Supply chain risk assessment and early warning method and system

Abstract

The invention provides a supply chain risk assessment and early warning method and system, and the method comprises the steps: collecting and integrating supplier productivity data, logistics customs clearance data and assembly factory production line operation data of a plurality of regions, so as to determine the correlation between key parts and multi-level supply nodes; then performing multi-link scanning according to node time axis information and geographic position parameters to obtain paths possibly forming delayed propagation; recognizing and locking core nodes causing multi-factor linkage risks by combining progressive analysis of production loads and assembly time sequences of key parts; and finally, dynamically reconstructing the logistics lines and the schedulable supplier resources to form an adjustment early warning scheme capable of meeting the assembly requirements of the key parts. According to the method, potential conflicts caused by multi-region and multi-node collaboration can be more comprehensively handled, and the risk of delivery delay caused by delivery time fluctuation is reduced.

Description

Technical Field

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

[0002] With the development of global economic integration and the deepening of industrial division of labor, the supply chain network has gradually shown multi-level, cross-regional and highly coordinated characteristics. In the fields of electronic products, automobiles, medical devices, etc., manufacturing companies often need to cooperate with parts suppliers, assembly plants and logistics service providers around the world across regions. Due to the large number and wide distribution of upstream and downstream nodes, the status fluctuations of each link may have a chain effect on the overall delivery, thereby causing potential supply chain disruptions or delays.

[0003] To reduce this type of risk, existing technologies usually adopt risk monitoring methods based on a single dimension or a single link, such as focusing only on the probability of delays in the logistics and transportation links, or only judging whether there is a supply shortage through historical order demand forecasts. However, this single-dimensional monitoring method is difficult to cover the comprehensive effects brought about by the simultaneous changes of multiple nodes in the supply chain network, and lacks systematic consideration of the interactions between different regions, different production lines, and different customs clearance environments. When the external environment fluctuates greatly, such as tariff adjustments, natural disasters, or changes in production processes, relying solely on traditional single-point risk assessments often fails to detect potential coupling risks in a timely manner, which can easily 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 problems in the field of supply chain risk assessment that the existing technology is difficult to cover cross-regional multi-node collaboration and lacks comprehensive consideration of sudden changes in the external environment because the monitoring method is only based on a single dimension or a single link; A first aspect of the present invention provides a supply chain risk assessment and early warning method, the supply chain risk assessment and early warning method comprising: Perform scenario-based mapping of supplier capacity data, logistics clearance data, and assembly plant production line operation data distributed in multiple regions to obtain a comprehensive data set that characterizes the correspondence between key components and multi-level supply nodes in the supply chain network; Based on the comprehensive data set, the timeline information and geographic location parameters of each supply node in the supply chain network are subjected to cross-link interactive scanning and processing to obtain a spatiotemporal correlation matrix covering the order dependency of the supply nodes and the potential delay propagation path; According to the spatiotemporal correlation matrix, the production load and assembly sequence of the key components are subjected to progressive coupling calculation processing to obtain correlation effect chain information indicating the multi-factor linkage risk of the key components; 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.

[0005] Optionally, in a first implementation of the first aspect of the present invention, the scenario-based mapping processing of the supplier capacity data, logistics clearance data, and assembly plant production line operation data distributed in multiple regions to obtain a comprehensive data set for characterizing the correspondence between key components and multi-level supply nodes in the supply chain network includes: According to the capacity cap and production line switching cycle in the supplier capacity data, capacity matching is performed on the order demand of key components to obtain an initial supply node list indicating the order range that each supplier can undertake and the corresponding capacity utilization rate; According to the initial supply node list, geographic time mapping is performed on the customs clearance location and customs clearance period parameters in the logistics customs clearance data to obtain a customs clearance association table representing the customs clearance rhythm and customs clearance restrictions of parts in different regions; Performing time sequence comparison processing on the production schedule, line change time and component assembly priority in the assembly plant production line operation data to obtain an assembly dependency index that indicates the degree of matching between the assembly load and the arrival time of key components; According to the customs clearance association table and assembly dependency index, scenario-based mapping processing is performed on the parts delivery link between the supply node and the assembly plant to obtain a comprehensive data set for characterizing the correspondence between key parts and multi-level supply nodes in the supply chain network.

[0006] Optionally, in a second implementation of the first aspect of the present invention, the scenario-based mapping process is performed on the parts delivery link between the supply node and the assembly plant according to the customs clearance association table and the assembly dependency index, and the comprehensive data set obtained for characterizing the corresponding relationship between key parts and multi-level supply nodes in the supply chain network includes: Performing interactive comparison processing on the checkpoint location and the customs clearance 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 plant; According to the assembly dependency index, the assembly priority of the parts and components is compared with the assembly factory production cycle execution period to obtain an assembly connection list indicating the degree of coordination between the parts arrival requirements and the assembly process; Perform path verification processing on the delivery capacity of the corresponding supply nodes in the customs clearance feasible section and the assembly connection list to obtain a delivery mapping index covering the parts transportation cycle and production scheduling window; According to the delivery mapping index, scenario-based mapping processing is performed on the parts delivery link between the supply node and the assembly plant to obtain a comprehensive data set for characterizing the corresponding relationship between key parts and multi-level supply nodes in the supply chain network.

[0007] Optionally, in a third implementation of the first aspect of the present invention, the cross-link interactive scanning and processing of the time axis information and geographic location parameters of each supply node in the supply chain network is performed according to the comprehensive data set to obtain a spatiotemporal correlation matrix covering the order dependency of the supply nodes and the potential delay propagation path, including: Performing time segmentation processing on the time axis information in the comprehensive data set to obtain a time series distribution record indicating the overlap between the supply node production capacity start-up period and the order delivery rhythm; According to the time series distribution record, a route comparison process is performed on the geographical location parameters corresponding to each supply node in the comprehensive data set to obtain a geographical mapping index indicating the connection relationship between the transportation path and the port between each supply node; Perform dependency connection comparison processing on the connection status of each supply node and the potential delay factors in the geographic mapping index to obtain a list of cross-supply node risk areas; According to the cross-supply node risk area list, the production scheduling connection and logistics connection between the supply nodes are subjected to cross-link interactive scanning processing to obtain a spatiotemporal correlation matrix.

[0008] Optionally, in a fourth implementation of the first aspect of the present invention, the cross-link interactive scanning process is performed on the production scheduling connection and logistics connection between the supply nodes according to the cross-supply node risk area list to obtain a spatiotemporal correlation matrix including: Centrally identify 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, a time period overlap comparison process is performed on the production scheduling sequence between the supply nodes and the assembly process sequence relationship to obtain a production scheduling comparison index indicating the degree of association of the assembly connection sequence; Performing line coupling processing on the production scheduling reference 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 sequence; According to the cross-link fusion records, interactive scanning and processing is performed on the production scheduling connection and logistics connection between supply nodes to obtain a spatiotemporal correlation matrix.

[0009] Optionally, in a fifth implementation of the first aspect of the present invention, the step of performing progressive coupling calculation processing on the production load and assembly sequence of the key components according to the spatiotemporal correlation matrix to obtain the correlation effect chain information indicating the multi-factor linkage risk of the key components includes: According to the spatiotemporal correlation matrix, load comparison processing is performed on the capacity utilization rate of the 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 balance state of component supply and demand; Performing timing calibration processing on the assembly supply nodes in the capacity load index and the production schedules 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 map, the sequence between supply nodes and the corresponding delivery cycle are aggregated to obtain a risk interval list that identifies possible delay accumulation of key components between multiple supply nodes; A progressive coupling calculation process is performed on the supply node dependencies in the risk interval list to obtain associated effect chain information indicating the multi-factor linkage risk of the key components.

[0010] Optionally, in a sixth implementation of the first aspect of the present invention, dynamically reconstructing 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 includes: Performing path comparison processing on the logistics routes with concentrated delays or conflicting supply nodes in the associated effect chain information to obtain alternative or avoidable transportation channels and the transportation time periods corresponding to the transportation channels; According to the replaceable transportation channels and the corresponding transportation time periods, priority screening is performed on the supply nodes to obtain a resource configuration list for cross-supply node scheduling of key components; Performing interactive connection processing on the candidate 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 process 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.

[0011] A second aspect of the present invention provides a supply chain risk assessment and early warning system, the supply chain risk assessment and early warning system comprising: The scenario mapping module is used to perform scenario-based mapping processing on supplier capacity data, logistics clearance data, and assembly plant production line operation data distributed in multiple regions, and obtain a comprehensive data set used to characterize the corresponding relationship between key components and multi-level supply nodes in the supply chain network; An interactive scanning module is used to perform cross-link interactive scanning processing on the time axis information and geographic location parameters of each supply node in the supply chain network according to the comprehensive data set, so as to obtain a spatiotemporal correlation matrix covering the order dependency of the supply nodes and the potential delay propagation path; A coupling calculation module, for performing progressive coupling calculation processing on the production load and assembly sequence of the key components according to the spatiotemporal correlation matrix, to obtain correlation effect chain information indicating the multi-factor linkage risk of the key components; The dynamic reconstruction module is used to dynamically reconstruct the logistics routes and schedulable supplier resources in the supply chain network according to the associated effect chain information, and obtain an adjustment warning plan based on the assembly requirements of the key components.

[0012] The above supply chain risk assessment and early warning method and system collects and integrates supplier capacity data, logistics 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 performs multi-link scanning based on node timeline information and geographic location parameters to obtain paths that may cause delayed propagation; then combines the progressive analysis of key component production load and assembly timing to identify and lock the core nodes that cause multi-factor linkage risks; finally, through dynamic reconstruction of logistics routes and schedulable supplier resources, an adjustment early warning plan that can meet the assembly needs of key components is formed. This method can more comprehensively deal with potential conflicts caused by multi-regional and multi-node collaboration, and reduce the risk of delivery delays caused by delivery date fluctuations.

[0013] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0014] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic diagram of a first embodiment of a supply chain risk assessment and early warning method in an embodiment of the present invention; Figure 2 Schematic diagram of an embodiment of a supply chain risk assessment and early warning system in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device end including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or device ends.

[0018] To facilitate understanding of this embodiment, a supply chain risk assessment and early warning method disclosed in an embodiment of the present invention is first described in detail. Figure 1 As shown, the method comprises the following steps: 101. Perform scenario-based mapping of supplier capacity data, logistics clearance data, and assembly plant production line operation data distributed in multiple regions to obtain a comprehensive data set that characterizes the corresponding relationship between key components and multi-level supply nodes in the supply chain network; In one embodiment of the present invention, the scenario-based mapping processing of the supplier 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 correspondence between key components and multi-level supply nodes in the supply chain network includes: performing capacity matching processing on the order demand of key components according to the capacity upper limit and production line switching cycle in the supplier capacity data, and obtaining an initial supply node list indicating the order range and corresponding capacity utilization rate that each supplier can undertake; performing geographic time mapping processing on the checkpoint location and customs clearance period parameters in the logistics customs clearance data according to the initial supply node list, and obtaining a customs clearance association table characterizing the customs clearance rhythm and customs clearance restrictions of components in different regions; performing time sequence comparison processing on the production scheduling cycle, line change time and component assembly priority in the assembly plant production line operation data, and obtaining an assembly dependency index for indicating the degree of matching between the assembly load and the arrival period of key components; performing scenario-based 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 dependency index, and obtaining a comprehensive data set for characterizing the correspondence between key components and multi-level supply nodes in the supply chain network.

[0019] Specifically, according to the capacity limit and production line switching cycle in the supplier capacity data, the capacity matching process is re-performed for the demand for key components, and an initial supply node list indicating the order range and corresponding capacity utilization rate that each supplier can undertake is obtained. represents the maximum available output of supplier i, D represents the total order demand, T represents its production line switching cycle, and defines It is used to measure the supplier's carrying capacity in theory. In order to subdivide the capacity usage in each period, the production line switching period can be divided into several intervals. , and calculate the allocable proportion of D in each interval ;If the same supplier cannot cover the demand in a certain interval, part of the order will be transferred to other supply nodes. After being combined with each other, a list of the quantities of key seasonal parts that each supplier can undertake in different time periods can be generated, and this information can be integrated into the initial supply node list. Additional weights are added to the calculation to ensure that urgently needed parts can receive production capacity allocation more quickly, so that the initial supply node list is more in line with actual demand.

[0020] Specifically, according to the initial supply node list, the checkpoint location and the customs clearance period parameters in the logistics customs clearance data are subjected to geographic time mapping processing to generate a customs clearance association table representing the customs clearance rhythm and customs clearance restrictions of parts in different regions. Let the checkpoint location coordinates be , opening hours are , the supply node and the assembly factory are respectively and The transport distance is defined as dist_{ikj}= , and will arrive at the time Comparison To determine whether the opening period constraint of the checkpoint is met, if there are multiple inspection links at the checkpoint, ω_{ik} can also be set to represent the customs clearance waiting coefficient, and score(i,k,j)=α Measure the time taken for the route. Traverse all feasible paths between the supply node and the assembly plant and select the best one, and write it into the customs clearance association table to record the working hours and additional inspection requirements of the logistics channel and the corresponding checkpoint.

[0021] Specifically, the production cycle, line change time and component assembly priority in the assembly plant production line operation data are processed in time sequence comparison to obtain the assembly dependency index used to match the assembly load with the arrival time of key components. m , the line change time sequence is , the component assembly priority set is , the estimated arrival time in the customs clearance association table is compared with T, if the arrival time falls within interval, it is determined that the production line can be connected to the production schedule; if the arrival time exceeds r, a line change downtime τ is inserted to quantify the additional waiting time. , a more advanced assembly sequence can be assigned within the production scheduling cycle to reduce the delay of key components in the waiting queue. This method combines the customs clearance process with the production scheduling process, and the output assembly dependency index presents the degree of coupling between the arrival time of each component and the production scheduling period of the assembly factory, providing a time sequence comparison basis for subsequent links to judge the overall assembly progress. If it is found that there is no available window for its components in the current production scheduling cycle, it can be added in the next batch. +1 for rescheduling to form a cycle-by-cycle iterative view of assembly arrangements.

[0022] Specifically, according to the customs clearance association table and assembly dependency index, scenario mapping processing is performed on the parts delivery link between the supply node and the assembly plant to obtain a comprehensive data set for characterizing the correspondence between key parts and components and multi-level supply nodes in the supply chain network. A matrix M can be established in the database, with its row index corresponding to the supply node and the column index corresponding to the assembly plant. M(i,j) stores the optimal transportation route and the expected arrival-assembly time period of the parts. If part x corresponds to multiple alternative paths in the customs clearance association table, multiple record entries are added at the M(i,j) position. The assembly dependency index is used to verify its compatibility with the production scheduling cycle to select a valid route. It represents the coordination degree of supply node i to factory j in the production scheduling cycle r, which is =g(dis , , ) form to comprehensively evaluate multiple parameters and write the evaluation results into a comprehensive data set. This comprehensive data set integrates data from multiple dimensions, including capacity configuration information, logistics clearance restrictions, and assembly scheduling priorities. It can provide accurate key component distribution and connection information for subsequent risk assessment, and presents a high degree of correlation readability in a multi-level supply chain network.

[0023] Furthermore, according to the customs clearance association table and the assembly dependency index, scenario-based mapping processing is performed on the parts delivery link between the supply node and the assembly plant to obtain a comprehensive data set for characterizing the correspondence between key parts and multi-level supply nodes in the supply chain network, including: interactive comparison processing is performed on the checkpoint location and customs clearance 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 plant; according to the assembly dependency index, the parts assembly priority and the assembly plant production cycle execution period comparison processing are performed to obtain an assembly connection list indicating the degree of coordination between the parts arrival demand and the assembly process; path verification processing is performed on the delivery capacity of the corresponding supply node in the customs clearance feasible section and the assembly connection list to obtain a delivery mapping index covering the parts transportation cycle and the production scheduling window; according to the delivery mapping index, scenario-based mapping processing is performed on the parts delivery link between the supply node and the assembly plant to obtain a comprehensive data set for characterizing the correspondence between key parts and multi-level supply nodes in the supply chain network.

[0024] Specifically, interactive comparison processing is performed on the checkpoint location and the customs clearance period parameters in the customs clearance association table to obtain a feasible customs clearance section that marks the geographical connection conditions between the supply node and the assembly plant. The customs clearance association table usually contains the coordinate information, opening period and customs clearance inspection requirements of multiple checkpoints. It is necessary to conduct feasibility analysis of route connection for these checkpoints one by one based on the respective geographical locations of the supply nodes and assembly plants. A record can be created for each checkpoint first, and the record contains data such as the longitude and latitude of the checkpoint, the type of acceptable goods, the start and end time of the opening period, and the inspection frequency. Subsequently, the distance between the location coordinates of the supply node and the checkpoint is evaluated, and the time when the goods depart from the supply node and arrive at the checkpoint is calculated based on the estimated transportation speed and the traffic control requirements that may be encountered. If the time falls within the opening period of the checkpoint, it means that the basic conditions for cross-checkpoint transportation are met in both space and time. This route can be included in the candidate route and assigned a customs connection mark; if the arrival time is earlier than the opening time or later than the checkpoint work deadline, it is necessary to record the expected waiting time or detour requirements and classify it as a route to be observed when outputting this step. For the same supply node leading to different checkpoints, it is necessary to perform a comparison independently for each checkpoint and summarize a list of several routes. Each route is annotated with details such as whether it meets the opening time, whether there is an inspection delay, and whether there are additional licensing requirements. If it is found that the geographical location of the checkpoint is 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 factory has the corresponding receiving or inspection capabilities for the remaining time of subsequent transportation.

[0025] Specifically, according to the assembly dependency index, the assembly priority of the parts and the production cycle of the assembly factory are compared and processed to obtain an assembly connection list indicating the degree of coordination between the arrival demand of the parts and the assembly process. The assembly dependency index usually contains the production time period of the assembly factory, the line change time and the priority parameters of the parts in the assembly process. The index needs to be matched with the feasible section identified in the previous step on the time axis. If the parts are determined to be able to arrive at the factory through a specific checkpoint during the customs clearance opening period, it is necessary to further determine whether their arrival time meets the specific window of the factory production scheduling. The production cycle can be subdivided into several process intervals, each of which has its own start and end time. The line change time is a special node, and it is necessary to distinguish the downtime caused by the assembly line conversion or debugging. The priority of the parts determines its queuing order in the production cycle. If the priority value is high, it will be placed in the assembly list in advance within the feasible process interval, while the parts with lower priority need to wait for the former to complete the assembly before entering the production line. In this way, one or more possible matching production scheduling windows can be determined for each component route that enters the factory through a feasible section. 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 final output assembly connection list will integrate the arrival time, priority, factory production cycle and line change time of the components into the same table, indicating the time correspondence between the components and the specific production line processes. This list can reflect which components can be assembled immediately after actual arrival, and which components need to be adjusted to the next round of production line process intervals due to scheduling conflicts, thereby completing the connection judgment between the arrival demand and the assembly process in the time dimension.

[0026] Specifically, the delivery capacity of the corresponding supply node in the feasible section for customs clearance and the assembly connection list is subjected to path verification processing to obtain a delivery mapping index covering the transportation cycle of parts and components and the production scheduling window. The path verification processing needs to combine information from both geographical and time dimensions, and gradually verify whether the overall flow of goods in the cross-border transportation and arrival at the factory production scheduling link meets the delivery capacity of the supply node itself. The delivery capacity of the supply node is related to its production capacity, and is also affected by the number of orders and assembly priority. It also includes the matching requirements of the shipping time and the customs clearance opening time. If the production capacity of a supply node is full in the current period or the order is delayed due to the switching of production lines, it is necessary to record the current status of being unable to ship in the scheduling table of the node. If the node is determined to be able to ship on time, it is verified with the feasible section for customs clearance, and the overlap between the time of arrival at the border after the node is sent and the border opening period is verified, and the potential queuing delay is calculated at the line change time or inspection period. Next, the customs clearance record after arriving at the checkpoint is compared with the connection status of the factory production scheduling window in the assembly connection list. If the parts enter the factory during the production cycle gap or priority channel, this route is considered a valid path and marked as "assembled". If time misalignment or insufficient node production capacity is found during the comparison process, the line will be marked as "temporarily unavailable" or "needs to be postponed" to ensure that it will not be mistakenly regarded as a feasible solution during the comprehensive selection later. The path verification process outputs a delivery mapping index, which lists the transportation and assembly connection situations that can be completed by each supply node in different time periods, and cooperates with the information of specific routes, checkpoint opening periods and factory production scheduling intervals to make the timing connection of the entire supply-customs clearance-assembly link more transparent.

[0027] Specifically, according to the delivery mapping index, the parts delivery link between the supply node and the assembly plant is subjected to scenario mapping processing to obtain a comprehensive data set for characterizing the corresponding relationship between key parts 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 node, the geographic and open data in the feasible section of customs clearance, and the priority and assembly progress judgment in the assembly connection list, and then summarize the feasible or partially feasible routes confirmed after path verification into a unified data structure. The data structure can be designed as a multidimensional array or a multi-relational table, the row index represents the supply node number and its delivery time period, the column index represents the assembly plant and its scheduling window, and the intermediate unit records the gateway route, waiting time and assembly priority information. If the same part has selectable routes and scheduling windows in multiple time periods, multiple rows of candidate states are written in the same node-factory combination unit, with the priority or time-consuming label of each candidate. Subsequently, by searching this comprehensive data set, we can quickly understand in which periods each component can pass through customs smoothly, whether it can be assembled online in time after arrival, and whether there are backup nodes or backup production cycles to achieve diversion when priorities conflict. This data set presents the multi-level relationship between key components and the supply chain network at a macro level, so that the geographical location, customs clearance channel, production cycle and assembly requirements of each node can be integrated in the same scenario.

[0028] 102. Based on the comprehensive data set, the time axis information and geographic location parameters of each supply node in the supply chain network are subjected to cross-link interactive scanning and processing to obtain a spatiotemporal correlation matrix covering the order dependency of the supply nodes and the potential delay propagation path; In one embodiment of the present invention, the time axis information and geographic location parameters of each supply node in the supply chain network are subjected to cross-link interactive scanning processing based on the comprehensive data set to obtain a spatiotemporal correlation matrix covering the sequential dependency and potential delay propagation path of the supply nodes, including: time period segmentation processing of the time axis information in the comprehensive data set to obtain a time series distribution record indicating the overlapping interval between the supply node production capacity start-up period and the order delivery rhythm; based on the time series distribution record, line comparison processing is performed on the geographic location parameters corresponding to each supply node in the comprehensive data set to obtain a geographic mapping index indicating the connection relationship between the transportation path and the port between each supply node; dependency connection comparison processing is performed on the connection status and potential delay factors of each supply node in the geographic mapping index to obtain a list of cross-supply node risk areas; based on the cross-supply node risk area list, production scheduling connection and logistics connection between supply nodes are subjected to cross-link interactive scanning processing to obtain a spatiotemporal correlation matrix.

[0029] Specifically, when the time axis information in the comprehensive data set is segmented into time periods, it is necessary to first extract the capacity start time, order receiving or delivery time, and production line conversion period of each supply node from the data set, and convert this information uniformly into a comparable time dimension. If a node has multiple capacity peaks or a line change shutdown interval in one day, it is necessary to subdivide it according to different time slices, and clarify the start and end points of each time slice by setting non-overlapping start and end time scales. In this way, it can be ensured that in a complete time series view, the capacity level and order delivery rhythm corresponding to each node in different time slices can be accurately quantified. Subsequently, it is necessary to compare it with the arrival period of the order demand. If the capacity start period of the node happens to cover the order time or lock time of a batch of orders, it is determined that there is an overlap interval between capacity and order in this period; if it is found that the capacity start period is later than the time when the order demand is generated, staggered delivery may occur after segmentation, and such "delayed" features need to be recorded in the form of annotations or comments. For scenes that cross zero o'clock or a production cycle in a day, they should also be assigned to the next time slice to avoid data overlap or missing. After determining the specific time slices of each node, these segmented information can be output as time series distribution records, and fields such as node identification, time period start and end, capacity value, and order demand value can be added to the records to intuitively reflect the matching of node capacity and orders. Time series distribution records can play a role in comparison and screening. If a node has no capacity in some time periods but receives a large number of orders, it will be highlighted in the record; if the node can meet orders in multiple time periods, these time periods can be further integrated for the next link to complete the data preparation required for sequential dependency and delay evaluation.

[0030] 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.

[0031] Specifically, when the connection status of each supply node in the geographic mapping index and the potential delay factors are compared and processed by dependency connection, and a list of risk areas across supply nodes is obtained, the geographic mapping index needs to be cross-correlated with the time series distribution record, and the route connection status in the spatial dimension and the node capacity and order demand status in the time dimension are unified under the same comparison framework. If two nodes are shown as capacity complementary relationships in different time slices, but their geographic mapping indexes expose traffic bottlenecks or port turnover restrictions, a high delay probability value will be obtained during the dependency connection comparison; if the geographic mapping index shows that there is a convenient route between the two nodes and the time series distribution record also shows that the capacity connection timing is just matched, it will be marked as a low-risk link. In order to improve the comparison logic, the delay trigger source can be monitored at the same time, such as the delay of fixed inspection at the checkpoint, the waiting time of multi-link multi-ticket cargo consolidation, or the transportation hub that is prone to congestion. If a route has road maintenance information during highway or railway transportation, or the checkpoint suspends customs clearance on certain dates, a "delay factor" will be superimposed during the dependency connection comparison, and the route will be included in the risk statistics object. After analyzing these connection comparison results, node pairs with high delay probability or high interdependence can be organized into a list of cross-supply node risk areas. Each area may represent a collection of several adjacent nodes or nodes along the same transportation channel, marking the areas that need special attention when allocating resources or production capacity, laying the data conditions for a deeper spatiotemporal correlation scan in the next step.

[0032] Specifically, according to the cross-supply node risk area list, the production scheduling connection and logistics connection between the supply nodes are subjected to cross-link interactive scanning and processing. When the spatiotemporal correlation matrix is ​​obtained, it is necessary to further check whether cascade delays or sequence crowding problems will occur based on the risk areas obtained by the previous comparison. If a node is delayed in delivery due to a change in capacity scheduling in a specific period, and the downstream node is already full of other orders in the next period, the connection between the two nodes may have a continuous impact on the entire transportation path. To evaluate this continuity, the time axis and the geographic coordinate axis can be cross-positioned in the matrix, and the different time periods corresponding to each node can be mapped to the row and column positions of the matrix, and the connection status, delay amplitude or possible conflict information can be marked in the cell. If the scanning results show that the production scheduling time sequence of multiple nodes conflicts with each other, a highlighted area will be formed in the matrix, indicating that these nodes are too dependent on each other in time or route selection. Through this interactive scanning method, it is possible to check which nodes have high loads in the same time period and which nodes have repeated queues or blockages at the logistics gateway under the same visualization structure, thereby discovering potential paths for large-scale delay propagation. The final spatiotemporal correlation matrix includes the temporal distribution of production capacity, geographical connection information and delayed connection probability, showing the complexity of the connection between nodes in the entire network. It has an intuitive auxiliary role in judging the sequential dependencies between multiple nodes and the diffusion mechanism of delays in multi-level supply networks.

[0033] Furthermore, according to the cross-supply node risk area list, the production scheduling connection and logistics connection between supply nodes are interactively scanned across links to obtain a spatiotemporal association matrix, including: performing centralized identification processing on the supply node connection relationship 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 time period overlap comparison processing on the production scheduling sequence and assembly process sequence between supply nodes to obtain a production scheduling comparison index indicating the degree of association of assembly connection timing; performing line coupling processing on the production scheduling comparison index and logistics connection information to obtain a cross-link fusion record that can indicate the synchronization status of the transportation path and assembly timing; according to the cross-link fusion record, interactively scanning processing is performed on the production scheduling connection and logistics connection between supply nodes to obtain a spatiotemporal association matrix.

[0034] Specifically, a centralized identification process is performed on the supply node connection relationship 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. This step requires first reading all node pairs listed in the cross-supply node risk area list, as well as the mutual influence data of these node pairs in the time and space dimensions, and then filtering and aggregating them in two directions: geographical proximity and business process correlation. If an area is marked as prone to order squeeze or delivery delay, all nodes in the area must be included in the centralized identification process. A record can be created for each node first, and the record includes attributes such as the node's unique identifier, available production capacity information, upstream and downstream order relationships, and coordinate positions. If the list shows that multiple nodes have interdependent scheduling resources or shared logistics routes, their connection strength is calculated during identification, and a connection link data with a weight is formed. Subsequently, it is necessary to locate several nodes with relatively concentrated geographic coordinates in the same area, and match the specific longitude and latitude of these nodes with potential conflict points one by one. If it is found that the adjacent nodes all indicate a surge in order volume in a certain period, the records of this period are classified as high-risk periods, and annotations such as "high load" or "emergency dispatch" are added to the node labels. If individual nodes are marked as repeatedly delayed or have quality failures in the list, they can also be assigned separate risk levels in the identification table. By merging and sorting multiple connection links, the node combinations that need the most attention can be screened out and summarized into a risk supply node table. The table can be presented in the form of a table or database, and contains the geographic coordinates of each node, adjacent node references, recent order throughput, and connection strength, so as to clarify which nodes are very likely to trigger conflicts or delays in the same area or the same business chain segment.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 103. Perform progressive coupling calculation processing on the production load and assembly sequence of the key components according to the spatiotemporal correlation matrix to obtain correlation effect chain information indicating multi-factor linkage risks of the key components; In one embodiment of the present invention, the step of performing progressive coupling calculation processing on the production load and assembly sequence of the key components according to the spatiotemporal association matrix to obtain the associated effect chain information indicating the multi-factor linkage risk of the key components includes: performing load comparison processing on the capacity utilization of the supply nodes and the demand changes of the key components at different supply nodes according to the spatiotemporal association matrix to obtain a capacity load index indicating the overall distribution of the production load and the balance state of supply and demand of the components; performing timing calibration processing on the assembly supply nodes in the capacity load index and the production schedules of the corresponding assembly plants 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 between the supply nodes and the corresponding delivery cycles according to the assembly timing mapping diagram to obtain a risk interval list indicating that delay accumulation may occur between multiple supply nodes for key components; performing progressive coupling calculation processing on the supply node dependencies in the risk interval list to obtain the associated effect chain information indicating the multi-factor linkage risk of the key components.

[0039] Specifically, according to the spatiotemporal association matrix, the capacity utilization rate of the supply node and the demand changes of key components at different supply nodes are subjected to load comparison processing to obtain the capacity load index indicating the overall distribution of production load and the balance state of component supply and demand. This process requires first extracting the capacity utilization rate information of each supply node in different time slices and the demand for key components assigned to the node from the spatiotemporal association matrix. If supply node A operates at high load in the first time slice and undertakes a large number of orders, while node B is still idle in the same time slice, node A will be identified as full load or tense during the load comparison processing, and node B will be identified as dispatchable or with spare capacity. In order to accurately reveal the degree of consumption of production resources by component demand, it is necessary to count the types and quantities of orders that each node has undertaken in the time slice, and compare them with the maximum capacity limit of the node, its switching cycle, equipment load limit and other parameters. If it is found that the demand for components has reached the critical value of the node's carrying capacity, it will be recorded as a warning level in the load comparison result, and if it has not reached the critical value, it will be recorded as being able to continue to undertake. This comparison process not only examines the load situation of each node itself, but also compares the differences in the demand for the same parts of all nodes in the same period, so as to distinguish whether the overall supply is sufficient and whether there is overheating or surplus at local nodes when summarizing. Finally, the actual load capacity and capacity limit of each node, the corresponding key component name and demand ratio are listed in the capacity load index according to the node number or time slice order, and the current supply and demand balance status is marked with text or numerical values. If a node is obviously overloaded, it will be specially marked in the index to prompt subsequent links to pay extra attention.

[0040] Specifically, the assembly supply node in the capacity load index and the production schedule of the corresponding assembly factory are subjected to timing calibration processing to obtain an assembly timing mapping diagram reflecting the assembly sequence and the delivery rhythm of each supply node. This step requires mapping the load data of each node in the capacity load index with the production schedule of the assembly factory, so as to identify which nodes have the capacity output conditions in a certain period of time and whether the corresponding parts can be assembled online in the factory according to the production schedule. If the assembly supply node is fully loaded in this period of time and still receives additional orders, there will be a production scheduling conflict or assembly sequence delay in the mapping diagram; if the node is available in this period of time and the factory production schedule shows that there is a suitable gap, the output of the node can be arranged in the nearest production scheduling window to reduce the waiting time of the parts. The timing calibration process not only considers the docking of a single node with the factory, but also synchronously compares the capacity carrying capacity of the surrounding nodes for the same parts. If the remaining nodes have more sufficient capacity and are geographically closer to the factory, they will be preferentially connected to the corresponding assembly period of the factory in the mapping diagram, thereby improving the flow efficiency of orders in the overall supply chain. Each connection line in the map represents the collaborative channel between the supply node and the assembly plant, and is accompanied by the assembly sequence or assembly cycle number to reflect whether the parts can be successfully put online as planned. If there are multiple consecutive nodes with tight production capacity in the same time period and the factory does not have enough assembly space, these lines will overlap or cross, indicating a high risk of conflict. The map can expand the time and node number on the two-dimensional coordinates, or record the connection relationship and priority through multiple columns in the table, making the timing scheduling relationship of the entire assembly chain clearer and more intuitive.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] Specifically, when performing path comparison processing on logistics routes with concentrated delays or supply node conflicts in the associated effect chain information, it is necessary to first parse the route and node combinations marked as frequent delay sources or bottleneck links in the associated effect chain, and extract their corresponding transportation path attributes at the time and geographical levels. If multiple sections of a route are displayed as delay-intensive points, it means that it is difficult for the route to complete fast passage in certain time periods. It is necessary to split all time period details of the route and search for traffic, checkpoints and loading restrictions in each time period. If it is found that adjacent nodes or transfer points have serious queues or inspection backlogs in certain time periods, these time periods will be marked with conflict or high load. Subsequently, it is necessary to match other parallel alternative channels for each problematic route, such as adjacent highway trunk lines, railways or multimodal transport schemes, and compare the number of customs clearance ports, estimated transportation time, and specific time when vehicles can be dispatched or shipped at upstream nodes for each scheme. If an alternative route can avoid known backlog points, it is considered as an alternative or avoidable channel, and the differentiated time period from the original route is recorded in the data structure. This differentiated time period can be represented as a set of start and end time segments to illustrate that the alternative channel can avoid peak or fault areas during this part of time. In order to present the comparison results more accurately, additional checks on transportation costs and vehicle allocation resources can be added to distinguish whether the channel is only feasible in geographical space or is more in line with the requirements in terms of cost and duration. If the alternative route also encounters loading conflicts during a specific period of time, other routes will continue to be searched until an alternative solution with obvious timeliness advantage or lower conflict is found, or the route is marked as temporarily infeasible. The alternative or avoidance channels extracted one by one and their corresponding time periods will be integrated into the path comparison output, providing a range of transportation options that can be switched for the next stage of screening the priority of supply nodes.

[0046] Specifically, when the priority of the supply nodes is screened according to the replaceable transportation channels and the corresponding transportation time periods, and the resource configuration list for the cross-supply node scheduling of key components is obtained, it is necessary to comprehensively evaluate whether each supply node has production capacity or storage margin in different time periods, and match the replaceable channels with the logistics connection capabilities of the nodes themselves. If a node has production or delivery conditions in the current time period, but cannot use the replaceable channels that have just been identified, it will be classified as "low priority" or "unreachable" during the screening; if another node has both production capacity and logistics capabilities connected to the replacement line in the same time period, it will be marked as "high priority". At this time, it is also necessary to compare the relative distance and customs clearance time limit between the nodes to determine which node will bring a shorter turnaround time when assigning the key component order. If multiple nodes meet the high priority standard, these nodes can be retained in the resource configuration list at the same time to form an alternative set that can be called in parallel. If some nodes have unsaturated production capacity in this period, but are geographically very remote and need to pass through multiple checkpoints or transfer points to reach the assembly plant, they will be downgraded in the priority calculation due to the expected delay. After sorting or classifying the nodes in this way, a resource allocation list will be generated, listing the capacity or inventory that each node can provide for key components in different time periods, as well as the corresponding replacement channel time period information. This list will help coordinate the multi-point supply model in the future, and ultimately find a better scheduling strategy for key components by comparing the feasibility of mutual replacement between nodes and the urgency of assembly needs.

[0047] Specifically, when performing interactive connection processing on the alternative supply nodes in the resource configuration list and obtaining feasible countermeasures for assembly timing switching and logistics synchronization under the multi-point supply mode, it is necessary to map the production scheduling, replacement routes and timing requirements of the assembly factory between different nodes to the same time axis for comprehensive calculation. If node A cannot meet the urgent order in the current period, but node B has production capacity in the same period and can use the replacement channel to transport the parts to the assembly factory within a reasonable time limit, then the order of key parts can be partially or completely allocated to node B in the interactive connection processing. If both node C and node D can participate in the supply, but their locations are not much different from the factory, it is necessary to further determine whether the production scheduling and logistics occupation time 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 more unobstructed between C and the factory, C is preferred, and vice versa. For those situations where batch merging and shipment may be adopted, it is necessary to splice the production scheduling information of adjacent time periods together, and then align them one by one with the time period details of the feasible routes. If the assembly cycle can just accommodate multi-point supply, it is marked as feasible. After this interactive connection process is completed, a feasible countermeasure document will be output, showing the supply plan formed based on different node combinations, and indicating the order volume of each node, the transportation period of the route used, and the specific time of arrival at the factory. If some node combinations have strong dependencies or extremely high route overlaps, corresponding comments will be added to the feasible countermeasures to remind them to avoid or split them in the next step of dynamic reconstruction.

[0048] Specifically, 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. When the adjustment warning plan based on the assembly demand of the key components is obtained, each node combination in the feasible countermeasures and its corresponding assembly connection sequence and logistics period need to be recalculated in the overall network environment. If some nodes participate in multiple batches of supply in the same period, it is necessary to determine whether the production line of the node can operate continuously or whether additional line change time is required during reconstruction; if it also conflicts with another batch of assembly tasks, it is necessary to insert a buffer period or use parallel lines for diversion. If it is found that some nodes have alternative suppliers or alternative production lines in 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 key components will not cause assembly stagnation in the case of peak or sudden gaps. Once the linkage correction of all nodes-lines-assembly windows is completed, an adjustment warning plan will be generated. This plan clearly lists the best supply node selection, transportation routes and arrival times for key components in a specific period, and provides secondary or tertiary scheduling plans that can be used in emergencies. If it is found that there are insufficient alternative resources at certain nodes or routes, a warning sign will be displayed in the plan to indicate the need to explore new supply channels or expand logistics routes in the future.

[0049] In this embodiment, the supplier capacity data, logistics clearance data and assembly plant production line operation data in multiple regions are collected and integrated to determine the association between key components and multi-level supply nodes; then, multi-link scanning is performed based on the node timeline information and geographic location parameters to obtain the path that may cause delayed propagation; then, the core nodes that cause multi-factor linkage risks are identified and locked in combination with the progressive analysis of the production load and assembly sequence of key components; finally, the logistics routes and dispatchable supplier resources are dynamically reconstructed to form an adjustment warning plan that can meet the assembly needs of key components. This method can more comprehensively deal with potential conflicts caused by multi-regional and multi-node collaboration, and reduce the risk of delivery delays caused by delivery date fluctuations.

[0050] The supply chain risk assessment and early warning method in the embodiment of the present invention is described above. The supply chain risk assessment and early warning system in the embodiment of the present invention is described below. Figure 2 In one embodiment of the present invention, a supply chain risk assessment and early warning system includes: The scenario mapping module 201 is used to perform scenario mapping processing on the supplier capacity data, logistics 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; Interactive scanning module 202, used to perform cross-link interactive scanning processing on the time axis information and geographic location parameters of each supply node in the supply chain network according to the comprehensive data set, to obtain a spatiotemporal correlation matrix covering the order dependency of the supply nodes and the potential delay propagation path; A coupling calculation module 203 is used to perform progressive coupling calculation processing on the production load and assembly sequence of the key components according to the spatiotemporal correlation matrix to obtain correlation effect chain information indicating the multi-factor linkage risk of the key components; The dynamic reconstruction module 204 is used to dynamically reconstruct the logistics routes and schedulable supplier resources in the supply chain network according to the associated effect chain information, and obtain an adjustment warning plan based on the assembly requirements of the key components.

[0051] In an embodiment 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 supplier capacity data, logistics 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 performs multi-link scanning based on node timeline information and geographic location parameters to obtain paths that may cause delayed propagation; then combines the progressive analysis of key component production load and assembly timing to identify and lock the core nodes that cause multi-factor linkage risks; finally, dynamically reconstructs logistics routes and schedulable supplier resources to form an adjustment and early warning plan that can meet the assembly needs of key components. This method can more comprehensively deal with potential conflicts caused by multi-regional and multi-node collaboration, and reduce the risk of delivery delays caused by delivery date fluctuations.

[0052] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0053] 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 is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0054] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A supply chain risk assessment and early warning method, characterized in that: The supply chain risk assessment and early warning method includes: Perform scenario-based mapping of supplier capacity data, logistics clearance data, and assembly plant production line operation data distributed in multiple regions to obtain a comprehensive data set that characterizes the correspondence between key components and multi-level supply nodes in the supply chain network; Based on the comprehensive data set, the timeline information and geographic location parameters of each supply node in the supply chain network are subjected to cross-link interactive scanning and processing to obtain a spatiotemporal correlation matrix covering the order dependency of the supply nodes and the potential delay propagation path; According to the spatiotemporal correlation matrix, the production load and assembly sequence of the key components are subjected to progressive coupling calculation processing to obtain correlation effect chain information indicating the multi-factor linkage risk of the key components; 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.

2. The supply chain risk assessment and early warning method according to claim 1 is characterized in that: The scenario-based mapping of supplier capacity data, logistics 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: According to the capacity cap and production line switching cycle in the supplier capacity data, capacity matching is performed on the order demand of key components to obtain an initial supply node list indicating the order range that each supplier can undertake and the corresponding capacity utilization rate; According to the initial supply node list, geographic time mapping is performed on the customs clearance location and customs clearance period parameters in the logistics customs clearance data to obtain a customs clearance association table representing the customs clearance rhythm and customs clearance restrictions of parts in different regions; Performing time sequence comparison processing on the production schedule, line change time and component assembly priority in the assembly plant production line operation data to obtain an assembly dependency index that indicates the degree of matching between the assembly load and the arrival time of key components; According to the customs clearance association table and assembly dependency index, scenario-based mapping processing is performed on the parts delivery link between the supply node and the assembly plant to obtain a comprehensive data set for characterizing the correspondence between key parts and multi-level supply nodes in the supply chain network.

3. The supply chain risk assessment and early warning method according to claim 2 is characterized in that: According to the customs clearance association table and the assembly dependency index, scenario mapping processing is performed on the parts delivery link between the supply node and the assembly factory to obtain a comprehensive data set for characterizing the corresponding relationship between key parts and multi-level supply nodes in the supply chain network, including: Performing interactive comparison processing on the checkpoint location and the customs clearance 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 plant; According to the assembly dependency index, the assembly priority of the parts and components is compared with the assembly factory production cycle execution period to obtain an assembly connection list indicating the degree of coordination between the parts arrival requirements and the assembly process; Perform path verification processing on the delivery capacity of the corresponding supply nodes in the customs clearance feasible section and the assembly connection list to obtain a delivery mapping index covering the parts transportation cycle and production scheduling window; According to the delivery mapping index, scenario-based mapping processing is performed on the parts delivery link between the supply node and the assembly plant to obtain a comprehensive data set for characterizing the corresponding relationship between key parts and multi-level supply nodes in the supply chain network.

4. The supply chain risk assessment and early warning method according to claim 1 is characterized in that: According to the comprehensive data set, the time axis information and geographic location parameters of each supply node in the supply chain network are subjected to cross-link interactive scanning processing to obtain a spatiotemporal correlation matrix covering the order dependency of the supply nodes and the potential delay propagation path, including: Performing time segmentation processing on the time axis information in the comprehensive data set to obtain a time series distribution record indicating the overlap between the supply node production capacity start-up period and the order delivery rhythm; According to the time series distribution record, a route comparison process is performed on the geographical location parameters corresponding to each supply node in the comprehensive data set to obtain a geographical mapping index indicating the connection relationship between the transportation path and the port between each supply node; Perform dependency connection comparison processing on the connection status of each supply node and the potential delay factors in the geographic mapping index to obtain a list of cross-supply node risk areas; According to the cross-supply node risk area list, the production scheduling connection and logistics connection between the supply nodes are subjected to cross-link interactive scanning processing to obtain a spatiotemporal correlation matrix.

5. The supply chain risk assessment and early warning method according to claim 4 is characterized in that: According to the cross-supply node risk area list, the production scheduling connection and logistics connection between the supply nodes are subjected to cross-link interactive scanning processing to obtain a spatiotemporal correlation matrix including: Centrally identify 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, a time period overlap comparison process is performed on the production scheduling sequence between the supply nodes and the assembly process sequence relationship to obtain a production scheduling comparison index indicating the degree of association of the assembly connection sequence; Performing line coupling processing on the production scheduling reference 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 sequence; According to the cross-link fusion records, interactive scanning and processing is performed on the production scheduling connection and logistics connection between supply nodes to obtain a spatiotemporal correlation matrix.

6. The supply chain risk assessment and early warning method according to claim 1 is characterized in that: The step of performing progressive coupling calculation processing on the production load and assembly sequence of the key components according to the spatiotemporal correlation matrix to obtain the correlation effect chain information indicating the multi-factor linkage risk of the key components includes: According to the spatiotemporal correlation matrix, load comparison processing is performed on the capacity utilization rate of the 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 balance state of component supply and demand; Performing timing calibration processing on the assembly supply nodes in the capacity load index and the production schedules 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 map, the sequence between supply nodes and the corresponding delivery cycle are aggregated to obtain a risk interval list that identifies possible delay accumulation of key components between multiple supply nodes; A progressive coupling calculation process is performed on the supply node dependencies in the risk interval list to obtain associated effect chain information indicating the multi-factor linkage risk of the key components.

7. The supply chain risk assessment and early warning method according to claim 1 is characterized in that: The dynamically reconstructing 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 includes: Performing path comparison processing on the logistics routes with concentrated delays or conflicting supply nodes in the associated effect chain information to obtain alternative or avoidable transportation channels and the transportation time periods corresponding to the transportation channels; According to the replaceable transportation channels and the corresponding transportation time periods, priority screening is performed on the supply nodes to obtain a resource configuration list for cross-supply node scheduling of key components; Performing interactive connection processing on the candidate 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 process 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.

8. A supply chain risk assessment and early warning system, characterized in that: The supply chain risk assessment and early warning system includes: The scenario mapping module is used to perform scenario-based mapping processing on supplier capacity data, logistics clearance data, and assembly plant production line operation data distributed in multiple regions, and obtain a comprehensive data set used to characterize the corresponding relationship between key components and multi-level supply nodes in the supply chain network; An interactive scanning module is used to perform cross-link interactive scanning processing on the time axis information and geographic location parameters of each supply node in the supply chain network according to the comprehensive data set, so as to obtain a spatiotemporal correlation matrix covering the order dependency of the supply nodes and the potential delay propagation path; A coupling calculation module, for performing progressive coupling calculation processing on the production load and assembly sequence of the key components according to the spatiotemporal correlation matrix, to obtain correlation effect chain information indicating the multi-factor linkage risk of the key components; The dynamic reconstruction module is used to dynamically reconstruct the logistics routes and schedulable supplier resources in the supply chain network according to the associated effect chain information, and obtain an adjustment warning plan based on the assembly requirements of the key components.

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