the collection process, the reliability of the stored data cannot be guaranteed. Therefore, how to analyze the reliability of the data content before completing the chain storage and adjust the block selection process for chain storage to ensure the data analysis efficiency of the subsequent access process is an urgent problem to be solved by technical personnel in this field.

[0003] Chinese patent publication number CN118677700A discloses a blockchain-based industrial Internet of Things (IIoT) data encryption and storage method, which relates to the field of IIoT data processing. The method includes the following steps: S1: sharding IIoT data to obtain data shard information, and digitizing the data shard information to obtain digital information; S2: dynamically establishing network routing 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; and S4: when users upload data, verifying and storing it through client software or by calling a data upload interface, monitoring newly added local data in real time, and sending data synchronization requests to neighboring nodes. However, the above scheme 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 conditions 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 during subsequent access processes. Summary of the Invention

[0004] To this end, the present invention provides a blockchain-based privatized platform data processing method to overcome the problem in the prior art that it fails to determine targeted data verification and storage strategies based on the actual conditions of different categories of industrial data, resulting in low reliability and analysis efficiency of the data stored on the chain, and further leading to poor data analysis efficiency in subsequent access processes.

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

[0006] Determine the data verification index based on the monitoring continuity index and the auxiliary monitoring index, and determine the data verification method for each platform verification data based on the data verification index, whether to conduct aggregate verification analysis or radiation verification analysis for the platform verification data;

[0007] During the aggregation verification analysis, the verification analysis method of the aggregated 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 and the reference weight coefficient, or to determine the data reliability coefficient based on the anomaly correlation degree of each anomaly collection moment, or to determine the data reliability coefficient based on the data anomaly index and the reference anomaly independence index;

[0008] During the radiation verification analysis, the data analysis strategy for the radiation verification data is determined based on the data change index and the reference difference index: determining the data reliability coefficient based on the stage matching coefficient of each evaluation reference data, or determining the data reliability coefficient based on the data stability index of each evaluation reference data;

[0009] Under the verification completion condition, the block allocation method of the verification data of each platform that has completed the verification analysis is determined according to the auxiliary assembly parameters, which is to determine the target upload block according to the link assistance ratio and the reference reliability coefficient, or to determine the target upload block according to the reference assistance index;

[0010] Under the block setting condition, the platform verification data that completes the determination of the target upload block and its corresponding data reliability coefficient are uploaded in coordination, and the block setting condition is that the platform verification data completes the determination of the target upload block.

[0011] Furthermore, under the 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;

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

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

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

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

[0016] Furthermore, for a single platform verification data, if the data verification index of the platform verification data is greater than the preset data verification index, a collective verification analysis is performed on the 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 aggregation parameters and the data anomaly index to determine the data reliability coefficient of the aggregated verification data.

[0018] The data anomaly index is determined based on the abnormal time series ratio 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, a 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 is negatively correlated with the verification difference.

[0021] 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 less than or equal to the preset data anomaly index, the data reliability coefficient of the aggregated verification data is determined according to the anomaly correlation degree of each abnormal collection moment of the aggregated verification data;

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

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

[0024] For a single abnormal collection moment, if the abnormal independence index of the abnormal collection moment is greater than the preset abnormal independence index, it is determined that defect compensation is performed on the value of the aggregated verification data obtained at the abnormal collection moment.

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

[0026] The platform verification data is recorded as radiation verification data. The data analysis strategy of the radiation verification data is determined based on the data change index and the reference difference index to determine the data reliability coefficient of the 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 is positively correlated with the reference stage matching coefficient.

[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 according to the data stability index of each evaluation reference data;

[0030] The data reliability coefficient is positively correlated with the reference data stability index, and the reference data stability index is the average value of the data stability indexes of various evaluation reference data of the radiation verification data.

[0031] Furthermore, under the verification completion condition, 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] Platform verification data for a single completed verification analysis,

[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 based on 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 that the platform verification data exists to complete the verification analysis

[0036] Compared with the prior art, the beneficial effect of the present invention lies in that the technical solution of the present 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, thereby ensuring that the data verification method for the platform verification data is more in line with the actual working scenario, thereby improving the accuracy of the data reliability coefficient of the determined platform verification data, and uploading it in coordination with the data reliability coefficient during subsequent chain storage, and providing a reference basis for the selection of the target upload block. The present invention improves the data analysis efficiency of the privatization platform in the process of analyzing production link problems.

[0037] Furthermore, the present invention determines the data verification index of each platform verification data based on the monitoring continuity index and the auxiliary monitoring index. The data verification index is used to characterize the frequency of platform verification data collection and the range of data changes, and then to characterize the reference data existing in the platform verification data. In this way, the data verification method is selected to make it more in line with the actual data situation. The present invention improves the accuracy of the data reliability coefficient of the determined platform verification data.

[0038] Furthermore, in the present invention, for platform verification data with a larger data verification index, a targeted verification and analysis method is determined based on the auxiliary assembly parameters and the data anomaly index to further ensure the accuracy of the data reliability coefficient of the determined platform verification data. When the auxiliary assembly parameters and the data anomaly index are both large, the data reliability coefficient is determined by referring to the changes in the auxiliary data and the fit between the time stages corresponding to the data anomalies. When the auxiliary assembly parameters are large but the data anomaly index is small, there is no long data anomaly stage. Therefore, based on the reference to the auxiliary data, whether the occurrence of anomalies at each abnormal collection moment is reasonable is analyzed to determine the data reliability coefficient. When the auxiliary assembly parameters are small, effective auxiliary verification cannot be performed through other data. Therefore, the data reliability coefficient is determined based on the data anomaly index and the reference anomaly independence index to indicate the effectiveness for verification of other data. The present invention improves the accuracy of the data reliability coefficient of the determined platform verification data.

[0039] Furthermore, in the present invention, for platform verification data with a smaller data verification index, the data analysis strategy of the 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 larger and the change situation is more obvious. Therefore, it is more reasonable to analyze the reliability of its abnormal data based on the change situation, and select the data analysis strategy in a targeted manner to ensure the accuracy of the data reliability coefficient of the determined platform verification data.

[0040] Furthermore, the present invention determines the block allocation method of the platform verification data that has completed the verification analysis based on the auxiliary assembly 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, it is uploaded in coordination with the data reliability coefficient during the chain storage process, thereby providing a reference for the subsequent data access process. The present invention improves the data analysis efficiency of the privatization platform in the process of analyzing production problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Schematic diagram of the data processing method of the privatized platform based on blockchain of the present invention;

[0042] Figure 2 This is a flow chart of the data verification method for determining verification data of various platforms according to the data verification index of the present invention;

[0043] Figure 3 A flow chart for determining the data analysis strategy for the radiation validation data based on the data change index and the reference difference index;

[0044] Figure 4This is a flow chart of the method for determining block allocation of verification data of each platform that has completed verification analysis according to auxiliary aggregation parameters of the present invention. DETAILED DESCRIPTION

[0045] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0046] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain 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 the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating 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 does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0048] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0049] See also Figures 1 to 4 As shown, the present invention provides a data processing method for a privatized platform based on blockchain, comprising:

[0050] Determine the data verification index based on the monitoring continuity index and the auxiliary monitoring index, and determine the data verification method for each platform verification data based on the data verification index, whether to conduct aggregate verification analysis or radiation verification analysis for the platform verification data;

[0051] During the aggregation verification analysis, the verification analysis method of the aggregated 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 and the reference weight coefficient, or to determine the data reliability coefficient based on the anomaly correlation degree of each anomaly collection moment, or to determine the data reliability coefficient based on the data anomaly index and the reference anomaly independence index;

[0052] During the radiation verification analysis, the data analysis strategy for the radiation verification data is determined based on the data change index and the reference difference index: determining the data reliability coefficient based on the stage matching coefficient of each evaluation reference data, or determining the data reliability coefficient based on the data stability index of each evaluation reference data;

[0053] Under the verification completion condition, the block allocation method of the verification data of each platform that has completed the verification analysis is determined according to the auxiliary assembly parameters, which is to determine the target upload block according to the link assistance ratio and the reference reliability coefficient, or to determine the target upload block according to the reference assistance index;

[0054] Under the block setting condition, the platform verification data that completes the determination of the target upload block and its corresponding data reliability coefficient are uploaded in coordination, and the block setting condition is that the platform verification data completes the determination of the target upload block.

[0055] Among them, the present invention is used to perform data verification and storage planning for each platform verification data in the target management platform before uploading it to the chain for storage. The target management platform is a platform that stores and manages access to various monitoring data in the production and manufacturing process of the industrial park based on blockchain technology. The monitoring data that needs to be uploaded to the blockchain is recorded as platform verification data. Each platform verification data is correspondingly set with an upload verification period and a data collection period. For a single platform verification data, the value of the platform verification data is obtained at the end of the data collection period. At the end of the upload verification period, the set of values ​​of the platform verification data obtained each time during the upload verification period is recorded as the data verification set of the platform verification data, and verification analysis is performed on the data verification set. The values ​​of the upload verification period and the data collection period of each platform verification data can be set by the user according to the actual work scenario. How to set the upload verification period and the data collection period for each platform verification data is content that is easy to understand for those skilled in the art and is not elaborated here. The categories of platform verification data in the present invention include but are not limited to: equipment operation data, production quality data, equipment performance data, and quality inspection data.

[0056] Several verification and analysis records are applied in the present invention, and any verification and analysis record records the data verification index, anomaly correlation index, auxiliary assembly parameters, data anomaly index, anomaly independence index, fitting reference coefficient, evaluation reference coefficient, data change index, reference difference index, change stage overlap, auxiliary assembly parameters and the proportion of relevant monitoring data in the process of verification and analysis of each platform verification data of the target management platform at least once, and each verification and analysis record corresponds to a qualified mark, which records whether the efficiency and accuracy of the data processing of the chain storage process meets the user requirements. It can be understood that the user can determine whether the efficiency and accuracy of the data processing of the chain storage process meet the requirements based on self-set indicators. For example, the self-set indicator can be but is not limited to the chain execution coefficient. The chain execution coefficient is the number of times that data confidence situation warnings occur in the block under the premise of meeting the data upload efficiency.

[0057] Specifically, under the 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;

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

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

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

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

[0062] Among them, for a single platform verification data, the data verification index = 1 / (monitoring continuity index + auxiliary monitoring index), the monitoring continuity index = the duration of the data collection cycle used for collecting data by the platform verification data in the current upload verification cycle / the duration of the upload verification cycle, the auxiliary monitoring index = (the maximum value of the data coverage range of the platform verification data - the minimum value of the data coverage range of the platform verification data) / the 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 when the target management platform is produced and manufactured, and the minimum value of the data coverage range is the minimum value allowed for the platform verification data when the target management platform is produced and manufactured. How to set the data coverage range of each platform verification data is content that is easy for technical personnel in this field to understand.

[0063] Specifically, for a single platform verification data, if the data verification index of the platform verification data is greater than the preset data verification index, a collective verification analysis will be conducted on the platform verification data, 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 aggregation parameters and the data anomaly index to determine the data reliability coefficient of the aggregated verification data.

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

[0066] The value of the preset data verification index can be determined by the user according to the actual work scenario. For example, the user can set it according to the verification analysis record. The higher the user's requirements for the efficiency and accuracy of data processing in the 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. The verification analysis record for the aggregated verification analysis of the platform verification data is recorded as the analysis reference record. The average value of the data verification index of each platform verification data in the analysis reference record that meets the user's requirements for the efficiency and accuracy of data processing in the chain storage process is recorded as the preset data verification index.

[0067] For a single platform verification data, the auxiliary aggregation parameter is the number of auxiliary verification data of the platform verification data, the auxiliary verification data is the platform verification data whose abnormal correlation index with the platform verification data is greater than the preset abnormal correlation index, and the abnormal correlation index between any platform verification data other than the platform verification data and the platform verification data = the number of times the data abnormality indexes of the above two platform verification data are greater than the preset data abnormality index at the same time during the relevant evaluation stage / the total number of times the data abnormality indexes of the above two platform verification data are greater than the preset data abnormality index during the relevant evaluation stage. The end time of the relevant evaluation stage is the time when the platform verification data is determined to be aggregated for verification analysis. The value of the duration can be determined by the user according to the actual work scenario, and a value of the duration of the relevant evaluation stage is provided. The duration of the relevant evaluation stage is 20 times the upload verification period of the assembled verification data. The value of the preset abnormality correlation index can be determined by the user according to the actual work scenario. For example, the user can set it according to the verification analysis record. The higher the user's requirements for the efficiency and accuracy of the data processing of the chain storage process, the larger the value of the preset abnormality correlation index. A method for determining the value of the preset abnormality correlation index is provided, and the average value of the abnormality correlation index of each auxiliary verification data in the verification analysis record that meets the user's requirements for the efficiency and accuracy of the data processing of the chain storage process is recorded as the preset abnormality correlation index;

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

[0069] Specifically, 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;

[0070] The data reliability coefficient is negatively correlated with the verification difference.

[0071] Among them, the values ​​of the preset auxiliary assembly 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 record. The higher the user's requirements for the efficiency and accuracy of the data processing of the chain storage process, the larger the value of the preset auxiliary assembly parameter and the smaller the value of the preset data anomaly index. A method for determining the value of the preset auxiliary assembly parameter is provided, and the verification analysis record for determining the data reliability coefficient of the assembly verification data based on the verification difference of each reference auxiliary data and the reference weight coefficient is recorded as the assembly verification record. The minimum value of the auxiliary assembly parameter of the assembly verification data in the assembly verification record that meets the user's requirements for the efficiency and accuracy of the data processing of the chain storage process is recorded as the preset auxiliary assembly parameter. A method for determining the value of the preset data anomaly index is provided, and the maximum value of the data anomaly index of the assembly verification data in the assembly verification record that meets the user's requirements for the efficiency and accuracy of the data processing of the chain storage process is recorded as the preset data anomaly index.

[0072] For single-item assembly verification data, if the auxiliary assembly parameter is greater than the preset auxiliary assembly parameter and the data anomaly index is greater than the preset data anomaly index, the data reliability coefficient is the sum of the verification difference of each reference auxiliary data and the reference weight coefficient corresponding to each reference auxiliary data. The reference auxiliary data is the auxiliary verification data with a data anomaly index less than the preset reference anomaly index. The data verification set of the reference auxiliary data in the current upload verification cycle is obtained. For single-item reference auxiliary data, the verification difference is the absolute value of the difference between the change stage ratio of the reference auxiliary data and the abnormal moment ratio of the assembly verification data. The abnormal moment ratio = (the number of abnormal collection moments of the assembly verification data in the current verification analysis cycle) / the number of abnormal collection moments of the assembly verification data The number of collections within the current verification and analysis cycle, the change phase ratio = the interval between the first change collection time and the last change collection time of the reference auxiliary data / the length of the current verification and analysis cycle; for the end time of any data collection cycle within the current verification and analysis cycle, if the data change degree is greater than the preset data change degree, the end time of the data collection cycle is recorded as the change collection time; the data change degree = the absolute value of the difference between the end time of the data collection cycle and the end time of the previous data collection cycle / the value obtained at the end time of the previous data collection cycle of the data collection cycle; the reference weight coefficient = 1 / (data anomaly index of the auxiliary aggregation parameter + 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 work 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 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, and a value for the preset data change degree is provided. The value of the preset data change degree is 0.05. A method for determining the value of the preset reference anomaly index is provided, and 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 chain storage process is recorded as the preset reference anomaly index.

[0074] Specifically, 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 of the aggregated verification data is determined according to the anomaly correlation degree of each abnormal collection moment of the aggregated verification data;

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

[0076] Among them, for single-item assembly verification data, if the auxiliary assembly parameter is greater than the preset auxiliary assembly parameter and the data anomaly index is less than or equal to the preset data anomaly index, the anomaly correlation of each abnormal collection moment of the assembly verification data is detected, and the reference anomaly correlation is the average value of the anomaly correlation of each abnormal collection moment. For a single abnormal collection moment, the anomaly correlation = 1 / the average value of the anomaly correlation duration of each reference auxiliary data of the assembly verification data. For a single reference auxiliary data, the anomaly correlation duration is the interval between the abnormal collection moment and its reference moment. The reference moment is a changed collection moment of the reference auxiliary data closest to the abnormal collection moment within the current verification analysis cycle.

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

[0078] For a single abnormal collection moment, if the abnormal independence index of the abnormal collection moment is greater than the preset abnormal independence index, it is determined that defect compensation is performed on the value of the aggregated verification data obtained at the abnormal collection moment.

[0079] Among them, for single-item aggregate verification data, when the auxiliary aggregation parameter is less than or equal to the preset auxiliary aggregation parameter, the data reliability coefficient is negatively correlated with the abnormal reference index, the abnormal reference index = data abnormality index of the aggregate verification data / reference abnormal independence index, the reference abnormal independence index is the average of the abnormal independence indexes of each abnormal collection moment of the aggregate verification data, for a single abnormal collection moment, the abnormal independence index = 1 / (abnormal correlation index + abnormal correlation duration index), the abnormal correlation parameter = the number of abnormal collection moments within the relevant analysis range / the number of collection moments within the relevant analysis range, the abnormal correlation duration index = the minimum value of the interval duration between the abnormal collection moment and each abnormal collection moment in the current verification analysis cycle / the duration of the verification analysis cycle, the middle moment of the relevant analysis range is the abnormal collection moment, the duration of the relevant analysis range can be determined by the user according to the actual work scenario, and a relevant analysis range duration is provided, which is 5% of the duration of the verification analysis cycle;

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

[0081] The values ​​of the preset abnormal 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 according to the verification and analysis records. The higher the user's requirements for the efficiency and accuracy of data processing in the chain storage process, the larger the value of the preset abnormal independence index. A method for determining the value of the preset abnormal independence index is provided, and the verification and analysis record for defect compensation of the numerical value of the aggregated verification data obtained at the abnormal collection moment is recorded as an independent reference record. The minimum value of the abnormal independence index at each abnormal collection moment in the independent reference record that meets the user's requirements for the efficiency and accuracy of data processing in the chain storage process is recorded as the preset abnormal independence index. A method for determining the value of the preset fitting reference coefficient is provided, and the average value of the fitting reference coefficient 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 chain storage process is recorded as the preset fitting reference coefficient.

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

[0083] The platform verification data is recorded as radiation verification data. The data analysis strategy of the radiation verification data is determined based on the data change index and the reference difference index to determine the data reliability coefficient of the 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 is determined according to the stage matching coefficient of each evaluation reference data;

[0085] The data reliability coefficient is positively correlated with the reference stage matching coefficient.

[0086] Among them, for a single item of 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 of the current upload verification cycle | / the maximum value of the radiation verification data obtained in the previous upload verification cycle of the current upload verification cycle; the reference difference index is the average value of the data difference index of each collection order, which is calculated for the absolute value of the difference between the values ​​obtained in the same collection order in the current upload verification cycle and the previous upload verification cycle, and is recorded as the data difference index of the corresponding collection order; the collection order is the order in which the radiation verification data is collected within the upload verification cycle to which it belongs; the evaluation reference data is other platform verification data whose evaluation reference coefficient is greater than the preset evaluation reference coefficient; for a single item of platform verification data, the evaluation reference coefficient is the product of the data anomaly index of the platform verification data and the reference evaluation index; the reference evaluation index is the number of times the platform verification data is used to determine the data reliability coefficient of the radiation verification data;

[0087] The values ​​of the preset evaluation reference coefficient, the preset data change index and the 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 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, and 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 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, and the verification analysis record for determining the data reliability coefficient of the radiation verification data according to the stage matching coefficient of each evaluation reference data is recorded as the radiation reference record. 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 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, and 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 chain storage process is recorded as the preset reference difference index.

[0088] For single radiation verification data, 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 by division can be set by the user according to the actual work scenario. The reference stage matching coefficient is the average value of the stage matching coefficients of each verification stage. For a single verification stage, the values ​​obtained by collecting each evaluation reference data of the radiation verification data within the time range corresponding to the verification stage are obtained. The stage matching coefficient is the number of change overlap data of the verification stage. The change overlap data is the evaluation reference data whose change stage overlap is greater than the preset change stage overlap. For a single evaluation reference data, the change stage overlap = change association duration / verification stage duration. The shortest duration between the abnormal collection time of the evaluation reference data and the abnormal collection time of the radiation verification data in 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 according to the actual work scenario. For example, the user can set it according to the verification analysis record. The higher the user's requirements for the efficiency and accuracy of data processing in the 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, and the average value of the change stage overlap of each change overlap data in the verification analysis record that meets the user's requirements for the efficiency and accuracy of data processing in the chain storage process is recorded 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 is 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, and the reference data stability index is the average value of the data stability indexes of various evaluation reference data of the radiation verification data.

[0092] Among them, for single 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 single evaluation reference data, the data stability index = the maximum value of the evaluation reference data obtained during the current upload verification cycle / (the maximum value of the evaluation reference data obtained during the current upload verification cycle - the minimum value of the evaluation reference data obtained during the current upload verification cycle). If the maximum value of the evaluation reference data obtained during the current upload verification cycle is consistent with the minimum value, the data stability index is recorded as 1.

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

[0094] Platform verification data for a single completed verification analysis,

[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 based on 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 completion condition is that there is platform verification data to complete the verification analysis.

[0098] Among them, for a single platform verification data that has completed verification analysis, the auxiliary assembly parameter is the number of other platform verification data involved in the process of determining the data reliability coefficient of the platform verification data. If the auxiliary assembly parameter of the platform verification data that has completed verification analysis is greater than the preset auxiliary assembly parameter, the allocation priority coefficient of each to-be-matched block is determined according to the link auxiliary ratio and the reference reliability coefficient. For a single to-be-matched block, the allocation priority coefficient is the product of the link auxiliary ratio and the reference reliability coefficient. The link auxiliary ratio = the number of link auxiliary blocks of the to-be-matched block / the number of link blocks of the to-be-matched block. The link auxiliary block is a link block that stores any platform verification data involved in the verification analysis process of the platform verification data. The link block is a block that has a link relationship with the to-be-matched block. The reference reliability coefficient is the average of the data reliability coefficients of the platform verification data stored in each link auxiliary block. Average, the block to be allocated with the largest allocation priority coefficient is recorded as the target upload block for the platform verification data. If the auxiliary assembly parameter of the platform verification data that has completed the verification analysis is less than or equal to the preset auxiliary assembly parameter, the allocation priority coefficient of each block to be matched is determined according to the reference auxiliary index, and the block to be allocated with the largest allocation priority coefficient is recorded as the target upload block for the platform verification data. For a single block to be allocated, the allocation priority coefficient = 1 / reference auxiliary index, and the reference auxiliary index is the average value of the auxiliary assembly parameters of the platform verification data stored in each linked block of the block to be allocated. The block to be allocated is the 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 data set and data reliability coefficient of the platform verification data in the current verification analysis cycle are uploaded in coordination. How to store the data on the chain is easy for those skilled in the art to understand and will not be elaborated here.

[0099] The value of the preset auxiliary assembly 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 assembly parameter is provided. The verification analysis record of the target upload block of the platform verification data determined according to the link auxiliary ratio and the reference reliability coefficient is recorded as the allocation reference record, and the minimum value of the auxiliary assembly 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 chain storage process is recorded as the preset auxiliary assembly parameter.

[0100] Thus far, the technical solutions of the present invention have been described in conjunction with 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 may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0101] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A data processing method for a privatized platform based on blockchain, characterized in that: include: Determine the data verification index based on the monitoring continuity index and the auxiliary monitoring index, and determine the data verification method for each platform verification data based on the data verification index, whether to conduct aggregate verification analysis or radiation verification analysis for the platform verification data; During the aggregation verification analysis, the verification analysis method of the aggregated 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 and the reference weight coefficient, or to determine the data reliability coefficient based on the anomaly correlation degree of each anomaly collection moment, or to determine the data reliability coefficient based on the data anomaly index and the reference anomaly independence index; During the radiation verification analysis, the data analysis strategy for the radiation verification data is determined based on the data change index and the reference difference index: determining the data reliability coefficient based on the stage matching coefficient of each evaluation reference data, or determining the data reliability coefficient based on the data stability index of each evaluation reference data; Under the verification completion condition, the block allocation method of the verification data of each platform that has completed the verification analysis is determined according to the auxiliary assembly parameters, which is to determine the target upload block according to the link assistance ratio and the reference reliability coefficient, or to determine the target upload block according to the reference assistance index; Under the block setting condition, the platform verification data that completes the determination of the target upload block and its corresponding data reliability coefficient are uploaded in coordination, and the block setting condition is that the platform verification data completes the determination of the target upload block.

2. The data processing method of the privatized platform based on blockchain according to claim 1 is characterized in that: Under the 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 collection interval index of the platform verification data; The auxiliary monitoring index is determined based on the data coverage of the verification data of each platform; The data verification index is negatively correlated with the monitoring continuity index and the auxiliary monitoring index respectively; The data verification condition is that the target management platform has platform verification data that needs to be stored on the chain.

3. The data processing method of the privatized platform based on blockchain according to claim 2 is characterized in that: For single platform verification data, if the data verification index of the platform verification data is greater than the preset data verification index, a collective verification analysis will be conducted on the platform verification data, including: 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 aggregation parameters and the data anomaly index to determine the data reliability coefficient of the aggregated verification data. The data anomaly index is determined based on the abnormal time series ratio and the missing monitoring index.

4. The data processing method of a privatized platform based on blockchain according to claim 3 is 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 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; The data reliability coefficient is negatively correlated with the verification difference.

5. The data processing method of a privatized platform based on blockchain according to claim 4 is 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 of the aggregated verification data is determined according to the anomaly correlation degree of each abnormal collection moment of the aggregated verification data; The data reliability coefficient is positively correlated with the reference anomaly correlation.

6. The data processing method of a privatized platform based on blockchain according to claim 5, characterized in that: If the auxiliary aggregation parameter of the aggregated verification data is less than or equal to the preset auxiliary aggregation parameter, the data reliability coefficient of the aggregated verification data is determined according to the data anomaly index and the reference anomaly independence index, and whether to perform defect compensation for each abnormal collection moment is determined according to the anomaly independence index; For a single abnormal collection moment, if the abnormal independence index of the abnormal collection moment is greater than the preset abnormal independence index, it is determined that defect compensation is performed on the value of the aggregated verification data obtained at the abnormal collection moment.

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

8. The data processing method of a privatized platform based on blockchain 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 is determined according to the stage matching coefficient of each evaluation reference data; The data reliability coefficient is positively correlated with the reference stage matching coefficient.

9. The data processing method of a privatized platform based on blockchain 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 is 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, and the reference data stability index is the average value of the data stability indexes of various evaluation reference data of the radiation verification data.

10. The data processing method of a privatized platform based on blockchain according to claim 9, characterized in that: Under the verification completion condition, the block allocation method of the verification data of each platform that has completed the verification analysis is determined according to the auxiliary assembly parameters; Platform verification data for a single completed verification analysis, 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 based on 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 completion condition is that there is platform verification data to complete the verification analysis.

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