A Blockchain-Based Method for Securely Processing Bidding and Tendering Data

Abstract

The present invention discloses a method for secure processing of bidding data based on blockchain, belonging to the field of data encryption technology. Specifically, it includes: dividing the original data into logical blocks along the time axis, and generating a dynamic feature code with a hierarchical relationship by non-linearly fusing the current data hash value with the last feature segment of the previous block. Constructing a three-dimensional verification topology network including a time-stamped vertical chain, a content-associated horizontal tree, and an environmental parameter space matrix. Establishing a dynamic verification channel, activating the cross-verification of the three-anchor point association path when retrieving data, and generating an integrity proof. A self-destruction mechanism is implanted during verification, and the data block is gradually erased in case of anomalies. Finally, a traceability map is generated, the key parameters are encrypted and stored dispersedly in multiple domains, and the hash digest of the storage domain coordinates is recorded through the blockchain; the present invention is applicable to high-security electronic bidding scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of data encryption, and particularly to a method for secure processing of bidding data based on blockchain. Background Art

[0002] In today's digital age, as an important way for resource allocation and transaction conclusion in the market economy, the bidding activity is gradually transforming from the traditional offline mode to the online mode. With the rapid development of information technology, the amount of data generated during the bidding process has increased explosively, covering sensitive and critical data such as the qualification information of bidding enterprises, quotation details, and technical solutions. These data not only relate to the business secrets and interests of the participants, but also have a significant impact on the fairness, impartiality, and transparency of the entire bidding market. Therefore, ensuring the security of bidding data has become a key issue to be solved urgently in the development of digital bidding.

[0003] Currently, in the aspect of secure processing of bidding data, some related technologies and methods have been applied. Some enterprises adopt traditional data encryption technologies, such as symmetric encryption and asymmetric encryption algorithms, to encrypt and store bidding data during transmission to prevent the data from being stolen or tampered with during transmission. There are also some systems that introduce access control mechanisms to restrict access rights to bidding data through user authentication and authorization management. In addition, due to its characteristics of decentralization, immutability, and traceability, blockchain technology has also begun to be applied to the secure processing of bidding data, and some solutions use the distributed ledger of blockchain to record the operation history of bidding data to enhance the credibility and transparency of the data.

[0004] Although traditional data encryption technologies can protect the confidentiality of data to a certain extent, their security is gradually challenged in the face of increasingly complex network attack means. Although the access control mechanism can restrict the access rights of data, it cannot effectively prevent internal personnel from violating regulations and data leakage. And the existing secure processing solutions for bidding data based on blockchain often only focus on the recording and traceability of data, lacking an effective verification mechanism for data integrity and authenticity, and it is difficult to deal with the situation where data is maliciously tampered with or forged. In addition, when data anomalies occur, the existing solutions lack effective emergency handling and data recovery mechanisms, and cannot protect data security and restore the normal use of data in a timely manner. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for secure processing of bidding data based on blockchain, and solve the following technical problems:

[0006] Traditional data encryption technologies focus on the recording and traceability of data, lacking an effective verification mechanism for data integrity and authenticity, and it is difficult to deal with the situation where data is maliciously tampered with or forged.

[0007] The object of the present invention can be achieved by the following technical solutions:

[0008] A method for secure processing of bidding data based on blockchain, comprising the following steps:

[0009] The original data is sliced into continuous logical data blocks along the time axis, and each data block generates a dynamic feature code containing a hierarchical relationship. The dynamic feature code is generated by non-linearly fusing the current data hash value with the last feature segment of the previous block.

[0010] A three-dimensional verification topology network is constructed, and three verification anchor points are generated for each data block: a longitudinal verification chain marked with a timestamp, a horizontal verification tree associated with the data content, and a spatial verification matrix bound to external environment parameters.

[0011] A dynamic verification channel is established. When data is retrieved, the associated verification paths of the three anchor points are synchronously activated, and a data integrity proof is generated through path cross-verification.

[0012] A self-destruction trigger mechanism is implanted during the verification process. When an abnormal verification path topology structure is detected, gradient erasure processing is automatically performed on the abnormal data block.

[0013] A data traceability map is generated. The key parameters of the verification process are encrypted and dispersed for storage in multiple independent storage domains, and the hash digest of the storage domain coordinates is recorded through blockchain nodes.

[0014] As a further solution of the present invention: The process of generating the dynamic feature code includes:

[0015] The content of the data block is divided into three parts: a leading area, a core area, and a verification area. The leading area extracts the 12-byte feature segment at the end of the previous data block as a fusion seed. The core area generates a variable-length hash value weighted based on content sensitivity. The verification area collects the system clock signal and environmental noise parameters to generate a random perturbation factor. The feature code is generated through a three-stage fusion algorithm: First, a cyclic shift cross-operation is performed on the fusion seed and the previous segment of the hash value. Then, the operation result and the perturbation factor are subjected to modular exponentiation superposition. Finally, the superposition value is divided into left and right halves for mirror reflection recombination to form an irreversible feature code with spatio-temporal correlation.

[0016] As a further solution of the present invention: The construction of the three-dimensional verification topology network includes:

[0017] The longitudinal verification chain is formed by connecting the feature codes of consecutive data blocks end to end. Each node contains a timestamp verification value and a data block length fingerprint. The horizontal verification tree builds a branch structure according to data types, and each branch node stores a set of associated hash values of data blocks of the same type. The spatial verification matrix generates a three-dimensional coordinate verification graph based on the data storage location. Each coordinate point contains a geographical location hash, a device fingerprint, and an environmental electromagnetic feature. The three form a verification vector through orthogonal projection. The verification paths in the three dimensions need to meet the topological consistency condition simultaneously during data access.

[0018] As a further solution of the present invention: The activation process of the dynamic verification channel includes:

[0019] When a data access request is initiated, the spatio-temporal feature elements in the request parameters are automatically identified. The verification marks of the three nodes before and after the current data block are extracted from the longitudinal chain, the last five operation records of the same type of data are retrieved from the horizontal tree, and an environmental feature snapshot of the storage device is obtained from the spatial matrix. The three groups of parameters are input into the path verification model. The model calculates the deviation degree between the current request and the normal operation by comparing with the historical operation mode library. When the deviation degree exceeds the threshold, enhanced verification is triggered, and the consecutive feature codes of the adjacent five data blocks are required to be provided.

[0020] As a further solution of the present invention: The specific implementation of the gradient erasure process includes:

[0021] After detecting data anomalies, first lock the abnormal data block and the three data blocks associated before and after it, and generate an erasure gradient template: The first gradient performs feature code confusion processing on the abnormal block, decomposing the original feature code into eight fragments and randomly permuting their positions; the second gradient perturbs the hash values of the adjacent blocks, resetting the verification association while preserving the integrity of the data content; the third gradient implants misleading marks in the storage domain coordinate records, and at the same time retains an encrypted copy of the real coordinates. The original data can be restored only after passing through triple reverse verification.

[0022] As a further solution of the present invention: The generation process of the data traceability map includes:

[0023] Record the four-dimensional feature vectors of each data operation, including the operation time magnitude, the operation terminal hardware fingerprint, the network environment feature waveform, and the data change trajectory topology map. The feature vectors are divided into three dimensions and encrypted respectively: The time dimension uses forward rolling encryption, the spatial dimension uses a dynamic key bound to environmental parameters, and the content dimension implements sharding threshold encryption. The encrypted data blocks are scattered and stored in geographically isolated storage nodes through the backpropagation algorithm. Each node only stores an invalidated copy of the data segment. The complete data needs to be reversely reorganized through the storage path recorded by the blockchain.

[0024] As a further solution of the present invention: The independent storage domain specifically includes:

[0025] Each storage domain implements a differentiated data protection scheme. The first type of storage domain adopts time - dimension sharding storage, slicing data according to the operation time and dispersing the slices to different physical regions; the second type of storage domain implements content - associated storage, forcibly separating and storing data blocks with logical associations; the third type of storage domain deploys a decoy data generation system to automatically create data copies with the same characteristics as the real data but invalid content. After the access logs of all storage domains are processed by heterogeneous encryption algorithms, uncorrelated verification tags are generated and stored in blockchain nodes.

[0026] As a further solution of the present invention: when it is detected that the same type of topological anomaly exists in three consecutive verification cycles, the data self - healing program is started. First, the feature template of the normal operation mode is extracted from the historical verification records, and then the abnormal data stream is subjected to mirror reconstruction. The reconstruction process adopts a dual - verification strategy: forward verification calculates the theoretical value according to the feature code generation rule, and reverse verification infers the reasonable value through the correlation of adjacent data blocks. When the results of the two - way verification converge, a repair patch is automatically generated and all associated verification paths are updated, while retaining the encrypted mirror of the original data for audit traceability.

[0027] The beneficial effects of the present invention:

[0028] The present invention divides the original data along the time axis and generates a dynamic feature code. This feature code is obtained by non - linear fusion operation of the current data hash value and the last - bit feature segment of the previous block, which can ensure the uniqueness and non - tampering of the data. Moreover, the irreversible feature code with spatio - temporal correlation generated by the three - stage fusion algorithm enhances the complexity and security of the data features. The constructed three - dimensional verification topology network includes a longitudinal verification chain marked with time stamps, a transverse verification tree associated with data content, and a spatial verification matrix bound with external environment parameters, which can verify data from multiple dimensions to ensure the consistency of data in terms of time, content, and environment. The dynamic verification channel synchronously activates the associated verification paths of the three anchors when data is retrieved, and generates a data integrity proof through path cross - verification, which can effectively identify abnormal access requests and improve the security of data access. The implanted self - destruction trigger mechanism performs gradient erasure processing on abnormal data blocks when detecting abnormal topological structures of verification paths, including feature code confusion, hash value perturbation, and implanting misleading marks, which can prevent further data leakage and malicious exploitation. The generated data traceability map encrypts the key parameters of the verification process and stores them dispersedly, combined with the hash digest of the blockchain node recording the storage domain coordinates, to achieve data traceability and multi - domain secure storage. At the same time, different independent storage domains adopt differentiated data protection schemes, such as time - dimension sharding storage, content - associated storage, and deploying a decoy data generation system, etc., which enhances the anti - attack ability of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The present invention will be further described below with reference to the accompanying drawings.

[0030] Figure 1 It is a schematic flowchart of the present invention. Specific embodiments

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0032] Please refer to Figure 1 As shown, the present invention is a method for secure processing of bidding and tendering data based on blockchain, including the following steps:

[0033] The original data is sliced into continuous logical data blocks along the time axis. A dynamic feature code with a hierarchical relationship is generated for each data block, which is obtained through the non-linear fusion operation of the current data hash value and the last feature segment of the previous block. This operation endows the feature code with uniqueness and coherence, preventing data from being tampered with or forged.

[0034] A three-dimensional verification topology network is constructed. Each data block corresponds to the generation of three verification anchor points: a longitudinal verification chain marked by a time stamp, connecting the feature codes of consecutive data blocks end to end. The nodes contain the time stamp verification value and the data block length fingerprint to ensure the integrity of the data in the time dimension; a horizontal verification tree associated with the data content, building a branch structure according to the data type. The branch nodes store the set of associated hash values of the same type of data blocks to verify the logical relationship of the data content; a spatial verification matrix bound by external environment parameters, generating a three-dimensional coordinate verification map based on the data storage location. The coordinate points contain the geographical location hash, device fingerprint, and environmental electromagnetic characteristics, forming a verification vector through orthogonal projection. When accessing data, the verification paths in the three dimensions need to meet the topological consistency conditions simultaneously.

[0035] A dynamic verification channel is established. When data is retrieved, the verification paths associated with the three anchor points are automatically synchronized and activated. The verification marks of the three nodes before and after the current data block are extracted from the longitudinal chain, the last five operation records of the same type of data are retrieved from the horizontal tree, and a snapshot of the environmental characteristics of the storage device is obtained from the spatial matrix. The input path verification model is compared with the historical operation mode library to calculate the deviation degree. When the deviation degree exceeds the threshold, enhanced verification is triggered, requiring the provision of the consecutive feature codes of the adjacent five data blocks to generate a data integrity proof.

[0036] During the verification process, a self-destruction trigger mechanism is implanted. When an abnormal verification path topology structure is detected, gradient erasure processing is automatically performed on the abnormal data block. The abnormal data block and the three associated data blocks before and after it are locked. First, the feature code of the abnormal block is gradient-confused, decomposed into eight fragments and randomly permuted; second, the hash values of adjacent blocks are gradient-disturbed, the verification association is reset while keeping the data content intact; third, misleading markers are implanted in the storage domain coordinate record, and at the same time, an encrypted copy of the real coordinates is saved. Triple reverse verification is required to restore the original data.

[0037] Generate a data traceability map, record the four-dimensional feature vectors of each data operation, including the operation time magnitude, the hardware fingerprint of the operation terminal, the characteristic waveform of the network environment, and the topology map of the data change trajectory. The feature vectors are encrypted in three dimensions. The time dimension is encrypted by forward rolling, the space dimension uses environmental parameters to bind a dynamic key, and the content dimension implements sharding threshold encryption. After encryption, the data blocks are scattered and stored in geographically isolated storage nodes through the backpropagation algorithm. The nodes only store invalid copies of data fragments. The complete data needs to be reversely recombined through the hash digest of the storage domain coordinates recorded by the blockchain nodes to achieve secure data storage and traceability.

[0038] In a preferred embodiment of the present invention, the dynamic feature code generation process includes:

[0039] First, the data block content is scientifically divided into three parts: the leading area, the core area, and the check area. In the leading area, the 12-byte feature segment at the end of the previous data block is accurately extracted and used as the core seed for subsequent fusion operations. This 12-byte feature segment contains the key feature information of the previous data block, laying a foundation for constructing the coherence and relevance between data. The core area is responsible for generating a variable-length hash value weighted based on content sensitivity. Since the content of bidding data is complex and diverse, and different parts have different sensitivities, the hash value generated by this weighting method can more accurately reflect the characteristics of the core content of the data block, enhancing the sensitivity of the hash value to changes in data content. The check area collects the system clock signal and environmental noise parameters to generate a random perturbation factor. The system clock signal reflects the time characteristics of data processing, while the environmental noise parameters reflect the external environmental characteristics during data generation or processing. The random perturbation factor generated by combining the two adds additional complexity and randomness to the feature code.

[0040] Subsequently, a feature code is generated through a sophisticated three-stage fusion algorithm. In the first stage, a cyclic shift cross-operation is performed between the fusion seed and the front segment of the hash value. This operation method deeply interweaves the features of the previous data block with the core content features of the current data block, initially establishing a close connection in both the time dimension and the content dimension. In the second stage, the operation result of the first stage is subjected to modular exponentiation superposition with the perturbation factor. Modular exponentiation operation has high complexity. Superposing the operation result with the perturbation factor further enhances the unpredictability of the feature code, making it difficult to be cracked or forged. In the last stage, the superposed value is divided into left and right halves and subjected to mirror reflection recombination. After this operation, an irreversible feature code with spatio-temporal correlation is finally formed. This irreversibility ensures that once the feature code is generated, the original data cannot be reversely restored by conventional means, greatly enhancing the security of the data.

[0041] In another preferred embodiment of the present invention, the construction of the three-dimensional verification topology network includes:

[0042] The longitudinal verification chain is naturally formed by connecting the feature codes of consecutive data blocks end to end. Each node carefully contains a timestamp verification value and a data block length fingerprint. The timestamp verification value accurately records the time point when the data block is generated or updated, which not only helps to strictly verify the order and continuity of the data in the time dimension but also provides key time clues for subsequent data auditing and tracing. The data block length fingerprint identifies the data block from the perspective of the data volume. Once an abnormal change occurs in the data block length, it can be quickly detected, effectively ensuring the integrity of the data in the time dimension.

[0043] The transverse verification tree establishes a branch structure according to the data type, and this structural design meets the requirements of the classification management of bidding and tendering data. Each branch node stores a set of associated hash values of data blocks of the same type. In this way, when it is necessary to verify the logical relationship between a certain data block and other data blocks of the same type, the relevant hash values can be quickly obtained from the corresponding branch node for comparison. For example, when verifying the consistency of the same type of qualification certificate data in a tender document, the transverse verification tree can play an efficient role in verifying the logical relationship of the data content.

[0044] The spatial verification matrix generates a three-dimensional coordinate verification map according to the data storage location. Each coordinate point contains a geographical location hash, a device fingerprint, and an environmental electromagnetic feature. The geographical location hash accurately identifies the geographical location information where the data is stored, the device fingerprint records the unique hardware features of the storage device, and the environmental electromagnetic feature reflects the electromagnetic characteristics of the data storage environment. The three form a verification vector through orthogonal projection. This verification method of multi-factor fusion comprehensively verifies the data from the spatial and environmental dimensions. When accessing data, the verification paths in the three dimensions need to simultaneously meet the topological consistency conditions. Only when the verifications in the longitudinal, transverse, and spatial dimensions all pass, the legitimacy of the data access request is recognized, comprehensively ensuring the security of the data.

[0045] In another preferred embodiment of the present invention, the activation process of the dynamic verification channel includes:

[0046] When a data access request is initiated, the spatio-temporal feature elements in the request parameters are identified. These spatio-temporal feature elements contain key information such as the time when the request is initiated and the geographical location of the initiating terminal, providing a basic clue for subsequent verification. The verification marks of the three nodes before and after the current data block are extracted from the longitudinal chain. These verification marks carry the key verification information of the data block in the time dimension, helping to judge the rationality of the current data block in the time series. The recent five operation records of the same type of data are retrieved from the horizontal tree. By analyzing the recent operation conditions of the same type of data, it is possible to better judge whether the current data operation requested conforms to the conventional operation mode of this type of data. The environmental feature snapshot of the storage device is obtained from the space matrix, which can ensure the consistency between the environment of the current data access and the data storage environment.

[0047] Then, these three groups of parameters are input into a carefully constructed path verification model. This model pre-stores a rich historical operation mode library. By deeply comparing the current request parameters with the historical operation modes, the deviation degree between the current request and the conventional operation is accurately calculated. When the deviation degree exceeds the pre-set threshold, it means that there is an abnormal risk in the current data access request. At this time, enhanced verification is immediately triggered. The enhanced verification requires providing the consecutive feature codes of the adjacent five data blocks. By further verifying the feature codes of the adjacent data blocks, the legitimacy of the data access request is confirmed again from the perspectives of data coherence and relevance, thereby generating a more reliable data integrity proof, effectively preventing illegal data access behaviors, and effectively ensuring the security of bidding data.

[0048] In another preferred embodiment of the present invention, the specific implementation of the gradient erasure process includes:

[0049] When data anomalies are detected, the abnormal data block and the three data blocks associated with it before and after are accurately locked. The reason for choosing to lock the three data blocks before and after is that there are often close logical connections between data, and abnormal data may affect the surrounding data. Expanding the locked range helps to comprehensively handle potential risks.

[0050] After the data block is locked, an erasure gradient template is immediately generated. The first gradient mainly performs feature code confusion processing on the abnormal block. Specifically, the original feature code is carefully decomposed into eight fragments, and then a random algorithm is used to permute the positions of these fragments. This operation makes the feature code of the abnormal block completely lose its original structure. Even if an attacker obtains these fragments, it is difficult to restore the original feature code, greatly increasing the difficulty of data cracking.

[0051] The second gradient focuses on adjacent blocks. A hash value perturbation operation is performed on the adjacent blocks, and during this process, the integrity of the data content is ingeniously preserved. The hash value is adjusted through a specific algorithm to reset the verification association between the adjacent blocks and the abnormal block. In this way, the influence of the abnormal block is effectively isolated, preventing the spread of abnormal data in the associated data and ensuring that the availability of normal data is not affected.

[0052] The third gradient is to implant misleading markers in the storage domain coordinate records. Generate seemingly reasonable but actually incorrect coordinate information to interfere with the potential attacker's judgment of the data storage location. At the same time, the real coordinates are encrypted and a copy is saved. Only after strict triple reverse verification confirms that the operation is legal can the real coordinates be obtained and used to restore the original data. This gradient erasure processing method with multiple layers of defense adds multiple protection barriers to data security, effectively preventing data leakage and malicious exploitation.

[0053] In another preferred embodiment of the present invention, the generation process of the data traceability map includes:

[0054] Detailedly record the four-dimensional feature vectors of each data operation. The operation time magnitude is accurate to an extremely fine time scale, which can accurately reflect the moment when the data operation occurs and provide a high-precision basis for tracing in the time dimension. The operation terminal hardware fingerprint records the unique hardware characteristics of the device that executes the data operation. Whether it is the CPU model of the computer, the motherboard serial number, or the IMEI code of the mobile device, etc., are all included in the record range, which helps to determine the device that initiates the data operation. The network environment feature waveform captures the electromagnetic characteristics of the network environment when the data is transmitted, such as changes in signal strength, frequency, etc., providing clues for analyzing the data transmission path and environment. The data change trajectory topology graph intuitively shows the change path and logical relationship of the data during the operation, such as the operation sequence and mutual association of data modification, deletion, addition, etc.

[0055] After recording these feature vectors, they are divided into three dimensions and encrypted separately. In the time dimension, a forward-rolling encryption method is adopted. This encryption method continuously updates the encryption key over time. After each data operation, a new key is generated based on the new time factor to encrypt the subsequent data, so that the encryption states of the data in the time dimension are different at different time points, enhancing the security of the data. In the space dimension, a dynamic key bound to environmental parameters is used. Environmental parameters such as the geographical location and electromagnetic interference of surrounding devices where the data is stored are incorporated into the key generation process, and the key changes when the environmental parameters change, further ensuring the confidentiality of the data in the space dimension. In the content dimension, piecewise threshold encryption is implemented. The data content is divided into multiple segments, and different encryption thresholds are set for each segment. Only when the corresponding threshold conditions are met can the segment data be decrypted, greatly increasing the difficulty of cracking the data content.

[0056] The encrypted data blocks are scattered and stored in geographically isolated storage nodes through the backpropagation algorithm. These storage nodes are distributed in different geographical locations and are physically isolated from each other, reducing the risk of complete data leakage caused by an attack on a single storage node. Each node only stores an invalidated copy of the data segment, and these copies cannot directly restore the complete data. Only by reverse recombining through the storage path recorded in the blockchain can the data segments scattered in each node be recombined into the complete data, realizing the secure storage and efficient traceability of the data.

[0057] In another preferred embodiment of the present invention, the independent storage domain specifically includes:

[0058] Each storage domain implements a differential data protection scheme. The first type of storage domain adopts a time-dimensional slice storage strategy. It slices the data according to the operation time and then scatters these slices to different physical regions. For example, the data slices generated in the morning are stored in the storage device in Area A, and the data slices generated in the afternoon are stored in the storage device in Area B, which is farther away. This method disperses the data in both time and space. Even if the storage device in a certain physical region fails or is attacked, it will only affect the data slices corresponding to the corresponding time period, and will not cause the loss or leakage of the overall data.

[0059] The second type of storage domain implements content-related storage. This method forcibly separates and stores data blocks with logical associations. For example, in the tendering and bidding data, although the qualification documents and quotation documents of the tendering enterprise are logically related, they are stored in different storage areas respectively. In this way, when a certain data block is tampered with or leaked for some reason, the associated data blocks can still remain secure, avoiding the chain risk brought by data correlation.

[0060] The third type of storage domain deploys a decoy data generation system. This automatically creates data copies that have the same characteristics as real data but with invalid content. These decoy data are indistinguishable from real data in appearance, being extremely similar in terms of data format, size, signature, etc., but the actual content is invalid. When an attacker attempts to obtain data, they are likely to be misled by this decoy data, consuming their attack resources and time, thereby protecting the security of the real data. In addition, the access logs of all storage domains are processed through heterogeneous encryption algorithms. The heterogeneous encryption algorithm combines multiple different encryption methods to generate uncorrelated verification tokens and stores them in blockchain nodes. This makes it difficult for attackers to obtain data access patterns or crack storage domain information by analyzing access logs, further enhancing the security of independent storage domains.

[0061] In another preferred embodiment of the present invention, when the same type of topology anomaly is detected in three consecutive verification cycles, the data self-healing program is quickly started. First, the feature templates of the normal operation mode are extracted from the historical verification records. These historical records detail various characteristic information during past data operation processes. The system analyzes and filters out the typical characteristics in the normal operation mode to construct the feature templates, providing a reference standard for subsequent data reconstruction.

[0062] Then, mirror reconstruction is performed on the abnormal data stream. The reconstruction process adopts a dual-verification strategy. Forward verification calculates the theoretical value based on the signature generation rule. The system forwards the signature of the abnormal data block according to the previously set dynamic signature generation rule to obtain the theoretically expected signature value. Reverse verification inversely infers the reasonable value through the correlation of adjacent data blocks. Due to the close association between data, by analyzing the characteristics and logical relationships of adjacent normal data blocks, the possible reasonable value of the abnormal data block is inversely inferred. When the results of the two-way verification converge, that is, the forward calculation value and the reverse inference value tend to be the same, a repair patch is automatically generated. This repair patch can accurately repair the error part of the abnormal data block, restoring it to a normal state. At the same time, all associated verification paths are updated to ensure the consistency and accuracy of the entire data verification system. In addition, to meet the requirements of subsequent audit and traceability, the encrypted mirror of the original data is retained and can be retrieved for review at any time when needed, providing strong support for data security management.

[0063] The above has described in detail an embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as defining the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A blockchain-based method for secure processing of bidding and tendering data, characterized in that, Including the following steps: The original data is sliced into continuous logical data blocks according to the time axis, and each data block generates a dynamic feature code including a hierarchical relationship. The dynamic feature code is generated by non-linearly fusing the current data hash value with the last feature segment of the previous block; Construct a three-dimensional verification topology network, and each data block correspondingly generates three verification anchor points: a longitudinal verification chain marked by a timestamp, a horizontal verification tree associated with data content, and a spatial verification matrix bound by external environment parameters; Establish a dynamic verification channel. When data is retrieved, the associated verification paths of the three anchor points are synchronously activated, and a data integrity proof is generated through path cross-verification; A self-destruction trigger mechanism is implanted during the verification process. When an abnormal verification path topology structure is detected, gradient erasure processing is automatically performed on the abnormal data block; Generate a data traceability map, encrypt the key parameters of the verification process and disperse them for storage in multiple independent storage domains, and record the hash digest of the storage domain coordinates through blockchain nodes; The construction of the three-dimensional verification topology network includes: The longitudinal verification chain is formed by connecting the dynamic feature codes of consecutive data blocks end to end. Each node contains a timestamp check value and a data block length fingerprint; the horizontal verification tree establishes a branch structure according to data types, and each branch node stores a set of associated hash values of data blocks of the same type; the spatial verification matrix generates a three-dimensional coordinate verification map according to the data storage location. Each coordinate point contains a geographical location hash, a device fingerprint, and an environmental electromagnetic feature. The three form a verification vector through orthogonal projection. The verification paths in the three dimensions need to simultaneously meet the topological consistency condition during data access.

2. The method for secure processing of bidding and tendering data based on blockchain according to claim 1, wherein The process of generating the dynamic feature code includes: The content of the data block is divided into three parts: a leading area, a core area, and a verification area. The leading area extracts the last 12-byte feature segment of the previous data block as a fusion seed. The core area generates a variable-length hash value weighted based on content sensitivity. The verification area collects the system clock signal and environmental noise parameters to generate a random perturbation factor. A dynamic feature code is generated through a three-stage fusion algorithm: first, perform a cyclic shift cross-operation on the fusion seed and the previous segment of the hash value, then perform a modular exponentiation superposition on the operation result and the perturbation factor, and finally divide the superposition value into left and right halves and perform mirror reflection recombination to form an irreversible dynamic feature code with spatio-temporal correlation.

3. A method for secure processing of bidding and tendering data based on blockchain according to claim 1, characterized in that, The process of activating the dynamic verification channel includes: When a data access request is initiated, the spatio-temporal feature elements in the request parameters are automatically identified. The verification marks of the three nodes before and after the current data block are extracted from the longitudinal chain, the last five operation records of the same type of data are retrieved from the horizontal tree, and an environmental feature snapshot of the storage device is obtained from the spatial matrix. The three groups of parameters are input into the path verification model. This model calculates the deviation degree between the current request and the normal operation by comparing with the historical operation mode library. When the deviation degree exceeds the threshold, enhanced verification is triggered, and the continuous dynamic feature codes of the adjacent five data blocks are required to be provided.

4. A blockchain-based bidding data security processing method according to claim 1, characterized in that, The specific implementation of the gradient erasure processing includes: After detecting data anomalies, first lock the anomalous data block and the three data blocks associated with it before and after, and generate an erasure gradient template: The first gradient performs dynamic signature obfuscation on the anomalous block, decomposing the original dynamic signature into eight fragments and randomly permuting their positions; the second gradient perturbs the hash values of adjacent blocks, resetting the verification associations while preserving the integrity of the data content; the third gradient implants misleading markers in the storage domain coordinate records, while retaining an encrypted copy of the true coordinates, and the original data can only be restored after passing a triple reverse verification.

5. A method for secure processing of bidding and tendering data based on blockchain according to claim 1, characterized in that, The generation process of the data traceability map includes: Record the four-dimensional feature vectors of each data operation, including the operation time magnitude, the hardware fingerprint of the operation terminal, the waveform of the network environment characteristics, and the topology map of the data change trajectory. Divide the feature vectors into three dimensions and encrypt them separately: The time dimension uses forward rolling encryption, the space dimension uses a dynamic key bound to environmental parameters, and the content dimension implements sharding threshold encryption. The encrypted data blocks are scattered and stored in geographically isolated storage nodes through the backpropagation algorithm. Each node only stores an invalidated copy of the data segment, and the complete data needs to be reconstructed reversely through the storage path recorded in the blockchain.

6. The method for secure processing of bidding and tendering data based on blockchain according to claim 1, wherein The independent storage domain specifically includes: Each storage domain implements a differentiated data protection scheme. The first type of storage domain uses time-dimensional sharding storage, slicing the data according to the operation time and dispersing it to different physical regions; the second type of storage domain implements content-associated storage, forcibly separating and storing data blocks with logical associations; the third type of storage domain deploys a decoy data generation system, automatically creating data copies with the same characteristics as the real data but invalid content. After the access logs of all storage domains are processed by heterogeneous encryption algorithms, unassociable verification markers are generated and stored in the blockchain nodes.

7. A method for secure processing of bidding and tendering data based on blockchain according to claim 1, characterized in that, When the same type of topological anomaly is detected in three consecutive verification cycles, start the data self-healing program. First, extract the feature template of the normal operation mode from the historical verification records, and then perform mirror reconstruction on the anomalous data stream. The reconstruction process uses a dual-verification strategy: Forward verification calculates the theoretical value according to the dynamic signature generation rule, and reverse verification infers the reasonable value through the association of adjacent data blocks. When the results of the two-way verification converge, automatically generate a repair patch and update all associated verification paths, while retaining an encrypted mirror of the original data for audit tracing.

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