Supply Chain Risk Assessment and Early Warning Method and System

Abstract

The present invention provides a supply chain risk assessment and early warning method and system. The method includes: collecting and integrating supplier production capacity data, logistics customs clearance data, and assembly plant production line operation data in multiple regions to determine the association between key components and multi-level supply nodes; then performing multi-link scanning based on node timeline information and geographical location parameters to obtain paths that may form delay propagation; further combining progressive analysis of the production load of key components and the assembly timing to identify and lock the core nodes that cause multi-factor linkage risks; finally, dynamically reconstructing logistics routes and schedulable supplier resources to form an adjustment and early warning plan that can meet the assembly requirements of key components. This method can more comprehensively address potential conflicts brought about by multi-region and multi-node collaboration, and reduce the risk of delivery delays caused by delivery date fluctuations.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain, and particularly to a supply chain risk assessment and early warning method and system. Background Art

[0002] With the development of global economic integration and the continuous deepening of industrial division of labor, the supply chain network gradually shows the characteristics of multi-level, cross-regional and high coordination. In the fields of electronic products, automobiles, medical devices, etc., manufacturing enterprises often need to carry out cross-regional collaborative cooperation with component suppliers, assembly plants and logistics service providers all over the world. Due to the large number of upstream and downstream nodes and wide distribution, the state fluctuation of each link may have a chain effect on the overall delivery, thus triggering potential supply chain interruption or delay risks.

[0003] To reduce such risks, existing technologies usually adopt risk monitoring methods based on a single dimension or a single link. For example, only the delay probability of the logistics transportation link is concerned, or only historical order demand forecasting is used to judge whether there is a supply shortage. However, this single-dimensional monitoring method is difficult to cover the comprehensive effects brought by the simultaneous changes of multiple nodes in the supply chain network, and also lacks a systematic consideration of the interaction effects between different regions, different production lines and different customs clearance environments. When there are large fluctuations in the external environment, such as tariff adjustments, natural disasters or production process changes, relying solely on traditional single-point risk assessment often fails to detect potential coupling risks in a timely manner, which is likely to lead to large-scale delays in subsequent assembly progress and market supply. Summary of the Invention

[0004] The main purpose of the present invention is to solve the technical problem that in the field of supply chain risk assessment of the existing technology, due to the monitoring method being only based on a single dimension or a single link, it is difficult to cover the coordination of multi-node across regions and lacks a comprehensive consideration of sudden changes in the external environment;

[0005] The first aspect of the present invention provides a supply chain risk assessment and early warning method, and the supply chain risk assessment and early warning method includes:

[0006] Performing scenario mapping processing on the production capacity data of suppliers, logistics customs clearance data and production line operation data of assembly plants distributed in multiple regions to obtain a comprehensive data set for characterizing the corresponding relationship between key components and multi-level supply nodes in the supply chain network;

[0007] According to the comprehensive data set, performing cross-link interaction scanning processing on the time axis information and geographical location parameters of each supply node in the supply chain network to obtain a spatio-temporal correlation matrix covering the sequential dependence of supply nodes and potential delay propagation paths;

[0008] According to the spatio-temporal correlation matrix, perform progressive coupling calculation processing on the production load and assembly timing of the key components to obtain correlation effect chain information indicating the multi-factor linkage risk of the key components;

[0009] According to the correlation effect chain information, perform dynamic reconstruction processing on the logistics routes and schedulable supplier resources in the supply chain network to obtain an adjustment warning plan based on the assembly requirements of the key components.

[0010] Optionally, in the first implementation manner of the first aspect of the present invention, the scenario mapping processing of the supplier production capacity data, logistics customs clearance data, and assembly plant production line operation data distributed in multiple regions to obtain a comprehensive data set for characterizing the corresponding relationship between the key components and multi-level supply nodes in the supply chain network includes:

[0011] According to the production capacity upper limit and production line switching cycle in the supplier production capacity data, perform production capacity matching processing on the order demand quantity of the key components to obtain an initial supply node list indicating the order range that each supplier can undertake and the corresponding production capacity utilization rate;

[0012] According to the initial supply node list, perform geo-time mapping processing on the port location and customs clearance time period parameters in the logistics customs clearance data to obtain a customs clearance correlation table characterizing the customs clearance rhythm and customs clearance restrictions of the components in different regions;

[0013] Perform timing comparison processing on the production scheduling cycle, line change time, and component assembly priority in the assembly plant production line operation data to obtain an assembly dependence index for indicating the matching degree between the assembly load and the arrival time period of the key components;

[0014] According to the customs clearance correlation table and the assembly dependence index, perform scenario mapping processing on the component delivery link between the supply node and the assembly plant to obtain a comprehensive data set for characterizing the corresponding relationship between the key components and multi-level supply nodes in the supply chain network.

[0015] Optionally, in the second implementation manner of the first aspect of the present invention, the scenario mapping processing of the component delivery link between the supply node and the assembly plant according to the customs clearance correlation table and the assembly dependence index to obtain a comprehensive data set for characterizing the corresponding relationship between the key components and multi-level supply nodes in the supply chain network includes:

[0016] Perform interactive comparison processing on the port location and customs clearance time period parameters in the customs clearance correlation table to obtain a customs clearance feasible section marking the geographical connection conditions between the supply node and the assembly plant;

[0017] According to the assembly dependency index, perform a comparison process on the component assembly priority and the production scheduling cycle execution period of the assembly factory to obtain an assembly connection list indicating the degree of cooperation between component arrival requirements and assembly processes;

[0018] Perform a path verification process on the delivery capabilities of the corresponding supply nodes in the customs clearance feasible section and the assembly connection list to obtain a delivery mapping index covering the component transportation cycle and the production scheduling window;

[0019] According to the delivery mapping index, perform a scenario mapping process on the component delivery link between the supply node and the assembly factory to obtain a comprehensive data set for characterizing the corresponding relationship between key components and multi-level supply nodes in the supply chain network.

[0020] Optionally, in the third implementation manner of the first aspect of the present invention, the cross-link interaction scanning process of the time axis information and geographical location parameters of each supply node in the supply chain network according to the comprehensive data set to obtain a spatio-temporal correlation matrix covering the sequential dependency of supply nodes and potential delay propagation paths includes:

[0021] Perform a time period segmentation process on the time axis information in the comprehensive data set to obtain a timing distribution record indicating the overlapping interval between the production capacity start time period of the supply node and the order delivery rhythm;

[0022] According to the timing distribution record, perform a line comparison process on the geographical location parameters corresponding to each supply node in the comprehensive data set to obtain a geographical mapping index indicating the transportation path and port connection relationship between supply nodes;

[0023] Perform a dependency connection comparison process on the connection situation and potential delay factors of each supply node in the geographical mapping index to obtain a cross-supply node risk area list;

[0024] According to the cross-supply node risk area list, perform a cross-link interaction scanning process on the production scheduling connection and logistics connection between supply nodes to obtain a spatio-temporal correlation matrix.

[0025] Optionally, in the fourth implementation manner of the first aspect of the present invention, the cross-link interaction scanning process of the production scheduling connection and logistics connection between supply nodes according to the cross-supply node risk area list to obtain a spatio-temporal correlation matrix includes:

[0026] Perform a centralized identification process on the supply node connection relationship in the cross-supply node risk area list to obtain a risk supply node table covering the potential conflict points of supply nodes and their corresponding geographical locations;

[0027] According to the risk supply node table, perform a comparison process on the scheduling order between supply nodes and the sequence relationship of assembly processes in terms of time period overlap to obtain a scheduling comparison index indicating the degree of correlation of assembly connection timing;

[0028] Perform a line coupling process on the scheduling comparison index and the logistics connection information to obtain a cross-link integration record that can indicate the synchronization status of the transportation path and the assembly timing;

[0029] According to the cross-link integration record, perform an interactive scanning process on the scheduling connection and the logistics connection between supply nodes to obtain a spatio-temporal correlation matrix.

[0030] Optionally, in the fifth implementation manner of the first aspect of the present invention, the progressive coupling calculation process of the production load and the assembly timing of the key components according to the spatio-temporal correlation matrix to obtain the correlation effect chain information indicating the multi-factor linkage risk of the key components includes:

[0031] According to the spatio-temporal correlation matrix, perform a load comparison process on the production capacity utilization rate of the supply node and the demand changes of the key components at different supply nodes to obtain a production capacity load index indicating the overall distribution of the production load and the supply and demand balance status of the components;

[0032] Perform a timing calibration process on the assembly supply nodes in the production capacity load index and the scheduling periods of the corresponding assembly factories to obtain an assembly timing mapping diagram reflecting the assembly sequence and the delivery rhythm of each supply node;

[0033] According to the assembly timing mapping diagram, perform an interval aggregation process on the sequence and the corresponding delivery cycles between supply nodes to obtain a risk interval list identifying the possible delay accumulation of the key components among multiple supply nodes;

[0034] Perform a progressive coupling calculation process on the supply node dependency relationship in the risk interval list to obtain the correlation effect chain information indicating the multi-factor linkage risk of the key components.

[0035] Optionally, in the sixth implementation manner of the first aspect of the present invention, the dynamic reconstruction process of the logistics lines and the schedulable supplier resources in the supply chain network according to the correlation effect chain information to obtain an adjustment warning plan based on the assembly requirements of the key components includes:

[0036] Perform a path comparison process on the logistics lines with concentrated delays or supply node conflicts in the correlation effect chain information to obtain replaceable or avoidable transportation channels and the corresponding transportation time periods of the transportation channels;

[0037] According to the replaceable transportation channels and the corresponding transportation time periods, perform priority screening on the supply nodes to obtain a resource allocation list for scheduling key components across supply nodes;

[0038] Perform interactive connection processing on the alternative supply nodes in the resource allocation list to obtain feasible countermeasures for assembly timing switching and logistics synchronization in the multi-point supply mode;

[0039] According to the feasible countermeasures, perform dynamic reconstruction on the connection sequence of key components in the assembly link and the coordination windows of each supply node to obtain an adjustment warning plan based on the assembly requirements of the key components.

[0040] The second aspect of the present invention provides a supply chain risk assessment and warning system, and the supply chain risk assessment and warning system includes:

[0041] A scenario mapping module for performing scenario mapping on the supplier production capacity data, logistics customs clearance data, and assembly plant production line operation data distributed in multiple regions to obtain a comprehensive data set representing the corresponding relationship between key components and multi-level supply nodes in the supply chain network;

[0042] An interactive scanning module for performing cross-link interactive scanning on the time axis information and geographical location parameters of each supply node in the supply chain network according to the comprehensive data set to obtain a spatio-temporal correlation matrix covering the sequential dependence of supply nodes and potential delay propagation paths;

[0043] A coupling calculation module for performing progressive coupling calculation on the production load and assembly timing of the key components according to the spatio-temporal correlation matrix to obtain correlation effect chain information indicating the multi-factor linkage risk of the key components;

[0044] A dynamic reconstruction module for performing dynamic reconstruction on the logistics lines and schedulable supplier resources in the supply chain network according to the correlation effect chain information to obtain an adjustment warning plan based on the assembly requirements of the key components.

[0045] The above-mentioned supply chain risk assessment and early warning method and system collect and integrate supplier production capacity data, logistics customs clearance data, and assembly plant production line operation data in multiple regions to determine the association between key components and multi-level supply nodes; then perform multi-link scanning based on node timeline information and geographical location parameters to obtain paths that may form delay propagation; then combine the progressive analysis of the production load of key components and the assembly timing to identify and lock the core nodes that cause multi-factor linkage risks; finally, through the dynamic reconstruction of logistics routes and schedulable supplier resources, an adjustment early warning plan that can meet the assembly requirements of key components is formed. This method can more comprehensively address potential conflicts brought about by multi-region and multi-node collaboration, and reduce the risk of delivery delays caused by delivery schedule fluctuations.

[0046] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification, claims, and drawings.

[0047] To make the above objectives, features, and advantages of the present invention more obvious and understandable, the following specifically gives preferred embodiments and, in conjunction with the accompanying drawings, the detailed description is as follows. Description of the Drawings

[0048] Figure 1 It is a schematic diagram of the first embodiment of the supply chain risk assessment and early warning method in the embodiment of the present invention;

[0049] Figure 2 It is a schematic diagram of an embodiment of the supply chain risk assessment and early warning system in the embodiment of the present invention. Detailed Embodiments

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0051] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes other unlisted steps or units, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0052] To facilitate the understanding of this embodiment, a supply chain risk assessment and early warning method disclosed in the embodiments of the present invention will be introduced in detail first. As Figure 1 shown, this method includes the following steps:

[0053] 101. Perform scenario mapping processing on the supplier production capacity data, logistics customs clearance data, and assembly plant production line operation data distributed in multiple regions to obtain a comprehensive data set for characterizing the corresponding relationship between key components and multi-level supply nodes in the supply chain network;

[0054] In an embodiment of the present invention, the performing scenario mapping processing on the supplier production capacity data, logistics customs clearance data, and assembly plant production line operation data distributed in multiple regions to obtain a comprehensive data set for characterizing the corresponding relationship between key components and multi-level supply nodes in the supply chain network includes: performing production capacity matching processing on the order demand volume of key components according to the production capacity upper limit and production line switching cycle in the supplier production capacity data to obtain an initial supply node list indicating the order range that each supplier can undertake and the corresponding production capacity utilization rate; performing geographical time mapping processing on the port location and customs clearance time period parameters in the logistics customs clearance data according to the initial supply node list to obtain a customs clearance association table characterizing the customs clearance rhythm and customs clearance restrictions of components in different regions; performing timing comparison processing on the production scheduling cycle, line change time, and component assembly priority in the assembly plant production line operation data to obtain an assembly dependence index for indicating the matching degree between the assembly load and the arrival time period of key components; performing scenario mapping processing on the component delivery link between the supply node and the assembly plant according to the customs clearance association table and the assembly dependence index to obtain a comprehensive data set for characterizing the corresponding relationship between key components and multi-level supply nodes in the supply chain network.

[0055] Specifically, perform production capacity matching processing on the order demand volume of key components according to the production capacity upper limit and production line switching cycle in the supplier production capacity data to obtain an initial supply node list indicating the order range that each supplier can undertake and the corresponding production capacity utilization rate. Let represent the maximum available output of supplier i, D represent the total order demand, T represent its production line switching cycle, and define ( ) to measure the bearing capacity of the supplier in the theoretical state. In order to subdivide the production capacity occupancy in each time period, the production line switching time period can be split into several intervals , and the allocable ratio to D is calculated separately in each interval ; if the same supplier cannot cover the demand in a certain interval, a part of the order will be transferred to other supply nodes. In After being combined with each other, a list containing the quantities of key components that each supplier can undertake within different time periods can be generated, and this information is integrated into an initial supply node list. For high-priority requirements of the same component, additional weights can be added to the operation of to ensure that urgently needed components can obtain production capacity tilt more quickly, so that the initial supply node list can better meet the actual requirements.

[0056] Specifically, according to the initial supply node list, geographical-time mapping processing is performed on the customs location and customs clearance time period parameters in the logistics customs clearance data to generate a customs clearance correlation table representing the customs clearance rhythm and customs clearance restrictions of components in different regions. Let the customs location coordinates be , the opening period be , the supply node and the assembly plant are represented by and respectively. Define the transportation distance as , and compare the arrival time with to determine whether it meets the opening period constraints of this customs. If there are multiple inspection links at the customs, the customs clearance waiting coefficient ω_{ik} can also be set, and score(i,k,j)=α is used to measure the route time consumption. Traverse all feasible paths between the supply node and the assembly plant and select the optimal item, and write it into the customs clearance correlation table to record the working periods of the logistics channel and the corresponding customs and additional inspection requirements.

[0057] Specifically, perform timing comparison processing on the production scheduling cycle, line change time, and component assembly priority in the production line operation data of the assembly plant to obtain an assembly dependence index for indicating the matching degree between the assembly load and the arrival time period of key components. Let the set of production scheduling cycles be m , the line change time sequence be , the set of component assembly priorities be , and compare one by one through the expected arrival time in the correlation table and T. If the arrival time falls within the interval, it is determined that it can connect to this production line scheduling; if the arrival time crosses the line change time , insert a line change downtime τ to quantify the additional waiting. For component priority , a more forward assembly order can be allocated within the production scheduling cycle to reduce the detention of key components in the waiting queue. By combining the customs clearance process with the production scheduling process in this way, the output assembly dependence index presents the coupling degree between the arrival time point of each component and the production scheduling period of the assembly plant, providing a timing comparison basis for judging the overall assembly progress in subsequent links. If it is found that there is no available window for its components within the current production scheduling cycle, it can be in the next batch +1 is rescheduled to form a per-cycle iterative assembly arrangement view.

[0058] Specifically, according to the customs clearance association table and the assembly dependency index, perform scenario mapping processing on the component delivery link between the supply node and the assembly factory to obtain a comprehensive data set for characterizing the corresponding relationship between key components and multi-level supply nodes in the supply chain network. A matrix M can be established in the database, where the row index corresponds to the supply node, the column index corresponds to the assembly factory, and M(i,j) stores the optimal transportation route of the component and the estimated arrival-assembly matching period. If component x corresponds to multiple alternative paths in the customs clearance association table, multiple record entries are added at the position of M(i,j). And use the assembly dependency index to check its compatibility with the production scheduling cycle to select an effective route. Denote indicating the cooperation degree of supply node i to factory j within the production scheduling cycle r, which is determined by =g(dis , , ) to comprehensively evaluate multiple parameters, and write the evaluation results into the comprehensive data set. This comprehensive data set completes the organic integration of various dimensional data, including production capacity configuration information, logistics customs clearance restrictions, and assembly scheduling priorities, and can provide accurate key component distribution and connection conditions for subsequent risk judgment, showing high correlation readability in the multi-level supply chain network.

[0059] Further, the performing scenario mapping processing on the component delivery link between the supply node and the assembly factory according to the customs clearance association table and the assembly dependency index to obtain a comprehensive data set for characterizing the corresponding relationship between key components and multi-level supply nodes in the supply chain network includes: performing interactive comparison processing on the checkpoint location and customs clearance time period parameters in the customs clearance association table to obtain a customs clearance feasible section marking the geographical connection conditions between the supply node and the assembly factory; according to the assembly dependency index, performing time period comparison processing on the component assembly priority and the assembly factory production scheduling cycle to obtain an assembly connection list indicating the matching degree between the component arrival demand and the assembly process; performing path verification processing on the corresponding supply node delivery capabilities in the customs clearance feasible section and the assembly connection list to obtain a delivery mapping index covering the component transportation cycle and the production scheduling window; according to the delivery mapping index, performing scenario mapping processing on the component delivery link between the supply node and the assembly factory to obtain a comprehensive data set for characterizing the corresponding relationship between key components and multi-level supply nodes in the supply chain network.

[0060] Specifically, perform an interactive comparison process on the checkpoint locations and customs clearance time period parameters in the customs clearance association table to obtain the customs clearance feasible sections that mark the geographical connection conditions between the supply nodes and the assembly plants. The customs clearance association table usually contains the coordinate information, opening hours, and customs clearance inspection requirements of multiple checkpoints. It is necessary to conduct a feasibility analysis of route connection for each of these checkpoints based on the respective geographical locations of the supply nodes and the assembly plants. A record can be established for each checkpoint first, and the record contains data such as the longitude and latitude of the checkpoint, the types of goods that can be accepted, the start and end times of the opening hours, and the inspection frequency. Subsequently, evaluate the distance between the location coordinates of the supply node and this checkpoint, and calculate the time when the goods arrive at this checkpoint from the supply node according to the estimated transportation speed and possible traffic control requirements. If this time falls within the opening hours of the checkpoint, it indicates that the basic conditions for cross-checkpoint transportation are met in both space and time, and this route can be included in the candidate routes and assigned a customs clearance connection mark; if the arrival time is earlier than the opening time or later than the closing time of the checkpoint work, it is necessary to record the expected waiting duration or the detour requirement, and classify it as a route to be observed when outputting in this step. For the situation where the same supply node leads to different checkpoints, it is necessary to perform the comparison independently for each checkpoint and summarize to obtain a list containing several routes, and each route is attached with detailed notes such as whether it meets the opening time, whether there is an inspection delay, and whether there are additional permit requirements. If it is found that the geographical location of the checkpoint is relatively close to the assembly plant, it is also necessary to check the terminal distance from the checkpoint to the assembly plant at the same time to determine whether the remaining time for subsequent transportation is within the corresponding receiving or inspection capacity of the factory.

[0061] Specifically, according to the assembly dependency index, a comparison process is performed between the assembly priorities of components and the scheduling periods of the assembly plant to obtain an assembly connection list indicating the degree of coordination between component arrival requirements and assembly processes. The assembly dependency index usually includes the scheduling time period of the assembly plant, the line change time, and the priority parameters of components during the assembly process. This index needs to be matched with the feasible sections identified in the previous step on the time axis. If a component is determined to be able to reach the factory through a specific checkpoint within the customs clearance opening period, it is necessary to further determine whether its arrival time meets the specific window of the factory's production schedule. The production scheduling period can be divided into several process intervals, each with its own start and end times, and the line change time is a special node where it is necessary to separately identify the downtime periods caused by the conversion or commissioning of the assembly line. The priority of components determines their queuing order in the production scheduling period. If the priority value is high, they will be included in the assembly list in advance within the feasible process intervals, while components with lower priorities need to wait for the former to complete assembly before entering the production line. Thus, for each component route entering the factory through a customs clearance feasible section, one or more possible matching production scheduling windows can be determined. If the arrival time spans the line change time, the corresponding line change delay amount needs to be recorded in the assembly dependency index. The finally output assembly connection list will integrate the arrival time, priority, factory production scheduling period, and line change time of components into the same table, indicating the temporal correspondence between components and specific production line processes. This list can show which components can be immediately assembled online after actual arrival and which components need to be adjusted to the next round of production line process intervals due to production scheduling conflicts, thereby completing the connection determination between arrival requirements and assembly processes in the time dimension.

[0062] Specifically, perform path verification processing on the delivery capabilities of the corresponding supply nodes in the customs clearance feasible section and the assembly connection list to obtain a delivery mapping index that covers the component transportation cycle and the production scheduling window. The path verification processing needs to combine information from two dimensions: geography and time, and gradually check whether the overall flow of goods during cross-border transportation and arrival at the factory for production scheduling meets the delivery capabilities of the supply nodes themselves. The delivery capabilities of supply nodes are not only related to their production capacity but also affected by the order quantity and assembly priority, and also include the matching requirements for the shipping time to connect with the customs clearance opening time. If a supply node's production capacity is full in the current period or the order is delayed due to production line switching, the status of being unable to ship currently needs to be recorded in the scheduling table of this node. If the node determines that it can ship on time, it is mutually verified with the customs clearance feasible section to check the overlap between the time of arrival at the customs after leaving the node and the customs opening period, and calculate the potential queuing delay at the line change time or inspection period. Next, compare the customs clearance record after arriving at the customs with the connection situation of the factory production scheduling window in the assembly connection list. If the components are in the production scheduling cycle gap or priority channel after entering the factory, this route is regarded as a valid path and marked as the "assemblable" state. If a time misalignment or insufficient node production capacity is found during the comparison process, the route is marked with "temporarily unavailable" or "needs to be postponed" to ensure that it will not be wrongly regarded as a feasible solution during the later comprehensive selection. This path verification processing outputs a delivery mapping index, which lists the transportation and assembly connection situations that each supply node can complete in different periods, and coordinates with the information of specific routes, customs opening periods, and factory production scheduling intervals to make the timing connection of the entire supply-customs clearance-assembly link more transparent.

[0063] Specifically, according to the delivery mapping index, a scenario mapping process is performed on the component delivery link between the supply nodes and the assembly plants to obtain a comprehensive data set for characterizing the corresponding relationship between key components and multi-level supply nodes in the supply chain network. This scenario mapping needs to integrate the actual production capacity and scheduling information of the supply nodes, the geographical and opening data in the feasible customs clearance sections, and the priority and assembly progress determination in the assembly connection list, and then summarize the routes that are confirmed to be feasible or partially feasible after path verification processing into a unified data structure. This data structure can be designed as a multi-dimensional array or a multi-relational table. The row index represents the supply node number and its shipping time period, the column index represents the assembly plant and its production scheduling window, and the intermediate unit records the customs route, waiting time, and assembly priority information. If there are selectable routes and production scheduling windows for the same component in multiple time periods, multiple candidate states are written in the same node-plant combination unit, along with the priority or time-consuming label for each candidate. Subsequently, by retrieving this comprehensive data set, it is possible to quickly understand in which time periods each component can pass through customs smoothly, whether it can be assembled online in a timely manner after arrival, and whether there are alternative nodes or alternative production scheduling periods to achieve diversion when priority conflicts occur. This data set macroscopically presents the multi-level association relationship between key components and the supply chain network, enabling the geographical location, customs clearance channels, production scheduling period, and assembly requirements of each node to be integrated in the same scenario.

[0064] 102. According to the comprehensive data set, perform a cross-link interaction scanning process on the time axis information and geographical location parameters of each supply node in the supply chain network to obtain a spatio-temporal association matrix covering the sequential dependence and potential delay propagation paths of the supply nodes;

[0065] In an embodiment of the present invention, the performing a cross-link interaction scanning process on the time axis information and geographical location parameters of each supply node in the supply chain network according to the comprehensive data set to obtain a spatio-temporal association matrix covering the sequential dependence and potential delay propagation paths of the supply nodes includes: performing a time period segmentation process on the time axis information in the comprehensive data set to obtain a timing distribution record indicating the overlapping interval between the production capacity start time period of the supply node and the order delivery rhythm; according to the timing distribution record, performing a line comparison process on the geographical location parameters corresponding to each supply node in the comprehensive data set to obtain a geographical mapping index indicating the transportation path and port connection relationship between each supply node; performing a dependence connection comparison process on the connection situation and potential delay factors of each supply node in the geographical mapping index to obtain a list of cross-supply node risk regions; according to the list of cross-supply node risk regions, performing a cross-link interaction scanning process on the production scheduling connection and logistics connection between the supply nodes to obtain a spatio-temporal association matrix.

[0066] Specifically, when performing time period segmentation processing on the timeline information in the comprehensive data set, it is necessary to first extract the production capacity start time, order reception or shipment time, and production line conversion period of each supply node from the data set, and uniformly convert this information to a comparable time dimension. If a node has multiple production capacity peaks or there are line change and downtime intervals within a day, it needs to be segmented according to different time slices, and the start and end time stamps that do not overlap with each other are set to clarify the start and end points of each time slice. This can ensure that in a complete time series view, the production capacity level and order delivery rhythm corresponding to each node in different time slices can be accurately quantified. Subsequently, it is necessary to compare with the arrival period of the order demand. If the production capacity start period of the node exactly covers the order placement time or locking time of a certain batch of orders, it is determined that there is an overlapping interval between production capacity and orders during this period; if it is found that the production capacity start period is later than the order demand generation time, there may be a situation of off-peak delivery after segmentation, and this "delay" feature needs to be recorded in the form of markings or annotations. For scenarios that span zero o'clock or a production cycle in a day, they should also be classified into the next time slice to avoid data overlap or missing. After determining the specific time slices where each node is located, these segmented information can be output as a time series distribution record, and fields such as node identification, start and end of the period, production capacity value, and order demand value are added to the record to intuitively reflect the matching situation between node production capacity and orders. The time series distribution record can play a role in comparison and screening. If a node has no production capacity but receives a large number of orders in some periods, it will be highlighted in the record; if the node can meet the orders in multiple periods, these periods can be further integrated for the next link to complete the data preparation required for sequential dependency and delay assessment.

[0067] Specifically, according to the time-series distribution record, the geographic location parameters corresponding to each supply node in the comprehensive data set are processed by line comparison. When the geographic mapping index indicating the connection relationship between the transportation path and the port between each supply node is obtained, the coordinate data of the node needs to be compared with the possible transportation route, and the path needs to be spliced in combination with the coordinate information of the customs clearance port or key logistics hub. If a node is located in an inland area and is far away from a port or international gateway, it is necessary to calculate the geographical connection link passing through multiple transfer points during line comparison processing, including different modes of transportation such as road, rail or air. In this way, a multi-segment path information from node to node can be generated, and each segment of the path is accompanied by different timeliness and cost references. If a node has spare capacity in a specific time period and its geographical location is adjacent to the main traffic artery, it will show a shorter transportation time and a more stable connection relationship in the control processing output, thus marking the direct or semi-direct connection attributes of the node and the surrounding nodes in the geographic mapping index; if another node has spare capacity but is geographically remote and requires additional intermodal transport or multiple transfers to reach the checkpoint, a more complex connection structure will appear in the mapping index. In order to support the subsequent multi-line return or emergency dispatch, it is necessary to expand the geographic mapping index with additional fields, such as transfer station codes, checkpoint opening hours, and road condition fluctuations. If it is found that some routes are unavailable due to traffic control during a specific period, such situations need to be marked as restricted routes to identify potential delay triggers later. The index can eventually be presented in the data structure in the form of node pairs (NodeA→NodeB), which focuses on whether the two can form a transportation channel for fast direct connection and safe customs clearance in terms of geographical location, and further associates the transportation window according to the capacity period in the time series distribution record.

[0068] Specifically, when performing the dependency connection comparison process on the connection status of each supply node in the geographical mapping index and potential delay factors to obtain the list of cross-supply node risk regions, it is necessary to cross-correlate the geographical mapping index with the time-series distribution record, and unify the route connection status in the spatial dimension and the node production capacity and order demand status in the time dimension into the same comparison framework. If two nodes show a complementary production capacity relationship in different time slices, but traffic bottlenecks or port turnover restrictions are exposed in their geographical mapping index, a high delay probability value will be obtained during the dependency connection comparison; if the geographical mapping index shows that there is a convenient route between these two nodes and the time-series distribution record also indicates that the production capacity docking timing is exactly matched, it will be marked as a low-risk link. To improve the comparison logic, the delay trigger sources can be monitored simultaneously, such as the fixed inspection delay at the checkpoint, the waiting time for consolidating multiple shipments in multiple links, or the transportation hubs prone to congestion. If road maintenance information appears during the highway or railway transportation of a certain route, or the checkpoint suspends customs clearance on some dates, a "delay factor" will be superimposed during the dependency connection comparison, and this route will be included in the risk statistics object. After analyzing these connection comparison results, the node pairs with high delay probability or excessive interdependence can be sorted into the list of cross-supply node risk regions. Each region may represent a set of several adjacent nodes or nodes along the same transportation route, marking out the areas that need special attention during resource allocation or production capacity distribution, laying the data conditions for the next deeper spatio-temporal correlation scan.

[0069] Specifically, when performing cross - link interactive scanning processing on the production scheduling connection and logistics connection between supply nodes according to the cross - supply - node risk area list to obtain a spatio - temporal correlation matrix, it is necessary to further examine whether cascade - type delays or sequential squeezing problems will occur on the basis of the previously compared risk areas. If the shipment of a certain node is postponed due to a change in production capacity scheduling within a specific time period, and the downstream node has already scheduled other orders in the next time period, the connection between the two nodes may have a continuous impact on the entire transportation route. To evaluate this continuity, the time axis and geographical coordinate axis can be cross - located in the matrix, mapping the different time periods corresponding to each node to the row and column positions of the matrix, and marking the connection status, delay amplitude, or possible conflict information in the cells. If the scanning result shows that the production scheduling time sequences of multiple nodes conflict with each other, a highlighted area will be formed in the matrix, indicating that these nodes are highly dependent on each other in terms of time or route selection. Through this interactive scanning method, it is possible to check in the same visualization structure which nodes have high loads in the same time period, and which nodes have repeated queuing or congestion at logistics checkpoints, so as to discover potential paths for large - scale delay propagation. The finally obtained spatio - temporal correlation matrix includes the production capacity time - sequence distribution, geographical connection information, and delay connection probability, showing the complexity of the connections between nodes within the entire network, and having an intuitive auxiliary effect on judging the sequential dependence relationship between multiple nodes and the diffusion mechanism of delays in a multi - level supply network.

[0070] Further, the cross - link interactive scanning processing of the production scheduling connection and logistics connection between supply nodes according to the cross - supply - node risk area list to obtain a spatio - temporal correlation matrix includes: performing centralized identification processing on the supply - node connection relationships in the cross - supply - node risk area list to obtain a risk - supply - node table covering potential conflict points of supply nodes and corresponding geographical locations; according to the risk - supply - node table, performing a time - period coincidence comparison processing on the production scheduling order between supply nodes and the sequence relationship of assembly processes to obtain a production - scheduling comparison index indicating the degree of temporal correlation of assembly connections; performing line coupling processing on the production - scheduling comparison index and logistics connection information to obtain a cross - link fusion record indicating the synchronization status of transportation routes and assembly time sequences; and performing interactive scanning processing on the production scheduling connection and logistics connection between supply nodes according to the cross - link fusion record to obtain a spatio - temporal correlation matrix.

[0071] Specifically, perform centralized identification processing on the supply node connection relationships in the cross-supply node risk area list to obtain a risk supply node table covering the potential conflict points of supply nodes and their corresponding geographical locations. This step requires first reading all node pairs listed in the cross-supply node risk area list and the mutual influence data of these node pairs in the spatio-temporal dimension, and then filtering and aggregating in two directions: geographical adjacency and business process correlation. If a certain area is marked as prone to order congestion or delivery delay, all nodes in this area need to be included in the centralized identification processing. Records can be established for each node first, and the records include attributes such as the unique identifier of the node, available production capacity information, upstream and downstream order relationships, and coordinate positions. If the list shows that multiple nodes have interdependent production scheduling resources or share logistics routes, the connection strength is calculated during identification and a connection link data with weights is formed. Subsequently, several nodes with relatively concentrated geographical coordinates need to be located within the same area, and the specific longitude and latitude of these nodes are matched one by one with the potential conflict points. If it is found that adjacent nodes both indicate a sharp increase in the order volume during a certain period, the records of this period are classified as high-risk periods, and annotations such as "high load" or "emergency scheduling" are added to the node labels. If individual nodes are marked as repeatedly experiencing delays or quality failures in the list, a separate risk level can also be assigned in the identification table. Through the merging and sorting of multiple connection links, the node combinations that need the most attention can be screened out and summarized into a risk supply node table. This table can be presented in the form of a table or a database and contains the geographical coordinates of each node, adjacent node references, recent order throughput, and connection strength, so as to clarify which nodes are extremely likely to trigger conflict or delay events in the same area or the same business chain segment.

[0072] Specifically, according to the risk supply node table, the production scheduling sequence between the supply nodes and the assembly process sequence are compared and processed based on the overlap of the time periods, and a production scheduling comparison index indicating the degree of correlation of the assembly connection sequence is obtained. This step requires mapping the high-load or potentially conflicting nodes in the risk supply node table with their most recent production scheduling cycle or assembly cycle, and checking whether these nodes simultaneously schedule a large number of orders or perform the assembly of key parts in the same time window. The production scheduling plan of the node can be represented as a set of time intervals, each of which contains the start and end times and the number of parts that can be undertaken, and the assembly process sequence can be represented by a sequence to reflect the priority or dependency of different parts. If two nodes are in a high-load state at the same time and the time intervals overlap, a "conflict index" is marked during the comparison, and the value can be calculated based on multiple factors such as node distance, logistics connection channel, and degree of homogeneity of parts. If the production schedule of a node is exactly staggered with the assembly cycle of another node, it can be recorded as "time sequence mutual exclusion" in the production schedule comparison index, indicating that the two will not compete with each other, but there is also a lack of collaborative opportunities; if there is a common time window between the two and there is overlap in the demand for parts, the interval will be marked as "time sequence squeeze". For situations where parts need to be transported across nodes, it is also necessary to consider whether the node has switched to the next process or ended the production schedule before the transfer is completed. If it is found during the comparison process that the same assembly process is executed successively on multiple nodes, their connection points can be identified so that the degree of connection of the assembly sequence can be clearly indicated in the index. Through these comparison results, a production schedule comparison index can be generated, in which the type of time period overlap is indicated for each pair of nodes, and the assembly sequence or the degree of competition for parts is indicated, so as to further evaluate how to allocate production capacity and logistics resources in the macro network.

[0073] Specifically, the production scheduling comparison index and the logistics connection information are processed by line coupling to obtain a cross-link fusion record that can indicate the synchronization status of the transportation path and the assembly sequence. The production scheduling comparison index usually only focuses on the overlap of the production scheduling period or the sequence of processes between nodes. It needs to be combined with the transportation path and time efficiency at the logistics level to fully present the multi-link connection on the supply chain network. The production scheduling interval reflected by each node in the index can be first connected with its corresponding logistics connection data table. The logistics connection data table records the feasible routes, estimated time consumption, and customs clearance or inspection period requirements from the node to other nodes or checkpoints. If the production scheduling comparison index shows that two nodes jointly process the same type of parts in a certain period, it is necessary to check whether there is a route between them that meets the transportation demand in the period during the line coupling processing, and calculate the difference between the actual arrival time and the assembly start time. If the difference is too large, it means that the transportation cannot be synchronized with the assembly progress; if the difference is negative and the node can only assemble after accepting the parts, it means that there is a queue of assembly processes, which may cause a short wait. Line coupling also needs to consider the impact of geographical location on transportation time. If the distance between two nodes is far or multiple checkpoints are required, the time consumption will increase significantly; if the distance is close and the line is unobstructed, fast operation can be achieved. After the coupling process is completed, a cross-link fusion record is generated for each node pair or process connection, recording their timing status in terms of transportation and assembly, as well as the actual connection status in terms of geographical location or production line load. If there are alternative routes or batch transportation plans, the feasibility evaluation results should also be noted in the record.

[0074] Specifically, according to the cross-link fusion record, the production scheduling connection and logistics connection between the supply nodes are interactively scanned and processed to obtain a spatiotemporal correlation matrix. This step requires the fusion records output by the previous step to be aggregated into a unified spatiotemporal coordinate system, and the key information of different dimensions such as production scheduling, assembly, and transportation are interactively integrated to construct a matrix structure with a time axis and a space axis. The production capacity, assembly action, and transportation path status of the corresponding node in the time slice can be marked in the matrix unit according to the geographical coordinates or business serial number of the node as the row index, and the time interval or assembly stage as the column index. If a node has a production scheduling conflict or a logistics breakpoint in the time slice, a special identifier or delay value can be written in the matrix unit. If the node can successfully complete the docking of parts in the time slice, a regular status code is written. As the scan is unfolded row by row or column by column, it can be detected which nodes affect each other in the same time slice, and which nodes are continuously congested or waiting in multiple time slices, thereby summarizing a larger range of delay propagation phenomena. If it is found that adjacent units are continuously affected in time and space, it can be determined as a manifestation of the cascade effect. The spatiotemporal correlation matrix can be presented in a visualization tool, providing analysts with a macro-view of the entire supply chain network, listing the connection status, logistics circulation routes and assembly execution levels of each node in each time period.

[0075] 103. Perform progressive coupling calculation processing on the production load and assembly timing of the key components according to the spatio-temporal correlation matrix to obtain correlation effect chain information indicating the multi-factor linkage risk of the key components;

[0076] In an embodiment of the present invention, the performing progressive coupling calculation processing on the production load and assembly timing of the key components according to the spatio-temporal correlation matrix to obtain correlation effect chain information indicating the multi-factor linkage risk of the key components includes: performing load comparison processing on the production capacity utilization rate of the supply nodes and the demand changes of the key components at different supply nodes according to the spatio-temporal correlation matrix to obtain a production capacity load index indicating the overall distribution of the production load and the supply and demand balance state of the components; performing timing calibration processing on the assembly supply nodes and the production scheduling periods of the corresponding assembly plants in the production capacity load index to obtain an assembly timing mapping diagram reflecting the assembly sequence and the delivery rhythm of each supply node; performing interval aggregation processing on the sequence and the corresponding delivery cycles between the supply nodes according to the assembly timing mapping diagram to obtain a list of risk intervals indicating the possible delay accumulation of the key components between multiple supply nodes; performing progressive coupling calculation processing on the supply node dependency relationship in the list of risk intervals to obtain correlation effect chain information indicating the multi-factor linkage risk of the key components.

[0077] Specifically, according to the spatio-temporal correlation matrix, a load comparison process is performed on the capacity utilization rate of supply nodes and the demand changes of key components at different supply nodes to obtain a capacity load index indicating the overall distribution of production load and the supply-demand balance state of components. This process first extracts the capacity utilization rate information of each supply node in different time slices from the spatio-temporal correlation matrix and the required quantity of key components allocated to this node. If supply node A has high-load operation and undertakes a large number of orders in the first time slice, while node B is still idle in the same time slice, then during the load comparison process, node A will be marked as fully loaded or in a tight state, and node B will be marked as schedulable or having surplus capacity. To accurately reveal the consumption degree of component demand on production resources, it is necessary to count the order types and quantities undertaken by each node in this time slice and compare them with parameters such as the maximum production capacity limit of the node, its switching cycle, and equipment load limit. If it is found that the component demand has reached the critical value of the node's carrying capacity, it is recorded as a warning level in the load comparison result; if it has not reached the critical value, it is recorded as being able to continue to undertake. This comparison process not only examines the load situation of each node itself, but also horizontally compares the differences in the demand acceptance of the same components by all nodes within the same period, so as to distinguish whether the overall supply is sufficient and whether there are overheated or excessive local nodes during the summary. Finally, in the capacity load index, the actual carrying capacity and production capacity limit of each node, the corresponding names of key components, and the proportion of demand are listed in the order of node number or time slice, and its current supply-demand balance state is marked with text or numerical values. If a certain node has obvious overloaded operation, it will be specially marked in the index to prompt subsequent links to pay extra attention.

[0078] Specifically, perform a timing calibration process on the assembly supply nodes in the production capacity load index and the production scheduling periods of the corresponding assembly plants to obtain an assembly timing mapping diagram that reflects the assembly sequence and the delivery rhythm of each supply node. This step requires mapping the load data of each node in the production capacity load index to the production scheduling cycle of the assembly plant, so as to identify which nodes have the production capacity output conditions during a certain period, and whether the corresponding components can be put on the assembly line in the factory according to the production plan. If the assembly supply node is fully loaded during this period and still receives additional orders, there will be a production scheduling conflict or a delay in the assembly sequence in the mapping diagram; if the node has spare capacity during this period and the factory production schedule shows a suitable gap, the output of this node can be arranged in the nearest production scheduling window to reduce the waiting time of the components. The timing calibration process not only considers the docking of a single node with the factory, but also synchronously compares the production capacity bearing conditions of surrounding nodes for the same components. If the production capacity of other nodes is more sufficient and their geographical location is closer to the factory, they will be preferentially connected to the corresponding assembly period of the factory in the mapping diagram, so as to improve the circulation efficiency of orders in the overall supply chain. Each connection line in the mapping diagram represents the collaboration channel between the supply node and the assembly plant, and is accompanied by the assembly sequence or the assembly cycle number to reflect whether the components can be successfully put on the assembly line as planned. If there are multiple consecutive nodes with tight production capacity during the same period and the factory does not have enough assembly space, these lines will overlap or cross, indicating a high risk of conflict. The mapping diagram can unfold time and node numbers on a two-dimensional coordinate, or record the connection relationship and priority through multiple columns of fields in a table, making the timing scheduling relationship of the entire assembly chain clearer and more intuitive.

[0079] Specifically, according to the assembly timing map, the order between the supply nodes and the corresponding delivery cycle are processed by interval aggregation to obtain a list of risk intervals that may cause delay accumulation between multiple supply nodes for key components. This step requires first retrieving the upstream and downstream connection information between all nodes in the mapping map, and clarifying the time span required for key components to arrive at node B or assembly factory through logistics transportation after node A completes production in each time period. If node A is shipped in the t1 period, and node B has production or assembly space only in the t2 period, it is necessary to determine whether the difference between t2-t1 and necessary links such as transportation and customs clearance introduce additional delays. If the above difference is too large and multiple nodes are idling while waiting for each other or in the process of timing alternation, it indicates that there is a high concentration of delivery period accumulation risk in these intervals. When performing interval aggregation, these waiting or idling periods need to be merged, and then they need to be sorted according to the order of node connection to determine whether the subsequent nodes are also in a situation where the production scheduling or assembly period does not match. If three or four consecutive node production windows are staggered, causing parts to wait in multiple locations, these superimposed delay intervals are marked in the aggregate list, and the names of key parts and corresponding process types are specified. For highly sensitive parts (for example, they are irreplaceable to the next layer of processes), higher risk factors can be given in the list to indicate that they are difficult to quickly transfer or redeploy once delayed in a multi-node network. This risk interval list provides aggregated time period data for subsequent progressive coupling calculations, and allows analysts to quickly identify which links are most likely to experience cumulative delays and potential chain reactions.

[0080] 104. According to the associated effect chain information, the logistics routes and schedulable supplier resources in the supply chain network are dynamically reconstructed to obtain an adjustment warning plan based on the assembly requirements of the key components.

[0081] In one embodiment of the present invention, the logistics routes and schedulable supplier resources in the supply chain network are dynamically reconstructed according to the associated effect chain information to obtain an adjustment warning plan based on the assembly requirements of the key components, including: path comparison processing is performed on the logistics routes where delays are concentrated or supply node conflicts appear in the associated effect chain information to obtain replaceable or avoidable transportation channels and transportation time periods corresponding to the transportation channels; priority screening is performed on the supply nodes according to the replaceable transportation channels and the corresponding transportation time periods to obtain a resource configuration list for scheduling key components across supply nodes; interactive connection processing is performed on the alternative supply nodes in the resource configuration list to obtain feasible countermeasures for assembly timing switching and logistics synchronization under the multi-point supply mode; according to the feasible countermeasures, the connection sequence of key components in the assembly link and the collaborative windows of each supply node are dynamically reconstructed to obtain an adjustment warning plan based on the assembly requirements of the key components.

[0082] Specifically, when performing path comparison processing on the logistics routes with delay concentration or supply node conflicts in the associated effect chain information, it is necessary to first analyze the combinations of routes and nodes marked as frequent delay sources or bottleneck links in the associated effect chain, and extract the corresponding transportation path attributes at the time and geographical levels. If multiple intervals of a route are shown as delay-intensive points, it indicates that the route is difficult to complete rapid passage within several time periods. It is necessary to split out all the time period details of this route and retrieve the traffic, checkpoint, and loading restrictions for each time period respectively. If it is found that adjacent nodes or transfer points have serious queuing or inspection backlogs during certain time periods, these time periods will be marked with conflict or high-load indicators. Subsequently, it is necessary to match other parallel alternative channels for each problematic route, such as adjacent highway main lines, railways, or multimodal transport plans, and compare the number of customs clearance ports, the estimated transportation duration, and the specific departure or shipment time at the upstream nodes of each plan. If an alternative route can avoid the known backlog points, it is regarded as a replaceable or avoidable channel, and the differential time period between it and the original route is recorded in the data structure. This differential time period can be represented as a set of start and end time segments to indicate that using this replacement channel within this part of the time can avoid peak or faulty areas. To present the comparison results more accurately, an additional check on transportation costs and vehicle allocation resources can be added to distinguish whether the channel is only feasible in the geographical space or also more in line with requirements in terms of cost and duration. If the replacement route also encounters loading conflicts during specific time periods, then continue to search for other routes until a replaceable solution with obvious time efficiency advantages or a lower degree of conflict is found, or mark this route as temporarily infeasible. The replaceable or avoidable channels extracted one by one and their corresponding time periods will be integrated into the path comparison output, providing a range of transport options for switching in the next stage of screening the priority of supply nodes.

[0083] Specifically, when performing a priority screening process on supply nodes according to the replaceable transportation channels and corresponding transportation time periods to obtain a resource allocation list for the cross-supply node scheduling of critical components, it is necessary to comprehensively evaluate whether each supply node has production capacity or storage surplus during different time periods, and match the replaceable channels with the logistics connection capabilities of the nodes themselves. If a node has the conditions for production or shipment during the current time period but cannot use the just-identified replaceable channels, it will be classified into the "low priority" or "inaccessible" category during the screening; if another node has both production capacity and logistics capabilities connected to the replacement route during the same time period, it will be marked as "high priority". At this time, it is also necessary to compare the relative distances and customs clearance time limits between nodes to determine which node will result in a shorter turnover time when allocating critical component orders. If multiple nodes meet the high priority criteria, these nodes can be retained simultaneously in the resource allocation list to form an alternative set that can be called in parallel. If some nodes have unsaturated production capacity during this time period but are located very remotely and need to pass through multiple checkpoints or transfer points to reach the assembly plant, they will be downgraded due to excessive expected delays in the priority calculation. After sorting or classifying each node in this way, a resource allocation list will be generated, listing the production capacity or inventory that each node can provide for critical components during different time periods, as well as the information on the matching replacement channel time periods. This list helps to coordinate the multi-point supply mode in the next step, and ultimately find a better scheduling strategy for critical components by comparing the mutual replacement feasibility between nodes and the urgency of assembly requirements.

[0084] Specifically, when performing interactive connection processing on the alternative supply nodes in the resource allocation list to obtain feasible countermeasures for assembly timing switching and logistics synchronization in the multi-point supply mode, it is necessary to map the production scheduling arrangements, replacement routes, and timing requirements of the assembly plant among different nodes onto the same timeline for comprehensive calculation. If Node A cannot meet the emergency order in the current period, but Node B has production capacity in the same period and can transport parts to the assembly plant within a reasonable time limit using the replacement channel, then in the interactive connection processing, part or all of the order for critical parts can be allocated to Node B. If both Node C and Node D can participate in the supply, but their locations are not very different from the factory, it is necessary to further determine whether the production scheduling and logistics occupation periods of the two overlap with the assembly window. If the production cycle of Node C is earlier than that of D, and the replacement channel is smoother between C and the factory, then C is preferred, and vice versa. For those situations where batch merging and shipping may be possible, it is necessary to splice the production scheduling information for adjacent periods together and then align it one by one with the period details of the feasible routes. If the assembly cycle can exactly accommodate multi-point supply, it is marked as feasible. After this interactive connection processing, a feasible countermeasure document will be output, showing the supply plans formed based on different node combinations, and indicating the order acceptance volume of each node, the transportation period of the used route, and the specific arrival time at the factory in the plan. If there are strong dependencies or extremely high route overlaps in some node combinations, corresponding notes will be added in the feasible countermeasures to remind to avoid or split them during the next dynamic reconstruction.

[0085] Specifically, according to the feasible countermeasures, when performing dynamic reconstruction processing on the connection sequence of critical parts in the assembly link and the coordination window of each supply node to obtain an adjustment warning plan based on the assembly requirements of the critical parts, it is necessary to re-calculate each node combination in the feasible countermeasures and its corresponding assembly connection sequence and logistics period in the overall network environment. If some nodes participate in multi-batch supply in the same period, it is necessary to determine during the reconstruction whether the production line of this node can operate continuously or whether an additional line change time needs to be set; if it also conflicts with another batch of assembly tasks at the same time, it is necessary to insert a buffer period or use a parallel line for diversion. If alternative suppliers or alternative production lines are found for some nodes during this dynamic reconstruction process, they can also be inserted into the connection sequence according to the previous priority screening results, so as to ensure that the assembly of critical parts will not be stagnated in case of peaks or sudden shortages. Once the linkage correction of all nodes - routes - assembly windows is completed, an adjustment warning plan will be generated. This plan clearly lists the best supply node selection, transportation route, and arrival time for critical parts within a specific period, and gives secondary or tertiary scheduling plans that can be adopted in case of emergencies. If it is found that the alternative resources for some nodes or routes are insufficient, a warning sign will be displayed in the plan to prompt the need to explore new supply channels or expand logistics routes in the future.

[0086] In this embodiment, by collecting and integrating the supplier production capacity data, logistics customs clearance data, and assembly plant production line operation data of multiple regions, the association between key components and multi-level supply nodes is determined; then, based on the node timeline information and geographical location parameters, multi-link scanning is performed to obtain the paths that may form delay propagation; combined with the progressive analysis of the production load and assembly timing of key components, the core nodes that cause multi-factor linkage risks are identified and locked; finally, by dynamically reconstructing the logistics routes and schedulable supplier resources, an adjustment warning plan that can meet the assembly requirements of key components is formed. This method can more comprehensively address the potential conflicts brought about by multi-region and multi-node collaboration and reduce the risk of delivery delays caused by delivery schedule fluctuations.

[0087] The supply chain risk assessment and warning method in the embodiment of the present invention has been described above. Next, the supply chain risk assessment and warning system in the embodiment of the present invention will be described. Please refer to Figure 2 , an embodiment of the supply chain risk assessment and warning system in the embodiment of the present invention includes:

[0088] A scenario mapping module 201, configured to perform scenario mapping processing on the supplier production capacity data, logistics customs clearance data, and assembly plant production line operation data distributed in multiple regions, and obtain a comprehensive data set for characterizing the corresponding relationship between key components and multi-level supply nodes in the supply chain network;

[0089] An interactive scanning module 202, configured to perform cross-link interactive scanning processing on the timeline information and geographical location parameters of each supply node in the supply chain network according to the comprehensive data set, and obtain a spatio-temporal correlation matrix covering the sequential dependence of supply nodes and potential delay propagation paths;

[0090] A coupling calculation module 203, configured to perform progressive coupling calculation processing on the production load and assembly timing of the key components according to the spatio-temporal correlation matrix, and obtain association effect chain information indicating the multi-factor linkage risk of the key components;

[0091] A dynamic reconstruction module 204, configured to perform dynamic reconstruction processing on the logistics routes and schedulable supplier resources in the supply chain network according to the association effect chain information, and obtain an adjustment warning plan based on the assembly requirements of the key components.

[0092] In the embodiments of the present invention, the supply chain risk assessment and early warning system runs the above-mentioned supply chain risk assessment and early warning method. The supply chain risk assessment and early warning system collects and integrates the production capacity data of suppliers, logistics customs clearance data, and production line operation data of assembly plants in multiple regions to determine the association between key components and multi-level supply nodes; then performs multi-link scanning based on node timeline information and geographical location parameters to obtain paths that may form delay propagation; then combines the progressive analysis of the production load of key components and the assembly timing to identify and lock the core nodes that cause multi-factor linkage risks; finally, through the dynamic reconstruction of logistics routes and schedulable supplier resources, an adjustment and early warning plan that can meet the assembly requirements of key components is formed. This method can more comprehensively address potential conflicts brought about by multi-region and multi-node collaboration, and reduce the risk of delivery delays caused by delivery date fluctuations.

[0093] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described system or device and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0094] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0095] The above is the case. The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A supply chain risk assessment and early warning method, characterized in that The described supply chain risk assessment and early warning method includes: Performing scenario mapping processing on the production capacity data of suppliers, logistics customs clearance data, and production line operation data of assembly plants distributed in multiple regions to obtain a comprehensive data set for characterizing the corresponding relationship between key components and multi-level supply nodes in the supply chain network; According to the comprehensive data set, performing cross-link interaction scanning processing on the time axis information and geographical location parameters of each supply node in the supply chain network to obtain a spatio-temporal correlation matrix covering the sequential dependence of supply nodes and potential delay propagation paths; According to the spatio-temporal correlation matrix, performing load comparison processing on the production capacity utilization rate of supply nodes and the demand changes of key components at different supply nodes to obtain a production capacity load index indicating the overall distribution of production load and the supply-demand balance state of components; performing timing calibration processing on the assembly supply nodes in the production capacity load index and the production scheduling periods of the corresponding assembly plants to obtain an assembly timing mapping diagram reflecting the assembly sequence and the delivery rhythm of each supply node; according to the assembly timing mapping diagram, performing interval aggregation processing on the sequence and corresponding delivery cycles between supply nodes to obtain a list of risk intervals indicating possible delay accumulation of key components between multiple supply nodes; performing progressive coupling calculation processing on the supply node dependence relationship in the risk interval list to obtain correlation effect chain information indicating the multi-factor linkage risk of the key components; According to the correlation effect chain information, performing dynamic reconstruction processing on the logistics routes and schedulable supplier resources in the supply chain network to obtain an adjustment early warning plan based on the assembly requirements of the key components.

2. The supply chain risk assessment and early warning method according to claim 1, characterized in that The performing scenario mapping processing on the production capacity data of suppliers, logistics customs clearance data, and production line operation data of assembly plants distributed in multiple regions to obtain a comprehensive data set for characterizing the corresponding relationship between key components and multi-level supply nodes in the supply chain network includes: According to the production capacity upper limit and production line switching cycle in the supplier production capacity data, performing production capacity matching processing on the order demand quantity of key components to obtain an initial supply node list indicating the order range that each supplier can undertake and the corresponding production capacity utilization rate; According to the initial supply node list, performing geographical time mapping processing on the port location and customs clearance period parameters in the logistics customs clearance data to obtain a customs clearance correlation table characterizing the customs clearance rhythm and customs clearance restrictions of components in different regions; Performing timing comparison processing on the production scheduling cycle, line change time, and component assembly priority in the production line operation data of the assembly plant to obtain an assembly dependence index for indicating the matching degree between the assembly load and the arrival time of key components; According to the customs clearance correlation table and the assembly dependence index, performing scenario mapping processing on the component delivery link between the supply node and the assembly plant to obtain a comprehensive data set for characterizing the corresponding relationship between key components and multi-level supply nodes in the supply chain network.

3. The supply chain risk assessment and early warning method according to claim 2, wherein The according to the customs clearance correlation table and the assembly dependence index, performing scenario mapping processing on the component delivery link between the supply node and the assembly plant to obtain a comprehensive data set for characterizing the corresponding relationship between key components and multi-level supply nodes in the supply chain network includes: Perform an interactive comparison process on the checkpoint locations and customs clearance time period parameters in the customs clearance association table to obtain a customs clearance feasible section that marks the geographical connection conditions between the supply nodes and the assembly plants; According to the assembly dependency index, perform a time period comparison process on the component assembly priority and the production scheduling cycle of the assembly plant to obtain an assembly connection list that indicates the degree of coordination between the component arrival requirements and the assembly processes; Perform a path verification process on the delivery capabilities of the corresponding supply nodes in the customs clearance feasible section and the assembly connection list to obtain a delivery mapping index that covers the component transportation cycle and the production scheduling window; According to the delivery mapping index, perform a scenario mapping process on the component delivery link between the supply nodes and the assembly plants to obtain a comprehensive data set that characterizes the corresponding relationship between the key components and the multi-level supply nodes in the supply chain network.

4. The supply chain risk assessment and early warning method according to claim 1, wherein The cross-link interactive scanning process of the time axis information and geographical location parameters of each supply node in the supply chain network according to the comprehensive data set to obtain a spatio-temporal correlation matrix covering the sequential dependency of supply nodes and the potential delay propagation path includes: Perform a time period segmentation process on the time axis information in the comprehensive data set to obtain a timing distribution record that indicates the overlapping interval between the production capacity start time period of the supply node and the order delivery rhythm; According to the timing distribution record, perform a line comparison process on the geographical location parameters corresponding to each supply node in the comprehensive data set to obtain a geographical mapping index that shows the transportation path and port connection relationship between each supply node; Perform a dependency connection comparison process on the connection conditions and potential delay factors of each supply node in the geographical mapping index to obtain a cross-supply node risk area list; According to the cross-supply node risk area list, perform a cross-link interactive scanning process on the production scheduling connection and logistics connection between the supply nodes to obtain a spatio-temporal correlation matrix.

5. The supply chain risk assessment and early warning method according to claim 4, characterized in that, The cross-link interactive scanning process of the production scheduling connection and logistics connection between the supply nodes according to the cross-supply node risk area list to obtain a spatio-temporal correlation matrix includes: Perform a centralized identification process on the supply node connection relationships in the cross-supply node risk area list to obtain a risk supply node table that covers the potential conflict points of the supply nodes and the corresponding geographical locations; According to the risk supply node table, perform a time period overlap comparison process on the production scheduling order between the supply nodes and the sequence relationship of the assembly processes to obtain a production scheduling comparison index that indicates the degree of temporal correlation of the assembly connection; Perform a line coupling process on the production scheduling comparison index and the logistics connection information to obtain a cross-link fusion record that can indicate the synchronization status of the transportation path and the assembly timing; According to the cross-link fusion record, perform an interactive scanning process on the production scheduling connection and logistics connection between the supply nodes to obtain a spatio-temporal correlation matrix.

6. The supply chain risk assessment and early warning method according to claim 1, wherein The dynamic reconstruction process of the logistics lines and schedulable supplier resources in the supply chain network according to the correlation effect chain information to obtain an adjustment warning plan based on the assembly requirements of the key components includes: Perform path comparison processing on the logistics routes with delayed concentration or supply node conflicts in the associated effect chain information to obtain replaceable or avoidable transportation channels and the corresponding transportation time periods of the transportation channels; According to the replaceable transportation channels and the corresponding transportation time periods, perform priority screening processing on the supply nodes to obtain a resource allocation list for scheduling key components across supply nodes; Perform interactive connection processing on the alternative supply nodes in the resource allocation list to obtain feasible countermeasures for assembly time sequence switching and logistics synchronization in the multi-point supply mode; According to the feasible countermeasures, perform dynamic reconstruction processing on the connection sequence of key components in the assembly link and the coordination windows of each supply node to obtain an adjustment warning plan based on the assembly requirements of the key components.

7. A supply chain risk assessment and early warning system, characterized in that The supply chain risk assessment and early warning system includes: A scenario mapping module for performing scenario mapping processing on the supplier production capacity data, logistics customs clearance data, and assembly plant production line operation data distributed in multiple regions to obtain a comprehensive data set for characterizing the corresponding relationship between key components and multi-level supply nodes in the supply chain network; An interactive scanning module for performing cross-link interactive scanning processing on the time axis information and geographical location parameters of each supply node in the supply chain network according to the comprehensive data set to obtain a spatio-temporal correlation matrix covering the sequential dependence of supply nodes and potential delay propagation paths; A coupling calculation module for performing load comparison processing on the production capacity utilization rate of supply nodes and the demand changes of key components at different supply nodes according to the spatio-temporal correlation matrix to obtain a production capacity load index indicating the overall distribution of production load and the supply-demand balance state of components; perform timing calibration processing on the production scheduling time periods of the assembly supply nodes and the corresponding assembly plants in the production capacity load index to obtain an assembly time sequence mapping diagram reflecting the assembly sequence and the delivery rhythm of each supply node; according to the assembly time sequence mapping diagram, perform interval aggregation processing on the sequence and corresponding delivery cycles between supply nodes to obtain a risk interval list identifying possible delay accumulation of key components between multiple supply nodes; perform progressive coupling calculation processing on the supply node dependence relationship in the risk interval list to obtain associated effect chain information indicating the multi-factor linkage risk of the key components; A dynamic reconstruction module for performing dynamic reconstruction processing on the logistics routes and schedulable supplier resources in the supply chain network according to the associated effect chain information to obtain an adjustment warning plan based on the assembly requirements of the key components.

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