A secure supplier collaborative management system adapted to the needs of intelligent scheduling and discrete manufacturing.

Abstract

This invention relates to the field of supply chain allocation management technology and discloses a secure supplier collaborative management system adapted to the needs of intelligent scheduling discrete manufacturing. The system includes: determining production parameters within T cycles of a preset time period; generating a set of security constraints based on the reliability levels of N suppliers; constraining the production parameters using the set of security constraints to form a feasible scheduling domain; determining the reliability level of supplier n in the current cycle in real time; if the reliability level changes, then: determining K types of changes to the reliability level; extracting the set of supporting variables corresponding to the K types of changes; calculating the average default magnitude of each type of change to determine the dominant change type; generating updated security constraints based on the dominant change type; replacing the updated security constraints with the set of security constraints to form an updated set of security constraints; and generating an updated feasible scheduling domain based on the updated set of security constraints.

Description

Technical Field

[0001] This invention relates to the field of supply chain allocation management technology, and more specifically, to a secure supplier collaborative management system adapted to the needs of intelligent scheduling and discrete manufacturing. Background Technology

[0002] In modern manufacturing supply chain scheduling systems, companies typically rely on multiple suppliers for critical raw materials or components. To ensure overall delivery stability, OEMs categorize suppliers based on their historical delivery performance. A common approach is to classify suppliers into several tiers based on their On-Time Complete Delivery Rate (OTIF), such as Excellent (≥95%), Acceptable (90–95%), and Unacceptable (<90%). Each tier corresponds to different management strategies and inventory safety buffer standards. For example, higher-tier suppliers are allowed lower safety stock, while lower-tier suppliers need to increase safety stock to mitigate uncertainties.

[0003] Manufacturing companies' supply chain layouts often involve multiple raw materials, multi-level supporting relationships, and cyclical delivery structures. For example, a particular product may rely on two raw materials supplied by suppliers of different tiers, and these suppliers' ratings may change over different time periods. Once the rating of any one supplier fluctuates, its corresponding safety stock will be adjusted immediately, affecting the inventory configuration of related materials from that supplier. This, in turn, causes multiple safety stock constraint boundaries that construct the feasible production scheduling domain to shift or be restructured, rendering the current production scheduling solution invalid, ultimately leading to plan execution failure, replenishment interruption, or even loss of finished goods.

[0004] Due to the discrete, jump-like nature of supply chain management mechanisms, hierarchical classification means that inventory strategies are adjusted according to tiers rather than continuous indicators. For example, a supplier's on-time delivery rate decreasing by 0.1% may lead to an overall increase in safety stock of 20%. This results in a discontinuous, step-like structure of safety stock in the decision space, further introducing non-differentiable perturbations in the solution domain. Traditional linear scheduling methods or correction mechanisms based on stationary perturbations are difficult to implement and cannot handle large-scale, rule-driven mutations. Summary of the Invention

[0005] This invention provides a secure supplier collaborative management system that adapts to the needs of intelligent scheduling and discrete manufacturing, and solves the technical problems mentioned in the background art.

[0006] This invention provides a secure supplier collaborative management system adapted to the needs of intelligent scheduling and discrete manufacturing, comprising:

[0007] The data acquisition module is used to determine production parameters within a preset time period of T cycles, including: the planned production quantity of material s by supplier n in cycle t, the safety stock of material s, and the reliability level; where, , , , , , All are positive integers. Indicates the number of suppliers. Indicate the type and quantity of materials;

[0008] The production scheduling constraint module generates a set of safety constraints based on the reliability levels of N suppliers; it then applies these safety constraints to production parameters to form a feasible production scheduling domain.

[0009] The monitoring and management module is used to determine the reliability level of supplier n in real time for the current period; if the reliability level changes, the following steps are executed:

[0010] Determine the K change types for the reliability level;

[0011] Extract the set of supporting variables corresponding to the K changes in type;

[0012] Calculate the average default magnitude for each type of change to identify the dominant type of change;

[0013] Generate updated security constraints based on the dominant change type;

[0014] The updated security constraints are replaced with the security constraint set to form the updated security constraint set.

[0015] An updated production scheduling feasible region is generated based on the updated set of safety constraints.

[0016] Furthermore, based on the reliability levels of the N suppliers, a set of security constraints is generated, including:

[0017] Based on the reliability level of supplier n Query the corresponding safety stock coefficient ;

[0018] Calculate the lower safety stock limit of supplier n for material s in period t. ,as follows:

[0019]

[0020] in, This represents the standard deviation of the quantity of material s put into production by supplier n over a historical period.

[0021] Constructing safety constraints based on a safety stock lower bound: , This represents the planned production quantity of material s by supplier n in the t-th period;

[0022] Obtain M×N×T planned production quantities This constitutes the decision vector X;

[0023] Based on the decision vector X, the safety constraints are processed into parameter pairs to obtain parameter pairs. ;in, express The corresponding j-th unit vector, express The corresponding safety stock minimum;

[0024] A set of security constraints is formed based on J parameter pairs; among which, , j is a positive integer.

[0025] Furthermore, production parameters are constrained using a set of safety constraints to form a feasible production scheduling domain, including:

[0026] Calculate the vector difference based on the decision vector X. : ;

[0027] If the vector difference If ≤0, it is recorded as satisfying the constraint; otherwise, it is recorded as violating the constraint.

[0028] The feasible region for production scheduling is formed based on the parameters that satisfy the constraints.

[0029] Furthermore, the K types of changes to the reliability level are determined, including:

[0030] Determine each supplier in the current cycle and cycle Reliability level;

[0031] Calculate the current period and cycle The difference in reliability levels;

[0032] If the difference is less than 0, the type is changed to downgrade.

[0033] If the difference is 0, then change the type to "keep".

[0034] If the difference is greater than 0, the type is changed to upgrade.

[0035] Furthermore, extract the set of supporting variables corresponding to the K changes in type, including:

[0036] Based on the type of change, the decision vector X is classified to obtain the set of downgrade support variables. Maintain the set of supporting variables and upgrade support variable set .

[0037] Furthermore, the average default magnitude for each type of change is calculated to identify the dominant type of change, including:

[0038] Calculate the set of downgrade support variables respectively Maintain the set of supporting variables and upgrade support variable set The vector difference between each element in the vector;

[0039] right , and Calculate the vector difference and sum of values ​​respectively;

[0040] After processing the vector differences and the mean of the values, we obtain the following results: , and The corresponding average default margin;

[0041] The type of change with the largest average default rate is selected as the dominant type of change.

[0042] Furthermore, updated security constraints are generated based on the dominant change type, including:

[0043] The set of supporting variables corresponding to the dominant change type is taken as the set of feature supporting variables;

[0044] Extracting elements that violate constraints from the set of feature-supporting variables forms a default set. ;

[0045] Extract the g-th element from the default set and assign a default weight. , ;

[0046] Calculate the normal vector based on the default weight. , ;

[0047] The outward shift coefficient is determined based on the default weight and the normal vector. , ;

[0048] Based on the outward shift coefficients, update the safety constraints and obtain... ;in, .

[0049] Furthermore, the updated safety constraints are replaced with the updated safety constraint set to form an updated safety constraint set, including:

[0050] The updated security constraints replace the corresponding security constraints in the security constraint set to form an updated security constraint set. The updated production scheduling feasible region is obtained based on the updated security constraint set, and the production scheduling process is performed on the next cycle of the supply chain based on the updated production scheduling feasible region.

[0051] The beneficial effects of this invention are as follows: By employing a dynamic safety constraint update mechanism driven by reliability level jumps, real-time repair and continuous maintenance of the production scheduling feasible domain can be achieved. Supplier level changes serve as trigger signals to construct updated safety constraints, which are then added to the safety constraint set to form an updated production scheduling model. Therefore, in complex environments such as sudden supplier performance fluctuations and cross-stage production structures, the system can significantly improve scheduling robustness and decision-making response speed, while maintaining structural simplicity and implementation feasibility. It is particularly suitable for manufacturing scenarios with high-volume, high-frequency disturbances. Attached Figure Description

[0052] Figure 1 This is a relational diagram of the present invention. Detailed Implementation

[0053] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0054] like Figure 1 As shown, a secure supplier collaborative management system adapted to the needs of intelligent scheduling and discrete manufacturing includes:

[0055] The data acquisition module is used to determine production parameters within a preset time period of T cycles, including: the planned production quantity of material s by supplier n in cycle t, the safety stock of material s, and the reliability level; where, , , , , , All are positive integers. Indicates the number of suppliers. Indicate the type and quantity of materials;

[0056] The production scheduling constraint module generates a set of safety constraints based on the reliability levels of N suppliers; it then applies these safety constraints to production parameters to form a feasible production scheduling domain.

[0057] The monitoring and management module is used to determine the reliability level of supplier n in real time for the current period; if the reliability level changes, the following steps are executed:

[0058] Determine the K change types for the reliability level;

[0059] Extract the set of supporting variables corresponding to the K changes in type;

[0060] Calculate the average default magnitude for each type of change to identify the dominant type of change;

[0061] Generate updated security constraints based on the dominant change type;

[0062] The updated security constraints are replaced with the security constraint set to form the updated security constraint set.

[0063] An updated production scheduling feasible region is generated based on the updated set of safety constraints.

[0064] In one embodiment of the present invention, a set of security constraints is generated based on the reliability levels of N suppliers, including:

[0065] Based on the reliability level of supplier n Query the corresponding safety stock coefficient ;

[0066] Calculate the lower safety stock limit of supplier n for material s in period t. ,as follows:

[0067]

[0068] in, This represents the standard deviation of the quantity of material s put into production by supplier n over a historical period.

[0069] Constructing safety constraints based on a safety stock lower bound: , This represents the planned production quantity of material s by supplier n in the t-th period;

[0070] Obtain M×N×T planned production quantities This constitutes the decision vector X;

[0071] Based on the decision vector X, the safety constraints are processed into parameter pairs to obtain parameter pairs. ;in, express The corresponding j-th unit vector, express The corresponding safety stock minimum;

[0072] A set of security constraints is formed based on J parameter pairs; among which, , j is a positive integer.

[0073] It should be noted that the supplier's reliability level reflects its delivery stability (e.g., high reliability, medium reliability, and low reliability), and different levels correspond to preset safety stock coefficients. Safety stock factor It is a risk quantification indicator: the lower the reliability level, The larger the capacity, the more safety stock is needed to hedge against delivery fluctuations.

[0074] It should be noted that, It is the standard deviation of the historical production quantity of supplier n for material s, reflecting the fluctuation range of its production (the larger the standard deviation, the more unstable the production). The minimum safety stock threshold is quantitatively calculated by multiplying the risk coefficient and volatility (for suppliers with low reliability, due to...). (Larger, with a higher safety stock floor). This couples reliability with historical volatility, ensuring the safety stock aligns with the risk level.

[0075] It should be noted that, ,in This refers to the planned production output of supplier n for material s during cycle t. The planned production output must be no less than the safety stock level to ensure sufficient inventory buffer even if supplier production fluctuates (such as sudden production cuts) to prevent supply chain disruptions.

[0076] It should be noted that if the total number of suppliers is M, the total number of materials is N, and the number of planning cycles is T, then the total number of decision variables is... All Arrange them to form a decision vector X. Thus, the multidimensional discrete decision is transformed into a decision vector X.

[0077] It should be noted that the j-th decision variable Construct unit vectors In the decision vector X, only the j-th element is 1, and the rest are 0. At this point, the following condition is met: .make Then Transformed into linear constraints .

[0078] In detail, by mapping supplier reliability levels to safety factors and calculating differentiated safety stock minimums based on material demand volatility, a set of safety constraints with level awareness is constructed. This approach enables the production scheduling system to set corresponding inventory protection boundaries based on the stability of suppliers of different levels without introducing predictive models. This improves the system's structured adaptability to delivery uncertainties and effectively enhances the robustness and flexibility of the production scheduling feasible domain.

[0079] In one embodiment of the present invention, production parameters are constrained using a set of safety constraints to form a feasible production scheduling domain, including:

[0080] Calculate the vector difference based on the decision vector X. : ;

[0081] If the vector difference If ≤0, it is recorded as satisfying the constraint; otherwise, it is recorded as violating the constraint.

[0082] The feasible region for production scheduling is formed based on the parameters that satisfy the constraints.

[0083] It should be noted that, The actual difference between the safety threshold and the current production parameter value: If ,but (Satisfies constraints); if ,but (Violation of constraints).

[0084] The feasible production scheduling region is the intersection of all parameter pairs that satisfy the constraints. Each constraint... Corresponding to half-space in higher-dimensional space (with The region satisfying the inequality is a half-space (where the boundary is the boundary). The intersection of all half-spaces satisfying the constraints constitutes the feasible region of a convex polyhedron.

[0085] In detail, by systematically constraining production parameters and safety constraints, a linear feasible region for production scheduling is constructed, giving the scheduling model a stable and computable geometric boundary. This feasible region construction not only effectively unifies multi-dimensional safety stock constraints but also ensures that scheduling results always remain within the range required by the current supplier level. This maintains the feasibility of the model's solution space and consistency in operational execution even when reliability levels fluctuate, enhancing the scheduling robustness and real-time correction capabilities of the entire system.

[0086] In one embodiment of the present invention, determining K change types of the reliability level includes:

[0087] Determine each supplier in the current cycle and cycle Reliability level;

[0088] Calculate the current period and cycle The difference in reliability levels;

[0089] If the difference is less than 0, the type is changed to downgrade.

[0090] If the difference is 0, then change the type to "keep".

[0091] If the difference is greater than 0, the type is changed to upgrade.

[0092] It should be noted that, due to the cyclical fluctuations in the reliability levels of suppliers in the supply chain, the current cycle... and the previous cycle This serves as a comparison window to capture the latest dynamic changes in supplier reliability ratings. Reliability ratings need to be quantified in advance using standardized rules. For example, we can set C = 1, B = 2, and A = 3, with higher ratings representing higher reliability.

[0093] In detail, by identifying changes in supplier reliability levels in real time and standardizing them into a fixed number of change types (such as upgrade, maintain, and downgrade), the system achieves classified and abstract processing of external disturbance events. This provides a clear logical entry point for subsequent variable extraction and response strategy formulation, enhancing the system's sensitivity and responsiveness to level transitions.

[0094] In one embodiment of the present invention, the set of supporting variables corresponding to K change types is extracted, including:

[0095] Based on the type of change, the decision vector X is classified to obtain the set of downgrade support variables. Maintain the set of supporting variables and upgrade support variable set .

[0096] It should be noted that the set of downgrade support variables includes all downgraded suppliers. In the decision vector X, the dimension index, such as supplier A being downgraded, then all of its... Included in the set of downgrade support variables The set of support variables contains all the support suppliers' corresponding variables. In the decision vector X, the dimension index, such as supplier B being kept, then all of its... Included in the set of downgrade support variables The upgrade support variable set includes all variables corresponding to the supplier. In the decision vector X, the dimension index, such as supplier C being an upgrade, then all its... Included in the upgrade support variable set .

[0097] In one embodiment of the invention, calculating the average default magnitude for each type of change to determine the dominant type of change includes:

[0098] Calculate the set of downgrade support variables respectively Maintain the set of supporting variables and upgrade support variable set The vector difference between each element in the vector;

[0099] right , and Calculate the vector difference and sum of values ​​respectively;

[0100] After processing the vector differences and the mean of the values, we obtain the following results: , and The corresponding average default margin;

[0101] The type of change with the largest average default rate is selected as the dominant type of change.

[0102] In detail, a direct mapping relationship is established between changes in supplier level and production scheduling decision variables. By extracting the set of supporting variables affected by each type of change, the system can pinpoint the specific range of variables that need to be readjusted due to changes in level. This structured extraction method improves the targeting of facet generation and constraint patching, avoids redundant processing of irrelevant variables, and enhances the system's execution efficiency and boundary repair accuracy.

[0103] It should be noted that the set of variables supporting the downgrade... Maintain the set of supporting variables and upgrade support variable set For any set, sum the vector differences corresponding to all its indices to obtain the total default amount for that type. The total default amount reflects the sum of defaults across all production parameters under that change type and forms the basis for subsequent averaging calculations. Summation aggregates the scattered default data into a comparable scalar. For each set, the average default magnitude is calculated. The average default magnitude eliminates the impact of differences in set size (e.g., the downgrade support variable set may contain more suppliers, making a direct comparison of total default amounts unfair), making the severity of defaults across the three change types comparable (e.g., the average default magnitude of the downgrade support variable set may be much higher than that of the upgrade support variable set, indicating that its risk requires higher priority). The downgrade support variable set is then compared. Maintain the set of supporting variables and upgrade support variable set The corresponding average default magnitude determines the dominant change type, with the largest average default magnitude being identified. The dominant type corresponds to the risk group with the most severe defaults in the current production schedule (e.g., downgraded suppliers are more likely to have insufficient safety stock due to decreased reliability). Subsequent updates to safety constraints should prioritize designing facets for this type to ensure the accuracy of constraint adjustments.

[0104] In detail, by calculating the average default magnitude for each of the three types of change corresponding to the supporting variable sets, and selecting the type with the largest default magnitude as the dominant change type, a key focus mechanism in default response is achieved. This approach not only avoids the dilution effect caused by average allocation, but also focuses on adjusting the most significant risk sources, effectively improving the targeting of production scheduling feasibility domain repair and the convergence of control strategies.

[0105] In one embodiment of the present invention, generating updated security constraints based on the dominant change type includes:

[0106] The set of supporting variables corresponding to the dominant change type is taken as the set of feature supporting variables;

[0107] Extracting elements that violate constraints from the set of feature-supporting variables forms a default set. ;

[0108] Extract the g-th element from the default set and assign a default weight. , ;

[0109] Calculate the normal vector based on the default weight. , ;

[0110] The outward shift coefficient is determined based on the default weight and the normal vector. , ;

[0111] Based on the outward shift coefficients, update the safety constraints and obtain... ;in, .

[0112] In detail, based on the hyperplane default situation associated with the dominant change type, a unique new cut surface parameter pair is constructed through weighted combination to form updated security constraints. This new cut surface, constructed based on the actual default magnitude, can simultaneously cover multiple disturbed constraints, achieving batch, non-redundant security boundary correction while maintaining the linear structure, thus enhancing the system's rapid self-recovery capability under sudden level jumps.

[0113] It should be noted that the set of supporting variables corresponding to the dominant change type is used as the feature supporting variable set. Focusing on the variable group with the most prominent default risk in the current production schedule (such as production parameters of downgraded suppliers) avoids interference from irrelevant variables and improves the accuracy of constraint updates. Each element in the feature supporting variable set is traversed, and elements violating safety constraints are selected to obtain the default set. For the g-th element in the default set, a default weight is assigned. Variables with more severe defaults have higher weights, making the subsequent normal vector synthesis more closely aligned with the default direction that most needs to be corrected. The normal vector is calculated using a weighted average. ,molecular Unit vector of default variables According to the extent of the default Weighted summation (the more severe the default of a variable, the greater its contribution to the direction of the normal vector). Denominator The sum of the absolute values ​​of the weights is used for normalization to avoid abnormal normal vector magnitudes. The synthesized normal vector simultaneously reflects the weights of both the direction and severity of all default variables, ensuring that the new constraint accurately targets the root causes of defaults of the derivative type. Outward shift coefficient. , It is the L1 norm of the normal vector (the sum of the absolute values ​​of each element), which characterizes the strength of the normal vector. This is the step size by which a single defaulting variable needs to be shifted outward; the maximum value is taken to ensure that the constraints of all defaulting variables are satisfied. Calculate the threshold for the new constraint. This ultimately leads to updated security constraints. The revised decision vector... ,satisfy That is, it returns exactly to the feasible region defined by the new constraints, without excessively shrinking the original feasible region (only repairing the default part).

[0114] In one embodiment of the present invention, replacing the updated security constraint with the security constraint set to form an updated security constraint set includes:

[0115] The updated security constraints replace the corresponding security constraints in the security constraint set to form an updated security constraint set. The updated production scheduling feasible region is obtained based on the updated security constraint set, and the production scheduling process is performed on the next cycle of the supply chain based on the updated production scheduling feasible region.

[0116] It's important to note that the process involves locating the original constraint associated with the updated constraint within the safety constraint set, removing that original constraint, and inserting the newly generated updated safety constraint. The updated constraint is a modification of the same decision object, not a new independent constraint. This replacement operation avoids redundancy in the constraint set, ensuring that each constraint corresponds to a unique risk state. After replacement, the updated safety constraint set is strictly synchronized with the current supplier reliability and production parameter default status, eliminating conflicts between old constraints and new risks and ensuring the accuracy of production scheduling decisions. The production scheduling feasible region is the semi-space intersection of the safety constraint sets. After updating the safety constraint set, the boundary of the feasible region adjusts with the constraints (e.g., if the constraint on downgrading suppliers is stricter, the feasible region shrinks in the corresponding dimension). The new feasible region accurately reflects the safety boundary under the current risk, providing real-time and effective constraints for subsequent production scheduling optimization. Based on the updated production scheduling feasible region, the next cycle's production scheduling optimization is performed (e.g., solving for the cost-optimal solution through linear programming) to ensure that the production schedule meets the new constraints.

[0117] In detail, by atomically appending updated safety constraints to the existing set of safety constraints and simultaneously updating its matrix and vector representations, the newly generated production scheduling feasible domain is ensured to take effect immediately and seamlessly integrate with the original structure. This operation not only guarantees the consistency and timeliness of the system constraint data, but also enables the production scheduling system to continuously evolve and dynamically enhance its robustness during operation, thereby supporting stable, high-frequency intelligent production scheduling.

[0118] The embodiments of this example have been described above. However, this example is not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of this example, and all of them are within the protection scope of this example.

Claims

1. A secure supplier collaborative management system adapted to the needs of intelligent scheduling and discrete manufacturing, characterized in that: include: The data acquisition module is used to determine production parameters within a preset time period of T cycles, including: the planned production quantity of material s by supplier n in cycle t, the safety stock of material s, and the reliability level; where, , , , , , All are positive integers. Indicates the number of suppliers. Indicate the type and quantity of materials; Production scheduling constraint module, based on The reliability levels of each supplier are used to generate a set of security constraints, including: Based on the reliability level of supplier n Query the corresponding safety stock coefficient ; Calculate the lower safety stock limit of supplier n for material s in period t. ,as follows: ； in, This represents the standard deviation of the quantity of material s put into production by supplier n over a historical period. Constructing safety constraints based on a safety stock lower bound: , This represents the planned production quantity of material s by supplier n in the t-th period; Obtain M×N×T planned production quantities This constitutes the decision vector X; Based on the decision vector X, the safety constraints are processed into parameter pairs to obtain parameter pairs. ;in, express The corresponding j-th unit vector, express The corresponding safety stock minimum; A set of security constraints is formed based on J parameter pairs; among which, , , It is a positive integer; Production parameters are constrained using a set of safety constraints to form a feasible production scheduling region, including: Calculate the vector difference based on the decision vector X. : ; If the vector difference If the constraint is satisfied, it is recorded as satisfying the constraint; otherwise, it is recorded as violating the constraint. The production scheduling feasible region is formed based on the parameters that satisfy the constraints; The monitoring and management module is used to determine the reliability level of supplier n in real time for the current period; if the reliability level changes, the following steps are executed: Determine K change types for the reliability level; where the change types include: downgrade, maintain, and upgrade; Extract the set of supporting variables corresponding to the K changes in type, including: Based on the type of change, the decision vector X is classified to obtain the set of downgrade support variables. Maintain the set of supporting variables and upgrade support variable set ; Calculate the average default magnitude for each type of change to identify the dominant type of change, including: Calculate the set of downgrade support variables respectively Maintain the set of supporting variables and upgrade support variable set The vector difference between each element in the vector; right , and Calculate the vector difference and sum of values ​​respectively; After processing the vector differences and the mean of the values, we obtain the following results: , and The corresponding average default margin; The type of change with the largest average default rate is taken as the dominant type of change. Generate updated security constraints based on the dominant change type, including: The set of supporting variables corresponding to the dominant change type is taken as the set of feature supporting variables; Extracting elements that violate constraints from the set of feature-supporting variables forms a default set. ; Extract the g-th element from the default set and assign a default weight. , ; Calculate the normal vector based on the default weight. , ; The outward shift coefficient is determined based on the default weight and the normal vector. , ; Based on the outward shift coefficients, update the safety constraints and obtain... ;in, ; The updated security constraints are replaced with the security constraint set to form the updated security constraint set. An updated production scheduling feasible region is generated based on the updated set of safety constraints.

2. The secure supplier collaborative management system adapted to the needs of intelligent scheduling and discrete manufacturing as described in claim 1, characterized in that, The K types of changes that determine the reliability level include: Determine each supplier in the current cycle and cycle Reliability level; Calculate the current period and cycle The difference in reliability levels; If the difference is less than 0, the type is changed to downgrade. If the difference is 0, then change the type to "keep". If the difference is greater than 0, the type is changed to upgrade.

3. The secure supplier collaborative management system adapted to the needs of intelligent scheduling and discrete manufacturing as described in claim 1, characterized in that, The updated safety constraints are replaced with the updated safety constraint set to form the updated safety constraint set, which includes: The updated security constraints replace the corresponding security constraints in the security constraint set to form an updated security constraint set. The updated production scheduling feasible region is obtained based on the updated security constraint set, and the production scheduling process is performed on the next cycle of the supply chain based on the updated production scheduling feasible region.

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