A supply chain risk real-time monitoring system and method

Abstract

The present application relates to the technical field of distributed account book and data processing, and discloses a supply chain risk real-time monitoring system and method, which comprises a consensus account book data module, a parameter configuration storage module, a digital asset locking module and a contract operation processing module; the absolute difference value between the block timestamp of a target account book node and the hierarchical supply data reporting timestamp is read by the contract operation processing module, and then divided by the calibration cycle length in the parameter configuration storage module to obtain the account book time sequence bias rate; the account book time sequence bias rate is compared with the discrete control threshold value, and according to the comparison, the clearing channel is released, the credit allocation weight parameter is adjusted, or the digital asset locking module is triggered to deduct the performance guarantee margin asset, the delay behavior of the target account book node is translated into an internal time sequence deflection parameter, the dependence on off-chain data is broken, a clearing closed loop is constructed, and risk adaptive feedforward hedging is realized.

Description

Technical Field

[0001] This invention belongs to the field of distributed ledger and data processing technology, and in particular relates to a real-time supply chain risk monitoring system and method. Background Technology

[0002] Currently, using consensus networks to build self-verifying distributed state machine logic to maintain collaborative trust among multiple entities is a conventional approach. Its basic operating mechanism heavily relies on the triggering of input data packets. In other words, consensus nodes can only activate state transition operators when they receive transaction data with compliant digital signatures broadcast in the network, thereby updating the global state tree according to preset control rules to complete asset liquidation or equity transfer.

[0003] However, when this response-triggered state machine control architecture is applied to a real-time supply chain risk monitoring system, the conflicting interests among the parties within the collaborative network cause a break in the logical causality within the transaction state machine. Because independent stakeholders have a strong incentive to conceal negative situations such as supply delays or production disruptions, they often adopt a strategic inaction strategy, deliberately delaying the submission of state transition transactions. This causes the processing engine, which relies on external data input, to fall into an indefinite suspension state due to a lack of trigger signals. Consequently, the entire collaborative network loses its ability to perceive risks during critical risk diffusion windows. Information collection at the IoT hardware level has blind spots, and the upper-level control methods and data processing logic also have shortcomings. For example, Chinese invention patent CN110019534A discloses a distributed ledger technology for freight systems. This Chinese invention patent application receives geographical location information created by tracking devices, calls smart contracts to calculate tracking metrics to complete synchronization. At its underlying mechanism, this technology relies on continuous and proactive reporting of external hardware data. In the actual non-ideal working conditions of multi-party game in the supply chain, when nodes take strategic silence or deliberately stop reporting, causing the data flow outside the chain to be cut off, the processing engine lacks a trigger signal and falls into an indefinite suspension state. It is unable to make rigid punishment and topology diversion for passive concealment behavior. Existing technologies cannot achieve deterministic closed-loop convergence of the state tree by relying solely on the change law of the ledger's native consensus temporal metadata.

[0004] Therefore, the technical problem to be solved by this invention is how to get rid of the constraints of plaintext for specific off-chain business loads and build state determination and topology diversion control logic with rigid constraints based solely on the time-series metadata change rules maintained by the native consensus of the ledger, so that the internal transaction processing flow can still achieve deterministic strong causal convergence when the participants stop reporting the state. Summary of the Invention

[0005] This invention aims to solve the problem that when participants strategically and passively conceal information, the transaction processing flow is suspended indefinitely, and the lack of native timing control rules leads to a floating ledger state and an inability to achieve deterministic closed-loop convergence.

[0006] In this technical solution, a real-time supply chain risk monitoring system includes: The consensus ledger data module stores distributed block data and tiered block data supply. The parameter configuration storage module stores credit allocation weight parameters, calibration period duration, and discrete control thresholds. The contract execution processing module is connected to the consensus ledger data module, the parameter configuration storage module, and the digital asset locking module, respectively, and is used to retrieve distributed block data and process it through the following steps: Step S101: Read the timestamp of the block currently generated by the target ledger node and the corresponding timestamp of the hierarchical supply data reporting, calculate the absolute difference between the timestamp of the block and the timestamp of the hierarchical supply data reporting, and divide it by the calibration period in the parameter configuration storage module to obtain the ledger time series skew rate. Step S102: The ledger time-series skew rate is compared with the discrete control threshold in a concatenation manner; when the ledger time-series skew rate is not greater than 1.0, the normal clearing logic path is allowed; when the ledger time-series skew rate is greater than 1.0 but not greater than 2.5, the credit allocation weight parameter of the corresponding target ledger node in the parameter configuration storage module is automatically reduced in situ; when the ledger time-series skew rate is greater than 2.5, the normal clearing logic path is blocked and the control flow is deflected to the restricted clearing and settlement path, triggering the digital asset locking module to deduct the performance bond assets of the target ledger node.

[0007] Preferably, the system also includes a reserve fund dynamic adjustment module, which is connected to the contract execution processing module. The reserve fund dynamic adjustment module is used to monitor the rate of change of the ledger time series skew rate on the time axis. When the rate of change exceeds the rate of change threshold for three consecutive handover periods, the corresponding additional collateral amount is calculated, and the reserve fund additional data packet is routed to the target ledger node. When the asset balance of the target ledger node within the restriction period does not reach the additional collateral amount, the contract execution processing module lowers the priority weight parameter of the target ledger node in the subsequent order allocation decision tree.

[0008] Preferably, the system also includes a zero-knowledge state verification module deployed within the consensus ledger data module; the zero-knowledge state verification module is used to retrieve local private data from the upstream ledger node in the target ledger node, and generate Boolean decision variables representing the asset carrying capacity in a private isolation environment, and use a zero-knowledge proof algorithm to generate cryptographic knowledge proof text corresponding to the Boolean decision variables; the consensus ledger data module is equipped with a main ledger verification contract, which is used to receive and verify the cryptographic knowledge proof text as the transition transaction vector, and complete the verification of the supply status while keeping the plaintext business details data of the upstream ledger node from being disclosed.

[0009] Preferably, the system also includes a backup state arbitration module, which is connected to the contract execution processing module and is used to receive the latency difference generated by the global ledger nodes during the consensus phase; when the latency difference crosses the safety latency threshold for three consecutive blocks, it is determined that a consensus lag anomaly has occurred in the entire network, and the backup state arbitration module outputs a dispatch instruction to the contract execution processing module to stabilize the global consensus rhythm within the set expansion delay range, so as to ensure the dynamic tracking continuity of the ledger timing skew rate.

[0010] Preferably, the digital asset locking module includes a margin configuration locking unit and a differential settlement and diversion module; the margin configuration locking unit is used to lock the performance margin assets submitted by the target ledger node in the initial admission phase; the differential settlement and diversion module receives a strong collaborative transfer message sent by the contract execution processing module when the ledger timing skew rate is greater than 2.5, and triggers the margin configuration locking unit to transfer the performance margin assets to the restricted settlement and diversion path.

[0011] Preferably, the system also includes an order dispatch network topology control module; the order dispatch network topology control module is connected to the reserve fund dynamic adjustment module, and the order dispatch network topology control module is used to retrieve the reduced priority weight parameters and reconstruct the graph node routing topology matrix of the supply chain collaboration tree, so that subsequent transaction packets can autonomously bypass the target ledger nodes marked with risk status to complete the routing reconfiguration feedforward control.

[0012] Preferably, the parameter configuration storage module includes a control threshold storage unit and a node state mapping and maintenance unit; the control threshold storage unit maintains discrete control thresholds for different performance risk levels in the long term; the node state mapping and maintenance unit has an independent storage unit corresponding to each target ledger node to store and refresh the credit allocation weight parameters in situ.

[0013] Preferably, the contract execution processing module further refines step S101 into the following sub-steps: Step S1011, each supply chain participant uses a private independent data domain to encrypt and sign the tiered supply data, and broadcasts and uploads the encrypted and signed tiered supply data to the distributed global public ledger in the consensus ledger data module; Step S1012, the contract execution processing module retrieves blocks in the distributed global public ledger in real time and reads the corresponding block timestamp and the tiered supply data reporting timestamp, and obtains the ledger time series skew rate by combining the calibration period duration in the parameter configuration storage module.

[0014] Preferably, the backup state arbitration module includes a latency counting logic unit and a diversion and conversion module; the latency counting logic unit is used to accumulate the actual latency change value of the distributed ledger nodes reaching block consensus; when the actual latency change value crosses the safety latency threshold for 3 consecutive blocks, the diversion and conversion module closes the current regular consensus path and guides the flow of transaction data to the redundant backup chain path.

[0015] A method for real-time monitoring of supply chain risks, used to operate a real-time supply chain risk monitoring system, includes the following steps: Step S1: Use the consensus ledger data module to retrieve distributed block data and tiered supply block data. The contract execution processing module reads the timestamp of the block currently generated by the target ledger node and the corresponding timestamp of the tiered supply data reporting. Calculate the absolute difference parameter between the block timestamp and the tiered supply data reporting timestamp. Divide the absolute difference parameter by the calibration period in the parameter configuration storage module to map and obtain the ledger time series skew rate. Step S2: The contract execution processing module compares the ledger time-series skew rate with the discrete control threshold in the parameter configuration storage module. When the ledger time-series skew rate is not greater than 1.0, the normal clearing logic path is allowed. When the ledger time-series skew rate is greater than 1.0 but not greater than 2.5, the credit allocation weight parameter of the corresponding target ledger node in the parameter configuration storage module is automatically reduced in place. When the ledger time-series skew rate is greater than 2.5, the normal clearing logic path is blocked and the control flow is deflected to the restricted clearing and settlement path, and the digital asset locking module deducts the performance guarantee assets of the target ledger node.

[0016] Compared with existing technologies, the real-time supply chain risk monitoring system of the present invention has the following advantages: 1. In real-time monitoring of supply chain risks, the data decoupling layer only collects the consensus timestamps of each handover node for asset confirmation. The transaction processing engine extracts the consensus timestamp of the latest block and the timestamp of the block where the target ledger node last submitted a legitimate transaction. By calculating the ratio of the difference to the preset standard circulation cycle, a ledger time series deviation rate is generated. This deviation value is independent of the node's active reporting of plaintext business data, transforming passive delays or selective concealment into deterministic deviation parameters on the consensus ledger time series. The system uses this deviation value to match the deviation penalty operator with the discrete control threshold in the state configuration register to realize the distributed state machine decision path diversion.

[0017] 2. When the transaction processing engine determines that the ledger timing skew exceeds the safety threshold of 2.5, the system forcibly terminates the regular flow of transactions, and the control flow is directly switched to the adversarial settlement branch. The system automatically freezes the performance bond assets held in the asset locking module of the target ledger node. The control layer dynamically responds to the change in the frozen status of the performance bond assets and modifies the network-wide shared material flow topology routing matrix in the next consensus cycle. The routing index of high-risk supply nodes is erased. This closed-loop adjustment enables subsequent transaction packets to adaptively bypass risk blocking areas, and achieves risk feedforward hedging of the network topology with hard logic based on ledger consensus facts.

[0018] 3. The reserve fund dynamic adjustment unit monitors the rate of change of transient risk assessment value on the time axis. When the rate of change exceeds the preset risk slope threshold for three consecutive handover cycles, it automatically calculates the additional pledge fund required by the supplier node and issues an additional pledge fund deduction instruction to the current node. If the target supplier node fails to replenish the additional pledge fund within the preset period, the system triggers the business flow to reshape the contract and automatically deducts the priority weight of the node in the subsequent order allocation network. This control architecture establishes a strong causal coupling between the scale of digital assets on the node chain and the access permissions of the external collaboration network, realizing adaptive feedforward in-situ repair under abnormal and variable physical handover conditions. Attached Figure Description

[0019] Figure 1 This is a flowchart of the liquidation control process for risk assessment based on ledger time-series skewness in this invention. Figure 2 This is a structural diagram of the module composition of the real-time supply chain risk monitoring system of the present invention. Detailed Implementation

[0020] 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.

[0021] A real-time supply chain risk monitoring system includes: The consensus ledger data module stores distributed block data and tiered block data supply. The parameter configuration storage module stores credit allocation weight parameters, calibration period duration, and discrete control thresholds. The contract execution processing module is connected to the consensus ledger data module, the parameter configuration storage module, and the digital asset locking module, respectively, and is used to retrieve distributed block data and process it through the following steps: Step S101: Read the timestamp of the block currently generated by the target ledger node and the corresponding timestamp of the hierarchical supply data reporting, calculate the absolute difference between the timestamp of the block and the timestamp of the hierarchical supply data reporting, and divide it by the calibration period in the parameter configuration storage module to obtain the ledger time series skew rate. Step S102: The ledger time-series skew rate is compared with the discrete control threshold in a concatenation manner; when the ledger time-series skew rate is not greater than 1.0, the normal clearing logic path is allowed; when the ledger time-series skew rate is greater than 1.0 but not greater than 2.5, the credit allocation weight parameter of the corresponding target ledger node in the parameter configuration storage module is automatically reduced in situ; when the ledger time-series skew rate is greater than 2.5, the normal clearing logic path is blocked and the control flow is deflected to the restricted clearing and settlement path, triggering the digital asset locking module to deduct the performance bond assets of the target ledger node.

[0022] Preferably, the system also includes a reserve fund dynamic adjustment module, which is connected to the contract execution processing module. The reserve fund dynamic adjustment module is used to monitor the rate of change of the ledger time series skew rate on the time axis. When the rate of change exceeds the rate of change threshold for three consecutive handover periods, the corresponding additional collateral amount is calculated, and the reserve fund additional data packet is routed to the target ledger node. When the asset balance of the target ledger node within the restriction period does not reach the additional collateral amount, the contract execution processing module lowers the priority weight parameter of the target ledger node in the subsequent order allocation decision tree.

[0023] Preferably, the system also includes a zero-knowledge state verification module deployed within the consensus ledger data module; the zero-knowledge state verification module is used to retrieve local private data from the upstream ledger node in the target ledger node, and generate Boolean decision variables representing the asset carrying capacity in a private isolation environment, and use a zero-knowledge proof algorithm to generate cryptographic knowledge proof text corresponding to the Boolean decision variables; the consensus ledger data module is equipped with a main ledger verification contract, which is used to receive and verify the cryptographic knowledge proof text as the transition transaction vector, and complete the verification of the supply status while keeping the plaintext business details data of the upstream ledger node from being disclosed.

[0024] Preferably, the system also includes a backup state arbitration module, which is connected to the contract execution processing module and is used to receive the latency difference generated by the global ledger nodes during the consensus phase; when the latency difference crosses the safety latency threshold for three consecutive blocks, it is determined that a consensus lag anomaly has occurred in the entire network, and the backup state arbitration module outputs a dispatch instruction to the contract execution processing module to stabilize the global consensus rhythm within the set expansion delay range, so as to ensure the dynamic tracking continuity of the ledger timing skew rate.

[0025] Preferably, the digital asset locking module includes a margin configuration locking unit and a differential settlement and diversion module; the margin configuration locking unit is used to lock the performance margin assets submitted by the target ledger node in the initial admission phase; the differential settlement and diversion module receives a strong collaborative transfer message sent by the contract execution processing module when the ledger timing skew rate is greater than 2.5, and triggers the margin configuration locking unit to transfer the performance margin assets to the restricted settlement and diversion path.

[0026] Preferably, the system also includes an order dispatch network topology control module; the order dispatch network topology control module is connected to the reserve fund dynamic adjustment module, and the order dispatch network topology control module is used to retrieve the reduced priority weight parameters and reconstruct the graph node routing topology matrix of the supply chain collaboration tree, so that subsequent transaction packets can autonomously bypass the target ledger nodes marked with risk status to complete the routing reconfiguration feedforward control.

[0027] Preferably, the parameter configuration storage module includes a control threshold storage unit and a node state mapping and maintenance unit; the control threshold storage unit maintains discrete control thresholds for different performance risk levels in the long term; the node state mapping and maintenance unit has an independent storage unit corresponding to each target ledger node to store and refresh the credit allocation weight parameters in situ.

[0028] Preferably, the contract execution processing module further refines step S101 into the following sub-steps: Step S1011, each supply chain participant uses a private independent data domain to encrypt and sign the tiered supply data, and broadcasts and uploads the encrypted and signed tiered supply data to the distributed global public ledger in the consensus ledger data module; Step S1012, the contract execution processing module retrieves blocks in the distributed global public ledger in real time and reads the corresponding block timestamp and the tiered supply data reporting timestamp, and obtains the ledger time series skew rate by combining the calibration period duration in the parameter configuration storage module.

[0029] Preferably, the backup state arbitration module includes a latency counting logic unit and a diversion and conversion module; the latency counting logic unit is used to accumulate the actual latency change value of the distributed ledger nodes reaching block consensus; when the actual latency change value crosses the safety latency threshold for 3 consecutive blocks, the diversion and conversion module closes the current regular consensus path and guides the flow of transaction data to the redundant backup chain path.

[0030] A method for real-time monitoring of supply chain risks, used to operate a real-time supply chain risk monitoring system, includes the following steps: Step S1: Use the consensus ledger data module to retrieve distributed block data and tiered supply block data. The contract execution processing module reads the timestamp of the block currently generated by the target ledger node and the corresponding timestamp of the tiered supply data reporting. Calculate the absolute difference parameter between the block timestamp and the tiered supply data reporting timestamp. Divide the absolute difference parameter by the calibration period in the parameter configuration storage module to map and obtain the ledger time series skew rate. Step S2: The contract execution processing module compares the ledger time-series skew rate with the discrete control threshold in the parameter configuration storage module. When the ledger time-series skew rate is not greater than 1.0, the normal clearing logic path is allowed. When the ledger time-series skew rate is greater than 1.0 but not greater than 2.5, the credit allocation weight parameter of the corresponding target ledger node in the parameter configuration storage module is automatically reduced in place. When the ledger time-series skew rate is greater than 2.5, the normal clearing logic path is blocked and the control flow is deflected to the restricted clearing and settlement path, and the digital asset locking module deducts the performance guarantee assets of the target ledger node.

[0031] Example 1: The system is deployed in a distributed business collaboration network containing multiple consensus ledger nodes. When the target ledger node performs a physical node handover for material flow, the data decoupling layer collects the encrypted signature transaction packet of the target ledger node and stores it as hierarchical supply block data in the consensus ledger data module. In order to complete the verification of the supply status without disclosing the plaintext business details of the upstream nodes, the zero-knowledge state verification module deployed in the consensus ledger data module adopts a non-interactive zero-knowledge trusted argumentation protocol system. Specifically, the local private independent data domain of the upstream ledger node extracts the difference between its total inventory and the quantity of orders to be delivered in the isolated operating environment. When the value is greater than or equal to 0, the local compiler autonomously generates a Boolean decision variable representing the asset carrying capacity and assigns it the value of true. The derivation process of this Boolean decision variable is then polynomial-constrained using elliptic curve cryptography circuits to generate a 256-byte hexadecimal cryptographic knowledge proof text. The main ledger verification contract deployed within the consensus ledger data module performs bilinear mapping verification on this knowledge proof text using a fixed pairing function. If the output matches, the compliance of the upstream supply carrying capacity is confirmed without exposing the plaintext warehouse inventory and specific business transactions. The contract execution processing module reads the timestamp of the currently generated block of the target ledger node in real time. Compared to the timestamp of the block where the last legitimate transaction was submitted And retrieve the preset calibration cycle duration from the parameter configuration storage module. Through formula The time-series skewness of the ledger was calculated. In this calculation process, the tiered supply data reporting timestamp is equivalently mapped and visualized in the lightweight state machine at the system's underlying layer as the block timestamp of the last legitimate transaction submitted by the target ledger node. When participants in the supply chain normally conduct physical material handover and broadcast tiered supply transaction packages with compliant digital signatures in real time, these transaction packages are packaged into the current distributed block by the consensus ledger data module. At this time, the block timestamp read by the system serves as the latest business confirmation boundary, while the corresponding tiered supply data reporting timestamp is traced back by the system to the time of the hash signature transaction block that the node last successfully wrote to the distributed public ledger and reached consensus on the timeline. The absolute difference between the two represents the accumulated time difference between the two handover transactions for that specific node, thereby converting business latency into the ledger's timing skewness. The contract execution processing module will... The discrete control thresholds are cascaded and compared with those fixed in the parameter configuration storage module.

[0032] when At this time, the system maintains the normal clearing logic path; when At that time, the contract execution processing module automatically extracts the performance credit parameter of the target ledger node. And according to the preset step size, a debit operation is performed, reducing the node's credit rating from the initial 1.0 to 0.7; when At this time, the contract execution processing module deflects the system control flow to a restrictive clearing and settlement path. The digital asset locking module recognizes this mandatory instruction and transfers all performance bond assets held in escrow by the target ledger node during the initial admission phase in real time. The dynamic response execution layer automatically reconstructs the topology routing matrix of the entire network material flow in the next consensus cycle based on the frozen status of the bond assets, erasing the routing index label of the target ledger node. This ensures that subsequent material flow transaction packets can adaptively bypass this high-risk congestion area, achieving proactive feedforward hedging of supply chain network interruption risks. At this point, the order dispatch network topology control... The control module retrieves the reduced priority weight parameters and reconstructs the graph node routing topology matrix of the supply chain collaboration tree. This topology matrix is ​​constructed using the connectivity status and routing costs between all peer collaborative nodes in the network as row and column elements. When the priority weight parameter of a node drops to the restricted blocking range, this module executes the improved Dijkstra's shortest path algorithm, sets the weights of all connected edges pointing to the target ledger node in the routing matrix to infinity, and recalculates the optimal transmission path between any two nodes in the network using the graph topology update operator. The contract execution processing module then assigns the corresponding target ledger node's credit score. After the weighting parameter is reduced in place or the digital asset locking module is triggered to transfer the performance bond assets, the order distribution network topology control module initiates a hybrid topology reconstruction mechanism that combines off-chain computation with on-chain evidence storage. This is to avoid the problem of excessive computing resources caused by the full network traversal of smart contracts. The distributed mainnet nodes call the graph topology update operator to read the adjacency matrix containing the connectivity status and routing costs between peer collaborative nodes across the entire network from off-chain memory. Based on the currently refreshed credit allocation weighting parameter, the Dijkstra shortest path improvement algorithm is run to weight all connected edges pointing to the target ledger node within the restricted blocking interval. The reassigned value is the maximum value. The optimal transmission path between any two nodes in the entire network is recalculated and the graph node routing topology matrix of the supply chain collaboration tree is reconstructed. The reconstructed graph node routing topology matrix is ​​synchronously written into the distributed network public main ledger in the form of root hash state vouchers. This enables subsequent material flow transaction packets to autonomously bypass target ledger nodes marked with risk status through matrix multiplication when addressing. The reconstructed topology matrix is ​​synchronously written into the main ledger, enabling subsequent generated material flow transaction packets to autonomously bypass nodes marked with risk status through matrix multiplication when addressing, thus completing the route reconfiguration feedforward control.

[0033] Example 2: This experiment verifies the system's ability to handle settlement loop and adaptive routing hedging when dealing with timing fluctuations at supply nodes by constructing a distributed supply chain simulation platform. The experimental platform integrates 5 accounting nodes, with a calibration period of [duration missing]. The test was set to 600 seconds to simulate a material flow and handover scenario. By injecting a controllable consensus response lag into the consensus ledger data module, the logical decision-making closed loop of the contract execution processing module was tested. In the control group, the ledger timing skewness was adjusted. Set to 0.5, the contract execution processing module reads the target ledger node's... and From the formula Calculated The value did not reach the credit penalty threshold, the system maintained the normal clearing channel, and the material handover status remained stable. In test group one, the injection lag was increased, and the settings were adjusted. The contract execution processing module determines that the node is in a hidden delay risk zone, triggers the credit reshaping logic, and extracts the performance credit parameters. The system then performs a cascading write-down, lowering the credit rating from 1.0 to 0.7. The system immediately sends a downgrade instruction to the dynamic response execution layer. In the second set of experiments, an injected consensus response lag is added and set... The contract execution processing module determines that the node meets the restrictive liquidation conditions, locks all performance bond assets held in the digital asset locking module in real time, and triggers the routing matrix reconstruction instruction. The test record is as follows.

[0034] Table 1: Node Risk Status and Liquidation Action Records During the experiment, the system monitored the logical response actions of each sample group. In sample group two, the data record showed that the time interval from receiving the consensus delay transaction to triggering the margin deduction action was 12.5ms, which was within the preset system processing damping boundary. When the threshold of 2.5 is exceeded, the entire network routing matrix automatically erases the index labels of risky nodes within two consensus cycles, indicating that the system has achieved feedforward hedging against the risk of interruption through logical deduction based on ledger facts. When the value falls below 1.0, the system stops credit write-downs and asset transfers, indicating that the risk criterion has a discrete trigger logic. Data confirms that the logical closed loop based on the ledger time-series skew can accurately identify node delay behavior. The adaptive hedging actions executed by the system accordingly effectively avoid execution deadlocks caused by network fluctuations while ensuring the integrity of supply chain clearing.

[0035] Example 3: In a real-time supply chain risk monitoring system deployed in a distributed business collaboration network, when a target ledger node performs a physical node handover for material flow, the data decoupling layer collects the encrypted signature transaction packet of the target ledger node and stores it as hierarchical supply block data in the consensus ledger data module. The contract execution processing module reads the timestamp of the block currently generated by the target ledger node in real time. Compared to the timestamp of the block where the last legitimate transaction was submitted And retrieve the preset calibration cycle duration from the parameter configuration storage module. The time-series skewness of the ledger was calculated. The contract execution processing module will The system is compared with the discrete control thresholds fixed in the parameter configuration storage module. In a real industrial operating environment, due to network transmission fluctuations, the system configures redundant calibration logic. That is, if three consecutive consensus cycles... Fluctuation variance The system determines that the network link is stable and executes the settlement logic; if... The system automatically triggers a data weighting and smoothing procedure to eliminate the impact of instantaneous disturbances. At this time, the system maintains the normal clearing logic path; when At that time, the contract execution processing module automatically extracts the performance credit parameter of the target ledger node. The credit rating is reduced from 1.0 to 0.7 based on a preset step size. At this time, the contract execution processing module switches the control flow to the restricted liquidation and settlement path, and the digital asset locking module transfers all performance guarantee assets held by the node in real time. The data weighted smoothing program adopts a sliding window weighted average model. When the variance of the ledger time-series skewness fluctuation reaches or exceeds the threshold of 0.05 for three consecutive consensus periods, the program automatically extracts three historical time-series skewness values ​​within the current window and assigns them weights of 0.2, 0.3, and 0.5 respectively, based on the oldest to the most recent time. When the program executes, it multiplies the skewness value of each period by its corresponding weight coefficient and performs an accumulation and summation operation. The calculation outputs a smoothed and calibrated scalar time-series skewness value, which is used as an effective input for subsequent cascaded comparisons. By reducing numerical spikes caused by instantaneous jitter or occasional congestion in physical network transmission, it prevents smart contracts from making misjudgments in unstable link environments. The dynamic response execution layer automatically reconstructs the topology routing matrix of the entire network material flow in the next consensus cycle based on the frozen status of the margin assets, and erases the routing index label of the target ledger node, ensuring that subsequent material flow transaction packets adaptively bypass the high-risk congestion area, thereby realizing the supply chain network's proactive feedforward hedging against the risk of interruption.

[0036] In the verification experiment for the above logic, the system was run in a simulated 5-node distributed network, and the following settings were implemented. By artificially injecting random disturbances to simulate actual supply delays, when the test sample group When the value is 0.8, the clearing channel remains in normal status and asset lock-up is not triggered. When set to 1.8, the system triggers a credit reshaping procedure, recording and displaying the node's... The update to version 0.7 was successful, and the change was synchronized to all nodes on the main ledger, confirming the immediate effectiveness of the degradation strategy. This was achieved even when interference was injected. When the performance reaches 3.2, the contract execution engine triggers the margin deduction action within a 12.5ms response time and simultaneously modifies the routing matrix. Comparative sample data shows that, before the introduction of volatility variance calibration logic, network congestion caused... When the value is artificially inflated, the system's false positive rate is 4.2%; (Introducing... After the calibration mechanism was implemented, the false positive rate dropped to 0.08%, verifying the engineering necessity of the redundant calibration logic in resisting noise disturbances. This embodiment, through the combination of discrete control threshold and dynamic data weighted smoothing procedure, realizes deterministic quantitative constraints on the timing dissipation behavior of supply nodes, ensuring the operational stability of the supply chain clearing closed loop under complex physical conditions.

[0037] Example 4: In a distributed industrial supply chain network application scenario, there is a delay in state data synchronization between system nodes due to network transmission jitter. This delay is directly related to the logical breakpoint of material handover confirmation. To solve this technical problem, the system deploys an initial state definition procedure. Before conducting supply chain risk monitoring, the system obtains the clock synchronization accuracy index of each accounting node. If the node clock synchronization deviation exceeds 50ms, a preset NTP time calibration is performed to ensure that the time axes of all nodes participating in consensus are physically aligned. At the same time, to ensure the engineering determinism of parameter settings, this system constructs a calibration procedure based on deviation deflection according to historical statistical patterns. Through offline experiments, the average node response delay data under typical supply chain business scenarios is statistically analyzed, and this statistical mean is defined as the calibration period duration. Based on the physical benchmark, the contract execution processing module reads the current block timestamp of the target ledger node when performing specific monitoring tasks. and corresponding transaction confirmation timestamp Calculate the deviation index To clarify the execution process of smart contracts, this system configures the smart contract execution logic as a defined rule-based decision-making process: when When the value is between 1.0 and 2.0, the system introduces a weight decay compensation operator to update the node credit parameter. The formula for calculating the weight is as follows: ,in, With a preset weight control coefficient, and the calculation logic being triggered entirely based on the lag quantification of node delivery responses, the system achieves closed-loop convergence of the control process when dealing with implicit delay risks in the supply chain by configuring the control logic as a linear decision step. In tests handling more than 1,000 concurrent delivery transactions, the state transition accuracy of this mechanism remained stable at over 99%. In actual engineering deployment, the preset weight control coefficient was specifically assigned a value of 0.15. This value was chosen to ensure a gradual penalty effect of the node credit parameter when dealing with implicit delay risks. When the deviation index fluctuates within the mild delay range of 1.0 to 2.0, the coefficient of 0.15 can ensure that the single deduction of the credit parameter is strictly limited to within 15%, which can both impose rigid financial and authority constraints on delayed nodes and reserve sufficient buffer space to avoid directly removing compliant suppliers from the supply chain network due to occasional network congestion.

[0038] The system further introduces boundary condition handling strategies to ensure the stability of the monitoring logic under abnormal environments. When a consensus is reached, the latency fluctuation value... The consensus cycle exceeded the safety threshold for three consecutive cycles. When the system determines that the network is in an uncertain connection state, the contract execution processing module forcibly triggers the arbitration locking logic, and... The logical decision branch switches from the regular clearing path to the static polling verification path. The trigger logic for this switch is: if the network jitter index continuously crosses the safety damping boundary, then stop... Instead of performing transient deflection calculations, the system uses discrete timestamp proof sequences to perform data integrity verification. The parameter setting logic in this process is as follows: When the network consensus latency falls below 400ms, the system determines that the network has recovered to within the safety threshold of the physical link, then exits the protection state machine and resets the timing deflection calculation flow. This ensures that the risk judgment logic is based solely on trusted physical layer timing data. During the process of the backup state arbitration module outputting allocation instructions to the contract execution processing module, the smart contract layer stabilizes the global consensus rhythm by injecting intervention into the control parameters of the underlying point-to-point communication layer protocol of the network. This allocation instruction includes a dynamic expansion time window parameter. When a consensus delay anomaly occurs across the network, the contract execution processing module calls the underlying consensus engine's interface to temporarily adjust the originally fixed 3-second block production wait delay limit to a 5-second expansion delay range. By increasing the data collection tolerance of distributed ledger nodes during the consensus broadcast phase, block verification voting messages delayed due to link congestion are allowed to be effectively received within the expansion window. This prevents network nodes from triggering a chain reaction of view switching failures due to frequent timeouts, successfully stabilizing the global consensus tempo within a controllable fluctuation range during initial system access. The initial phase initiates a physical clock alignment procedure for each ledger node. Each distributed consensus node implements high-frequency time synchronization via a network time protocol, controlling the clock synchronization deviation between all nodes to remain within 50 milliseconds. The backup state arbitration module uses a latency counting logic unit to continuously accumulate the actual latency variation value of the distributed ledger nodes reaching block consensus. When a momentary congestion occurs in the network transmission physical link, causing the consensus latency variation value to exceed the 500-millisecond safety latency threshold for three consecutive blocks, the backup state arbitration module outputs a dispatch instruction to the contract execution processing module, invoking the underlying consensus engine. The interface temporarily adjusts the fixed 3-second block production wait delay limit to a 5-second expansion delay range, increasing the tolerance of distributed ledger nodes for data collection during the consensus broadcast phase. This allows block verification voting messages delayed due to link congestion to be written into the ledger within the expansion window, stabilizing the global consensus temporal rhythm within the set expansion delay range and maintaining the continuity of dynamic tracking of ledger timing skew. By defining clear state transition thresholds and discretized execution processes, this strategy eliminates the logical dangling problem caused by link uncertainty at the execution end of smart contracts, achieving precise engineering control for monitoring supply chain risk events.

[0039] Example 5: The material flow between current supply chain nodes is represented as discrete time-series transaction flows in the distributed ledger. When the system is deployed in a real-time supply chain risk monitoring scenario, a standardized calibration procedure for node status needs to be executed to identify risks associated with abnormal time-series behaviors. Before supply chain business commences, the contract execution processing module reads and fixes the calibration period duration. This value is 600s. When the system detects that the target ledger node has committed a business transaction, the contract execution processing module executes step one, which involves extracting the current consensus block timestamp of the target ledger node. Block timestamp of the last valid transaction of this node The contract execution processing module calculates the ledger timing skewness based on the formula. ,in, The consensus timestamp of the latest block recorded by the consensus ledger data module. The block timestamp of the last hash-signed transaction executed by the target ledger node. The preset standard circulation cycle duration, As a dimensionless time-series skewness indicator, to address data loss due to target ledger nodes not actually broadcasting, this invention pre-sets a default reporting state tree before system operation. When a target ledger node fails to broadcast the current tiered supply data reporting timestamp within a limited physical handover window due to silence, the time-sharing listener of the contract execution processing module will automatically intercept this data loss anomaly. An off-chain time-sharing monitoring process written in an industrial-grade development language runs at the distributed network consensus node layer. This process subscribes to the latest block generation event of the distributed network's public main ledger through the underlying network remote procedure call interface. An internally configured polling counter with a cycle length set to one-tenth of the calibration cycle length is used. When the polling counter is triggered and the time-sharing monitoring process intercepts the missing tiered supply data reporting timestamp of the target ledger node in the current block data, the consensus node uses its private key to sign a cryptographic signature on the risk detection transaction and broadcasts it to the consensus ledger data module. The risk detection transaction then triggers the contract execution processing module to obtain the current... The consensus block timestamp is used to automatically extract the latest consensus clock from the preceding blocks of the entire public ledger. This latest consensus clock is then used as a virtual default reporting timestamp and forcibly aligned to the starting point of the corresponding physical delivery deadline. This drives the contract execution module to perform subsequent subtraction operations to obtain the ledger time skew value in the absence of off-chain data. Based on the latest consensus clock from the preceding blocks in the entire public ledger, a virtual default reporting timestamp with a penalty is automatically generated as a substitute input. The value of this virtual default reporting timestamp is forcibly aligned to the starting point of the physical delivery deadline agreed upon in the supply scheduling contract signed by the target ledger node during the initial admission phase. This ensures that the absolute difference parameter between the block timestamp and the corresponding tiered supply data reporting timestamp can maintain physical readability and deterministic subtraction operation flow even under extreme concealment conditions of off-chain data breakage. This allows the ledger time skew rate to diverge strongly causally towards a high value range, forcibly activating the subsequent adversarial liquidation and settlement branch.

[0040] Calculated Then, the contract execution module performs step two, which involves classifying and mapping the node states based on preset discrete conditions to determine the network. When the contract processing module determines that the target ledger node is in a standard fulfillment state, the regular clearing logic path remains open, and material collaboration transactions continue to flow. The aforementioned discrete control thresholds of 1.0 and 2.5 are based on offline statistical and regression analysis of over 10,000 regular material handover cycles in the historical supply chain collaboration network. Threshold 1.0 represents that the physical delivery cycle of the target ledger node fully conforms to the standard flow cycle duration, belonging to a completely delay-free safety boundary. When the ledger timing skewness is greater than 1.0 and not greater than 2.5, it indicates that the node's delivery delay is between 1.0 and 2.5 times the standard cycle. Statistics show that this range is mostly caused by occasional logistics and warehousing congestion, and the node still has the willingness to fulfill its obligations. Therefore, a mild penalty measure of reducing the credit allocation weight parameter in situ is adopted. When the skewness exceeds 2.5, the delivery delay has reached more than 2.5 times the standard cycle. Statistically, the probability of the node experiencing capacity collapse or strategic malicious concealment exceeds 95%. Based on this, the system defines it as a forced termination state. When the contract execution processing module determines that the target ledger node is in a state of hidden risk, it calls the credit allocation weight parameter stored in the parameter configuration storage module. Perform a weight deduction operation to... The resource allocation decision tree was adjusted from 1.0 to 0.7 to limit the resource allocation share of this node in subsequent collaborations. The order allocation decision tree is deployed within the business flow reshaping contract and adopts a multi-layer conditional judgment chain architecture. Its underlying input parameters include the supplier node's historical fulfillment delivery rate, the scale of on-chain digital asset margin, the current physical material storage load, and the priority weight parameters refreshed and output by the contract execution processing module. When executing the allocation decision, the first-level fork condition of this decision tree divides the candidate nodes into a high-priority group, a regular scheduling group, and a restricted blocking group based on the size of the priority weight parameters. When the priority weight parameter of the target ledger node is cascaded down from the standard 1.0 to the implicit risk range of 0.7, the routing generation operator of this decision tree will automatically reduce its row vector score in the multi-criteria decision matrix, causing its comprehensive ranking in the candidate queue to be postponed. This results in the newly generated material purchase order package, when traversing the decision tree nodes, preferentially flowing to the peer collaboration node with complete weight parameters, forcibly compressing the resource allocation ratio of the abnormal node at the system bottom layer.

[0041] when When the contract execution processing module determines that the target ledger node is in a forced termination state, it blocks the regular clearing logic path and deflects the control flow to the restricted clearing and settlement path. At the same time, the contract execution processing module sends an asset transfer instruction to the digital asset locking module. Based on this instruction, the digital asset locking module deducts the performance bond assets pre-stored by the target ledger node. The order dispatch network topology control module receives the status update message, reconstructs the routing topology matrix of the supply chain collaboration tree, and the system removes the associated records of the target ledger node from the routing index table. When subsequent supply chain transaction flows perform network routing addressing, they autonomously avoid the target ledger node, realizing the immediate isolation of risk nodes and the reconfiguration of business flows.

[0042] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A real-time supply chain risk monitoring system, characterized in that, include: The consensus ledger data module stores distributed block data and tiered block data supply. The parameter configuration storage module stores credit allocation weight parameters, calibration period duration, and discrete control thresholds. The contract execution processing module is connected to the consensus ledger data module, the parameter configuration storage module, and the digital asset locking module, respectively, and is used to retrieve distributed block data and process it through the following steps: Step S101: Read the timestamp of the block currently generated by the target ledger node and the corresponding timestamp of the hierarchical supply data reporting, calculate the absolute difference between the timestamp of the block and the timestamp of the hierarchical supply data reporting, and divide it by the calibration period in the parameter configuration storage module to obtain the ledger time series skew rate. Step S102: The ledger time-series skew rate is compared with the discrete control threshold in a concatenation manner; when the ledger time-series skew rate is not greater than 1.0, the normal clearing logic path is allowed; when the ledger time-series skew rate is greater than 1.0 but not greater than 2.5, the credit allocation weight parameter of the corresponding target ledger node in the parameter configuration storage module is automatically reduced in situ; when the ledger time-series skew rate is greater than 2.5, the normal clearing logic path is blocked and the control flow is deflected to the restricted clearing and settlement path, triggering the digital asset locking module to deduct the performance bond assets of the target ledger node.

2. The supply chain risk real-time monitoring system according to claim 1, characterized in that, The system also includes a reserve fund dynamic adjustment module, which is connected to the contract execution processing module. The reserve fund dynamic adjustment module is used to monitor the rate of change of the ledger time series skew rate on the time axis. When the rate of change exceeds the change rate threshold for three consecutive handover periods, the corresponding additional collateral amount is calculated, and the reserve fund additional data packet is routed to the target ledger node. When the asset balance of the target ledger node within the restriction period does not reach the additional collateral amount, the contract execution processing module lowers the priority weight parameter of the target ledger node in the subsequent order allocation decision tree.

3. The real-time supply chain risk monitoring system according to claim 1, characterized in that, The system also includes a zero-knowledge state verification module deployed within the consensus ledger data module; The zero-knowledge state verification module retrieves local private data from the upstream ledger node in the target ledger node and generates Boolean decision variables representing the asset carrying capacity in a private isolated environment. It then uses a zero-knowledge proof algorithm to generate cryptographic knowledge proof text corresponding to the Boolean decision variables. The consensus ledger data module deploys a main ledger verification contract, which receives and verifies the cryptographic knowledge proof text as the transition transaction vector. It completes the verification of the supply status while keeping the plaintext business details data of the upstream ledger node confidential.

4. The real-time supply chain risk monitoring system according to claim 1, characterized in that, The system also includes a backup state arbitration module, which is connected to the contract execution processing module and is used to receive the latency difference generated by the global ledger nodes during the consensus phase. When the latency difference crosses the safety latency threshold for three consecutive blocks, it is determined that a consensus lag anomaly has occurred across the entire network. The backup state arbitration module outputs a dispatch instruction to the contract execution processing module to stabilize the global consensus rhythm within the set expansion delay range, ensuring the dynamic tracking continuity of the ledger timing skew.

5. The real-time supply chain risk monitoring system according to claim 1, characterized in that, The digital asset locking module includes a margin configuration locking unit and a differential settlement and diversion module. The margin configuration locking unit is used to lock the performance margin assets submitted by the target ledger node during the initial admission phase. When the ledger timing skew rate is greater than 2.5, the differential settlement and diversion module receives a strong collaborative transfer message sent by the contract execution processing module and triggers the margin configuration locking unit to transfer the performance margin assets to the restricted settlement and diversion path.

6. The supply chain risk real-time monitoring system according to claim 2, characterized in that, The system also includes an order dispatch network topology control module; the order dispatch network topology control module is connected to the reserve fund dynamic adjustment module, and the order dispatch network topology control module is used to retrieve the reduced priority weight parameters and reconstruct the graph node routing topology matrix of the supply chain collaboration tree, so that subsequent transaction packets can autonomously bypass the target ledger nodes marked with risk status to complete the routing reconfiguration feedforward control.

7. The supply chain risk real-time monitoring system according to claim 1, characterized in that, The parameter configuration storage module includes a control threshold storage unit and a node state mapping and maintenance unit. The control threshold storage unit maintains discrete control thresholds that divide different performance risk levels in the long term. The node state mapping and maintenance unit has an independent storage unit corresponding to each target ledger node to store and refresh the credit allocation weight parameters in situ.

8. The supply chain risk real-time monitoring system according to claim 1, characterized in that, The contract execution processing module breaks down step S101 into the following sub-steps: Step S1011, each supply chain participant uses a private independent data domain to encrypt and sign the tiered supply data, and broadcasts and uploads the encrypted and signed tiered supply data to the distributed global public ledger in the consensus ledger data module; Step S1012, the contract execution processing module retrieves blocks in the distributed global public ledger in real time and reads the corresponding block timestamp and the tiered supply data reporting timestamp, and obtains the ledger time series skew rate by combining the calibration period duration in the parameter configuration storage module.

9. A real-time supply chain risk monitoring system according to claim 4, characterized in that, The backup state arbitration module includes a latency counting logic unit and a traffic splitting and conversion module; the latency counting logic unit is used to accumulate the actual latency variation value of the distributed ledger nodes reaching block consensus; When the actual latency variation value crosses the safety latency threshold for three consecutive blocks, the diversion and conversion module closes the current regular consensus path and guides the transaction data to the redundant backup chain path.

10. A method for real-time monitoring of supply chain risks, used to operate the real-time supply chain risk monitoring system of claim 1, characterized in that, Includes the following steps: Step S1: Use the consensus ledger data module to retrieve distributed block data and tiered supply block data. The contract execution processing module reads the timestamp of the block currently generated by the target ledger node and the corresponding timestamp of the tiered supply data reporting. Calculate the absolute difference parameter between the block timestamp and the tiered supply data reporting timestamp. Divide the absolute difference parameter by the calibration period in the parameter configuration storage module to map and obtain the ledger time series skew rate. Step S2: The contract execution processing module compares the ledger time-series skew rate with the discrete control threshold in the parameter configuration storage module. When the ledger time-series skew rate is not greater than 1.0, the normal clearing logic path is allowed. When the ledger time-series skew rate is greater than 1.0 but not greater than 2.5, the credit allocation weight parameter of the corresponding target ledger node in the parameter configuration storage module is automatically reduced in place. When the ledger time-series skew rate is greater than 2.5, the normal clearing logic path is blocked and the control flow is deflected to the restricted clearing and settlement path, and the digital asset locking module deducts the performance guarantee assets of the target ledger node.

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