Blockchain-based vaccine production whole-process data traceability management method and system

Abstract

This invention belongs to the field of biopharmaceutical management and public health safety supervision technology. It discloses a blockchain-based method and system for data traceability management of the entire vaccine production process. The method includes: a regulatory server acquiring production business timestamps reflecting the progress of vaccine batch production; production nodes collecting business data packets representing the process status of specific vaccine production procedures; inputting a dynamic random supervision factor as an initial perturbation parameter into a preset secure hash algorithm logic; storing the current block hash value and associated business data packets as an irreversible traceability data block; and the regulatory server calling a smart contract to extract the current block hash value from the traceability data block. This invention establishes a causal interlocking mechanism between process data and physical environment background, blocking the path for production entities to input non-real business data using virtual simulation or protocol replay technology.

Description

Technical Field

[0001] This invention relates to a blockchain-based method and system for data traceability management throughout the entire vaccine production process, belonging to the field of biomedical management and public health safety supervision technology. Background Technology

[0002] Establishing a high-confidence data traceability system for the entire vaccine production process is crucial to ensuring quality control. Currently, the industry generally adopts distributed ledger technology, which encapsulates process parameters during inactivation, cultivation, filling, and distribution into hash digests and distributes them to consensus nodes. The immutability of the ledger is used to achieve administrative evidence preservation and quality traceability. However, in administrative supervision practice, existing traceability management methods face inherent constraints in ensuring the authenticity of the source. Because administrative regulatory agencies and production entities are physically separated, the production side has the operational space to logically fit or offline beautify process parameters before the data is uploaded to the blockchain. This information asymmetry means that existing passive evidence preservation schemes can only verify the stability of data within the ledger and cannot verify whether the data truly reflects the physical processes at the moment of its generation.

[0003] To address the challenges of verifying the authenticity of data sources, existing technological evolution paths largely focus on expanding sensor deployment density or continuously refining sampling granularity. Practice shows that such linear improvements relying solely on hardware stacking, while significantly increasing system communication load and data processing costs, still fail to address the core issue. Due to the lack of causal relationship auditing between business data and production environment characteristics, the possibility of pre-installed compliant data being injected by the production side using protocol replay technology cannot be ruled out. This deep information asymmetry often leads administrative audit logic into a fundamental blind spot when faced with pre-fitted, seemingly highly logically consistent fabricated business facts. Besides the limitations of hardware monitoring, software control logic also needs to address… Logical forgery exhibits a lag. For example, Chinese invention patent application CN110084626A discloses a blockchain-based vaccine production supervision method. It uses production parameter hash storage and broadcasts with the signature of regulatory agencies. Analysis reveals that this scheme is based on unidirectional maintenance of business data integrity. The input source for hash operations relies on static records on the production side. Due to the lack of strong real-time coupling between external physical random entropy and business behavior, when the production side uses a preset compliance parameter model protocol for replay or data injection, it cannot determine the physical exclusivity of data generation from the algorithm's underlying layer. It over-trusts a single production record node. Administrative supervision faces a fundamental obstacle in judging forged facts with logical consistency, and cannot achieve the verification of the authenticity of production behavior.

[0004] Therefore, the technical problem to be solved by this invention is how to construct a management mechanism that logically couples the physical quantities of production processes with the random quantities of equipment operating environment, so as to ensure that each traceability certificate carries the causal interlocking characteristics at the physical level, thereby realizing the leap from passive evidence storage to active auditing of administrative supervision. Summary of the Invention

[0005] To address the problems in the background technology, the technical solution of the present invention is as follows: A blockchain-based method for full-process data traceability management of vaccine production, comprising the following steps: Step S1: The monitoring server obtains the production business timestamp reflecting the production progress of the vaccine batch, and uses it as a trigger command. It also performs discretization sampling processing by collecting uncontrollable third-party physical noise sources that are geographically isolated from the production node, generates a dynamic random supervision factor that is uniquely corresponding to the production business timestamp in time sequence, and pushes it to the trusted computing environment of the production node through an asymmetric encrypted link. Step S2: The production node collects business data packets representing the process status of a specific vaccine production process, reads the identification code from the operator's digital certificate, and encapsulates the business data packets and the identification code with asymmetric encryption. Step S3: The production node opens a microsecond-level digital synchronization lock based on the production business timestamp through the trusted computing environment. If and only if the received dynamic random supervision factor is aligned with the generation clock of the local business data packet within a preset logical window, the dynamic random supervision factor is used as the initial perturbation parameter and input into the preset secure hash algorithm logic to perform nonlinear transformation operation on the business data packet to generate the current block hash value with spatiotemporal logic locking characteristics. Step S4: The production node obtains the hash value of the preceding block stored in the distributed ledger, uses the current block hash value as a logical verification pointer to the hash value of the preceding block for chain-like time-series anchoring, and stores the current block hash value and the associated business data packet as an irreversible traceability data block. Step S5: The monitoring server calls the smart contract to extract the current block hash value from the traceability data block, verifies the consistency between the current block hash value and the preset logical mapping between the dynamic random monitoring factor, and determines whether the business data packet conforms to the business management logic constraints corresponding to the production business timestamp based on the consistency comparison result.

[0006] Preferably, in step S2, associating and encapsulating the business data packet with the operator's digital certificate includes: reading the operator's digital identity private key share and the real-time monitoring values ​​of the production process; performing step numerical mapping processing on the real-time monitoring values ​​to generate a fixed-length discrete encoded string; homomorphically superimposing the fixed-length discrete encoded string onto the code array of the digital identity private key share according to the rules of finite algebraic operation ring domain, and performing operations on the superimposed array through a threshold signature algorithm to generate a consensus authorization signature with process site environment binding attributes; and writing the consensus authorization signature into the business data packet.

[0007] Preferably, the dynamic random monitoring factor is generated by the monitoring server collecting environmental thermal noise signals and performing discretization transformation processing, and the effective usage time of the dynamic random monitoring factor is no greater than the preset production cycle time, so as to ensure that the traceability data block has a unique logical generation window in the distributed ledger.

[0008] Preferably, before step S5, the method further includes: extracting the business logic association matrix between heterogeneous production units in the vaccine production line from the production node; analyzing the dynamic coupling relationship between multiple process parameters in the business data packet based on the business logic association matrix, determining whether it conforms to the preset production process constraint logic, and outputting the verification result.

[0009] Preferably, the smart contract is pre-configured with a directed acyclic graph model of the vaccine production process, which is used in step S5 to verify the production access status and logical migration legality of the current process based on the logical node where the traceability data block is located, so as to prevent process jumps or time reversals in the distributed ledger for business data packets.

[0010] Preferably, after step S4, the method further includes: synchronously distributing traceability data blocks to cold chain logistics nodes and disease control supervision nodes; when vaccine batches are transferred and switched, completing multi-party joint arbitration through asynchronous Byzantine fault tolerance algorithm to confirm that the business facts corresponding to the production business timestamp have been reached through the entire network's logical consensus.

[0011] Preferably, in step S1, the monitoring server pushes the dynamic random monitoring factor to the trusted computing environment of the production node through an asymmetric encrypted communication link to ensure that the dynamic random monitoring factor is not intercepted during the distribution process and to ensure that the dynamic random monitoring factor stored in the production node cannot be illegally read.

[0012] Preferably, the traceability data block includes: a data header containing the current block hash value, block index value, and pointer to the previous block; and a data body containing encrypted production business timestamps, vaccine production environment monitoring parameters, and batch codes of process materials, thereby realizing structured evidence storage of all elements of the vaccine production process.

[0013] Preferably, in step S5, when the verification result of logical mapping consistency is inconsistent, the regulatory server generates an abnormal alarm signal and locks the process status bit of the corresponding production business timestamp in the distributed ledger to block the logical migration path from the current process to the subsequent process, so that the subsequent production process cannot be started due to the lack of compliant preceding state migration instructions, thereby realizing real-time online interception of non-compliant vaccine production behavior.

[0014] A blockchain-based data traceability management system for the entire vaccine production process includes: The supervision factor generation module is used to obtain the production business timestamps that reflect the progress of vaccine batch production, and to perform discretization sampling processing by collecting third-party physical noise sources to generate dynamic random supervision factors that are uniquely corresponding to the production business timestamps in time sequence. The business data encapsulation module is used to collect business data packets that represent the process status of a specific vaccine production process, read the identity recognition code in the operator's digital certificate, and encapsulate the business data packets and the identity recognition code with asymmetric encryption. The block hash calculation module is used to input the dynamic random supervision factor as the initial perturbation parameter into the preset secure hash algorithm logic, process the business data packets to complete the nonlinear transformation operation, and generate the current block hash value with spatiotemporal logic locking characteristics. The data time-series anchoring module is used to obtain the hash value of the previous block stored in the distributed ledger, use the current block hash value as a logical verification pointer to the previous block hash value to complete the chained time-series anchoring, and store the current block hash value and the associated business data packets in the distributed ledger as irreversible traceability data blocks. The logical consistency verification module is used to call the smart contract, extract the current block hash value from the traceability data block, verify the consistency between the current block hash value and the preset logical mapping between the dynamic random supervision factor, and determine whether the business data packet conforms to the business management logical constraints corresponding to the production business timestamp based on the consistency comparison results.

[0015] Compared with the prior art, the beneficial effects of the present invention are: First, in the data traceability management of the entire vaccine production process, by injecting the random environmental feature sequence of the production equipment into the hash root node calculation process of the process parameter set, a causal interlocking mechanism between process data and physical environment background is established, so that each piece of evidence data carries a physical fingerprint with spatiotemporal exclusivity. This blocks the path for production entities to enter non-real business data using virtual simulation or protocol replay technology at the administrative supervision logic layer, and solves the problem of logical disconnect between data records and real business behavior in the time sequence in the traditional traceability system.

[0016] Second, relying on distributed ledger smart contract technology, this invention constructs a joint arbitration mechanism among production nodes, quality assurance nodes, and remote monitoring nodes by pre-setting a directed acyclic graph model for vaccine production and combining it with an asynchronous Byzantine fault-tolerant algorithm. This mechanism ensures that the state transition of vaccine batches is based on multi-party logical consistency verification, shifting the intervention point of administrative supervision from delayed post-event auditing to instantaneous verification during the process, thus eliminating the risk of information lag and regulatory blind spots in the administrative supervision process.

[0017] Third, by introducing cross-validation logic based on the correlation matrix of physical quantities, the system can deeply analyze the dynamic coupling relationship of physical parameters between different mechanisms in the production line, determine the logical rationality of sensor-collected data, and enable the management system to identify data injection attacks such as isolated changes of single parameters without changing the existing hardware sensors, thereby improving the logical robustness of the traceability management system in complex industrial environments. Attached Figure Description

[0018] Figure 1 This invention relates to a flowchart of the steps involved in data traceability and logical verification of the entire vaccine production process. Figure 2 This is a logical state transition and risk truncation diagram of the traceability management system involved in this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0020] Example 1: This example relates to a blockchain-based method for managing the entire vaccine production data traceability process, including the following steps: Step S1: The monitoring server obtains the production business timestamp reflecting the production progress of the vaccine batch, and uses it as a trigger command. It also performs discretization sampling processing by collecting uncontrollable third-party physical noise sources that are geographically isolated from the production node, generates a dynamic random supervision factor that is uniquely corresponding to the production business timestamp in time sequence, and pushes it to the trusted computing environment of the production node through an asymmetric encrypted link. Step S2: The production node collects business data packets representing the process status of a specific vaccine production process, reads the identification code from the operator's digital certificate, and encapsulates the business data packets and the identification code with asymmetric encryption. Step S3: The production node opens a microsecond-level digital synchronization lock based on the production business timestamp through the trusted computing environment. If and only if the received dynamic random supervision factor is aligned with the generation clock of the local business data packet within a preset logical window, the dynamic random supervision factor is used as the initial perturbation parameter and input into the preset secure hash algorithm logic to perform nonlinear transformation operation on the business data packet to generate the current block hash value with spatiotemporal logic locking characteristics. Step S4: The production node obtains the hash value of the preceding block stored in the distributed ledger, uses the current block hash value as a logical verification pointer to the hash value of the preceding block for chain-like time-series anchoring, and stores the current block hash value and the associated business data packet as an irreversible traceability data block. Step S5: The monitoring server calls the smart contract to extract the current block hash value from the traceability data block, verifies the consistency between the current block hash value and the preset logical mapping between the dynamic random monitoring factor, and determines whether the business data packet conforms to the business management logic constraints corresponding to the production business timestamp based on the consistency comparison result.

[0021] In step S2 of this embodiment, associating and encapsulating the business data packet with the operator's digital certificate includes: reading the operator's digital identity private key share and the real-time monitoring values ​​of the production process; performing step numerical mapping processing on the real-time monitoring values ​​to generate a fixed-length discrete encoding string; homomorphically superimposing the fixed-length discrete encoding string onto the code array of the digital identity private key share according to the rules of the finite algebra operation ring domain, and performing operations on the superimposed array through the threshold signature algorithm to generate a consensus authorization signature with process site environment binding attributes; and writing the consensus authorization signature into the business data packet.

[0022] The dynamic random monitoring factor described in this embodiment is generated by the monitoring server collecting environmental thermal noise signals and performing discretization transformation. The effective usage time of the dynamic random monitoring factor is no greater than the preset production cycle time, so as to ensure that the traceability data block has a unique logical generation window in the distributed ledger.

[0023] Before step S5, this embodiment further includes: extracting the business logic association matrix between heterogeneous production units in the vaccine production line from the production node; analyzing the dynamic coupling relationship between multiple process parameters in the business data packet based on the business logic association matrix, determining whether it conforms to the preset production process constraint logic, and outputting the verification result.

[0024] The smart contract described in this embodiment has a pre-built directed acyclic graph model of the vaccine production process, which is used in step S5 to verify the production access status and logical migration legality of the current process based on the logical node where the traceability data block is located, so as to prevent process jumps or time reversals in the distributed ledger for business data packets.

[0025] This embodiment, after step S4, also includes: synchronously distributing traceability data blocks to cold chain logistics nodes and disease control supervision nodes; when vaccine batches are transferred and switched, multi-party joint arbitration is completed through asynchronous Byzantine fault tolerance algorithm to confirm that the business facts corresponding to the production business timestamp have been reached through the entire network's logical consensus.

[0026] In step S1 of this embodiment, the monitoring server pushes the dynamic random monitoring factor to the trusted computing environment of the production node through an asymmetric encrypted communication link to ensure that the dynamic random monitoring factor is not intercepted during the distribution process and to ensure that the dynamic random monitoring factor stored in the production node cannot be illegally read.

[0027] The traceability data block described in this embodiment includes: a data header containing the current block hash value, block index value, and pointer to the previous block; and a data body containing encrypted production business timestamps, vaccine production environment monitoring parameters, and batch codes of process materials, thereby realizing structured evidence storage of all elements of the vaccine production process.

[0028] In step S5 of this embodiment, when the verification result of logical mapping consistency is inconsistent, the regulatory server generates an abnormal alarm signal and locks the process status bit of the corresponding production business timestamp in the distributed ledger, thereby blocking the logical migration path from the current process to the subsequent process, so that the subsequent production process cannot be started due to the lack of compliant preceding state migration instructions, thus realizing real-time online interception of non-compliant vaccine production behavior.

[0029] This embodiment relates to a blockchain-based data traceability management system for the entire vaccine production process, including: The supervision factor generation module is used to obtain the production business timestamps that reflect the progress of vaccine batch production, and to perform discretization sampling processing by collecting third-party physical noise sources to generate dynamic random supervision factors that are uniquely corresponding to the production business timestamps in time sequence. The business data encapsulation module is used to collect business data packets that represent the process status of a specific vaccine production process, read the identity recognition code in the operator's digital certificate, and encapsulate the business data packets and the identity recognition code with asymmetric encryption. The block hash calculation module is used to input the dynamic random supervision factor as the initial perturbation parameter into the preset secure hash algorithm logic, process the business data packets to complete the nonlinear transformation operation, and generate the current block hash value with spatiotemporal logic locking characteristics. The data time-series anchoring module is used to obtain the hash value of the previous block stored in the distributed ledger, use the current block hash value as a logical verification pointer to the previous block hash value to complete the chained time-series anchoring, and store the current block hash value and the associated business data packets in the distributed ledger as irreversible traceability data blocks. The logical consistency verification module is used to call the smart contract, extract the current block hash value from the traceability data block, verify the consistency between the current block hash value and the preset logical mapping between the dynamic random supervision factor, and determine whether the business data packet conforms to the business management logical constraints corresponding to the production business timestamp based on the consistency comparison result. In this invention, the uncontrollable physical noise source of the third party is collected in real time by a hardware random number generator deployed on the regulatory server side. Specific sources include: thermal noise generated by semiconductor quantum devices, which utilizes voltage fluctuations generated by the thermal motion of electrons inside the resistor, exhibiting high non-periodicity and unpredictability at the microsecond scale; atmospheric radioactive decay or electromagnetic background noise, which is atmospheric electrostatic discharge or residual noise from cosmic background radiation collected by a dedicated antenna; and photon shot noise, which utilizes quantum fluctuation signals generated by the randomness of photon arrival time when a photosensitive device receives a constant light source. The third party refers to the noise generation environment and the physical site of vaccine production in terms of administrative management, geographical space, and At the network physical layer, all systems are in a heterogeneous state. This ensures that the entropy sequence generated by the noise source is not interfered with by the electromagnetic environment of the production unit or by human operation. To ensure the unpredictability of the physical noise source, this system establishes a dual physical isolation mechanism: First, the independence of the generation domain, that is, the physical noise source is generated by an entropy source server deployed in the government cloud or a third-party high-reliability laboratory environment. This ensures that the random number generation process is independent of the production node in terms of physical space and management authority. Moreover, the generated physical quantities are based on a spontaneous process of quantum mechanics or the second law of thermodynamics, which cannot be simulated by algorithm pre-deduction or logical deduction. Second, the one-way spatiotemporal anchoring of the distribution domain, which means that the dynamic random supervision factor is pushed to the trusted computing environment of the production node through an asymmetric encrypted link. Since the action of the supervision server to collect noise and the production business timestamp are digitally synchronized and locked at the microsecond level, even if the production end has powerful computing capabilities, it cannot predict the random physical state at that moment before the supervision factor is issued.

[0030] Example 2: In the continuous operation of DPT vaccine inactivated production supervision, the administrative supervision agency is physically detached from the production site. The conventional discrete on-site inspection and post-event report verification mechanism faces a systemic threat of distorted underlying data sources. Due to the time lag between the production-side quality control node and the data uploading action, when non-compliant fluctuations occur in the temperature or pressure indicators inside the inactivation vessel, the production node has the physical conditions to refit the process parameter curve and replay the protocol data packet using the local offline simulation environment. The evidence storage mechanism that relies solely on the integrity of the sensor data itself cannot logically distinguish the objective physical spatiotemporal context of data generation, causing the administrative supervision to lose the basic ability to determine the objectivity of the source when faced with offline data fitting.

[0031] When the monitoring server obtains the production business timestamp reflecting the progress of vaccine batch inactivation, it simultaneously performs discretization sampling by collecting uncontrollable physical noise sources from remote third parties, generating a dynamic random monitoring factor that uniquely corresponds to the production business timestamp in time sequence. The production node collects business data packets representing the current inactivation temperature and pressure, reads the identification code from the operator's digital certificate, and performs asymmetric encryption association and encapsulation of the business data packets and the identification code. In the underlying architecture of this asymmetric encryption association operation, the system has a pre-set quantization mapping logic that converts continuous industrial signals into hash features. The system controller directly captures the steady-state inactivation vessel pressure simulation parameters and, according to the preset discretization segmentation criteria, inputs the first eight bits of the input data into the micro-analog-to-digital converter module. The output is a fixed-length discrete encoded string, and according to the basic rules of the finite algebra operation ring domain, the string is homomorphically superimposed and fused into the code array of the current operator's digital identity private key share. This specific modulus shift and homomorphic integration operation forces the operator to leave the pure authorization recognition password based on the binding weight attribute of the objective field environment of the process, and completes the penetration from the continuous physical monitoring dimension to the underlying data of abstract signature authentication. The system uses the dynamic random supervision factor as the initial perturbation parameter to input the preset secure hash algorithm logic, processes the business data packet to generate the current block hash value with spatiotemporal logic locking characteristics, obtains the previous block hash value stored from the distributed ledger, and uses the current block hash value as a logical verification pointer to the previous block hash value to complete the chain-like temporal anchoring.This mechanism asymmetrically fuses unpredictable physical noise sequences representing external administrative oversight with internal process parameters within a closed production environment. The injection of external dynamic random monitoring factors superimposes external physical characteristics into internal business data packets, making it impossible to unidirectionally reverse-engineer these characteristics. This allows for the synchronous deduction and construction of physical environment noise values ​​at specific microsecond-level spatiotemporal nodes by intervening in offline forged inactivation process curves. The forced causal interlocking between these two data sources within the hash logic resolves the inherent contradiction of local collaborative tampering risks arising from unidirectional evidence storage records under remote silent monitoring. This causal interlocking mechanism relies on a precise timing synchronization protocol within the secure computing domain to achieve strong logical-level binding. The system utilizes a network clock interrupt control mechanism as a synchronization trigger base to ensure the local sensor generation process... The hardware clock tag of the service data packet can achieve precise alignment with the random noise feature clock stamp issued by the monitoring server at the microsecond level, thus transforming the physical isolation in the spatial dimension into a temporal consistency constraint in the computational dimension. When the hardware clock tag of the service data packet generated by the local sensor achieves microsecond-level digital synchronization lock with the thermal noise feature clock stamp pushed by the monitoring server, the hash logic entry is allowed to concurrently extract these two different sources for computation. If the production side fabricates parameters offline, the system's computation layer will directly trigger hash collapse because it cannot intercept the unique thermal noise sequence issued within that microsecond instant as a valid initial hash seed. By utilizing the temporal consistency check in cryptography, the spatial physical isolation gap is transformed into an insurmountable spatiotemporal computational obstacle mechanism at the algorithm's underlying level.

[0032] Once the irreversible traceable data block containing the current block hash value and associated business data packets is synchronously distributed to the external regulatory node, the logical consistency verification module calls the smart contract to extract the current block hash value, verifies the consistency between the current block hash value and the preset logical mapping between the dynamic random supervision factor, and when the consistency comparison meets the business management logic constraints corresponding to the production business timestamp, it allows the business data packet to complete the process state migration according to the preset directed acyclic graph model of vaccine production process. This data traceability management method transcends the inherent hardware upgrade path of simply optimizing single sensor encryption or high-frequency sampling, shifting the focus of administrative supervision and defense from protecting the static historical data itself to actively auditing the spatiotemporal exclusive state at the moment of data generation. By reconstructing the temporal verification logic of business data entry, it transforms the management dilemma of preventing forged data into the computational obstacle of reconstructing a high-dimensional physical random entropy vector, and establishes a system principle that relies on external uncontrollable environmental variables to constrain the internal closed-loop production logic.

[0033] Example 3: In this example, under the simulated production verification conditions of 50 million doses of DPT vaccine per year for aseptic inactivation, the administrative regulatory agency is physically detached from the production site. The discrete on-site monitoring and post-production report verification mechanism carries the risk of distorted underlying data sources. There is a time lag between the production-side quality control nodes and the data upload process. When non-compliant fluctuations occur in the temperature or pressure indicators within the inactivation vessel, the production node has the physical conditions to refit the process parameter curves and replay the protocol data packets using a local offline simulation environment. The unidirectional evidence storage mechanism relying on sensor data integrity cannot distinguish the objective physical and spatiotemporal context of data generation, resulting in a lack of basic ability for administrative supervision to determine the objectivity of the source when facing offline data fitting. The regulatory server is deployed on the government cloud, and edge computing nodes are distributed on the production site. The experiment sets up a control group and the sample group of this invention. To ensure the engineering referenceability of the experiment, this experimental environment is built on a government cloud platform (simulated) using a Kunpeng 920 processor. The test involved a monitoring center and five high-performance industrial gateways with integrated ARM architecture as edge production nodes. The sample data source was extracted from the actual sensor history of the PLC (Programmable Logic Controller) in the DPT vaccine inactivation process. The test sample size covered 500 consecutive production batches, totaling 1,000,000 business data packets. Before the test started, all edge nodes synchronized with the monitoring center at the microsecond level via NTP (Network Time Protocol) and completed sensor zero-point drift compensation as a necessary prerequisite for data acquisition. The control group used conventional blockchain notarization logic containing business data and operator identification codes. Gaussian white noise with a signal-to-noise ratio of 20dB was superimposed in the signal acquisition link to simulate a real industrial electromagnetic environment. The monitoring server collected thermal noise from a high-vacuum physical experimental environment as an external physical noise source and distributed dynamic random supervision factors through an asymmetric encrypted link. The sampling period Δt and the production cycle T were set. cycle The logical mapping relationship; based on the balance principle of data acquisition real-time performance and reducing distributed ledger storage overhead, when T cycle When the value is 30 seconds, the Δt value is set to 3 seconds using the tenfold oversampling principle to cover communication delay fluctuations caused by industrial electromagnetic environment.

[0034] This invention simulates offline fitting attack strengths Iatk with offsets of 10%, 30%, and 50% in the sample group. Iatk represents the percentage deviation of the attack data from the actual process parameters. The monitoring server outputs a dynamic random supervision factor Df in real time. Df is a random numerical feature with time attributes. This invention's sample group reads the business data packet Bp representing the current inactivation temperature Ta and pressure Pa, and uses Df as the initial perturbation parameter input to the secure hash algorithm logic to calculate the current block hash value Hcur. Ta is the inactivation temperature, Pa is the pressure inside the reactor, Bp is the digital package data, and Hcur is the hash feature string. Data measurements show that when Iatk is 10%, the hash matching degree of the control group remains above 95.2%, and no data replay behavior is detected. Due to the unpredictability introduced by Df, the calculated Hcur and the pre-fitted hash value of the attack node have a Hamming distance of over 128 bits in binary. For the logic implementation of interception judgment, this invention quantifies the degree of deviation through the conversion relationship between Hamming distance and hash matching degree. The specific conversion formula is defined as follows: in, This indicates the hash matching degree generated by the reconstruction verification; This represents two sets of hash values ​​(in this example, ). The binary Hamming distance between the prefitted hash value and the attack hash value; This represents the total number of bits output by the secure hash algorithm (this embodiment uses the SHA-256 algorithm, therefore...). ), measured by the experiment If the number reaches 128 bits or more, it can be calculated according to the above formula that... A bit flip rate of 50% or lower in a 256-bit hash space indicates that the attacker's forged data and the system's real-time generated verification value are statistically completely decoupled, which is far below the system's preset 95% security threshold, thus triggering the system's interception response to the fitted action.

[0035] When the entropy intensity Ep of the external physical noise sequence increases from 128 bits to 512 bits, the measured time cost for an attacking node to forge a single traceability data block increases from 1.2 seconds to over 3600 seconds, exceeding the state transition time limit of the vaccine production process; among which, E p To characterize the noise characteristics using binary bit length, the test data reports the inflection point of nonlinear performance, when D f When the byte length exceeds 1024 bits, the time T consumed by the monitoring server to verify the consistency of the logic... chk The timeout increased dramatically from 15.2ms to 850.6ms; among which, T chk To verify the computation time, excessive entropy injection induced a nonlinear expansion of the system's processing load, causing a lag in the determination of business management logic constraints. The experiment determined that the optimal working window was D.f With a length between 256 and 512 bits, this range balances the anti-counterfeiting strength of external physical features with the response speed of the administrative audit system. The test results confirm that using external random variables to construct a causal interlocking mechanism enhances the physical exclusivity of the evidence records, completing the technical logic loop from passive evidence storage to proactive auditing.

[0036] Example 4: In this example, within a collaborative vaccine production process encompassing inactivation and purification / packaging, production units in different geographical locations generate asynchronous business data packets. Due to physical differences in production rhythms between processes, the system faces management risks such as reversed logical order of production records and malicious skipping of critical processes. The monitoring server obtains standard process parameters corresponding to heterogeneous production units in the production line, determines the logical topological relationship between the inactivation vessel and the centrifuge, and extracts a business logic association matrix representing the mandatory prerequisite order of material flow. Based on the business logic association matrix, the system constructs a directed acyclic graph model of the vaccine production process, where logical vertices correspond to the inactivation unit and the purification unit, respectively. The system then integrates the heterogeneous production units in the vaccine production line... The units are mapped to matrix row nodes and column nodes respectively. When the physical sensor network detects an irreversible material flow pipeline between a specific upstream and downstream production unit, the corresponding row and column coordinate element in the matrix is ​​set to one; otherwise, the element at that coordinate is set to zero. After completing the Boolean mapping and matrix quantization of the above node pipeline topology, the system performs cross-process physical parameter consistency audits on the row and column nodes marked as valid pathways in the matrix. In specific implementation, the system does not monitor a single parameter in isolation, but extracts heterogeneous process feature sequences that span adjacent task sequences and performs correlation calibration based on the dynamic evolution trend between features. For example, the system uses a high-weighted monitoring pathway to simultaneously acquire information from the upstream inactivation reactor. The system analyzes the instantaneous pressure drop slope during the discharge cycle and the feed rate increase of the downstream purification centrifuge during the same period. It then uses a Pearson correlation coefficient model to calculate the linear covariance coefficient of these fluctuations, quantifying the process coupling strength. This coefficient is used as a feature value characterizing the legitimacy of the pathway and written back into the business logic association matrix. Specifically, the system simultaneously extracts the instantaneous pressure drop slope of the upstream inactivation vessel during the discharge cycle and the feed rate increase of the downstream purification centrifuge during the same period. A Pearson correlation analysis model is established between the heterogeneous simulated quantities. By calculating the Pearson correlation coefficient between the fluctuation sequences of the instantaneous pressure drop slope and the feed rate increase, the linear correlation strength between the two is characterized. The Pearson correlation coefficient calculated through calibration is used as the second attribute characterizing the process coupling strength and written back to a specific cell in the previously constructed association matrix to build a numerical verification base for the legality of joint changes in multidimensional process parameters. The quantified value of this correlation coefficient calculated through calibration is used as the second attribute characterizing the process coupling strength and written back to a specific cell in the previously constructed association matrix to build a numerical verification base for the legality of joint changes in multidimensional process parameters. The system constructs a directed acyclic graph model of the vaccine production process based on the business logic association matrix, with logical vertices corresponding to the inactivation unit and purification unit, respectively. The system sets a time window threshold interval [t] for the directed edges connecting the vertices of the inactivation unit and the purification unit. min ,t maxThis interval is determined based on the minimum effective duration of inactivation and the maximum duration of material transfer specified in the vaccine pharmacopoeia; when the production node uploads data containing the production business timestamp and business data packet B... p When tracing the source data block, the logical consistency verification module calls the smart contract to parse the business data packet B. p The system extracts the material batch identification code and equipment operation status code generated by the inactivation unit; the smart contract traverses the directed acyclic graph model of the vaccine production process based on the material batch identification code, locates the current purification node, and traces back to its corresponding inactivation predecessor node; the system obtains the state transition flag of the inactivation predecessor node from the distributed ledger to confirm that it is in the completed state; the system synchronously calculates the time difference ΔT between the current production business timestamp and the process completion timestamp recorded by the inactivation predecessor node, and determines whether the difference meets the preset constraint condition: t min ≤ΔT≤t max Where ΔT is the time difference between two consecutive production nodes, t min t is the preset lower limit of process flow time. max This is the preset upper limit for process flow time.

[0037] When the difference determination result shows that ΔT is within the interval and the state transition flag of the inactivation unit is valid, the smart contract outputs an instruction to allow the purification unit to be started; this mechanism transforms abstract logical constraints into numerical domain comparison operators based on physical time constants, through a time window threshold interval [t min ,t maxTo establish causal anchoring between multi-task sequential flows, the system addresses potential logical inconsistencies in business records stored in the ledger due to clock drift or human intervention. Furthermore, business management logic constraints are implemented by transforming abstract production specifications into concrete spatiotemporal coupling operators: On one hand, a pre-defined directed acyclic graph model of vaccine production processes within smart contracts is used to forcibly anchor the material flow path in spatial dimensions. By comparing the current process with the preceding completed processes recorded in the distributed ledger, the system ensures that production nodes strictly adhere to the irreversible process sequence. On the other hand, the system synchronously retrieves the current production business timestamp and the preceding process completion timestamp, calculates the physical time difference between them in real time, and determines whether this difference is strictly within the safe time window threshold range defined by the minimum effective inactivation time and the material transfer limit time. This allows for the identification of offline fitting of process parameters from the logical level. To prevent protocol replay attacks, the system ensures that every piece of business data accurately reflects the objective rhythm and temporal causality of the physical production site. To support subsequent reverse calls without interfering with the publicly available standard structure of the original traceability data, the monitoring server, upon generating the monitoring factor, immediately utilizes a dedicated high-authority monitoring bypass channel to directly encapsulate and push the plaintext of the dynamically random monitoring factor held exclusively by itself, along with the corresponding production business timestamp bound to it, into an independent hidden snapshot record block reserved at the bottom layer of the distributed network ledger. This independent record storage operation forcibly separates the storage domain of the publicly available data structure of daily business transactions from that of the core original environmental factors. While maintaining the security of the non-privileged node access structure, it constructs and retains a legally valid arbitration comparison index source with full lifecycle traceability capabilities. The smart contract incorporates a reverse mapping verification logic with a secure hash algorithm, used to parse and extract the current block hash value H after obtaining the traceability data block. cur With production business timestamp T s The contract is based on T s The index address pointed to retrieves the corresponding dynamic random check factor D from the encrypted storage domain of the distributed ledger. f and call the stored business data packet B p The preset secure hash operation logic is repeatedly used to generate the hash value H to be tested. test , where H cur T is the original block hash value. s For production business timestamps, D f B is the generated dynamic random monitoring factor. p For the collected business data packets, H test For the reconstructed verification hash value; contract comparison H cur With H test Character consistency is determined when the consistency check is true and the current production business timestamp meets the time window threshold interval [t]. min ,t maxWhen the process status flag for the material batch in the distributed ledger is updated to allow migration, this processing logic automates the audit of the production-side reporting facts by closing the data generation process on the regulatory side.

[0038] Example 5: In this example, under the data management of the entire vaccine production process, which includes asynchronous production units and multi-level cold chain logistics nodes, the unaligned data packets directly collected by the system are affected by differences in the physical deployment environment of each node, local system clock accuracy deviation, and inherent thermal noise of the sensors. When applied to distributed ledger storage and process node traversal, these packets cause problems such as underlying data timing reference deviation and microwave interference masking production fluctuations. Before receiving business data packets and dynamic random monitoring factors, the system completes a pre-calibration process for the data entry point. This process covers the clock synchronization layer and the physical sampling layer. At the clock synchronization layer, the monitoring server, as the master clock source, sends heartbeat data packets with timing marks to the distributed production nodes through the network protocol. Each production node receives the heartbeat data packets and calculates the round-trip transmission delay. Based on the delay value, the local system clock is adjusted and corrected at the microsecond level, so that the production business timestamps of all nodes in the network are anchored to the same reference time axis after delay cancellation, eliminating misjudgments of the process sequence caused by clock frequency offset between nodes.

[0039] At the physical sampling layer, for the analog-to-digital conversion unit that collects external physical noise sources, the system continuously reads background noise data for 1000 sampling periods under no-load conditions and calculates the average amplitude M of the data. noise With variance V noise M noise As a zero-point compensation quantity, and V noise The sampling suppression thresholds are written into the sampling registers respectively, where M noise V represents the average amplitude of the background noise. noise The variance represents the amplitude fluctuation of the background noise; when generating the dynamic random monitoring factor subsequently, the system removes variables with amplitudes in the range of V. noise Invalid components within the range and superimposed M noise This ensures that the initial disturbance parameters reflect the characteristics of external environmental variables; the system reconstructs the data format of business data packets uploaded by the production unit based on the process baseline; the monitoring server extracts the steady-state tolerance band of key process parameters at different process nodes and calculates the inactivation temperature T. a With pressure P a If the slope of change within the current sampling period is within the corresponding steady-state tolerance band, the system performs smoothing filtering on the original sampled data based on the median of the steady-state tolerance band to filter out redundant entropy increase caused by electromagnetic interference to the hash logic. The business data packets after clock correction, zero-bit padding and filtering, together with the production business timestamp, constitute the standardized basic data for inputting the secure hash algorithm. This process standardizes the underlying parameter locking path before the generation of traceability data blocks.

[0040] In the circulation supervision scenario involving the transfer and switching of vaccine batches, traceability data blocks are synchronously distributed to cold chain logistics nodes and disease control supervision nodes. The system uses an asynchronous Byzantine fault-tolerant algorithm to complete multi-party joint arbitration to prove that the business facts corresponding to the production business timestamp have passed the logical consensus of the entire network; the receiving node reads the current block hash value H of the batch to be transferred. cur The system initiates a consensus request to the network. The monitoring server and the third-party audit node, as consensus participants, retrieve the logical association matrix recorded in the distributed ledger to verify the integrity of the process in this batch, and output Boolean vote values ​​representing their own verification status to form a consensus vector V. vote , where H cur V is the hash value of the current block. vote V is a dimensional vector containing the voting results of n nodes; the system determines V. vote When the voting ratio of verified data is not less than 2 / 3, a consensus signature is generated and appended to the header of the traceability data block. This procedure uses multi-centralized verification weights to offset the possibility of local storage data tampering or record forgery by a single production node. In the dynamic monitoring scenario of vaccine batch transfer and switching, due to the dispersed physical locations of each monitoring node and the heterogeneity of the network environment, this invention adopts an asynchronous Byzantine fault-tolerant algorithm to solve the consensus problem of non-aligned data streams. The receiving node first extracts the current block hash value H from the RFID or QR code of the vaccine to be received. cur The system broadcasts a consensus request to all network monitoring nodes. Upon receiving the request, the monitoring server and third-party audit nodes, using their independent permissions, retrieve the corresponding logical association matrix from the distributed ledger. This matrix records the mandatory time-series characteristics of the batch of vaccines from inactivation, purification to packaging. Nodes then compare the H... cur Whether the corresponding process chain is complete, the output Boolean voting values ​​constitute the consensus vector V. vote In this embodiment, the proportion threshold is set to 2 / 3. That is, the system will automatically trigger the consensus signature operation only when more than 66.7% of the independent monitoring nodes confirm that the process logic of the batch is correct. The arbitration result will be permanently fixed in the data header of the traceability data block. This mechanism effectively offsets the local record forgery behavior that may exist in a single production node and ensures the objective authenticity of business facts during the transition.

[0041] Example 6: In this example, in an adaptive calibration scenario addressing process differences between different vaccine batches, the uniform time parameter faces the risk of logical misjudgment due to physical differences in the inactivation efficiency of different vaccine types. The system initiates an offline optimization program to determine the time window threshold range [t] for a specific vaccine type. min ,t maxThe numerical value; the regulatory server extracts the historical circulation records of this vaccine type from the distributed ledger and calculates the actual time difference sequence T between the completion of inactivation and the start of purification. hist The system defines the objective function L. loss To characterize the coupled effect of false positive and false negative rates, L is selected by searching within a search domain of 1 minute to 120 minutes. loss The value that reaches the global minimum is taken as t. min With t max The system iterates through the historical time difference sequences, extracting the ratio of operations outside the candidate interval but identified as compliant by experts as the false positive rate, and the ratio of failures inside the candidate interval but identified as exhibiting abnormal states such as material degradation as the false negative rate. The system uses a preset weighted summation algorithm to multiply the false negative rate by a first calibration penalty coefficient, add the false positive rate multiplied by a second calibration penalty coefficient, and perform a homogeneous summation of the two bias products. The final calculated and combined value is then explicitly defined as the objective function L. loss The basic assessment object is used to eliminate scale interference between multiple bias assessment dimensions. The calibration results for DPT vaccine batches show that... min For 15 minutes and t max The process takes 45 minutes and transforms static process experience into a dynamic control benchmark based on data feedback.

[0042] In the initial perturbation parameter injection stage of the secure hash algorithm logic, in order to eliminate the dynamic random monitoring factor D... f With business data packet B p To mitigate logical offsets during the fusion process, the system employs a bit-level nonlinear obfuscation algorithm; the input receiving length is L. df Dynamic random supervision factor D f Business data packets B in byte stream format p The system will use a dynamic random monitoring factor D. fThe data packet Bp is split into n equal-length sub-segments di, and the data packet Bp is cyclically shifted according to the offset indicated by the production service timestamp Ts. The offset is determined according to the basic algebra modulo operation rules. The controller extracts the last eight characters of the production service timestamp Ts and converts them into a decimal integer value. The integer value is then used to perform a modulo operation on the total byte length of the data packet Bp, and the non-negative remainder value is output as the actual offset number of bits to drive the underlying shift register. The system performs a logical XOR operation on the corresponding interval of each sub-segment di and the shifted data packet Bp to generate a hash operation input vector Vm. This process defines the injection path of external environment variables at the binary level, enabling the generated current block hash value Hcur to possess spatiotemporal locking characteristics, establishing the spatiotemporal exclusivity state at the moment of data generation. In step S3, the preset secure hash algorithm logic preferably adopts the national standard SM3 cryptographic hash algorithm or SHA-256 algorithm. To achieve the spatiotemporal logic locking characteristics, this invention constructs causal interlocking through the following specific parameter injection and nonlinear transformation mechanism. The parameter injection method is that the system adopts an XOR penetration injection method based on message extension blocks. The algorithm uses dynamic random supervision factors. With production business timestamp The sequence is concatenated and converted into a perturbation sequence aligned with the standard word length (32 bits) of the SM3 algorithm using a predefined homomorphic mapping function. During the message expansion phase of the algorithm, this perturbation sequence is injected into each group of message words by a cyclic XOR operation. In the generation logic, the initial random entropy is fully permeated before the compression function starts; the nonlinear transformation operation relies on the iterative compression function structure inside the hash algorithm. Taking SM3 as an example, a message block injected with perturbation parameters needs to undergo 64 rounds of nonlinear iterative operation. In each round, a Boolean function is used to perform the operation. and Perform bit-level logical operations, in conjunction with linear permutation functions. and Performing a cyclic left shift transformation; solidifying the spatiotemporal exclusive state, this injection method ensures that hash operations have an extremely high avalanche effect, even for business data packets. The content is completely identical. As long as the external physical noise entropy shifts by 1 bit at the microsecond level, after being amplified through 64 rounds of nonlinear transformations, the final generated current block hash value will be obtained. It will present completely random and uncorrelated results within a 256-bit numerical space. This computational irreversibility locks in the instantaneous physical context of data generation at the underlying algorithm level, causing any subsequent protocol replay or data fitting to produce a detection difference with a huge Hamming distance at the hash mapping level.

[0043] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0044] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A blockchain-based vaccine production whole-process data traceability management method, characterized in that, Includes the following steps: Step S1: The monitoring server obtains the production business timestamp reflecting the production progress of the vaccine batch, and uses it as a trigger command. It also performs discretization sampling processing by collecting uncontrollable third-party physical noise sources that are geographically isolated from the production node, generates a dynamic random supervision factor that is uniquely corresponding to the production business timestamp in time sequence, and pushes it to the trusted computing environment of the production node through an asymmetric encrypted link. Step S2: The production node collects business data packets representing the process status of a specific vaccine production process, reads the identification code from the operator's digital certificate, and encapsulates the business data packets and the identification code with asymmetric encryption. Step S3: The production node opens a microsecond-level digital synchronization lock through the trusted computing environment based on the production business timestamp. If and only if the received dynamic random supervision factor is aligned with the generation clock of the local business data packet within a preset logical window, the dynamic random supervision factor is used as the initial perturbation parameter input into the preset security hash to perform nonlinear transformation operation on the business data packet to generate the current block hash value with spatiotemporal logic locking characteristics. Step S4: The production node obtains the hash value of the preceding block stored in the distributed ledger, uses the current block hash value as a logical verification pointer to the hash value of the preceding block for chain-like time-series anchoring, and stores the current block hash value and the associated business data packet as an irreversible traceability data block. Step S5: The monitoring server calls the smart contract to extract the current block hash value from the traceability data block, verifies the consistency between the current block hash value and the preset logical mapping between the dynamic random monitoring factor, and determines whether the business data packet conforms to the business management logic constraints corresponding to the production business timestamp based on the consistency comparison result. 2.The blockchain-based vaccine production whole-process data traceability management method according to claim 1, characterized in that, In step S2, associating and encapsulating the business data packet with the operator's digital certificate includes: reading the operator's digital identity private key share and the real-time monitoring values ​​of the production process; performing step numerical mapping processing on the real-time monitoring values ​​to generate a fixed-length discrete encoded string; homomorphically superimposing the fixed-length discrete encoded string onto the code array of the digital identity private key share according to the rules of finite algebraic operation ring domain, and performing operations on the superimposed array through a threshold signature algorithm to generate a consensus authorization signature with process site environment binding attributes; and writing the consensus authorization signature into the business data packet. 3.The blockchain-based vaccine production whole-process data traceability management method according to claim 1, characterized in that, The dynamic random monitoring factor is generated by the monitoring server collecting environmental thermal noise signals and performing discretization transformation. The effective usage time of the dynamic random monitoring factor is no longer than the preset production cycle time, so as to ensure that the traceability data block has a unique logical generation window in the distributed ledger.

4. The blockchain-based method for full-process data traceability management of vaccine production as described in claim 1, characterized in that, Before step S5, the process also includes: extracting the business logic association matrix between heterogeneous production units in the vaccine production line from the production node; based on the business logic association matrix, analyzing the dynamic coupling relationship between multiple process parameters in the business data packet, determining whether it conforms to the preset production process constraint logic, and outputting the verification result.

5. The method for data traceability management of the entire vaccine production process based on blockchain according to claim 1, characterized in that, The smart contract is pre-configured with a directed acyclic graph model of the vaccine production process. In step S5, it is used to verify the production access status and logical migration legality of the current process based on the logical node where the traceability data block is located, so as to prevent process jumps or time reversals in the distributed ledger for business data packets.

6. The method for data traceability management of the entire vaccine production process based on blockchain according to claim 1, characterized in that, After step S4, the process also includes: synchronously distributing traceability data blocks to cold chain logistics nodes and disease control and supervision nodes; when vaccine batches are transferred and switched, multi-party joint arbitration is completed through asynchronous Byzantine fault tolerance algorithm to confirm that the business facts corresponding to the production business timestamp have been reached through the entire network's logical consensus.

7. The method for data traceability management of the entire vaccine production process based on blockchain according to claim 1, characterized in that, In step S1, the monitoring server pushes the dynamic random monitoring factor to the trusted computing environment of the production node through an asymmetric encrypted communication link to ensure that the dynamic random monitoring factor is not intercepted during the distribution process and to ensure that the dynamic random monitoring factor stored in the production node cannot be read illegally.

8. The method for data traceability management of the entire vaccine production process based on blockchain according to claim 1, characterized in that, The traceability data block includes: a data header containing the current block hash value, block index value, and pointer to the previous block; and a valid data body containing encrypted production business timestamps, vaccine production environment monitoring parameters, and batch codes of process materials, realizing structured evidence storage of all elements of the vaccine production process.

9. The blockchain-based method for full-process data traceability management of vaccine production according to claim 1, characterized in that, In step S5, when the verification result of logical mapping consistency is inconsistent, the regulatory server generates an abnormal alarm signal and locks the process status bit of the corresponding production business timestamp in the distributed ledger, thereby blocking the logical migration path from the current process to the subsequent process, so that the subsequent production process cannot be started due to the lack of compliant preceding state migration instructions, thus realizing real-time online interception of non-compliant vaccine production behavior.

10. A blockchain-based data traceability management system for the entire vaccine production process, used to implement the blockchain-based data traceability management method for the entire vaccine production process as described in claim 1, characterized in that, include: The supervision factor generation module is used to obtain the production business timestamps that reflect the progress of vaccine batch production, and to perform discretization sampling processing by collecting third-party physical noise sources to generate dynamic random supervision factors that are uniquely corresponding to the production business timestamps in time sequence. The business data encapsulation module is used to collect business data packets that represent the process status of a specific vaccine production process, read the identity recognition code in the operator's digital certificate, and encapsulate the business data packets and the identity recognition code with asymmetric encryption. The block hash calculation module is used to input the dynamic random supervision factor as the initial perturbation parameter into the preset secure hash algorithm logic, process the business data packets to complete the nonlinear transformation operation, and generate the current block hash value with spatiotemporal logic locking characteristics. The data time-series anchoring module is used to obtain the hash value of the previous block stored in the distributed ledger, use the current block hash value as a logical verification pointer to the previous block hash value to complete the chained time-series anchoring, and store the current block hash value and the associated business data packets in the distributed ledger as irreversible traceability data blocks. The logical consistency verification module is used to call the smart contract, extract the current block hash value from the traceability data block, verify the consistency between the current block hash value and the preset logical mapping between the dynamic random supervision factor, and determine whether the business data packet conforms to the business management logical constraints corresponding to the production business timestamp based on the consistency comparison results.

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