Block chain-based collection data storage and security management system

Abstract

The invention relates to the technical field of collection data management, and discloses a block chain-based collection data storage and security management system, which comprises a building module used for building a block chain network which comprises a plurality of participating organizations and setting a plurality of storage nodes in each participating organization, generating a management strategy of the block chain network according to all the storage nodes; the receiving module is used for receiving and processing the to-be-stored data of the whole collection process, selecting a corresponding storage node according to a processing result, and generating a data-node mapping table; the management module is used for uploading the processed to-be-stored data to the corresponding storage node in the block chain network according to the data feature-storage node mapping table and the management strategy, verifying the processed to-be-stored data, and judging whether a correction instruction is generated or not according to a verification result, so that the storage efficiency and the security level of the collected data are effectively improved, and the safety of the collected data is improved. And a powerful guarantee is provided for data security and reliable storage in the field of collection.

Description

Technical Field

[0001] This application relates to the field of procurement data management technology, and in particular to a blockchain-based procurement data storage and security management system. Background Technology

[0002] As a crucial link in economic activities, bidding and procurement generate data covering the entire process, including bidding announcements, bidding documents, tender documents, bid evaluation records, notices of award, and contract texts. This data is not only the core basis for compliance review of bidding and procurement activities, but also directly related to fair market competition and the legitimate rights and interests of both parties in the transaction.

[0003] In existing technologies, traditional procurement data management systems typically employ a centralized database architecture, lacking flexible storage strategies, resource allocation strategies, and security management strategies that match the importance of the data. This reduces the storage efficiency and security level of procurement data, failing to meet the stringent requirements for data security and reliability in the procurement field. Summary of the Invention

[0004] To address the aforementioned technical issues, this application provides a blockchain-based data storage and security management system for procurement. By establishing a blockchain network, setting up several storage nodes for each participating organization within the network, and generating management policies, the system uploads the data to be stored to the corresponding storage nodes according to the management policies and the data-node mapping table. Based on the verification results, it determines whether to generate a correction instruction, effectively improving the storage efficiency and security level of procurement data, and providing strong protection for data security and reliable storage in the procurement field.

[0005] In some embodiments of this application, a blockchain-based procurement data storage and security management system is provided, including:

[0006] A module for building a blockchain network, which includes several participating organizations, sets several storage nodes within each participating organization, and generates a management strategy for the blockchain network based on all storage nodes.

[0007] The receiving module is used to receive and process the data to be stored throughout the entire procurement process, select the corresponding storage node based on the processing results, and generate a data-node mapping table.

[0008] The management module is used to upload the processed data to be stored to the corresponding storage node in the blockchain network according to the data feature-storage node mapping table and management strategy, and to verify it. Based on the verification result, it determines whether to generate a correction instruction.

[0009] In some embodiments of this application, several storage nodes are configured within each participating organization, including:

[0010] Retrieve historical stored data packets for each participating organization;

[0011] Identify several data categories in the historical storage data packets, and divide the historical storage data packets into several storage sub-data packets according to the data categories;

[0012] Calculate the comprehensive storage evaluation value of each storage sub-data packet in the same participating organization and the first correlation coefficient between different storage sub-data packets;

[0013] The weight coefficient of each storage sub-data packet in the same participating organization is set according to the comprehensive storage evaluation value. The storage sub-data packets are sorted according to the weight coefficient, and a storage sub-data packet sequence is generated according to the sorting result.

[0014] The top-ranked storage sub-data packet is set as the corresponding storage backbone node of the participating organization, the remaining storage sub-data packets are set as the corresponding storage branch nodes of the participating organization, and the edges are set according to the first correlation coefficient.

[0015] Construct a storage tree graph for the corresponding participating organization based on the storage backbone node, several storage branch nodes, and corresponding edges of the same participating organization.

[0016] Based on the storage tree diagram of each participating organization, determine the corresponding number of storage nodes within that participating organization.

[0017] In some embodiments of this application, calculating the comprehensive storage evaluation value for each storage sub-data packet of the same participating organization includes:

[0018] Each storage sub-data packet is evaluated and analyzed based on several pre-defined storage evaluation indicators to obtain the storage evaluation value of each storage sub-data packet.

[0019] The association analysis is performed on all storage sub-data packets of the same participating organization to obtain the first association coefficient between different storage sub-data packets. Based on the first association coefficient, several first associated data packets for each storage sub-data packet are determined.

[0020] The storage sub-data packets of different participating organizations are correlated to obtain the second correlation coefficient between each storage sub-data packet and other storage sub-data packets of organizations with different parameters. Based on the correlation coefficient, several second associated data packets of each storage sub-data packet are determined.

[0021] The first compensation coefficient is generated based on the number of first associated data packets in the same storage sub-data packet, the corresponding first association coefficient, and the weight coefficient of each first associated data packet;

[0022] The second compensation coefficient is generated based on the number of second associated data packets in the same storage sub-data packet, the corresponding second association coefficient, and the weight coefficient of each second associated data packet;

[0023] A comprehensive compensation coefficient is generated based on the first compensation coefficient and the second compensation coefficient, and the storage evaluation value is corrected to obtain the comprehensive storage evaluation value of each storage sub-data packet.

[0024] In some embodiments of this application, several storage nodes within a corresponding participating organization are determined based on the storage tree diagram of each participating organization, including:

[0025] Generate a node sequence J based on the arrangement order of the stored sub-data packets, J = (j1, j2, ..., jn), where j1 is the main storage node, ji is the i-th storage branch node, and n is the number of nodes;

[0026] Construct a weight coefficient-preset radius mapping table, which includes several preset weight coefficients, and each preset weight coefficient is mapped to a corresponding preset radius.

[0027] Based on each node in the node sequence as the center, and the preset radius mapped by the weight coefficient of each node as the scanning radius, the storage tree spectrum is scanned to obtain the first-level scanning area of ​​each node.

[0028] The first-level scanning regions of adjacent nodes in the node sequence are analyzed for overlap. If there is an overlapping region, the overlapping region is assigned to the corresponding node based on the weight coefficient priority rule to obtain several second-level scanning regions.

[0029] Construct a sequence of secondary scanning regions and calculate the management coefficient for each secondary scanning region;

[0030] The number of storage nodes for the corresponding secondary scanning area is set according to the management coefficient, and several storage nodes for the corresponding participating organization are generated according to the total number of storage nodes for all secondary scanning areas of the same participating organization.

[0031] In some embodiments of this application, the coefficient to be managed includes:

[0032] Collect the historical storage data volume, the number of storage sub-data packets involved, the average weight coefficient, historical access frequency, and historical access duration for each secondary scan area;

[0033] Predicted storage coefficients are generated based on the amount of historical stored data in the same secondary scan area;

[0034] The predicted importance coefficient is generated based on the number of storage sub-data packets involved in the same secondary scan area and the average of the corresponding weight coefficients.

[0035] Predicted access coefficients are generated based on the historical access frequency and historical access duration of the same secondary scan area.

[0036] The predicted storage coefficient, predicted importance coefficient, and predicted access coefficient are processed with unified dimensions and combined with the corresponding weight coefficients to calculate the management coefficient for each secondary scan area.

[0037] In some embodiments of this application, a management strategy for the blockchain network is generated based on all storage nodes, including:

[0038] A regional association map of the blockchain network is constructed based on the secondary scanning areas of all participating organizations and the second association coefficients between nodes in different secondary scanning areas;

[0039] Generate weight coefficients for each secondary scan region based on the regional association map;

[0040] Based on the weight coefficients of all secondary scan regions and the preset management strategy generation rules, determine the management permission level of all storage nodes in each secondary scan region;

[0041] Based on the management permission level and the business needs and security requirements of the corresponding secondary scanning area, set the resource allocation strategy for all storage nodes in the corresponding secondary scanning area;

[0042] Each storage node's management sub-policy is generated based on its management permission level and resource allocation strategy;

[0043] The management strategy for the blockchain network is generated based on the management sub-strategies of all storage nodes.

[0044] In some embodiments of this application, the data to be stored in the entire procurement process is received and processed to obtain the data characteristics, standard data digest, standard evidence storage code and complete original data file of each data to be stored. The processing includes format verification and cleaning, feature extraction, encryption and digest processing.

[0045] In some embodiments of this application, the corresponding storage node is selected based on the processing result, including:

[0046] Determine the stored data characteristics of each secondary scan region;

[0047] By performing a similarity analysis between the data characteristics of the data to be stored and the data characteristics of the stored data in each secondary scan area, the fit between the data to be stored and each secondary scan area is obtained.

[0048] All secondary scan regions are sorted according to their fit, and the secondary scan region ranked first is set as the storage region for the data to be stored.

[0049] Calculate the real-time application coefficient of each storage node in the area to be stored, sort the storage nodes in each area according to the real-time application coefficient, and set the storage node with the first sorted value as the storage node for the data to be stored.

[0050] In some embodiments of this application, generating a data-node mapping table includes:

[0051] Collect the standard evidence code of the data to be stored and the node location of the selected storage node for each piece of data to be stored;

[0052] Establish a correspondence between the standard evidence code of the data to be stored and the node location of the storage node, forming a data-node mapping entry;

[0053] The data-node mapping entries corresponding to all the data to be stored are summarized to generate a data-node mapping table.

[0054] In some embodiments of this application, determining whether to generate a correction instruction based on the verification result includes:

[0055] Collect verification data packets within each secondary scanning area. The verification data packets include differences in data digests, evidence encoding, storage requirements, and security levels of the data to be stored uploaded by the storage nodes.

[0056] The differences in data digest, evidence encoding, storage requirements, and security level are quantified to obtain a first quantization value, a second quantization value, a third quantization value, and a fourth quantization value. These values ​​are then compared with their corresponding quantization value thresholds. If none of these values ​​are less than the corresponding quantization value threshold, no correction instruction is generated.

[0057] If a quantization value is found that is less than the corresponding quantization threshold, a corresponding correction instruction is generated.

[0058] The blockchain-based procurement data storage and security management system of this application has the following advantages compared with the prior art:

[0059] By building a blockchain network, setting up several storage nodes for each participating organization in the blockchain network, and generating management policies, the data to be stored is uploaded to the corresponding storage nodes according to the management policies and the data-node mapping table. Based on the verification results, it is determined whether to generate a correction instruction, which effectively improves the storage efficiency and security level of procurement data and provides a strong guarantee for data security and reliable storage in the procurement field. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of a blockchain-based procurement data storage and security management system in an embodiment of this application. Detailed Implementation

[0061] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0062] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0063] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0064] In the description of this application, it should be noted that, unless otherwise expressly 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 between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0065] like Figure 1 As shown in the figure, an embodiment of this application discloses a blockchain-based procurement data storage and security management system, comprising:

[0066] A module for building a blockchain network, which includes several participating organizations, sets several storage nodes within each participating organization, and generates a management strategy for the blockchain network based on all storage nodes.

[0067] The receiving module is used to receive and process the data to be stored throughout the entire procurement process, select the corresponding storage node based on the processing results, and generate a data-node mapping table.

[0068] The management module is used to upload the processed data to be stored to the corresponding storage node in the blockchain network according to the data feature-storage node mapping table and management strategy, and to verify it. Based on the verification result, it determines whether to generate a correction instruction.

[0069] In this embodiment, the participating organizations include the procuring party, the bidding party, the regulatory party, and the bid evaluation party.

[0070] In this embodiment, the data to be stored received by the receiving module throughout the entire procurement process includes, but is not limited to, basic information of the procurement project in the bidding announcement, bidding documents, bid documents, bid evaluation report, and bid winning notice, as well as text or non-text data such as bid document storage, process control records, bidding behavior records, qualification certificate storage, bid winning history, and global ledger copy. This data is then cleaned, deduplicated, and converted to obtain the data to be stored.

[0071] In some embodiments of this application, several storage nodes are configured within each participating organization, including:

[0072] Retrieve historical stored data packets for each participating organization;

[0073] Identify several data categories in the historical storage data packets, and divide the historical storage data packets into several storage sub-data packets according to the data categories;

[0074] Calculate the comprehensive storage evaluation value of each storage sub-data packet in the same participating organization and the first correlation coefficient between different storage sub-data packets;

[0075] The weight coefficient of each storage sub-data packet in the same participating organization is set according to the comprehensive storage evaluation value. The storage sub-data packets are sorted according to the weight coefficient, and a storage sub-data packet sequence is generated according to the sorting result.

[0076] The top-ranked storage sub-data packet is set as the corresponding storage backbone node of the participating organization, the remaining storage sub-data packets are set as the corresponding storage branch nodes of the participating organization, and the edges are set according to the first correlation coefficient.

[0077] Construct a storage tree graph for the corresponding participating organization based on the storage backbone node, several storage branch nodes, and corresponding edges of the same participating organization.

[0078] Based on the storage tree diagram of each participating organization, determine the corresponding number of storage nodes within that participating organization.

[0079] In this embodiment, the larger the comprehensive storage evaluation value, the larger the corresponding weight coefficient, and vice versa.

[0080] In this embodiment, when the first correlation coefficient is greater than the correlation coefficient corresponding to a strong correlation, the corresponding node is directly connected. Furthermore, the larger the first correlation coefficient is, the shorter the edge connecting the corresponding node is, and vice versa.

[0081] In this embodiment, the storage tree diagram can intuitively display the hierarchical relationship and degree of association between the main storage node and several branch storage nodes within each participating organization. This lays the foundation for determining the secondary scanning area and setting the number of storage nodes, clarifies the layout of storage nodes within each participating organization, improves the reliability and security of data storage, and facilitates subsequent data retrieval and management, providing strong support for the full lifecycle management of procurement data.

[0082] In some embodiments of this application, calculating the comprehensive storage evaluation value for each storage sub-data packet of the same participating organization includes:

[0083] Each storage sub-data packet is evaluated and analyzed based on several pre-defined storage evaluation indicators to obtain the storage evaluation value of each storage sub-data packet.

[0084] The association analysis is performed on all storage sub-data packets of the same participating organization to obtain the first association coefficient between different storage sub-data packets. Based on the first association coefficient, several first associated data packets for each storage sub-data packet are determined.

[0085] The storage sub-data packets of different participating organizations are correlated to obtain the second correlation coefficient between each storage sub-data packet and other storage sub-data packets of organizations with different parameters. Based on the correlation coefficient, several second associated data packets of each storage sub-data packet are determined.

[0086] The first compensation coefficient is generated based on the number of first associated data packets in the same storage sub-data packet, the corresponding first association coefficient, and the weight coefficient of each first associated data packet;

[0087] The second compensation coefficient is generated based on the number of second associated data packets in the same storage sub-data packet, the corresponding second association coefficient, and the weight coefficient of each second associated data packet;

[0088] A comprehensive compensation coefficient is generated based on the first compensation coefficient and the second compensation coefficient, and the storage evaluation value is corrected to obtain the comprehensive storage evaluation value of each storage sub-data packet.

[0089] In this embodiment, the storage evaluation indicators include, but are not limited to, historical storage volume, historical storage frequency, importance of historical stored data, application frequency, and data access frequency. Each storage evaluation indicator has corresponding reference data. The data in each storage sub-data packet that is related to each storage evaluation indicator is extracted and compared with the corresponding parameter data to obtain the score of each storage sub-data packet under each storage evaluation indicator. The storage evaluation value of each storage sub-data packet is obtained by combining the scores under each storage evaluation indicator. When the storage evaluation value is larger, it indicates that the historical storage volume of the corresponding storage sub-data packet is larger, the historical storage frequency is higher, the importance of historical stored data is higher, and the application frequency and data access frequency are also higher.

[0090] In this embodiment, a first correlation coefficient threshold and a second correlation coefficient threshold are preset. Storage sub-data packets with a first correlation coefficient greater than the first correlation coefficient threshold are set as the first associated data packets of the corresponding storage sub-data packets, and storage sub-data packets with a second correlation coefficient greater than the second correlation coefficient threshold are set as the second associated data packets of the corresponding storage sub-data packets. The first correlation coefficient threshold refers to the minimum correlation value for storage sub-data packets within the same participating organization to have a correlation relationship, and the second correlation coefficient threshold refers to the minimum correlation value for storage sub-data packets between different participating organizations to have a correlation relationship.

[0091] In this embodiment, the first compensation coefficient = Where z is the compensation conversion coefficient, n is the number of first associated data packets, and g1i is the first association coefficient of the i-th first associated data packet. q1i is the threshold of the first correlation coefficient, and q1i is the weight coefficient of the i-th first correlation data packet. The compensation conversion coefficient refers to converting the difference of the first correlation coefficient into a value with the same dimension as the first compensation coefficient. When the sum of the differences of the first correlation coefficients is larger, the corresponding first compensation coefficient is larger, and vice versa. The calculation formula of the second compensation coefficient is the same as above, and will not be repeated here.

[0092] In this embodiment, the weighting coefficient of the first compensation coefficient is 0.6, the weighting coefficient of the second compensation coefficient is 0.4, and the value range of both the first compensation coefficient and the second compensation coefficient is (0.8, 1.2).

[0093] In this embodiment, the comprehensive storage evaluation value = storage evaluation value × (first compensation coefficient × 0.6 + second compensation coefficient × 0.4). The storage evaluation value is corrected by this formula so that the comprehensive storage evaluation value can more comprehensively and accurately reflect the importance of the storage sub-data packets, laying the foundation for constructing the storage tree diagram and setting storage nodes.

[0094] In some embodiments of this application, several storage nodes within a corresponding participating organization are determined based on the storage tree diagram of each participating organization, including:

[0095] Generate a node sequence J based on the arrangement order of the stored sub-data packets, J = (j1, j2, ..., jn), where j1 is the main storage node, ji is the i-th storage branch node, and n is the number of nodes;

[0096] Construct a weight coefficient-preset radius mapping table, which includes several preset weight coefficients, and each preset weight coefficient is mapped to a corresponding preset radius.

[0097] Based on each node in the node sequence as the center, and the preset radius mapped by the weight coefficient of each node as the scanning radius, the storage tree spectrum is scanned to obtain the first-level scanning area of ​​each node.

[0098] The first-level scanning regions of adjacent nodes in the node sequence are analyzed for overlap. If there is an overlapping region, the overlapping region is assigned to the corresponding node based on the weight coefficient priority rule to obtain several second-level scanning regions.

[0099] Construct a sequence of secondary scanning regions and calculate the management coefficient for each secondary scanning region;

[0100] The number of storage nodes for the corresponding secondary scanning area is set according to the management coefficient, and several storage nodes for the corresponding participating organization are generated according to the total number of storage nodes for all secondary scanning areas of the same participating organization.

[0101] In this embodiment, the weight coefficient-preset radius mapping table is set according to preset algorithm rules. By analyzing historical data and actual storage requirements, the required preset radius range under different weight coefficients is determined, and a mapping table containing multiple sets of weight coefficients and their corresponding preset radii is constructed. This mapping table ensures that during the subsequent storage node generation process, the scanning radius of each node can be quickly and accurately determined based on its weight coefficient, thereby achieving effective scanning of the storage tree graph and reasonable division of storage nodes. When the weight coefficient is larger, the corresponding scanning radius is smaller, allowing nodes with larger weight coefficients to more finely divide their management areas during the scanning process, ensuring that important data is stored and managed more properly, and vice versa.

[0102] In this embodiment, when the overlapping area is the entire first-level scan area of ​​one of the nodes, the overlapping area is completely assigned to the preceding node; if the overlapping area is only a part of the first-level scan area, the overlapping area is obtained according to the weight coefficient priority rule, that is, the node with the larger weight coefficient is given priority. If the weight coefficients of two adjacent nodes are the same, the overlapping area is divided according to the order of the nodes in the node sequence, and the node that appears first obtains the overlapping area. The weight coefficient of the second-level scan area is consistent with the weight coefficient of the corresponding node.

[0103] In this embodiment, a storage node refers to a storage data unit in a corresponding secondary scanning area used for storage and management. These storage data units cover multiple categories of historical stored data in the secondary scanning area. Storage nodes in the same secondary scanning area have the same security level. By setting the number of storage nodes in different secondary scanning areas, storage resources can be flexibly configured according to actual storage needs and security level requirements to ensure the security and efficient storage of data throughout the entire procurement process.

[0104] In this embodiment, the management coefficient is calculated based on the amount of data to be stored in the secondary scanning area, the comprehensive storage evaluation value of the storage sub-data packets involved, and the data access frequency.

[0105] In this embodiment, the higher the management coefficient, the more storage nodes are set to ensure efficient data storage and fast access. Several storage nodes are generated for each secondary scanning area. These storage nodes undertake specific data storage tasks in the blockchain network to jointly ensure the secure and reliable storage of procurement data.

[0106] In some embodiments of this application, the coefficient to be managed includes:

[0107] Collect the historical storage data volume, the number of storage sub-data packets involved, the average weight coefficient, historical access frequency, and historical access duration for each secondary scan area;

[0108] Predicted storage coefficients are generated based on the amount of historical stored data in the same secondary scan area;

[0109] The predicted importance coefficient is generated based on the number of storage sub-data packets involved in the same secondary scan area and the average of the corresponding weight coefficients.

[0110] Predicted access coefficients are generated based on the historical access frequency and historical access duration of the same secondary scan area.

[0111] The predicted storage coefficient, predicted importance coefficient, and predicted access coefficient are processed with unified dimensions and combined with the corresponding weight coefficients to calculate the management coefficient for each secondary scan area.

[0112] In this embodiment, the more historical storage data there is, the larger the predicted storage coefficient is; the larger the number of storage sub-data packets involved and the greater the average weight coefficient of the corresponding data packets, the larger the predicted importance coefficient is; the higher the historical access frequency and the longer the historical access duration, the larger the predicted access coefficient is, and vice versa.

[0113] In this embodiment, the weighting coefficients for the predicted storage coefficient, the predicted importance coefficient, and the predicted access coefficient are 0.3, 0.4, and 0.3, respectively.

[0114] In this embodiment, based on the calculated management coefficient and the preset correspondence between the number of storage nodes and the management coefficient, the number of storage nodes corresponding to the secondary scanning area is set. This correspondence can be flexibly adjusted according to actual storage needs and security level requirements to meet storage needs in different scenarios, ensuring the safe and reliable storage of procurement data and supporting efficient data access and retrieval operations.

[0115] In some embodiments of this application, a management strategy for the blockchain network is generated based on all storage nodes, including:

[0116] A regional association map of the blockchain network is constructed based on the secondary scanning areas of all participating organizations and the second association coefficients between nodes in different secondary scanning areas;

[0117] Generate weight coefficients for each secondary scan region based on the regional association map;

[0118] Based on the weight coefficients of all secondary scan regions and the preset management strategy generation rules, determine the management permission level of all storage nodes in each secondary scan region;

[0119] Based on the management permission level and the business needs and security requirements of the corresponding secondary scanning area, set the resource allocation strategy for all storage nodes in the corresponding secondary scanning area;

[0120] Each storage node's management sub-policy is generated based on its management permission level and resource allocation strategy;

[0121] The management strategy for the blockchain network is generated based on the management sub-strategies of all storage nodes.

[0122] In this embodiment, a deep analysis of the regional association map is performed to identify key information such as the relative position, degree of association, and data interaction frequency of each secondary scanning region in the entire blockchain network. A preset weight calculation algorithm (which comprehensively considers multiple dimensions such as data traffic between regions, business relevance, and security risk level) is used to assign a weight coefficient to each secondary scanning region.

[0123] In this embodiment, the preset management strategy generation rules include preset management permission level standards corresponding to different weight coefficient ranges. For example, the weight coefficients are divided into three ranges: high, medium, and low, which correspond to level 1, level 2, and level 3 management permission levels, respectively. Level 1 management permission is used for confidential data, level 2 management permission is used for internal data, and level 3 management permission is used for public data. The storage, security, and permission levels corresponding to different levels of data increase with the increase of the management permission level, ensuring the secure storage and reasonable access of data in the blockchain network.

[0124] In this embodiment, storage, security, and access control specifically include using different levels of encryption algorithms to encrypt data stored on nodes to prevent illegal theft or tampering, backing up data on storage nodes, and storing backup data in multiple different geographical locations to prevent data loss. Different data access permissions are set for users at different levels based on their management access levels, ensuring that only authorized users can access the corresponding level of data. Strict identity verification and access log recording are performed during the access process. Through the comprehensive implementation of these management access control levels, comprehensive security is provided for the storage and management of procurement data in the blockchain network.

[0125] In this embodiment, the resource allocation strategy includes storage space allocation, computing resource allocation, and network bandwidth allocation, which are customized according to the business needs and security requirements of the secondary scanning area. For areas with large data storage volume, high access frequency, and high security requirements, more storage and computing resources are allocated to ensure their efficient operation; while for areas with small data volume and low access frequency, resource allocation is appropriately reduced to achieve optimized resource configuration.

[0126] In this embodiment, by integrating the management sub-strategies of all storage nodes, a complete and coordinated blockchain network management strategy is formed, providing comprehensive protection for the secure, reliable, and efficient storage and management of procurement data on the blockchain.

[0127] In some embodiments of this application, the data to be stored in the entire procurement process is received and processed to obtain the data characteristics, standard data digest, standard evidence storage code and complete original data file of each data to be stored. The processing includes format verification and cleaning, feature extraction, encryption and digest processing.

[0128] In this embodiment, the format verification and cleaning process mainly verifies the format of the data to be stored, removes data that does not meet the preset format requirements, and cleans data with errors or redundancy to ensure the standardization and accuracy of the data. The feature extraction process extracts key features from the verified and cleaned data, which can represent the core content and attributes of the data, and provides a basis for subsequent data classification, storage and management.

[0129] In this embodiment, encryption and digest processing employ advanced encryption algorithms to encrypt the data, ensuring data security during transmission and storage. Simultaneously, a standard data digest is generated, which uniquely identifies the original data, facilitating subsequent data verification and retrieval. A standard evidence code is assigned to each data item for unique identification and tracking management, ultimately resulting in a complete original data file that provides a solid data foundation for subsequent storage operations.

[0130] In some embodiments of this application, the corresponding storage node is selected based on the processing result, including:

[0131] Determine the stored data characteristics of each secondary scan region;

[0132] By performing a similarity analysis between the data characteristics of the data to be stored and the data characteristics of the stored data in each secondary scan area, the fit between the data to be stored and each secondary scan area is obtained.

[0133] All secondary scan regions are sorted according to their fit, and the secondary scan region ranked first is set as the storage region for the data to be stored.

[0134] Calculate the real-time application coefficient of each storage node in the area to be stored, sort the storage nodes in each area according to the real-time application coefficient, and set the storage node with the first sorted value as the storage node for the data to be stored.

[0135] In this embodiment, the real-time application coefficient is calculated based on multiple factors such as the current storage status of the storage node, the remaining storage space, the data transmission rate, and the historical storage efficiency. The larger the remaining storage space, the faster the data transmission rate, and the higher the historical storage efficiency, the larger the real-time application coefficient, and vice versa.

[0136] In this embodiment, the fit degree obtained through similarity analysis can accurately match the most suitable secondary scanning area for the data to be stored. After sorting the data by fit degree from large to small and selecting the storage area, the optimal storage node is further selected within the storage area based on the real-time application coefficient. This two-level screening mechanism greatly improves the rationality and efficiency of data storage.

[0137] In some embodiments of this application, generating a data-node mapping table includes:

[0138] Collect the standard evidence code of the data to be stored and the node location of the selected storage node for each piece of data to be stored;

[0139] Establish a correspondence between the standard evidence code of the data to be stored and the node location of the storage node, forming a data-node mapping entry;

[0140] The data-node mapping entries corresponding to all the data to be stored are summarized to generate a data-node mapping table.

[0141] In this embodiment, a data-node mapping table is constructed to facilitate subsequent data management and query operations, ensuring that each piece of data to be stored can accurately correspond to its storage node, providing strong support for the secure storage and efficient management of procurement data in the blockchain network.

[0142] In some embodiments of this application, determining whether to generate a correction instruction based on the verification result includes:

[0143] Collect verification data packets within each secondary scanning area. The verification data packets include differences in data digests, evidence encoding, storage requirements, and security levels of the data to be stored uploaded by the storage nodes.

[0144] The differences in data digest, evidence encoding, storage requirements, and security level are quantified to obtain a first quantization value, a second quantization value, a third quantization value, and a fourth quantization value. These values ​​are then compared with their corresponding quantization value thresholds. If none of these values ​​are less than the corresponding quantization value threshold, no correction instruction is generated.

[0145] If a quantization value is found that is less than the corresponding quantization threshold, a corresponding correction instruction is generated.

[0146] In this embodiment, data digest difference refers to the situation where the data digest uploaded by the storage node is inconsistent with the original data digest; evidence storage code difference refers to the situation where the evidence storage code uploaded by the storage node does not match the original evidence storage code; storage requirement difference refers to the situation where the storage resources requested by the storage node do not match the actual needs; and security level difference refers to the situation where the data security level uploaded by the storage node does not match the preset security level.

[0147] In this embodiment, the greater the differences in data digest, evidence encoding, storage requirements, and security level, the smaller the corresponding quantization value, and vice versa. The quantization value threshold refers to the minimum quantization value that meets the storage and security management requirements. When it is less than the quantization value threshold, it indicates that the data uploaded by the storage node has problems in terms of digest, evidence encoding, storage requirements, or security level, and cannot meet the strict requirements for data storage and security management in the procurement process.

[0148] In this embodiment, the correction instructions include, but are not limited to, requiring storage nodes to re-upload data, adjusting the number of storage nodes, adjusting storage resource allocation strategies, and updating security level settings, to ensure that the data on each storage node meets the preset standards and requirements, promptly identify and correct any problems that may occur during the storage process, thereby ensuring the efficient and stable operation of the entire procurement data storage and security management system, ensuring the integrity and reliability of procurement data in the blockchain network, and providing a solid data foundation for subsequent data query, use and other operations.

[0149] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A blockchain-based procurement data storage and security management system, characterized in that, include: A module for building a blockchain network, which includes several participating organizations, sets several storage nodes within each participating organization, and generates a management strategy for the blockchain network based on all storage nodes. The receiving module is used to receive and process the data to be stored throughout the entire procurement process, select the corresponding storage node based on the processing results, and generate a data-node mapping table. The management module is used to upload the processed data to be stored to the corresponding storage node in the blockchain network according to the data feature-storage node mapping table and management strategy, and to verify it. Based on the verification result, it determines whether to generate a correction instruction.

2. The blockchain-based procurement data storage and security management system as described in claim 1, characterized in that, Configure several storage nodes within each participating organization, including: Retrieve historical stored data packets for each participating organization; Identify several data categories in the historical storage data packets, and divide the historical storage data packets into several storage sub-data packets according to the data categories; Calculate the comprehensive storage evaluation value of each storage sub-data packet in the same participating organization and the first correlation coefficient between different storage sub-data packets; The weight coefficient of each storage sub-data packet in the same participating organization is set according to the comprehensive storage evaluation value. The storage sub-data packets are sorted according to the weight coefficient, and a storage sub-data packet sequence is generated according to the sorting result. The top-ranked storage sub-data packet is set as the corresponding storage backbone node of the participating organization, the remaining storage sub-data packets are set as the corresponding storage branch nodes of the participating organization, and the edges are set according to the first correlation coefficient. Construct a storage tree graph for the corresponding participating organization based on the storage backbone node, several storage branch nodes, and corresponding edges of the same participating organization. Based on the storage tree diagram of each participating organization, determine the corresponding number of storage nodes within that participating organization.

3. The blockchain-based procurement data storage and security management system as described in claim 2, characterized in that, Calculate the overall storage evaluation value for each storage sub-data packet within the same participating organization, including: Each storage sub-data packet is evaluated and analyzed based on several pre-defined storage evaluation indicators to obtain the storage evaluation value of each storage sub-data packet. The association analysis is performed on all storage sub-data packets of the same participating organization to obtain the first association coefficient between different storage sub-data packets. Based on the first association coefficient, several first associated data packets for each storage sub-data packet are determined. The storage sub-data packets of different participating organizations are correlated to obtain the second correlation coefficient between each storage sub-data packet and other storage sub-data packets of organizations with different parameters. Based on the correlation coefficient, several second associated data packets of each storage sub-data packet are determined. The first compensation coefficient is generated based on the number of first associated data packets in the same storage sub-data packet, the corresponding first association coefficient, and the weight coefficient of each first associated data packet; The second compensation coefficient is generated based on the number of second associated data packets in the same storage sub-data packet, the corresponding second association coefficient, and the weight coefficient of each second associated data packet; A comprehensive compensation coefficient is generated based on the first compensation coefficient and the second compensation coefficient, and the storage evaluation value is corrected to obtain the comprehensive storage evaluation value of each storage sub-data packet.

4. The blockchain-based procurement data storage and security management system as described in claim 3, characterized in that, Based on the storage tree diagram of each participating organization, determine several storage nodes within that organization, including: Generate a node sequence J based on the arrangement order of the stored sub-data packets, J = (j1, j2, ..., jn), where j1 is the main storage node, ji is the i-th storage branch node, and n is the number of nodes; Construct a weight coefficient-preset radius mapping table, which includes several preset weight coefficients, and each preset weight coefficient is mapped to a corresponding preset radius. Based on each node in the node sequence as the center, and the preset radius mapped by the weight coefficient of each node as the scanning radius, the storage tree spectrum is scanned to obtain the first-level scanning area of ​​each node. The first-level scanning regions of adjacent nodes in the node sequence are analyzed for overlap. If there is an overlapping region, the overlapping region is assigned to the corresponding node based on the weight coefficient priority rule to obtain several second-level scanning regions. Construct a sequence of secondary scanning regions and calculate the management coefficient for each secondary scanning region; The number of storage nodes for the corresponding secondary scanning area is set according to the management coefficient, and several storage nodes for the corresponding participating organization are generated according to the total number of storage nodes for all secondary scanning areas of the same participating organization.

5. The blockchain-based procurement data storage and security management system as described in claim 4, characterized in that, The coefficients to be managed include: Collect the historical storage data volume, the number of storage sub-data packets involved, the average weight coefficient, historical access frequency, and historical access duration for each secondary scan area; Predicted storage coefficients are generated based on the amount of historical stored data in the same secondary scan area; The predicted importance coefficient is generated based on the number of storage sub-data packets involved in the same secondary scan area and the average of the corresponding weight coefficients. Predicted access coefficients are generated based on the historical access frequency and historical access duration of the same secondary scan area. The predicted storage coefficient, predicted importance coefficient, and predicted access coefficient are processed with unified dimensions and combined with the corresponding weight coefficients to calculate the management coefficient for each secondary scan area.

6. The blockchain-based procurement data storage and security management system as described in claim 5, characterized in that, The management strategy for the blockchain network is generated based on all storage nodes, including: A regional association map of the blockchain network is constructed based on the secondary scanning areas of all participating organizations and the second association coefficients between nodes in different secondary scanning areas; Generate weight coefficients for each secondary scan region based on the regional association map; Based on the weight coefficients of all secondary scan regions and the preset management strategy generation rules, determine the management permission level of all storage nodes in each secondary scan region; Based on the management permission level and the business needs and security requirements of the corresponding secondary scanning area, set the resource allocation strategy for all storage nodes in the corresponding secondary scanning area; Each storage node's management sub-policy is generated based on its management permission level and resource allocation strategy; The management strategy for the blockchain network is generated based on the management sub-strategies of all storage nodes.

7. The blockchain-based procurement data storage and security management system as described in claim 6, characterized in that, The system receives and processes the data to be stored throughout the entire procurement process to obtain the data characteristics, standard data summary, standard evidence storage code, and complete original data file for each data to be stored. The processing includes format verification and cleaning, feature extraction, encryption, and summary processing.

8. The blockchain-based procurement data storage and security management system as described in claim 7, characterized in that, Based on the processing results, the corresponding storage nodes are selected, including: Determine the stored data characteristics of each secondary scan region; By performing a similarity analysis between the data characteristics of the data to be stored and the data characteristics of the stored data in each secondary scan area, the fit between the data to be stored and each secondary scan area is obtained. All secondary scan regions are sorted according to their fit, and the secondary scan region ranked first is set as the storage region for the data to be stored. Calculate the real-time application coefficient of each storage node in the area to be stored, sort the storage nodes in each area according to the real-time application coefficient, and set the storage node with the first sorted value as the storage node for the data to be stored.

9. The blockchain-based procurement data storage and security management system as described in claim 8, characterized in that, Generate a data-node mapping table, including: Collect the standard evidence code of the data to be stored and the node location of the selected storage node for each piece of data to be stored; Establish a correspondence between the standard evidence code of the data to be stored and the node location of the storage node, forming a data-node mapping entry; The data-node mapping entries corresponding to all the data to be stored are summarized to generate a data-node mapping table.

10. The blockchain-based procurement data storage and security management system as described in claim 9, characterized in that, Based on the verification results, determine whether to generate a correction instruction, including: Collect verification data packets within each secondary scanning area. The verification data packets include differences in data digests, evidence encoding, storage requirements, and security levels of the data to be stored uploaded by the storage nodes. The differences in data digest, evidence encoding, storage requirements, and security level are quantified to obtain a first quantization value, a second quantization value, a third quantization value, and a fourth quantization value. These values ​​are then compared with their corresponding quantization value thresholds. If none of these values ​​are less than the corresponding quantization value threshold, no correction instruction is generated. If a quantization value is found that is less than the corresponding quantization threshold, a corresponding correction instruction is generated.

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