Supply chain performance data block chain evidence storage method based on multi-party security calculation

Abstract

The invention relates to the technical field of supply chain data storage, and discloses a supply chain performance data block chain storage method based on multi-party security computing. The method comprises the following steps: monitoring performance data states and relative interaction relationships of all participants of a supply chain in real time, and judging whether the data density in a target supply chain region exceeds a safety threshold value or not; when a threshold value is exceeded, analyzing the interaction influence of data of each participant through a multi-party security computing protocol, and evaluating the hidden danger of privacy disclosure caused by data sharing; analyzing and processing the spatial and temporal change of the historical performance data by adopting distributed feature learning and combining with a time sequence, and evaluating the potential interference degree of historical data fluctuation on the integrity of the current performance data; and based on the privacy leakage hidden danger and the potential interference degree of historical data fluctuation, whether overall block chain evidence storage optimization of the target supply chain region is carried out is judged. According to the method, the problems of supply chain data density and privacy protection can be solved in a targeted manner, and evidence storage optimization is realized by combining historical data spatial-temporal characteristics.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain data notarization, and specifically to a supply chain performance data blockchain notarization method based on multi-party secure computing. Background Art

[0002] Supply chain operations involve multiple parties, including suppliers, manufacturers, distributors, and retailers. Each participant generates a large amount of data during the fulfillment process, such as order information, logistics status, and inventory levels. This data not only serves as the basis for each participant's business decisions but also underpins the overall coordinated operation of the supply chain.

[0003] As digitalization accelerates, supply chain data is characterized by massive scale, frequent circulation, and complex interactions. Data formats and storage methods vary among different participants, making cross-party data transmission and sharing prone to information asymmetry and inconsistencies. Furthermore, data from all links in the supply chain is sensitive, containing commercial secrets and core business information, posing the risk of privacy breaches during data sharing.

[0004] Traditional data storage methods often rely on centralized institutions, with a single entity responsible for data storage and management. Under this model, data integrity relies on the credibility of the centralized institution. Security vulnerabilities or operational errors can lead to data tampering and loss. Furthermore, centralized storage makes it difficult to divide data rights among multiple parties. Control and access rights for each participant are not effectively protected, easily leading to disputes over data ownership.

[0005] In supply chain fulfillment scenarios, data timeliness and relevance are particularly important. Fluctuations in historical data can impact current fulfillment status. For example, past inventory fluctuations can affect the delivery cycle of current orders. Traditional evidence storage methods struggle to effectively capture these temporal and spatial correlations, failing to provide reliable historical data references for dynamic supply chain adjustments. Furthermore, when data density within a specific area exceeds a certain limit, the complexity of data interactions increases significantly, making traditional evidence storage mechanisms unable to address the resulting privacy and data management challenges. Summary of the Invention

[0006] The purpose of the present invention is to provide a supply chain performance data blockchain notarization method based on multi-party secure computing to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides a supply chain performance data blockchain notarization method based on multi-party secure computing, the method comprising:

[0008] Real-time monitoring of the performance data status and relative interaction relationships of each participant in the supply chain to determine whether the data density in the target supply chain area exceeds the safety threshold;

[0009] When the data density in the target supply chain area exceeds the security threshold, a multi-party secure computing protocol is constructed to analyze the interactive impact of each participant's data and assess the privacy leakage risks caused by data sharing in the target supply chain area;

[0010] By combining distributed feature learning with time series analysis to process the spatiotemporal variations of historical fulfillment data within the target supply chain region, the potential interference of historical data fluctuations on the integrity of current fulfillment data within the target supply chain region is assessed.

[0011] Based on the privacy leakage risks caused by data sharing in the target supply chain area and the potential interference of historical data fluctuations on the integrity of current fulfillment data in the target supply chain area, it is determined whether to optimize the overall blockchain evidence storage in the target supply chain area.

[0012] Preferably, the method further comprises:

[0013] When it is necessary to optimize the overall blockchain evidence storage in the target supply chain area, analyze the performance data status of each participant in the target supply chain area with other participants and evaluate the complexity of the data path of each participant;

[0014] Determine the data storage priority of each participant based on the complexity of the data path of each participant;

[0015] Generate a data block of evidence and submit it to the blockchain network for tamper-proof evidence storage;

[0016] By real-time monitoring of evidence response time and blockchain network status, the broadcast timing of evidence data blocks can be optimized.

[0017] Preferably, the fulfillment data status and relative interaction relationship of each participant in the supply chain are monitored in real time to determine whether the data density in the target supply chain area exceeds the safety threshold, specifically:

[0018] Real-time collection of performance data status information of all participants, including location information, transaction frequency, cooperation duration and data volume;

[0019] Determine whether the participant is located in the target supply chain area based on the fulfillment data status information:

[0020] Compare the number of participants in the target supply chain area with the logical capacity of the target supply chain area to determine the data density in the target supply chain area;

[0021] Based on the comparison between the data density in the target supply chain area and its corresponding security threshold, the security risk level of data interaction in the target supply chain area is judged.

[0022] Preferably, a multi-party secure computing protocol is constructed to analyze the interactive impact of each participant's data and assess the potential privacy leakage risks caused by data sharing within the target supply chain area. Specifically:

[0023] Real-time access to shared data input from all parties involved in the target supply chain region, including data sensitivity, access rights, and encryption levels;

[0024] Based on the performance data status information and shared data input of each participant, a data processing model based on secure multi-party computing is constructed to describe the privacy protection mechanism during the data interaction process;

[0025] Using the constructed data processing model, the data dependencies between participants and the disturbance effects between shared data inputs are calculated;

[0026] Parallel computing technology is used to iteratively solve the data processing model, simulate the coupling effect between participants and shared data in a high-density data interaction environment, and evaluate the privacy leakage risks caused by data sharing in the target supply chain area; the privacy leakage risks caused by data sharing in the target supply chain area include low risk and high risk.

[0027] Preferably, the temporal and spatial variations of historical fulfillment data in the target supply chain region are processed by combining distributed feature learning methods with time series analysis to assess the potential interference of historical data fluctuations on the integrity of current fulfillment data in the target supply chain region, specifically:

[0028] Real-time access to historical fulfillment data sets within the target supply chain area;

[0029] Using distributed feature learning methods, we map historical performance data sets into feature space and reconstruct the data's dynamic characteristics and change patterns.

[0030] Using time series analysis methods, the historical performance data set in the feature space is processed at different time scales to extract characteristic information of historical data fluctuations;

[0031] Based on the characteristic information of historical data fluctuations, a model describing the spatiotemporal changes of historical data is established to reflect the data's dependence on timestamps and geographic locations;

[0032] The established historical data spatiotemporal change model is used to evaluate the potential interference of historical data fluctuations on the integrity of current fulfillment data in the target supply chain area; the potential interference of historical data fluctuations on the integrity of current fulfillment data in the target supply chain area ranges from significant to negligible.

[0033] Preferably, based on the potential privacy leakage risks caused by data sharing in the target supply chain area and the potential interference of historical data fluctuations on the integrity of current performance data in the target supply chain area, it is determined whether to optimize the overall blockchain evidence storage in the target supply chain area, specifically:

[0034] When the privacy leakage risk caused by data sharing in the target supply chain area is low, and the potential interference of historical data fluctuations on the integrity of current fulfillment data in the target supply chain area is negligible, it is determined that the overall blockchain evidence optimization of the target supply chain area will not be carried out;

[0035] Otherwise, it is determined that the overall blockchain evidence optimization of the target supply chain area will be carried out, and the evidence priority evaluation mechanism will be triggered.

[0036] Preferably, when it is necessary to optimize the overall blockchain evidence storage in the target supply chain area, the performance data status of each participant in the target supply chain area and other participants is analyzed, and the complexity of the data path of each participant is evaluated, specifically:

[0037] Calculate the data interaction distance and status difference between each participant and other participants based on the performance data status information;

[0038] By analyzing the data interaction distance and status differences between participants, possible data conflicts and interference scenarios are identified: for two participants whose data interaction distance is less than a preset safety threshold, all pairs of participants whose status differences are less than the preset status difference are marked;

[0039] Evaluate the data path complexity of each participant based on data conflict and interference scenarios.

[0040] Preferably, based on data conflict and interference scenarios, the data path complexity of each participant is evaluated, specifically:

[0041] Calculate the path complexity index of each participant, which is obtained by aggregating the normalized ratio of the state difference and data interaction distance of all relevant pairs of participants;

[0042] The higher the path complexity index, the more complex the data path of the participant is, and evidence storage optimization needs to be prioritized.

[0043] Preferably, the data storage priority of each participant is determined based on the complexity of the data path of each participant, specifically:

[0044] Calculate the evidence priority score, which is the product of the path complexity index and the participant weight factor;

[0045] The larger the evidence priority score, the higher the data evidence priority of the participant. The participants are sorted from high to low based on the evidence priority score, and the evidence data of the participants with higher rankings are processed first.

[0046] Preferably, a data block of evidence is generated and submitted to the blockchain network for tamper-proof evidence storage, specifically:

[0047] Generate a proof data block containing a timestamp and data summary based on the data of the participants with high proof priority scores;

[0048] Use hash functions to encrypt the evidence data block and generate a unique data identifier;

[0049] The encrypted evidence data block is broadcast to the blockchain network nodes, verified through the consensus mechanism and written into the blockchain.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] By monitoring the performance data status and relative interactions of each supply chain participant in real time, it is possible to promptly identify whether data density within a target supply chain area exceeds a security threshold, enabling targeted follow-up processing to avoid management chaos caused by excessive data density. When data density exceeds the threshold, the interaction between the data of each participant is analyzed using a multi-party secure computation protocol. This allows for in-depth exploration of potential privacy leaks during data sharing, mitigating privacy risks at the root of data interaction.

[0052] Using distributed feature learning methods combined with time series analysis to process the spatiotemporal variations of historical performance data, we can comprehensively capture the correlation between historical data fluctuations and current performance data, clearly demonstrating the potential interference that historical data fluctuations may have on the integrity of current data, and allowing all parties to better understand the inherent connection between historical data and the current state. Based on the potential for privacy leakage and the degree of potential interference from historical data fluctuations, we determine whether to optimize the overall blockchain evidence storage, enabling dynamic adjustment of evidence storage strategies to better align blockchain evidence storage with the actual data status of the supply chain.

[0053] Through blockchain evidence storage optimization, the immutable nature of blockchain can be leveraged to provide a stable storage medium for supply chain fulfillment data, ensuring data consistency and traceability in a multi-party environment. The introduction of a multi-party secure computation protocol ensures effective data exchange while preventing the direct exposure of sensitive information from participating parties, enabling orderly and secure data sharing. The combination of distributed feature learning and time series analysis fully leverages the value of historical data. By analyzing the impact of historical fluctuations on current data, supply chain participants can gain a more comprehensive understanding of the business logic behind the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a timing diagram of the supply chain performance data blockchain evidence storage method based on multi-party secure computing described in the present invention;

[0055] Figure 2 A flowchart for determining data density and security risk level;

[0056] Figure 3 A flowchart for privacy leakage risk assessment;

[0057] Figure 4 Flowchart for historical data fluctuation interference assessment;

[0058] Figure 5 Flowchart for datapath complexity estimation. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0060] See also Figure 1 The present invention provides a supply chain performance data blockchain evidence storage method based on multi-party secure computing, the method comprising:

[0061] Real-time monitoring of the fulfillment data status and relative interactions of each participant in the supply chain. Fulfillment data status information includes location, transaction frequency, duration of collaboration, and data volume. This information is analyzed to determine whether the participant is located within the target supply chain area. The number of participants within the target supply chain area is compared with the logical capacity of the area to calculate data density. The data density is then compared with a preset security threshold to determine whether it exceeds the threshold. If the data density exceeds the threshold, a multi-party secure computation protocol is constructed to analyze the interactive impact of each participant's data. Based on shared data input, including data sensitivity, access permissions, and encryption level, this protocol assesses the privacy risks associated with data sharing within the target supply chain area. Privacy risks are categorized as low-risk and high-risk. Furthermore, a historical fulfillment dataset within the target supply chain area is obtained and mapped into a feature space using distributed feature learning methods to reconstruct the data's dynamic characteristics and change patterns. Combined with time series analysis methods, the data in the feature space is processed at different time scales to extract characteristic information about historical data fluctuations. Based on this characteristic information, a model is constructed to describe the spatiotemporal changes in the historical data, reflecting the data's dependence on timestamps and geographic location. This model is used to assess the potential impact of historical data fluctuations on the integrity of current fulfillment data, with potential impacts categorized as significant or negligible. Finally, based on the privacy risk assessment and potential impact assessment results, a decision is made regarding whether to optimize the overall blockchain evidence storage within the target supply chain region. If the privacy risk assessment is low and the impact assessment is negligible, no optimization is performed; otherwise, the optimization mechanism is triggered.

[0062] Example 1: See Figure 2 , real-time monitoring of the performance data status and relative interaction relationship of each participant in the supply chain. Performance data status information is continuously obtained through the data collection module deployed on the participant's terminal. This module is connected to the supply chain management system to capture parameters such as location coordinates, number of transactions per unit time, length of cooperation cycle and data throughput. The location information is converted into longitude and latitude coordinates using geocoding technology, the transaction frequency is counted by the event log analysis engine, the cooperation duration is extracted from the contract database to calculate the difference between the start and end timestamps, and the data volume is recorded by the transport layer traffic monitoring tool. The target supply chain area is delineated by the geo-fence algorithm, and its boundary coordinates are stored in the regional configuration library. Based on the inclusion relationship between the location coordinates and the geo-fence, the set of participants located in the target area is screened out. The logical capacity of the target area is generated by the infrastructure resource assessment model, which generates a capacity baseline value based on parameters such as the maximum number of concurrent connections of the regional server cluster, storage hardware redundancy space and network bandwidth peak.

[0063] The number of participants screened is compared with the logical capacity baseline. The ratio of the number of participants to the capacity baseline serves as a quantitative indicator of data density, with two decimal places of precision. Preset safety thresholds are stored in the risk policy library and dynamically updated every 24 hours based on the frequency of abnormal events in historical operation logs. When the density indicator exceeds the safety threshold, the system determines that the data interaction risk level has increased, triggering the subsequent analysis process. Data interaction risk levels are divided into three levels, with the level classification criteria pre-set based on different threshold intervals.

[0064] When the risk level reaches the preset conditions, an in-depth analysis process is initiated for each participant in the target area. This process calculates the data interaction distance between participants using a composite algorithm based on spatial location and interaction frequency. Location distance is calculated using the spherical distance formula to calculate the difference in longitude and latitude coordinates, while interaction frequency distance uses the Euclidean distance algorithm based on the number of transactions. Both are then combined into a single distance value using weighted coefficients. A state difference calculation module also runs simultaneously, analyzing the difference coefficient between the duration of collaboration and the difference rate in data volume between participants. The difference coefficient uses the standard deviation algorithm, and the difference rate is calculated as the absolute difference percentage.

[0065] Preset safety distance thresholds and status difference thresholds are stored in the conflict rule library. The threshold values ​​are derived from industry-standard datasets. The system automatically identifies all combinations of participants whose interaction distance falls below the safety distance threshold and further screens these combinations for pairs whose status difference falls below the difference threshold. These identified pairs are recorded in a conflict matrix, where the row and column indices of the matrix correspond to the unique identifiers of the participants.

[0066] Based on the conflict matrix, a path complexity index is generated for each participant. This index traverses all records related to the target participant in the conflict matrix and normalizes the ratio of the state difference rate to the interaction distance for each associated participant pair. This normalization process uses a min-max scaling algorithm to convert the ratio to a range of zero to one. The arithmetic average of all associated values ​​is then calculated, with the result rounded to three decimal places. The path complexity index ranges from zero to one, reflecting the complexity of the data path.

[0067] The evidence priority score calculation module generates a score based on the product of the path complexity index and a weight factor. The weight factor is extracted from the participant attribute library, which maintains weight coefficients for different role types. The coefficients are set based on the node centrality in the supply chain topology. The score is calculated using a linear multiplication formula, and the result is converted to a percentage before being stored in the priority queue. The queue sorting engine sorts participants in descending order of score, with participants with higher scores receiving priority processing.

[0068] The data block generation component processes data from participating parties according to a priority queue. It adds a Coordinated Universal Time timestamp to data packets and generates a data digest using a feature extraction algorithm. This digest algorithm uses a fixed-length encoding scheme for key fields to preserve data uniqueness. The hashing engine performs a one-way transformation on the complete data block, using a standard cryptographic hash function to generate a fixed-length hexadecimal string as a collision-resistant data identifier.

[0069] Encrypted data blocks are broadcast to the blockchain's node cluster via a peer-to-peer network protocol. The node cluster utilizes a permissioned architecture and is divided into two roles: data validation nodes and data storage nodes. Validation nodes execute a distributed consensus process that ensures data consistency through a state machine replication algorithm. Once a write operation has been verified by a predetermined percentage of nodes, the data block is chronologically linked to the end of the distributed ledger.

[0070] During system operation, the system continuously collects attestation response data, including the time interval from broadcast initiation to blockchain confirmation, as well as network-level parameters such as the percentage of online nodes and data transmission rate. This response data is fed into a timing optimization controller, which adapts to network conditions by adjusting the broadcast interval of data packets. This interval is dynamically adjusted using an exponential backoff algorithm, automatically extending the interval when network latency exceeds a threshold. A priority queue manager simultaneously optimizes the order of data packet distribution, assigning priority transmission channels to high-scoring data blocks. Bandwidth resources for these transmission channels are allocated proportionally based on priority weights.

[0071] Example 2: See Figure 3 , obtaining shared data input from all participants in the target supply chain area in real time. Shared data is transmitted through a standardized interface, which uses an encrypted communication protocol to ensure transport layer security. The data input contains three structured fields: the data sensitivity field uses a three-level classification coding system, corresponding to the public level marked as P1, the internal level marked as P2, and the confidential level marked as P3; the access permission field records the role-based permission matrix, stored as a binary permission code; the encryption level field identifies the type of security algorithm currently applied, such as the symmetric encryption identifier AES and the homomorphic encryption identifier HE. After format verification, the input data is stored in the distributed cache area.

[0072] The data processing model based on secure multi-party computation adopts a layered architecture. The input layer is equipped with a data preprocessing module, which performs normalization on shared data: converting textual permission codes into numeric vectors and mapping discrete sensitivity codes into continuous weight factors. The computation layer deploys a privacy-preserving computation protocol, primarily consisting of a secret sharing subprotocol and an obfuscated circuit subprotocol. The secret sharing subprotocol partitions the input data into random number shards, with the number of shards corresponding to the total number of participating nodes. The obfuscated circuit subprotocol converts business logic into a Boolean circuit representation, hiding the logic through circuit gate encryption. The output layer is equipped with a risk quantification module, which generates a privacy breach probability index.

[0073] Data dependencies are modeled using a dynamic dependency graph. Dependency graph nodes represent participating entities, and node attributes contain a snapshot of the current data state. Directed edges between nodes record data transmission paths, with edge weights calculated by combining interaction frequency and data volume. A full-graph traversal algorithm is executed every ten minutes to update node connectivity. A correlation matrix is ​​generated based on the interaction history between entities, and matrix cell values ​​are dynamically refreshed using the Pearson correlation algorithm. Sensitivity testing is performed to the impact of perturbations on shared data inputs: small perturbations and mutations are applied to input fields, with the magnitude of the variation controlled to less than one percent, and the fluctuation range of output layer indicators is observed.

[0074] Parallel computing tasks are executed through a distributed computing framework. The framework's master node receives the complete data processing model parameter configuration and partitions the computational graph into independent subtasks. The task scheduler dispatches task packages based on the availability of node resources. Each task package contains a shard of input data and a set of computational instructions. The multi-threaded execution environment configures an elastic thread pool, whose size automatically adjusts based on task complexity. The iterative solution process sets an initial privacy parameter baseline, executes the core computational logic, and outputs intermediate results. The parameter adjustment engine compares the difference between the parameters to be optimized and the actual output to generate parameter fine-tuning vectors. The iterative termination condition is monitored by a convergence determinant; the computation is terminated when the fluctuation in the results of three consecutive iterations is less than 0.5%.

[0075] High-density data environment simulations utilize a stress testing model. The node coupling simulator generates peak load scenarios and constructs a stress dataset by geometrically amplifying actual interaction data. The coupling effect analysis module runs a covariance calculation program to determine the statistical correlation between participant behavior patterns and data characteristics. The Monte Carlo component implements a random sampling experiment: a random number generator is established to simulate thousands of data exchange events, randomly selecting two participants in each event to perform hypothetical data interactions. The probability of privacy leakage is statistically calculated based on the simulation results, with probability values ​​rounded to two decimal places.

[0076] The privacy breach risk assessment process encompasses two dimensions: probability grading and type determination. Probability grading compares calculated results to pre-defined intervals: probabilities below a certain percentage are classified as low risk, while those above a certain percentage are classified as high risk. The type determination module cross-analyzes data features and breach paths to extract a core set of risk characteristics. The risk report generator outputs a structured document with sections containing risk level labels, descriptions of key breach scenarios, and a list of key influencing factors. Report data is then simultaneously pushed to the blockchain evidence audit interface.

[0077] Example 3: See Figure 4 The historical performance data set within the target supply chain area is continuously acquired through a distributed data collection system, which is deployed at the data warehouse interface layer of each node in the supply chain. The data set contains three core data types: time series transaction records, logistics trajectory coordinate series, and inventory change logs. The transaction record fields include transaction timestamp, participant identifier, commodity code, and transaction amount; the logistics trajectory data records the spatial location sampling points of the transportation tool, and each sampling point contains latitude and longitude coordinates and collection time; the inventory change log records the time, item code, and quantity changes of warehouse entry and exit events. The data storage adopts a sharded architecture, divides data partitions by time range, and configures an independent copy management strategy for each partition. The data retrieval service provides a time range query interface and supports millisecond-level time precision filtering.

[0078] The distributed feature learning system is implemented using a two-layer network architecture. The underlying feature extraction network consists of a group of autoencoders operating in parallel, with each autoencoder instance deployed on an independent compute node. The encoder portion of the autoencoder consists of a five-layer fully connected neural network, with the hidden layer activation function using rectified linear units. The input layer receives a normalized window of historical data with a configurable window length, which defaults to 24 time units. The encoding process compresses the input data into a low-dimensional latent space, with a compression ratio set to one-eighth of the input dimension. The decoder network is symmetrical to the encoder structure and uses backpropagation to minimize reconstruction error. The feature space mapping process performs batch normalization to eliminate dimensionality differences between different data sources.

[0079] The time series analysis module utilizes a multi-scale processing pipeline. The short-term analysis unit processes hourly data and employs a sliding window mechanism to extract local fluctuation patterns. The window sliding step is set to six time units, with a 50% overlap. The medium-term analysis unit operates on daily data and applies a seasonal trend decomposition algorithm to isolate cyclical components. The long-term analysis unit processes monthly data and fits macro-trend curves using a state-space model. A multi-scale feature fuser aggregates analysis results at different granularities hierarchically, with the aggregation weights dynamically calculated using an attention mechanism. Spectral analysis is applied to the feature information extraction process to identify the dominant frequency components in data fluctuations.

[0080] An extended state-space representation is used to model the spatiotemporal changes in historical data. The model state variables contain both temporal and spatial components. The temporal component is processed by a temporal convolutional network, while the spatial component is modeled using a graph neural network. The temporal convolutional network is configured with a dilated causal convolution kernel, with four convolution layers, and an exponentially increasing dilation factor for each layer. The node features of the graph neural network include the geographic location codes and historical interaction features of the participants, while the edge weights reflect the strength of spatial associations. The model parameter training process utilizes a staged optimization strategy, first training the temporal network with fixed spatial network parameters, and then jointly fine-tuning all parameters. The training data is partitioned using a chronological partitioning method, with the most recent three months of data retained as the validation set.

[0081] The process of evaluating the integrity of the current data using a spatiotemporal variation model performs Monte Carlo sampling. The sampler generates simulated interference data that conforms to historical statistical characteristics, with the interference intensity controlled within 10% of the historical fluctuation range. The injection test mixes the simulated data into the real data stream in proportion, with the mixing ratio gradually increasing from 5% to 20%. The integrity detector compares the difference in feature distribution between the original data and the interfered data, and the difference measurement uses the improved Wasserstein distance D w :

[0082]

[0083] Where: K represents the total number of feature dimensions, F k represents the original cumulative distribution function of the kth feature, G k Represents the cumulative distribution function of the interference data. The potential interference grader determines the interference level based on a distance threshold, which is calibrated using historical benchmarking. The output is a discrete label: negligible, slight, or significant.

[0084] Matrix decision analysis is used to jointly assess privacy breach risks and data interference levels. The rows of the decision matrix represent privacy risk levels, while the columns represent interference levels. Matrix cells store predefined optimization strategy codes. The evaluation engine queries the matrix to retrieve decision rules. When any dimension reaches a preset alert level, an optimization flag is triggered. This optimization trigger activates the evidence priority assessment process and simultaneously sends a resource pre-allocation request to the blockchain network. The system maintains a real-time status dashboard, visually displaying the risk indicators and optimization status of each participant. The dashboard data is updated synchronously with the underlying monitoring module to ensure the timeliness of decision-making.

[0085] Example 4: See Figure 5During the data storage optimization process within the target supply chain, the complexity of the data paths between participants must be assessed through detailed analysis based on actual interaction data. Consider an electronics supply chain consisting of six core participants: chip supplier (P1), screen manufacturer (P2), assembly plant (P3), logistics service provider (P4), wholesaler (P5), and retailer (P6). The interaction data between these participants over the past quarter is recorded in an interaction relationship table.

[0086] Table 1: Participant interaction data record table.

[0087] Participants Interaction distance (km) Average number of daily interactions Data volume difference rate (%) Difference in cooperation duration (months) P1-P2 150 28 12.5 6 P1-P3 80 45 8.2 3 P2-P3 220 32 15.7 9 P3-P4 50 68 5.3 2 P4-P5 180 25 18.9 12 P5-P6 30 55 3.1 1

[0088] The system first sets a safety distance threshold of 100 kilometers, and status discrepancy thresholds for data volume discrepancy of 10% and collaboration duration discrepancy of 6 months. Based on these thresholds, it automatically identifies pairs of participants requiring special attention. For example, while the interaction distance of 80 kilometers for the pair P1-P3 falls below the safety threshold, their data volume discrepancy of 8.2% and collaboration duration discrepancy of 3 months do not exceed the corresponding thresholds, thus marking them as potential conflict pairs. Similarly, pairs P3-P4 and P5-P6 are also flagged because their interaction distances fall below the thresholds.

[0089] During the path complexity assessment phase, the system calculates the ratio of the state difference to the interaction distance for each participant. For example, assembly plant P3 interacts with chip supplier P1, screen manufacturer P2, and logistics service provider P4. For the P3-P1 pair, the comprehensive state difference value is calculated by taking the weighted average of the data volume difference rate and the difference in cooperation duration, and then dividing it by the interaction distance of 80 kilometers to obtain the ratio. Similarly, the ratios for P3-P2 and P3-P4 are calculated. Finally, these three ratios are normalized and averaged to obtain the path complexity index for P3.

[0090] When determining the priority of evidence storage, the system assigns weight factors based on the roles of the participants in the supply chain. For example, assembly plant P3, as a core node, receives a weight of 1.2, while logistics service provider P4, as an auxiliary node, receives a weight of 0.9. After multiplying the path complexity index by the weight factor, the evidence storage priority scores of the six participants are as follows: P3 (0.87), P1 (0.72), P5 (0.68), P6 (0.65), P2 (0.61), and P4 (0.58). Based on this ranking, the data of assembly plant P3 will receive the highest priority for evidence storage processing.

[0091] When generating a data block, the system extracts key features for each participant and generates a data summary. For example, for P3, its summary includes core information such as the average amount of the last ten transactions, a list of key partners, and current inventory turnover. This summary information, along with a millisecond-accurate timestamp, is packaged into a data block and encrypted to form a fixed-length hash value. The hash algorithm employs a collision-resistant design, ensuring that data blocks from different participants, even if the content is similar, will generate completely different identifiers.

[0092] After receiving these data blocks, validating nodes in the blockchain network first check the validity of the hash values ​​and then use a consensus algorithm to confirm the order in which the data blocks are written. The network utilizes a practical Byzantine fault-tolerance mechanism, requiring more than two-thirds of validating nodes to agree on the order of data blocks before a write operation is executed. Once successfully written, the data block is permanently recorded in the distributed ledger, forming an immutable record.

[0093] Throughout the entire process, the system continuously monitors the efficiency of the proof-keeping operation. For high-priority participants, such as P3, the system dynamically adjusts the broadcast frequency of their data blocks, prioritizing their transmission bandwidth during network congestion. It also records the time delay from each data block's generation to its successful on-chain upload. This delay data is used for subsequent network optimization analysis. If a participant's proof-keeping delay is detected to exceed a preset warning value, the system automatically adjusts its data transmission path or retry strategy.

[0094] This analysis method, based on actual interaction data, accurately identifies key nodes and potential points of conflict within the supply chain network, enabling targeted optimization of the blockchain evidence storage process. This entire process does not rely on any pre-set performance metrics or optimization goals, but rather dynamically adjusts evidence storage strategies based entirely on the actual interaction characteristics of the participants. This data-driven approach ensures that the evidence storage optimization plan is highly aligned with the actual operation of the supply chain.

[0095] Example 5: After the data notarization priority determination process of the participants in the target supply chain area is started, the system accesses the path complexity index database, which stores the numerical evaluation results generated by the distributed computing nodes in real time. The participant weight factors are stored in an independent attribute configuration library, which maintains a coefficient mapping table for different role types. Core manufacturing participants are assigned higher coefficients, and auxiliary service participants are assigned lower coefficients. The system traverses all participant identifiers in the area and performs a multiplication operation on each identifier: extracts the current path complexity index floating-point value from the index database, and obtains the corresponding weight coefficient value from the attribute library. The two are used to generate the original priority score through a floating-point multiplication processor. The original score is linearly mapped to the range of zero to one hundred through a percentage converter, and two decimal places of precision are retained during the conversion process. The calculation results are written to the priority sorting buffer.

[0096] The participant priority sequence generation module runs a stable sorting algorithm. The sorting engine reads the percentile scores of all participants from a cache and constructs a maximum heap using a heap sort data structure. During the heap sort process, only the positional references of the participant identifiers are exchanged to avoid large data copying. The sorting results form a priority queue data structure, with the head of the queue always pointing to the participant with the current highest score. The queue manager maintains a dynamic index mapping table to ensure incremental updates to the queue when new participants join or their status changes. The data storage scheduler extracts participant identifiers sequentially, starting from the head of the queue, and automatically advances the queue pointer to the next element after each extraction.

[0097] The process of generating a data block for evidence storage involves two parallel processes: timestamp embedding and data summary generation. The timestamp generator invokes the Precision Time Protocol service to obtain millisecond-level timing signals in Coordinated Universal Time. The timestamp value is formatted as a string structure that complies with the ISO8601 standard and includes a time zone offset identifier. Simultaneously, the data summary engine is activated, extracting status information within a specific time window from the fulfillment data warehouse based on the identifier of the currently processed party. The summary algorithm selects three core indicators: average transaction amount, number of active partners, and inventory turnover rate, and compresses them into a binary sequence of fixed byte length using a fixed-length encoding scheme. The timestamp string and summary byte stream are encapsulated into a structured data container.

[0098] The hash cryptographic processor utilizes a multi-stage mechanism. The preprocessor serializes the structured container into a byte array, padded to the standard block length. The initial hash value is generated as a cryptographically secure random number via the hardware security module. The encryption core executes the SHA-3512 algorithm, performing 64 rounds of permutation operations on the data block. Each round involves five nonlinear transformations: θ, ρ, π, χ, and ι, with the χ function introducing obfuscation properties. The final output hash value is a 512-bit binary sequence, encoded as a 128-character hexadecimal string that serves as a unique identifier. The encryption process is performed entirely within a trusted execution environment, and memory data is automatically erased after the operation.

[0099] The blockchain network access layer establishes a persistent connection pool. The broadcast manager retrieves a list of validating nodes from the connection pool and transmits data packets via a publish-subscribe model. The packet format adheres to a standardized transaction structure: a 56-byte identifier header, a variable-length payload, and a 64-byte digital signature. Signature generation uses the participant's registered elliptic curve private key and performs an ECDSA signature calculation based on the secp256k1 curve parameters. Network transmission utilizes a message queue middleware to ensure reliability, with a time-to-live threshold set for each message.

[0100] The consensus verification process is performed asynchronously within the validating node group. Node receivers synchronously receive broadcast packets and perform format compliance checks. The signature verification engine verifies the validity of the signature using the sender's public certificate. The state consistency verifier compares the latest state hashes of local ledger copies. Inter-node coordination utilizes a two-phase broadcast protocol: a proposal phase to gather initial votes and a commit phase for final confirmation. Verified data blocks are assigned incremental block heights and integrated into the global state trie via a Merkle tree builder. The ledger update module appends the new block to the chain structure and updates the world state database.

[0101] The evidence storage response monitoring system deploys distributed tracing probes. The timestamp collector sets five collection points along the critical path: the start time of data block generation, encryption completion time, broadcast transmission time, first response reception time, and on-chain confirmation time. A latency calculator automatically generates reports on the time difference between each stage. The network status sensor periodically collects twelve performance indicators of the verification node, including CPU utilization, memory usage, and network input and output throughput. Monitoring data is written to the time series analysis database in real time.

[0102] The broadcast timing optimization controller implements a closed-loop feedback mechanism. The delay analyzer calculates the average end-to-end delay of data blocks of different priorities and establishes a delay change trend model. The parameter regulator dynamically adjusts the base value of the broadcast interval based on the current network performance indicators: shortening the interval during network idle periods and extending the interval during congestion periods. The priority weighter assigns a transmission acceleration coefficient to high-priority data blocks, which is exponentially related to the priority score. The concurrency controller limits the broadcast concurrency per unit time and triggers traffic shaping when the bandwidth utilization threshold is exceeded. The retry policy manager implements a stepped backoff algorithm to set differentiated retry limits for data packets that fail to transmit based on their priority. All control parameters are visually displayed through the console interface, allowing administrators to manually adjust baseline parameters.

[0103] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0104] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A supply chain performance data blockchain evidence storage method based on multi-party secure computing, characterized by: The steps include: Real-time monitoring of the performance data status and relative interaction relationships of each participant in the supply chain to determine whether the data density in the target supply chain area exceeds the safety threshold; When the data density in the target supply chain area exceeds the security threshold, a multi-party secure computing protocol is constructed to analyze the interactive impact of each participant's data and assess the privacy leakage risks caused by data sharing in the target supply chain area; By combining distributed feature learning with time series analysis to process the spatiotemporal variations of historical fulfillment data within the target supply chain region, the potential interference of historical data fluctuations on the integrity of current fulfillment data within the target supply chain region is assessed. Based on the privacy leakage risks caused by data sharing in the target supply chain area and the potential interference of historical data fluctuations on the integrity of current fulfillment data in the target supply chain area, it is determined whether to optimize the overall blockchain evidence storage in the target supply chain area.

2. The supply chain performance data blockchain evidence storage method based on multi-party secure computing according to claim 1 is characterized in that: Also includes: When it is necessary to optimize the overall blockchain evidence storage in the target supply chain area, analyze the performance data status of each participant in the target supply chain area with other participants and evaluate the complexity of the data path of each participant; Determine the data storage priority of each participant based on the complexity of the data path of each participant; Generate a data block of evidence and submit it to the blockchain network for tamper-proof evidence storage; By real-time monitoring of evidence response time and blockchain network status, the broadcast timing of evidence data blocks can be optimized.

3. The supply chain performance data blockchain evidence storage method based on multi-party secure computing according to claim 1 is characterized in that: Real-time monitoring of the performance data status and relative interaction relationships of each participant in the supply chain to determine whether the data density in the target supply chain area exceeds the safety threshold, specifically: Real-time collection of performance data status information of all participants, including location information, transaction frequency, cooperation duration and data volume; Determine whether the participant is located in the target supply chain area based on the fulfillment data status information: Compare the number of participants in the target supply chain area with the logical capacity of the target supply chain area to determine the data density in the target supply chain area; Based on the comparison between the data density in the target supply chain area and its corresponding security threshold, the security risk level of data interaction in the target supply chain area is judged.

4. The supply chain performance data blockchain evidence storage method based on multi-party secure computing according to claim 3 is characterized in that: By building a multi-party secure computing protocol to analyze the interactive impact of each participant's data, we can assess the potential privacy leaks caused by data sharing within the target supply chain area. Specifically: Real-time access to shared data input from all parties involved in the target supply chain region, including data sensitivity, access rights, and encryption levels; Based on the performance data status information and shared data input of each participant, a data processing model based on secure multi-party computing is constructed to describe the privacy protection mechanism during the data interaction process; Using the constructed data processing model, the data dependencies between participants and the disturbance effects between shared data inputs are calculated; Parallel computing technology is used to iteratively solve the data processing model, simulate the coupling effect between participants and shared data in a high-density data interaction environment, and evaluate the privacy leakage risks caused by data sharing in the target supply chain area; the privacy leakage risks caused by data sharing in the target supply chain area include low risk and high risk.

5. The supply chain performance data blockchain evidence storage method based on multi-party secure computing according to claim 4 is characterized in that: By combining distributed feature learning with time series analysis to process the spatiotemporal variations of historical fulfillment data within the target supply chain region, we evaluate the potential interference of historical data fluctuations on the integrity of current fulfillment data within the target supply chain region. Specifically: Real-time access to historical fulfillment data sets within the target supply chain area; Using distributed feature learning methods, we map historical performance data sets into feature space and reconstruct the data's dynamic characteristics and change patterns. Using time series analysis methods, the historical performance data set in the feature space is processed at different time scales to extract characteristic information of historical data fluctuations; Based on the characteristic information of historical data fluctuations, a model describing the spatiotemporal changes of historical data is established to reflect the data's dependence on timestamps and geographic locations; The established historical data spatiotemporal change model is used to evaluate the potential interference of historical data fluctuations on the integrity of current fulfillment data in the target supply chain area; the potential interference of historical data fluctuations on the integrity of current fulfillment data in the target supply chain area ranges from significant to negligible.

6. The supply chain performance data blockchain evidence storage method based on multi-party secure computing according to claim 5 is characterized in that: Based on the potential privacy leakage caused by data sharing in the target supply chain area and the potential interference of historical data fluctuations on the integrity of current fulfillment data in the target supply chain area, it is determined whether to optimize the overall blockchain evidence storage in the target supply chain area. Specifically: When the privacy leakage risk caused by data sharing in the target supply chain area is low, and the potential interference of historical data fluctuations on the integrity of current fulfillment data in the target supply chain area is negligible, it is determined that the overall blockchain evidence optimization of the target supply chain area will not be carried out; Otherwise, it is determined that the overall blockchain evidence optimization of the target supply chain area will be carried out, and the evidence priority evaluation mechanism will be triggered.

7. The supply chain performance data blockchain evidence storage method based on multi-party secure computing according to claim 2 is characterized in that: When it is necessary to optimize the overall blockchain evidence storage in the target supply chain area, the performance data status of each participant in the target supply chain area and other participants is analyzed, and the complexity of the data path of each participant is evaluated, specifically: Calculate the data interaction distance and status difference between each participant and other participants based on the performance data status information; By analyzing the data interaction distance and status differences between participants, possible data conflicts and interference scenarios are identified: for two participants whose data interaction distance is less than a preset safety threshold, all pairs of participants whose status differences are less than the preset status difference are marked; Evaluate the data path complexity of each participant based on data conflict and interference scenarios.

8. The supply chain performance data blockchain evidence storage method based on multi-party secure computing according to claim 7 is characterized in that: Based on data conflict and interference scenarios, the data path complexity of each participant is evaluated, specifically: Calculate the path complexity index of each participant, which is obtained by aggregating the normalized ratio of the state difference and data interaction distance of all relevant pairs of participants; The higher the path complexity index, the more complex the data path of the participant is, and evidence storage optimization needs to be prioritized.

9. The supply chain performance data blockchain evidence storage method based on multi-party secure computing according to claim 2 is characterized in that: The data storage priority of each participant is determined based on the complexity of the data path of each participant, specifically: Calculate the evidence priority score, which is the product of the path complexity index and the participant weight factor; The larger the evidence priority score, the higher the data evidence priority of the participant. The participants are sorted from high to low based on the evidence priority score, and the evidence data of the participants with higher rankings are processed first.

10. The supply chain performance data blockchain evidence storage method based on multi-party secure computing according to claim 2 is characterized in that: Generate a data block of evidence and submit it to the blockchain network for tamper-proof evidence storage. Specifically: Generate a proof data block containing a timestamp and data summary based on the data of the participants with high proof priority scores; Use hash functions to encrypt the evidence data block and generate a unique data identifier; The encrypted evidence data block is broadcast to the blockchain network nodes, verified through the consensus mechanism and written into the blockchain.

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