Cross-mechanism data storage method and system based on block chain smart contract

Abstract

The invention provides a cross-mechanism data storage method and system based on a block chain smart contract, and relates to the technical field of data storage.The method comprises the steps that firstly, three-dimensional shape data of the surface of a data storage entity is collected, feature contrast is enhanced through coherent light processing, and enhanced feature data is generated; carrying out compression coding and baseline drift elimination by adopting pulse width modulation, carrying out Hash operation on the processed pulse sequence to generate a feature Hash value, and storing the feature Hash value and the original data in a cross-mechanism block chain; and when an institution initiates a data reading request, triggering a verification process through an intelligent contract, comparing the matching degree of a real-time hash value and a storage hash value, if the matching degree exceeds a tolerance range, triggering a warning mechanism, broadcasting an abnormal event to the whole network, and recording abnormal information into a block chain non-tampering log at the same time. According to the invention, the tamper-proof capability of cross-mechanism data storage and the reliability of entity identity verification are improved.

Description

Technical Field

[0001] This application relates to the field of data storage technology, and in particular to a cross-institutional data storage method and system based on blockchain smart contracts. Background Technology

[0002] In scenarios such as cross-border logistics that require collaborative operations among multiple institutions, each participant must ensure a unique and robust binding relationship between important data entities and their corresponding digital information, and achieve their reliable flow throughout the entire chain. Therefore, such scenarios place specific requirements on data storage methods, namely, they must have the ability to prevent tampering, be traceable, and be verified in real time among multiple institutions. At the same time, they also need to support a two-way verification mechanism between physical entities and digital identities, thereby effectively solving the authenticity and consistency issues that may arise during the multi-party flow of data.

[0003] Currently, there is a targeted solution that uses a distributed database combined with digital tag technology to assign a unique RFID tag to each physical item and store the tag number in a shared database after associating it with the item's complete information. Participating organizations scan the RFID tags of physical items with readers to obtain their numbers, and then query the shared database to verify whether the obtained item information is consistent with the records. All such operations are recorded and synchronized to each participating node through a consensus mechanism.

[0004] However, this scheme has certain limitations in terms of entity feature acquisition. It relies on pre-attached labels, which are susceptible to physical damage in practical applications, potentially disrupting the binding relationship between the item and the digital information. Furthermore, its verification process primarily focuses on matching label numbers, lacking direct perception and comparison of the physical characteristics of the actual item. This introduces the risk that the label may be authentic, but the attached item may have been replaced. In addition, existing methods offer limited support for multi-agency collaborative verification scenarios, failing to deeply integrate direct perception of entity features with a distributed consensus mechanism. Therefore, they struggle to meet the tamper-proof requirements of high-security scenarios. Summary of the Invention

[0005] This application provides a cross-institutional data storage method and system based on blockchain smart contracts to solve the problems of low tamper resistance and poor reliability of entity authentication in existing cross-institutional data storage technologies.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a cross-institutional data storage method based on blockchain smart contracts, comprising:

[0007] Collect raw data and three-dimensional topographic data of data storage entities that are transferred between multiple institutions;

[0008] The three-dimensional topography data is enhanced using a coherent light processing method to generate enhanced feature data;

[0009] The enhanced feature data is compressed and encoded using pulse width modulation to generate a compressed pulse sequence, and the compressed pulse sequence is then eliminated.

[0010] A hash operation is performed on the compressed pulse sequence after the elimination process to generate a feature hash value, and the feature hash value and the original data are stored together in a cross-institutional blockchain;

[0011] Based on smart contracts and combined with the stored data of cross-institutional blockchains, when any institution initiates a data read request, a verification process is executed to generate a real-time hash value. The smart contract then matches the real-time hash value with the feature hash value stored in the cross-institutional blockchain.

[0012] When the matching result does not meet the preset tolerance requirements, the warning mechanism of the smart contract is triggered and the abnormal event is broadcast to all institutional nodes in the cross-institutional blockchain. At the same time, the identification information, timestamp and characteristic deviation data of the abnormal institutional node are stored in the distributed immutable log of the cross-institutional blockchain.

[0013] Optionally, based on smart contracts and combined with stored data from cross-institutional blockchains, when any institution initiates a data read request, a verification process is executed to generate a real-time hash value. The smart contract then matches this real-time hash value with the characteristic hash value stored in the cross-institutional blockchain, including:

[0014] In smart contracts, a verification relationship is established based on the correspondence between the feature hash value and the stored data across institutional blockchains;

[0015] Based on the verification relationship, when any institution initiates a data reading request, the smart contract automatically triggers a feature acquisition instruction, and based on the feature acquisition instruction, acquires optical feature data of the surface of the current data storage entity.

[0016] Based on the optical feature data, a real-time hash value is generated;

[0017] The smart contract's verification and execution module uses a similarity calculation algorithm to perform a bit-by-bit comparison and difference analysis between the real-time hash value and the feature hash value stored in the blockchain to obtain a matching result.

[0018] Optionally, the step of using a similarity calculation algorithm to perform a bit-by-bit comparison and difference analysis between the real-time hash value and the feature hash value stored in the blockchain to obtain a matching result includes:

[0019] Calculate the Hamming distance between the real-time hash value and the corresponding bits of the feature hash value stored in the blockchain, and use the Hamming distance as a dissimilarity index.

[0020] A sliding window algorithm is used to compare the real-time hash value and the feature hash value stored in the blockchain in segments to identify local distribution patterns;

[0021] The local distribution pattern was analyzed to detect deviation characteristics;

[0022] The deviation characteristics, the difference index, and the local distribution pattern are comprehensively analyzed to generate a comprehensive matching score, which is the matching result.

[0023] Optionally, the step of using coherent light processing to enhance the three-dimensional topography data and generate enhanced feature data includes:

[0024] An interference fringe pattern is generated by illuminating the surface of the data storage entity with a coherent light source, and the brightness distribution data of the interference fringe pattern is recorded.

[0025] The brightness distribution data is subjected to phase-shift interferometry to generate a phase-shift interferogram;

[0026] Phase information is extracted from the phase-shifted interferogram using a phase calculation algorithm, and a phase modulation distribution map is generated based on the phase information.

[0027] The phase modulation distribution map is fused with the three-dimensional topography data, and an enhanced feature data is generated using a weighted superposition algorithm.

[0028] Optionally, the step of fusing the phase modulation distribution map with the three-dimensional topography data and generating enhanced feature data using a weighted superposition algorithm includes:

[0029] Calculate the corresponding adaptive weighting coefficient based on the phase gradient value of each pixel in the phase modulation distribution map;

[0030] Based on the adaptive weighting coefficients, a weighted superposition algorithm is used to perform pixel-by-pixel fusion calculations of the depth information of the three-dimensional topography data and the phase information of the phase modulation distribution map to generate preliminary optical feature data.

[0031] An anisotropic diffusion filtering algorithm is used to perform edge optimization processing on the preliminary optical feature data to obtain intermediate optical feature data;

[0032] The intermediate optical feature data is subjected to contrast enhancement and dynamic range adjustment to generate enhanced feature data.

[0033] Optionally, the step of compressing and encoding the enhanced feature data using pulse width modulation to generate a compressed pulse sequence, and then eliminating the compressed pulse sequence, includes:

[0034] Based on the data characteristics of the enhanced feature data, the pulse modulation parameters are dynamically adjusted;

[0035] Based on the adjusted pulse modulation parameters, the enhanced feature data is divided into segments according to a preset data block size, and each data block is converted into a modulation signal with a specific pulse width and pulse interval through pulse width modulation.

[0036] Differential Manchester encoding is performed on all the modulated signals to generate a compressed pulse sequence;

[0037] The compressed pulse sequence is eliminated using an adaptive baseline correction technique to obtain an eliminated compressed pulse sequence.

[0038] Optionally, the step of performing a hash operation on the compressed pulse sequence after the elimination process to generate a feature hash value includes:

[0039] The compressed pulse sequence after elimination is grouped by performing multiple rounds of compression function iteration. In each iteration round, bit rotation, modular addition and nonlinear logic function operations are performed to convert the compressed pulse sequence after elimination into a fixed-length intermediate hash value.

[0040] The intermediate hash value is input into a deep hash neural network, and the intermediate hash value is subjected to feature enhancement processing by the multilayer perceptron module in the deep hash neural network to obtain the target hash value.

[0041] The target hash values ​​of each group are combined by the cascaded processing module in the deep hash neural network to generate a feature hash value.

[0042] Secondly, this application provides a cross-institutional data storage system based on blockchain smart contracts, comprising:

[0043] The acquisition module is used to acquire the raw data of the data storage entity that flows between multiple institutions and the three-dimensional morphological data of the surface of the data storage entity.

[0044] The enhancement module is used to enhance the three-dimensional topography data using coherent light processing methods to generate enhanced feature data;

[0045] The encoding module is used to compress and encode the enhanced feature data using pulse width modulation to generate a compressed pulse sequence, and to eliminate the compressed pulse sequence.

[0046] The generation module is used to perform hash operations on the compressed pulse sequence after elimination processing, generate a feature hash value, and store the feature hash value and the original data together in a cross-institutional blockchain.

[0047] The execution module is used to execute a verification process based on smart contracts and combined with the stored data of cross-institutional blockchains. When any institution initiates a data read request, it generates a real-time hash value and matches the real-time hash value with the feature hash value stored in the cross-institutional blockchains through the smart contract.

[0048] The triggering module is used to trigger the warning mechanism of the smart contract and broadcast the abnormal event to all institutional nodes in the cross-institutional blockchain when the matching result does not meet the preset tolerance requirements. At the same time, it stores the identification information, timestamp, and characteristic deviation data of the abnormal institutional node in the distributed immutable log of the cross-institutional blockchain.

[0049] Thirdly, this application provides an electronic device, comprising:

[0050] Memory, used to store computer programs;

[0051] A processor is used to implement the steps of the cross-institutional data storage method based on blockchain smart contracts as described in the first aspect above when executing the computer program.

[0052] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the cross-institutional data storage method based on blockchain smart contracts as described in the first aspect above.

[0053] The cross-institutional data storage method based on blockchain smart contracts provided in this application has the following beneficial effects:

[0054] This application provides a high-precision physical feature data foundation for subsequent processing, ensuring the authenticity and uniqueness of feature sources, while improving the identification of surface micro-features and enhancing the anti-interference capability and reliability of optical feature data. Based on this, pulse width modulation effectively reduces the resource overhead of data transmission and storage, while ensuring the integrity and stability of the signal during transmission. By achieving immutable binding between entity features and business data, the distributed nature of blockchain ensures the trustworthiness and traceability of stored data. Furthermore, an automated decentralized entity authentication mechanism is established to ensure the consistency between the entity and stored information during data retrieval. Finally, it forms a rapid response and audit trail capability for abnormal situations, thereby enhancing the security and trustworthiness of the entire cross-institutional collaborative network.

[0055] Furthermore, this application automates and intelligentizes entity authentication through smart contracts, thereby ensuring the reliable execution of the verification process and improving the accuracy of verification results through algorithm comparison, thereby effectively preventing entity replacement or data tampering, and ultimately ensuring the security and trustworthiness of cross-organizational data access processes.

[0056] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 A flowchart illustrating a cross-institutional data storage method based on blockchain smart contracts, provided as an embodiment of this application;

[0059] Figure 2 A schematic diagram illustrating a specific implementation of a cross-institutional data storage method based on blockchain smart contracts, provided for an embodiment of this application;

[0060] Figure 3 This is a schematic diagram of the structure of a cross-institutional data storage system based on blockchain smart contracts, provided as an embodiment of this application. Detailed Implementation

[0061] In critical goods transfer scenarios involving collaboration among multiple institutions, existing evidence storage solutions based on digital tags and centralized databases have the following significant limitations: they rely on externally attached tag media for data binding, which can easily lead to the separation of physical and digital information due to tag damage or peeling; and the verification process only targets the tag number rather than the characteristics of the physical entity itself, making it difficult to effectively prevent the risk of physical replacement. At the same time, centralized verification mechanisms lack sufficient transparency and collaborative prevention and control capabilities in cross-institutional environments, resulting in insufficient security of the overall data storage and verification process.

[0062] To address the aforementioned issues, this application proposes a cross-institutional data storage method based on blockchain smart contracts. Its core lies in collecting high-precision three-dimensional topographic data of the data storage entity's surface, generating a unique feature hash value through optical enhancement, pulse coding, and hash calculation, and storing this hash value along with the original data in a cross-institutional blockchain. When any institution initiates a data read request, the smart contract automatically triggers a real-time optical feature acquisition and verification process, comparing the matching degree between the real-time generated hash value and the chain-stored hash value, and broadcasting warnings and recording tamper-proof anomaly logs in real-time under abnormal circumstances.

[0063] Therefore, this method abandons the reliance on external labels and directly utilizes the entity's own non-replicable optical characteristics to achieve data binding. It constructs a decentralized, automated, and reliable collaborative verification mechanism through blockchain and smart contract technologies, thereby fundamentally eliminating the possibility of entity replacement and data tampering. At the same time, it improves the data consistency guarantee and security operation and maintenance capabilities in multi-institutional scenarios, effectively solving the shortcomings of existing solutions in terms of entity binding reliability and cross-institutional collaborative efficiency.

[0064] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0065] The core of this application is to provide a cross-institutional data storage method based on blockchain smart contracts, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0066] Step 101: Collect the original data of the data storage entity that flows between multiple institutions and the three-dimensional morphology data of the data storage entity surface.

[0067] In step 101, the data storage entity refers to a physical object that flows between multiple institutions, such as important documents or goods packaging, whose surface has measurable physical characteristics.

[0068] The relationship between institutions and data storage entities is one of regulation and being regulated, verification and being verified. Specifically, multiple institutions jointly regulate and maintain the circulation process of data storage entities. Each institution shares the original data and feature hash value of the data storage entity through the blockchain network, and verifies the consistency between the optical features of the entity's surface and the data stored on the blockchain when reading the data, ensuring the authenticity and immutability of the data storage entity in the process of circulation across institutions.

[0069] Raw data refers to business information describing the data storage entity, such as document number, goods type, circulation record, and other text or numerical information; three-dimensional topographic data refers to the height, contour, and texture information of the surface microstructure of the data storage entity obtained through optical scanning, used to characterize its unique physical features.

[0070] For example, in a cross-border logistics scenario, when goods arrive at the transit warehouse in location A, staff use a high-resolution 3D scanner to scan the outer packaging surface of the goods from multiple angles to obtain 3D topographic data containing 1024×1024 points. At the same time, the original data of the goods are recorded, including information such as the product name "electronic components", the batch number "B2023-08", and the weight "25.6kg".

[0071] Step 102: The three-dimensional topography data is enhanced using a coherent light processing method to generate enhanced feature data.

[0072] In step 102, the enhanced feature data represents the processed data, which contains clearer surface feature information and has higher recognition compared to the pre-3D topography data.

[0073] It should be noted that the embodiments of this application do not limit the specific type of coherent light processing method, and can be set accordingly according to the actual situation.

[0074] In this embodiment, a coherent light source is used to illuminate the surface of the data storage entity at a specific angle to generate an interference fringe pattern. Then, a photoelectric sensor is used to collect light intensity distribution data, and a phase analysis algorithm is used to extract phase information. Finally, the phase information is weighted and fused with the original three-dimensional topography data to generate enhanced optical feature data.

[0075] For example, the three-dimensional topography data obtained in step 101 is input into a coherent light processing system, and a helium-neon laser with a wavelength of 632.8 nm is used to irradiate the surface of the cargo at a 45-degree angle. The phase distribution map is obtained through a four-step phase-shifting method, where the phase value is calculated using the following formula: ,in Indicates the phase value. arrive The light intensity values ​​are at phase shifts of 0°, 90°, 180°, and 270°, respectively. Then, the phase distribution map and the three-dimensional topography data are weighted and fused with a weight ratio of 0.6 to 0.4 to generate enhanced feature data of 1024×1024 pixels.

[0076] Step 103: Compress and encode the enhanced feature data using pulse width modulation to generate a compressed pulse sequence, and then eliminate the compressed pulse sequence.

[0077] In step 103, the compressed pulse sequence represents the data sequence generated after encoding, which has a small data volume and strong anti-interference ability.

[0078] It should be noted that the specific type of pulse width modulation method is not limited in the embodiments of this application, and can be set accordingly according to the actual situation.

[0079] In this embodiment, the pulse modulation parameters are dynamically adjusted according to the data characteristics of the enhanced feature data. First, the data is divided into blocks and converted into pulse width modulation signals. Then, differential Manchester encoding is performed to generate a compressed pulse sequence. Finally, adaptive filtering technology is used to eliminate baseline drift.

[0080] For example, the enhanced feature data generated in step 102 is divided into 256×256 pixel blocks, and pulse width modulation is used to convert each pixel value into a pulse width, where the pulse width... The calculation formula is: ,in Indicates the pulse width. Indicates the reference pulse width, such as 100ns. This represents the modulation coefficient, such as 0.5 ns / unit value. The pixel value is then represented; the generated pulse sequence is then differentially encoded using Manchester encoding, and an adaptive filter is used to eliminate baseline drift. The coefficients of this filter are adjusted in real time according to the signal characteristics.

[0081] Step 104: Perform a hash operation on the compressed pulse sequence after the elimination process to generate a feature hash value, and store the feature hash value and the original data together in the cross-institutional blockchain.

[0082] In step 104, the feature hash value represents the digital fingerprint of the surface features of the data storage entity, and the cross-institutional blockchain represents a distributed ledger system jointly maintained by multiple institutions.

[0083] In this embodiment of the application, a secure hash algorithm is performed on the processed pulse sequence, and a fixed-length hash value is generated through a multi-round iterative compression function. Then, the hash value is combined with the original data to form a data block, and finally, after verification by the consensus mechanism, it is written into the blockchain.

[0084] For example, based on the pulse sequence processed in step 103, a 256-bit feature hash value “a1b2c3d4e5f6...” is generated through 64 rounds of iterative computation using the SHA-256 hash algorithm. Then, this hash value is combined with the original data in step 101 to form a data block, which is finally written into the blockchain network jointly maintained by institutions A, B and C through the PBFT consensus mechanism.

[0085] Step 105: Based on the smart contract and combined with the stored data of the cross-institutional blockchain, when any institution initiates a data read request, a verification process is executed to generate a real-time hash value. The real-time hash value is then matched with the feature hash value stored in the cross-institutional blockchain through the smart contract.

[0086] In step 105, a smart contract refers to a computer program that automatically executes predefined rules; an institution refers to the various independent organizations or units participating in cross-institutional data storage; and a real-time hash value is a hash value generated during the data reading phase by collecting optical feature data of the surface of the data storage entity in real time and processing it through the same process.

[0087] In this embodiment of the application, when an institution initiates a read request, the smart contract automatically triggers the optical feature acquisition device to collect the current entity surface feature data. Then, it generates a real-time hash value through the same processing procedure as when it is stored, and then compares and analyzes it with the hash value stored in the blockchain.

[0088] For example, when organization B requests to read cargo data, the smart contract triggers a scanning device to re-collect the cargo surface features, which are then processed using the same coherent light processing and hash operation to generate a real-time hash value. The smart contract calculates the Hamming distance between this real-time hash value and the hash value stored in the blockchain, using the following formula: ,in Indicates Hamming distance, and The real-time hash value and the stored hash value are respectively the first Bit value.

[0089] Step 106: When the matching result does not meet the preset tolerance requirements, the warning mechanism of the smart contract is triggered and the abnormal event is broadcast to all institutional nodes in the cross-institutional blockchain. At the same time, the identification information, timestamp and feature deviation data of the abnormal institutional node are stored in the distributed immutable log of the cross-institutional blockchain.

[0090] In step 106, the preset tolerance requirement refers to the allowable error range for matching the feature hash value set in the smart contract, the warning mechanism refers to the alarm system triggered when an anomaly is detected, and the distributed immutable log refers to the record that is permanently stored on the blockchain and cannot be modified.

[0091] In this embodiment of the application, when the hash value matching degree exceeds the tolerance range, the smart contract automatically sends a warning message to all participating institutions and writes the abnormal details, including the institution identifier, timestamp, and deviation data, into the blockchain log.

[0092] For example, when the Hamming distance calculated in step 105 is greater than the preset threshold 8, the smart contract broadcasts an abnormal warning to institutions A, B and C, and writes the identifier of institution B, the current timestamp "2023-08-15, 14:30:25" and the deviation value 6 into the blockchain log.

[0093] This method ensures the authenticity and security of data storage in a multi-agency environment by executing the above process, and prevents the risks of data tampering and entity replacement, thereby improving the credibility and efficiency of cross-agency collaboration.

[0094] To address the issue of insufficient reliability in binding entity and digital information during cross-institutional data verification, some embodiments include step 105: Based on a smart contract and combined with the stored data of the cross-institutional blockchain, when any institution initiates a data read request, a verification process is executed to generate a real-time hash value. This real-time hash value is then matched with the characteristic hash value stored in the cross-institutional blockchain via the smart contract. Figure 2 As shown, it includes:

[0095] Step 201: In the smart contract, establish a verification relationship based on the correspondence between the feature hash value and the stored data of the cross-institutional blockchain.

[0096] In step 201, the verification relationship refers to the verification rule established in the smart contract that establishes a one-to-one correspondence between the feature hash value and the original data. This relationship ensures that each data storage entity has a unique feature identifier bound to it.

[0097] In this embodiment of the application, the smart contract reads the feature hash value and the corresponding original data stored in the blockchain, establishes a mapping table between the two, and sets verification rules and tolerance thresholds, thereby forming a complete verification protocol.

[0098] It should be noted that the embodiments of this application do not limit the content of the verification rules or the specific values ​​of the tolerance thresholds, and can be set accordingly according to the actual situation.

[0099] Step 202: Based on the verification relationship, when any institution initiates a data reading request, the smart contract automatically triggers a feature acquisition instruction, and based on the feature acquisition instruction, acquires the optical feature data of the surface of the current data storage entity.

[0100] In step 202, the feature acquisition instruction is a control command automatically issued by the smart contract, which is used to trigger the optical sensor to acquire and store real-time feature data of the physical surface; optical feature data refers to the raw optical feature information acquired in real time during the data reading phase.

[0101] In this embodiment of the application, when an organization initiates a data reading request, the smart contract generates a feature acquisition instruction based on the established verification relationship. This instruction controls the optical scanning device to scan the surface of the current data storage entity, thereby obtaining real-time optical feature data.

[0102] Step 203: Generate a real-time hash value based on the optical feature data.

[0103] In this embodiment of the application, the collected real-time optical feature data is sent to the hash operation unit, and after the same compression and encoding process, a real-time hash value with the same format as when it was stored is generated.

[0104] Step 204: The smart contract's verification and execution module uses a similarity calculation algorithm to perform a bit-by-bit comparison and difference analysis between the real-time hash value and the feature hash value stored in the blockchain to obtain a matching result.

[0105] In this embodiment, the execution module first verifies the bit-level comparison result between the real-time hash value and the stored hash value, calculates the specific difference index based on this, and performs a comprehensive analysis with the preset tolerance requirements, thereby finally outputting the matching result.

[0106] It should be noted that the embodiments of this application do not limit the specific expression and implementation process of the similarity calculation algorithm, and can be set accordingly according to the actual situation.

[0107] Here is a specific example:

[0108] In cross-border logistics scenarios, if institution B needs to verify electronic component goods that have been stored on the blockchain, the smart contract establishes verification conditions based on the correspondence between the feature hash value and the original data of the goods. For example, the condition specifies a tolerance threshold of 8 for the Hamming distance.

[0109] When Institution B issues a data read request, the smart contract automatically triggers a feature acquisition command and controls a high-resolution scanner to re-acquire the optical feature data of the current cargo outer packaging surface under conditions identical to the initial storage: a 632.8 nm wavelength laser and a 45-degree incident angle. Based on the acquired optical feature data, the smart contract continues to perform four-step phase-shifting interferometry and weighted fusion processing according to the processing flow at the time of storage, generating 1024×1024 pixel enhanced data, and then using pulse width modulation and hash operation to generate a real-time hash value.

[0110] Next, the verification execution module uses a similarity calculation algorithm to compare the real-time hash value with the feature hash value stored in the blockchain bit by bit, and calculates the Hamming distance. The final calculated Hamming distance value is 3, which is less than the preset tolerance threshold of 8. The matching result meets the tolerance requirement, and the smart contract determines that the verification is successful, thereby allowing institution B to access the cargo data.

[0111] In this embodiment of the application, the automated verification process through smart contracts can bind data storage entities with their digital information in real time and reliably, thereby ensuring efficiency and accuracy in cross-institutional data verification and effectively preventing the risks of entity replacement and data tampering, ultimately helping to improve the overall data security level in a multi-institutional collaborative environment.

[0112] To further improve the accuracy and reliability of hash value comparison, in some embodiments, step 204 involves using a similarity calculation algorithm to perform a bit-by-bit comparison and difference analysis between the real-time hash value and the feature hash value stored in the blockchain to obtain a matching result, including:

[0113] Step 301: Calculate the Hamming distance between the real-time hash value and the corresponding bits of the feature hash value stored in the blockchain, and use the Hamming distance as a dissimilarity index.

[0114] In step 301, the Hamming distance refers to the number of different characters at corresponding positions in two strings of equal length. In this application, it can specifically refer to the number of different bits between the real-time hash value and the stored hash value. The difference index is a preliminary difference measure calculated based on the Hamming distance.

[0115] In this embodiment of the application, the value of each bit in the real-time hash value and the feature hash value stored in the blockchain is compared bit by bit, the total number of bits that are different is counted, and the value obtained from this count is used as an indicator to evaluate the degree of difference between the two.

[0116] Step 302: Use a sliding window algorithm to compare the real-time hash value and the feature hash value stored in the blockchain in segments to identify local distribution patterns.

[0117] In step 302, the local distribution pattern refers to the distribution characteristics of the differential bits within each segment.

[0118] It should be noted that the embodiments of this application do not limit the specific expression and implementation process of the sliding window algorithm, and can be set accordingly according to the actual situation.

[0119] In this embodiment, a fixed-size sliding window is used to compare two hash values ​​in segments, and the specific number and distribution of the difference bits in each window are calculated in turn to identify the feature regions where the differences are more concentrated.

[0120] Step 303: Analyze the local distribution pattern and detect deviation characteristics.

[0121] In step 303, the deviation characteristic refers to the persistent deviation pattern caused by system factors.

[0122] In this embodiment of the application, by performing time-series analysis on the local difference data generated during multiple verification processes, moving averages and trend lines are calculated, and finally, it is detected whether specific deviation characteristics persist.

[0123] Step 304: Perform a comprehensive analysis of the deviation characteristics, the difference index, and the local distribution pattern to generate a comprehensive matching score, which is the matching result.

[0124] In step 304, the comprehensive matching score is the final similarity score calculated by combining multiple features, which is used to determine the matching result.

[0125] In this embodiment, the identified deviation characteristics, difference index, and local distribution pattern are input into the comprehensive evaluation model, and a final comprehensive matching score is generated through weighted calculation. It should be noted that this embodiment does not limit the structure or specific parameter design of the comprehensive evaluation model.

[0126] In this embodiment of the application, a multi-level difference analysis mechanism is used to assess the overall degree of difference while also identifying local difference characteristics and systematic deviation trends, thereby improving the accuracy and reliability of matching result judgment and effectively reducing the possibility of misjudgment caused by accidental factors or systematic errors.

[0127] To further improve the feature discrimination capability of three-dimensional topography data, in some embodiments, step 102: using a coherent light processing method to enhance the three-dimensional topography data and generate enhanced feature data, including:

[0128] Step 401: Illuminate the surface of the data storage entity with a coherent light source to generate an interference fringe pattern, and record the brightness distribution data of the interference fringe pattern.

[0129] In step 401, a coherent light source refers to a light source that can produce a stable interference phenomenon, the interference fringe pattern is a series of alternating bright and dark fringes formed by the superposition of light waves, and the brightness distribution data is a digital signal that records the intensity information of the fringes.

[0130] In this embodiment, a coherent light source is used to illuminate the surface of the data storage entity at a specific angle, and an interference fringe pattern is generated by means of the interference phenomenon of light. Then, a photoelectric sensor is used to record the brightness value of each point on the pattern, thereby generating a complete brightness distribution dataset.

[0131] Step 402: Perform phase-shift interferometry processing on the brightness distribution data to generate a phase-shift interferogram.

[0132] In step 402, the phase-shifted interferogram is an image sequence that records interference fringes under different phase differences.

[0133] In this embodiment of the application, a phase modulation device sequentially generates multiple reference lights with different phase shifts, which then interfere with the object light to form a series of interference patterns. The brightness distribution data corresponding to each phase shift is then recorded, and finally a complete set of phase-shifted interferograms is generated.

[0134] Step 403: Extract phase information from the phase-shifted interferogram using a phase calculation algorithm, and generate a phase modulation distribution map based on the phase information.

[0135] In step 403, the phase information represents the phase change data of the light wave, and the phase modulation distribution map is an image that records the phase values ​​of each point on the surface.

[0136] In this embodiment, a phase calculation algorithm is used to analyze and calculate multiple phase-shifted interferograms. Then, continuous phase distribution information is obtained by solving the phase wrapping equation, and based on this phase information, a phase modulation distribution map reflecting surface characteristics is finally generated. It should be noted that this embodiment does not limit the specific expression of the phase calculation algorithm; it can be set according to actual conditions.

[0137] Step 404: The phase modulation distribution map and the three-dimensional topography data are fused together, and an enhanced feature data is generated using a weighted superposition algorithm.

[0138] In this embodiment of the application, the phase modulation distribution map and the three-dimensional topography data are aligned according to their pixel positions, and then a weighted superposition algorithm is used to fuse the values ​​of the two according to a predetermined weight ratio, thereby generating enhanced feature data that simultaneously contains phase features and topography features.

[0139] In the embodiments of this application, coherent light processing can effectively improve the contrast and recognizability of surface micro-features, thereby enhancing the uniqueness and anti-interference ability of optical feature data, thus laying a high-quality data foundation for the subsequent generation of unique feature identifiers.

[0140] To further improve the quality and accuracy of feature fusion, in some embodiments, step 404 involves fusing the phase modulation distribution map with the three-dimensional topography data, and generating enhanced feature data using a weighted superposition algorithm, including:

[0141] Step 501: Calculate the corresponding adaptive weighting coefficients based on the phase gradient values ​​of each pixel in the phase modulation distribution map.

[0142] In step 501, the phase gradient value of each pixel is obtained by performing spatial differentiation on the phase modulation distribution map. Its meaning represents the rate of change of the phase value between adjacent pixels and is used to characterize the degree of surface features. The adaptive weight coefficient is a fusion weight value that is dynamically adjusted according to the gradient magnitude.

[0143] In this embodiment, the corresponding phase gradient value can be obtained by calculating the phase difference between each pixel and its neighboring pixels in the phase modulation distribution map. Then, based on the magnitude of the gradient value, the corresponding adaptive weight coefficient is calculated using a piecewise function, and higher weights are assigned to regions with larger gradient values. It should be noted that this embodiment does not limit the parameter design of the piecewise function or the specific number of segments; these can be set according to actual conditions.

[0144] Step 502: Based on the adaptive weighting coefficients, a weighted superposition algorithm is used to perform pixel-by-pixel fusion calculations of the depth information of the three-dimensional topography data and the phase information of the phase modulation distribution map to generate preliminary optical feature data.

[0145] In step 502, the depth information of the three-dimensional topography data is the raw measurement data obtained by multi-angle optical scanning, which represents the vertical distance information of each point on the surface of the data storage entity relative to the reference plane; the phase information is the phase value in the phase modulation distribution map, and the preliminary optical feature data is the feature data after preliminary fusion.

[0146] In this embodiment, the depth information of the three-dimensional topography data and the phase information of the phase modulation distribution map are mapped according to the pixel position. Then, a weighted superposition algorithm is used to calculate the weighted information based on the adaptive weight coefficient of each pixel to generate preliminary optical feature data.

[0147] Step 503: The anisotropic diffusion filtering algorithm is used to perform edge optimization processing on the preliminary optical feature data to obtain intermediate optical feature data.

[0148] In step 503, the intermediate optical feature data is the optimized feature data.

[0149] In this embodiment, an anisotropic diffusion filtering algorithm is used to process the preliminary optical feature data, and the diffusion coefficient is adaptively adjusted according to the image gradient, thereby achieving a strong smoothing effect in flat areas and effectively preserving details in edge areas, ultimately obtaining optimized intermediate optical feature data.

[0150] Step 504: Perform contrast enhancement and dynamic range adjustment on the intermediate optical feature data to generate enhanced feature data.

[0151] In step 504, the adaptive gamma correction function is a contrast enhancement function that is dynamically adjusted according to the image characteristics. It should be noted that this embodiment does not limit the specific expression used for this function, and it can be set according to the actual situation.

[0152] In this embodiment, the gamma correction parameters are dynamically calculated by analyzing the statistical characteristics of intermediate optical feature data. Then, a nonlinear transformation is applied to the data to enhance feature contrast and optimize dynamic range, ultimately generating the required enhanced feature data.

[0153] In this embodiment, adaptive weight allocation and optimization can effectively improve the quality of feature fusion, while enhancing the contrast and detail retention of the data, thereby providing a better data foundation for subsequent feature extraction and hashing operations.

[0154] To address the issues of efficient compression and reliable transmission of enhanced feature data, in some embodiments, step 103 involves: compressing and encoding the enhanced feature data using pulse width modulation to generate a compressed pulse sequence, and then eliminating the compressed pulse sequence, including:

[0155] Step 601: Dynamically adjust the pulse modulation parameters based on the data characteristics of the enhanced feature data.

[0156] In step 601, the data features of the enhanced feature data are obtained by performing multi-angle optical scanning and coherent light processing on the surface of the data storage entity. The meaning is that the data contains attributes such as statistical characteristics, frequency distribution, data size and dynamic range. These features determine the adjustment strategy of the pulse modulation parameters. The pulse modulation parameters include pulse repetition frequency parameters, duty cycle range parameters and modulation depth parameters.

[0157] In this embodiment, by analyzing the numerical distribution and variation characteristics of the enhanced feature data, the pulse modulation parameters are automatically adjusted according to the dynamic range and frequency characteristics of the data, so that the modulation parameters can better match the data characteristics.

[0158] Step 602: Based on the adjusted pulse modulation parameters, the enhanced feature data is divided into segments according to a preset data block size, and each data block is converted into a modulation signal with a specific pulse width and pulse interval through pulse width modulation.

[0159] In step 602, the preset data block size refers to the pre-defined size of the data processing unit; the "specific" in the specific pulse width and pulse interval refers to the parameter values ​​dynamically determined based on the data characteristics of the enhanced feature data.

[0160] Pulse width refers to the duration of each pulse, and pulse interval refers to the time interval between adjacent pulses. These parameter values ​​are determined by the content of the data block. Different data blocks will generate pulse signals with different time characteristics, thereby achieving accurate conversion from data to pulse signals.

[0161] In this embodiment of the application, the enhanced feature data is divided into multiple data blocks of a fixed size, and the values ​​in each data block are converted into pulse signals of corresponding width according to the adjusted pulse modulation parameters, while maintaining a fixed pulse interval, thereby generating a modulation signal sequence.

[0162] Step 603: Perform differential Manchester encoding on all the modulated signals to generate a compressed pulse sequence.

[0163] In the embodiments of this application, differential Manchester encoding is applied to all modulation signals, so that each data bit generates a level transition at the beginning, thereby generating a compressed pulse sequence with self-synchronization characteristics.

[0164] Step 604: The compressed pulse sequence is eliminated using adaptive baseline correction technology to obtain the eliminated compressed pulse sequence.

[0165] In this embodiment, adaptive filtering technology is used to process the compressed pulse sequence. By monitoring the changes in the signal baseline in real time and dynamically adjusting the filtering parameters, the baseline drift that may occur during transmission can be effectively eliminated, and a stable compressed pulse sequence can be obtained.

[0166] In this embodiment, adaptive parameter adjustment and encoding processing can effectively achieve data compression and signal optimization. While reducing the demand for data transmission and storage resources, it also ensures the reliability and stability of signal transmission, thereby laying a high-quality data foundation for subsequent processing.

[0167] To address the issues of feature representation and collision resistance in hash values, in some embodiments, step 104: performing a hash operation on the compressed pulse sequence after elimination processing to generate a feature hash value includes:

[0168] Step 701: Perform multiple iterations using a compression function to group the compressed pulse sequence after elimination processing. In each iteration, perform bit rotation, modular addition, and nonlinear logic function operations to convert the compressed pulse sequence after elimination processing into a fixed-length intermediate hash value.

[0169] In step 701, the compression function is an algorithm function that converts a variable-length input into a fixed-length output. It should be noted that this embodiment does not limit the specific expression used in this function, and it can be set according to the actual situation.

[0170] Multi-round iteration is a process that is repeatedly executed; bit rotation is a cyclic shift operation; and modulo addition is a modulo addition operation.

[0171] Nonlinear logic functions are nonlinear transformation functions. It should be noted that this embodiment does not limit the specific expression used for this function, and it can be set according to the actual situation.

[0172] Intermediate hash values ​​are temporary hash values ​​generated during the iteration process. The termination condition for the iteration is that all data groups have completed hash calculations and generated a final fixed-length hash value output.

[0173] Step 702: Input the intermediate hash value into the deep hash neural network, and perform feature enhancement processing on the intermediate hash value through the multilayer perceptron module in the deep hash neural network to obtain the target hash value.

[0174] In step 702, the target hash value is the hash value optimized by the neural network.

[0175] In this embodiment, the intermediate hash value is input into a deep hash neural network, and then nonlinear transformation and feature learning are performed through its multilayer perceptron module to enhance the feature representation ability and discriminative power of the hash value, and finally the optimized target hash value is obtained.

[0176] Step 703: Combine the target hash values ​​of each group through the cascaded processing module in the deep hash neural network to generate a feature hash value.

[0177] In step 703, the cascading processing module is a processing unit that connects and combines multiple data blocks.

[0178] In this embodiment of the application, by introducing feature enhancement processing of deep neural networks, the feature expression capability and uniqueness of hash values ​​are improved, and their collision resistance and security are also enhanced accordingly, thereby providing more reliable feature identification for blockchain storage.

[0179] Figure 3A schematic diagram of the structure of a cross-institutional data storage system based on blockchain smart contracts is provided for embodiments of this application, as shown below. Figure 3 As shown, the system includes:

[0180] The acquisition module 31 is used to acquire the original data of the data storage entity that flows between multiple institutions and the three-dimensional morphological data of the surface of the data storage entity.

[0181] The enhancement module 32 is used to enhance the three-dimensional topography data using a coherent light processing method to generate enhanced feature data.

[0182] The encoding module 33 is used to perform compression encoding processing on the enhanced feature data using pulse width modulation to generate a compressed pulse sequence, and to perform elimination processing on the compressed pulse sequence.

[0183] The generation module 34 is used to perform a hash operation on the compressed pulse sequence after the elimination process, generate a feature hash value, and store the feature hash value and the original data together in the cross-institutional blockchain.

[0184] The execution module 35 is used to execute a verification process based on a smart contract and combined with the stored data of the cross-institutional blockchain, so as to generate a real-time hash value when any institution initiates a data read request, and match the real-time hash value with the feature hash value stored in the cross-institutional blockchain through the smart contract.

[0185] Trigger module 36 is used to trigger the warning mechanism of the smart contract and broadcast the abnormal event to all institutional nodes in the cross-institutional blockchain when the matching result does not meet the preset tolerance requirements. At the same time, it stores the identification information, timestamp and feature deviation data of the abnormal institutional node in the distributed immutable log of the cross-institutional blockchain.

[0186] The cross-institutional data storage system based on blockchain smart contracts in this application is used to implement the aforementioned cross-institutional data storage method based on blockchain smart contracts. Therefore, the specific implementation of the cross-institutional data storage system based on blockchain smart contracts can be found in the embodiment section of the cross-institutional data storage method based on blockchain smart contracts mentioned above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0187] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the above-described cross-institutional data storage methods based on blockchain smart contracts.

[0188] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described cross-institutional data storage methods based on blockchain smart contracts.

[0189] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0190] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the embodiments of the cross-institutional data storage method based on blockchain smart contracts described above.

[0191] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0192] The foregoing has provided a detailed description of a cross-institutional data storage method and system based on blockchain smart contracts provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A cross-institutional data storage method based on blockchain smart contracts, characterized in that, include: Collect raw data and three-dimensional topographic data of data storage entities that are transferred between multiple institutions; The three-dimensional topography data is enhanced using a coherent light processing method to generate enhanced feature data; The enhanced feature data is compressed and encoded using pulse width modulation to generate a compressed pulse sequence, and the compressed pulse sequence is then eliminated. A hash operation is performed on the compressed pulse sequence after the elimination process to generate a feature hash value, and the feature hash value and the original data are stored together in a cross-institutional blockchain; Based on smart contracts and combined with the stored data of cross-institutional blockchains, when any institution initiates a data read request, a verification process is executed to generate a real-time hash value. The smart contract then matches the real-time hash value with the feature hash value stored in the cross-institutional blockchain. When the matching result does not meet the preset tolerance requirements, the warning mechanism of the smart contract is triggered and the abnormal event is broadcast to all institutional nodes in the cross-institutional blockchain. At the same time, the identification information, timestamp and characteristic deviation data of the abnormal institutional node are stored in the distributed immutable log of the cross-institutional blockchain.

2. The method according to claim 1, characterized in that, Based on smart contracts and combined with data stored across institutional blockchains, when any institution initiates a data read request, a verification process is executed to generate a real-time hash value. The smart contract then matches this real-time hash value with the characteristic hash value stored across the institutional blockchain, including: In smart contracts, a verification relationship is established based on the correspondence between the feature hash value and the stored data across institutional blockchains; Based on the verification relationship, when any institution initiates a data reading request, the smart contract automatically triggers a feature acquisition instruction, and based on the feature acquisition instruction, acquires optical feature data of the surface of the current data storage entity. Based on the optical feature data, a real-time hash value is generated; The smart contract's verification and execution module uses a similarity calculation algorithm to perform a bit-by-bit comparison and difference analysis between the real-time hash value and the feature hash value stored in the blockchain to obtain a matching result.

3. The method according to claim 2, characterized in that, The step of using a similarity calculation algorithm to perform a bit-by-bit comparison and difference analysis between the real-time hash value and the feature hash value stored in the blockchain to obtain a matching result includes: Calculate the Hamming distance between the real-time hash value and the corresponding bits of the feature hash value stored in the blockchain, and use the Hamming distance as a dissimilarity index. A sliding window algorithm is used to compare the real-time hash value and the feature hash value stored in the blockchain in segments to identify local distribution patterns; The local distribution pattern was analyzed to detect deviation characteristics; The deviation characteristics, the difference index, and the local distribution pattern are comprehensively analyzed to generate a comprehensive matching score, which is the matching result.

4. The method according to claim 1, characterized in that, The enhancement process of the three-dimensional topography data using a coherent light processing method to generate enhanced feature data includes: An interference fringe pattern is generated by illuminating the surface of the data storage entity with a coherent light source, and the brightness distribution data of the interference fringe pattern is recorded. The brightness distribution data is subjected to phase-shift interferometry to generate a phase-shift interferogram; Phase information is extracted from the phase-shifted interferogram using a phase calculation algorithm, and a phase modulation distribution map is generated based on the phase information. The phase modulation distribution map is fused with the three-dimensional topography data, and an enhanced feature data is generated using a weighted superposition algorithm.

5. The method according to claim 4, characterized in that, The step of fusing the phase modulation distribution map with the three-dimensional topography data and generating enhanced feature data using a weighted superposition algorithm includes: Calculate the corresponding adaptive weighting coefficient based on the phase gradient value of each pixel in the phase modulation distribution map; Based on the adaptive weighting coefficients, a weighted superposition algorithm is used to perform pixel-by-pixel fusion calculations of the depth information of the three-dimensional topography data and the phase information of the phase modulation distribution map to generate preliminary optical feature data. An anisotropic diffusion filtering algorithm is used to perform edge optimization processing on the preliminary optical feature data to obtain intermediate optical feature data; The intermediate optical feature data is subjected to contrast enhancement and dynamic range adjustment to generate enhanced feature data.

6. The method according to claim 1, characterized in that, The step of compressing and encoding the enhanced feature data using pulse width modulation to generate a compressed pulse sequence, and then eliminating the compressed pulse sequence, includes: Based on the data characteristics of the enhanced feature data, the pulse modulation parameters are dynamically adjusted; Based on the adjusted pulse modulation parameters, the enhanced feature data is divided into segments according to a preset data block size, and each data block is converted into a modulation signal with a specific pulse width and pulse interval through pulse width modulation. Differential Manchester encoding is performed on all the modulated signals to generate a compressed pulse sequence; The compressed pulse sequence is eliminated using an adaptive baseline correction technique to obtain an eliminated compressed pulse sequence.

7. The method according to claim 1, characterized in that, The step of performing a hash operation on the compressed pulse sequence after the elimination process to generate a feature hash value includes: The compressed pulse sequence after elimination is grouped by performing multiple rounds of compression function iteration. In each iteration round, bit rotation, modular addition and nonlinear logic function operations are performed to convert the compressed pulse sequence after elimination into a fixed-length intermediate hash value. The intermediate hash value is input into a deep hash neural network, and the intermediate hash value is subjected to feature enhancement processing by the multilayer perceptron module in the deep hash neural network to obtain the target hash value. The target hash values ​​of each group are combined by the cascaded processing module in the deep hash neural network to generate a feature hash value.

8. A cross-institutional data storage system based on blockchain smart contracts, characterized in that, include: The acquisition module is used to acquire the raw data of the data storage entity that flows between multiple institutions and the three-dimensional morphological data of the surface of the data storage entity. The enhancement module is used to enhance the three-dimensional topography data using coherent light processing methods to generate enhanced feature data; The encoding module is used to compress and encode the enhanced feature data using pulse width modulation to generate a compressed pulse sequence, and to eliminate the compressed pulse sequence. The generation module is used to perform hash operations on the compressed pulse sequence after elimination processing, generate a feature hash value, and store the feature hash value and the original data together in a cross-institutional blockchain. The execution module is used to execute a verification process based on smart contracts and combined with the stored data of cross-institutional blockchains. When any institution initiates a data read request, it generates a real-time hash value and matches the real-time hash value with the feature hash value stored in the cross-institutional blockchains through the smart contract. The triggering module is used to trigger the warning mechanism of the smart contract and broadcast the abnormal event to all institutional nodes in the cross-institutional blockchain when the matching result does not meet the preset tolerance requirements. At the same time, it stores the identification information, timestamp, and characteristic deviation data of the abnormal institutional node in the distributed immutable log of the cross-institutional blockchain.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the cross-institutional data storage method based on blockchain smart contracts as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the cross-institutional data storage method based on blockchain smart contracts as described in any one of claims 1 to 7.

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