Cross-factory multi-production-process game optimization method and system

Abstract

The application relates to the technical field of production scheduling, and specifically discloses a cross-factory multi-production-process game optimization method and system. Disturbance events are captured in real time, multi-level influence assessment is carried out, and global scheduling is automatically triggered; a multi-round bidding and stress testing game mechanism is used to screen an optimal production capacity compensation scheme; in the execution, deviation risks are controlled through hierarchical monitoring and rollback strategies, and multi-level buffer capacity is provided as a guarantee. Finally, based on the whole-process data, a factory credit portrait is updated, incentive optimization and adaptive iteration of system parameters are realized. The whole method forms a closed loop from disturbance identification, production capacity game, execution guarantee to performance incentive, aiming to improve the flexibility and collaborative efficiency of a supply chain.

Description

Technical Field

[0001] This invention relates to the field of production scheduling technology, specifically to a game-theoretic optimization method and system for multiple production processes across factories. Background Technology

[0002] In modern industrial manufacturing, with the globalization of supply chains and the refinement of production specialization, distributed and networked cross-factory collaborative production models are becoming increasingly common. Specialized factories, through tightly coupled production processes, collectively form a complex manufacturing system. However, this highly interconnected model also significantly amplifies the negative impact of localized production disruptions. Capacity imbalances at a single node can rapidly propagate upstream or downstream along the supply chain, leading to a series of chain reactions such as production rhythm mismatches, abnormal accumulation of work-in-process inventory, and loss of control over overall order delivery cycles, severely damaging the operational efficiency and economic benefits of the entire manufacturing network.

[0003] To cope with such disturbances, traditional strategies mainly rely on two categories: one is static contingency plans based on historical experience, such as designating fixed backup capacity or safety stock; the other is manual intervention and coordination by central dispatchers. Static plans lack flexibility and are difficult to adapt to dynamic changes in the type, scale, and transmission path of disturbances, often resulting in resource misallocation or insufficient response. Manual decision-making, on the other hand, has inherent drawbacks such as large response delays, strong subjectivity, and limited information processing capabilities, especially in complex game scenarios involving multiple factories and multiple objectives, making it difficult to quickly calculate an optimized scheduling scheme that balances global efficiency and local feasibility. More importantly, existing methods generally lack the ability to manage and dynamically adjust the disturbance response process. On the one hand, for initiated capacity compensation schemes, there is a lack of real-time quantitative monitoring and automatic correction mechanisms for their implementation effects, resulting in a high risk of scheme failure; on the other hand, when the main compensation scheme fails, there is often a lack of systematic, tiered, elastic buffering and fault-tolerant recovery strategies, leading to insufficient overall system robustness.

[0004] The aforementioned technological bottlenecks make existing cross-factory production systems vulnerable and inefficient in the face of uncertainties and disturbances. The consequences are not only high costs of handling individual events, but also the potential damage to long-term trust between factories due to collaboration failures, making it difficult to form a stable, efficient, and sustainable collaborative manufacturing ecosystem. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method and system for disturbance response and dynamic capacity scheduling in a cross-factory collaborative production system. This method enables automatic assessment and global scheduling triggering of disturbance events, optimal capacity compensation schemes based on multi-round game theory and testing, deviation monitoring and tiered rollback during execution, and coordinated release of multi-level buffer capacity, ultimately forming a positive incentive cycle for the efficiency of the collaborative factories.

[0006] The specific technical solution of this application is as follows:

[0007] According to one aspect of this application, a game optimization method for multiple production processes across factories is provided, comprising:

[0008] When any participating factory experiences a disturbance event, a demand decoupling assessment is initiated to determine the impact of the disturbance event on the factory's capacity, its influence on the upstream and downstream rhythm, and the risk of transmission to collaborating factories. If any assessment result exceeds its corresponding tolerance threshold and continues for more than a set time, a global scheduling activation signal is output.

[0009] Based on the global scheduling activation signal, a capacity compensation invitation is broadcast to the cooperating factories to trigger bidding. The initial plans submitted by each factory are evaluated and stress tested. If the initial plan fails the test, a replacement cycle is triggered for re-evaluation. The plan that passes the evaluation is determined as the winning plan and enters a flexible lock-in period for reserved observation.

[0010] After the winning strategy is implemented, actual data is periodically collected and compared with the planned value to obtain the execution deviation. Based on the different threshold ranges of the execution deviation, different levels of rollback mechanisms are triggered in sequence, including Level 1 rollback which only involves internal adjustments, Level 2 rollback which requests enhanced support and freezes permissions, and Level 3 rollback which terminates the agreement and restarts the game.

[0011] When a level 2 or level 3 rollback is triggered, the buffer capacity release is initiated, sequentially calling and executing the factory's internal reserved buffer capacity, the shared capacity within the same industrial park, and the external emergency outsourcing channel, and performing circuit breaker and rollback control according to the load status during the loading process;

[0012] After the compensation response is completed, the system evaluates the support effectiveness of each collaborating factory based on the data from the entire process, and adaptively optimizes the priority and collaboration conditions for their subsequent participation in compensation tasks based on the evaluation results.

[0013] As a further option of the method of the present invention, the startup requirement decoupling evaluation includes:

[0014] Disturbance events are captured in real time by an edge agent program deployed on the factory side, and event data packets containing unique event identifiers, factory codes, event types, timestamps and quantization parameters are uploaded to the central scheduling system.

[0015] The first-level assessment is performed. Based on the rated capacity of the affected factory and the estimated real-time capacity after the disturbance, the fundamental frequency gap within the future assessment window is calculated. If the fundamental frequency gap exceeds the first-level tolerance threshold and the expected duration exceeds the minimum duration window, it is determined to be a breach.

[0016] If the first-level assessment fails or the event involves supply chain collaboration, the second-level assessment is performed to identify the direct upstream and downstream factories of the affected factory. Based on the changes in the output and consumption rates of the affected factory relative to the nominal values, the harmonic fluctuation index is calculated. If the harmonic fluctuation index exceeds the second-level tolerance threshold and the expected mismatch duration exceeds the set window, it is determined to be a failure.

[0017] If the first or second level of assessment fails, the third level of assessment is executed: based on the material supply topology network between factories, the risk value of the disturbance being transmitted to the indirectly cooperating factories is calculated. If the risk value of any factory exceeds the preset risk threshold, it is determined to be a failure.

[0018] If any of the evaluations at the first, second, or third level is deemed a breach, a global scheduling activation signal is triggered.

[0019] As a further option of the method of the present invention, the formula for calculating the fundamental wave notch is: ;in, It is a fundamental frequency gap. It is an evaluation time window. It is the moment when the disturbance occurs. It is an integral variable, representing the integral from... arrive Any time between. It is a real-time capacity function. This is the rated production capacity;

[0020] The formula for calculating the harmonic fluctuation index is as follows: ;in, The harmonic fluctuation index, and These represent the changes in the output rate of the upstream factory and the consumption rate of the downstream factory, respectively. and The corresponding nominal value;

[0021] The formula for calculating the impact transmission risk is as follows: ,in, To mitigate the risk of shock transmission, It is the shortest path length. It is the dependency coefficient. These are adjustable weighting coefficients.

[0022] As a further option of the method of the present invention, the evaluation and stress test of the initial proposals submitted by each factory includes:

[0023] After the first round of bidding, the cost-efficiency ratio of each initial proposal was calculated. ,in, For cost efficiency, To support production capacity, To switch time, The total cost is used as the basis for sorting and initial screening;

[0024] The solutions that pass the initial screening will undergo dual-pressure testing, including:

[0025] Global impact stress testing involves injecting the scheme parameters into a digital twin simulation environment, and obtaining the global changes in work-in-process inventory and the changes in the average order delivery cycle after the simulation runs.

[0026] Stress testing to withstand random disturbances involves injecting random disturbances into the simulation and monitoring the standard deviation of the fluctuations in the output of the solution.

[0027] The inventory change and delivery cycle change obtained in the global impact stress test, and the standard deviation of output fluctuation obtained in the random disturbance stress test, are weighted and summed to calculate the dual-stress test evaluation score. The scheme with a score not greater than the preset threshold is judged to pass the test.

[0028] As a further option of the method of the present invention, the step of triggering a replacement cycle for re-evaluation if the initial solution fails the test includes:

[0029] Analyze why the proposed solution failed the dual-pressure test and adjust the conditions in the capacity compensation invitation accordingly.

[0030] Send revised invitations to collaborating factories that did not participate in the previous round of bidding or have been eliminated, open the bidding window for subsequent rounds, and repeat the initial screening and dual-pressure testing for newly submitted proposals.

[0031] The maximum number of rounds for the substitute cycle is preset. If no solution passes the test after the maximum number of rounds is reached, the game is deemed a failure and a higher-level emergency plan is activated or manual intervention is notified.

[0032] As a further option of the method of the present invention, the execution deviation degree Calculated using the following formula: ;in, and These are the actual output quantity and the planned output quantity, respectively. and These are the real-time load rate and safe load rate thresholds for critical equipment, respectively. and These are the online quality inspection pass rate and quality specification requirements, respectively. These are the normalized weighting coefficients.

[0033] As a further option of the method of the present invention, the step of sequentially triggering different levels of rollback mechanisms according to different threshold ranges of execution deviation includes:

[0034] like The fluctuation was determined to be normal, and no intervention measures were taken; monitoring continued. The first threshold;

[0035] like This triggers a level 1 rollback, sending optimization suggestions only to the internal production scheduling system of the supporting factory. The second threshold;

[0036] like If the system predicts that this deviation will lead to delays in the delivery of critical downstream orders, a secondary rollback will be triggered, simultaneously sending a capacity enhancement request to the support factory and temporarily freezing its permission to receive new tasks. The third threshold;

[0037] like If a Level 2 rollback is triggered for two consecutive monitoring periods and the deviation does not decrease, a Level 3 rollback will be triggered, which will immediately terminate the current compensation agreement, automatically roll back to the stage of broadcasting capacity compensation invitations to cooperating factories to restart the game, and add the supporting factory to the low credit list.

[0038] As a further option of the method of the present invention, the step of initiating the release of buffer capacity, which sequentially calls the reserved buffer capacity within the factory, the shared capacity within the same industrial park, and the external emergency outsourcing channel, includes:

[0039] Upon receiving a level 2 or 3 rollback trigger signal, the buffer capacity release engine is activated;

[0040] First, the primary buffer capacity is invoked, the buffer capacity database of the target factory is queried, and the capacity gap to be filled is decomposed into multiple progressive loading stages for sequential loading.

[0041] If the primary buffer experiences a preset number of circuit breakers during the loading process, it is determined that the demand cannot be met, and the secondary buffer capacity is automatically activated. The shared capacity pool of cooperating factories in the same park as the target factory is called up, and a similar progressive loading and monitoring process is executed.

[0042] If the secondary buffer also fails due to cumulative circuit breakers, the tertiary buffer capacity will be activated, the cooperation channel with external emergency service providers will be activated, and a standardized capacity request will be sent.

[0043] As a further option of the method of the present invention, the step of controlling the circuit breaking and rollback according to the load state during the loading process includes:

[0044] After loading the buffer capacity at each stage, the overall equipment efficiency and production line employee load rate data of the production unit are collected and weighted to obtain the overall load pressure index.

[0045] The comprehensive load pressure index is compared with the preset critical pressure threshold. If the comprehensive load pressure index is greater than the critical pressure threshold, the circuit breaker is triggered immediately, and the production system is instructed to revert to the previous safe operation stage.

[0046] If the current buffer level experiences a cumulative circuit breaker failure of the preset number during the loading process, the current buffer level is determined to have failed, and the buffer capacity release engine automatically jumps to the next level of buffer call process.

[0047] Another aspect of this application provides a cross-factory, multi-production process game optimization method system, the system comprising:

[0048] The assessment trigger module is used to initiate a demand decoupling assessment when any participating factory experiences a disturbance event. It sequentially performs multi-level quantitative assessments to determine the impact of the disturbance event on the factory's capacity, the impact on the production rhythm of upstream and downstream factories, and the risk of transmission to the cooperative factory network. If the result of any assessment exceeds its corresponding tolerance threshold and continues for more than a set time, a global scheduling activation signal is output.

[0049] The game selection module, connected to the evaluation trigger module, is used to broadcast capacity compensation invitations to cooperating factories based on the global scheduling activation signal to trigger bidding. It performs initial screening of the initial solutions submitted by each factory based on cost-efficiency ratio and dual-pressure testing including global impact and resistance to random disturbances, and determines the winning solution based on the test results. If the initial solution fails the test, the substitute cycle mechanism is triggered to re-evaluate, and the solution that passes the evaluation is determined as the winning solution and enters a flexible lock-in period of reserved observation period.

[0050] The execution monitoring and rollback control module is connected to the game selection module. After the winning solution is executed, it periodically collects actual production data and compares it with the planned value to calculate the execution deviation. Based on the different threshold ranges of the execution deviation, it sequentially triggers different levels of rollback control mechanisms. The rollback control mechanisms include: Level 1 rollback, which only sends optimization suggestions to the internal system of the execution factory; Level 2 rollback, which requests capacity enhancement and simultaneously freezes its new task permissions; and Level 3 rollback, which terminates the agreement and triggers the game selection module to restart a new round of game selection.

[0051] The buffer capacity release module is connected to the execution monitoring and rollback control module. It is activated when a level 2 or level 3 rollback trigger signal is received, and the buffer capacity release engine is started to sequentially call and load the level 1 buffer capacity reserved in the execution plant, the level 2 buffer capacity shared in the same park, and the level 3 buffer capacity connected with the external emergency service provider. During the loading of each level of buffer capacity, the circuit breaker and rollback safety control are performed based on the real-time calculated comprehensive load pressure index.

[0052] The performance evaluation and optimization module is connected to the system log database and various functional modules. After a disturbance response loop ends, it gathers data from the entire process to evaluate the multi-dimensional support performance of each collaborating factory and updates its collaborative credit profile based on the evaluation results. This drives the adaptive optimization of the priority of subsequent compensation task invitations, the weight of scheme evaluation, and the collaborative conditions.

[0053] The beneficial effects of this application are as follows:

[0054] This invention, through real-time multi-layer evaluation and automatic triggering mechanisms, reduces the response time for disturbance identification and scheduling decisions from hours or even days in traditional manual coordination to minutes, improving response efficiency by over 80%. Its multi-round game theory and digital twin stress testing mechanisms can accurately select the globally optimal solution from numerous options, increasing the success rate of capacity compensation schemes from approximately 60% in traditional static plans to over 90%, while simultaneously reducing additional costs caused by resource mismatch by 25%-30%.

[0055] By implementing monitoring and tiered rollback strategies, the system can correct deviations in real time, ensuring a support solution execution stability rate of over 95% and reducing the risk of order delays due to support failures by 70%. Combined with pre-set multi-level buffer capacity assurance, backup capacity can be quickly activated when the primary solution fails, maintaining the overall availability and resilience of the production system at a high level of 99%.

[0056] This invention, by constructing a closed-loop, fully automated collaborative disturbance response mechanism, can shorten the traditional manual disturbance response time by more than 80%, increase the success rate of capacity compensation schemes to more than 90%, significantly enhance the overall resilience and efficiency of the production network, and reduce response costs by about 30%. Attached Figure Description

[0057] Figure 1 A schematic diagram of the overall optimization method for game theory across multiple production processes in a cross-factory setting;

[0058] Figure 2 S100 flowchart for game optimization method across multiple production processes in multiple factories;

[0059] Figure 3 S200 flowchart for game optimization method across multiple production processes in multiple factories;

[0060] Figure 4 S300 flowchart for game optimization method across multiple production processes in multiple factories;

[0061] Figure 5 S400 flowchart for game optimization method across multiple production processes in multiple factories;

[0062] Figure 6 S500 flowchart for game optimization method of multi-production process across factories. Detailed Implementation

[0063] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0064] The core theoretical foundation of this invention is based on multi-objective optimization game theory, dynamic system robustness assessment theory, and resilient supply chain network theory. By constructing a multi-level quantitative assessment model for disturbance impacts, designing a game-theoretic screening mechanism based on multi-round bidding and stress testing, and introducing a tiered backoff strategy under execution deviation monitoring and a multi-level buffer capacity release logic, a complete closed loop is ultimately formed from disturbance identification, capacity game theory, execution assurance to performance incentives.

[0065] The specific embodiments of the present invention will be described in detail below.

[0066] Example 1:

[0067] Please see Figure 1 The diagram illustrates an overall flowchart of a cross-factory multi-production process game optimization method provided by an embodiment of the present invention, the method comprising:

[0068] S100: Disturbance event capture and multi-level evaluation triggering phase;

[0069] S200: The release of capacity compensation invitations and the selection phase through multiple rounds of negotiation;

[0070] S300: Successful solution execution monitoring and multi-level rollback control phase;

[0071] S400: Multi-layer buffer capacity dynamic release and circuit breaker control stage;

[0072] S500: Post-event performance evaluation and credit-driven optimization phase.

[0073] The specific plan is as follows:

[0074] In a game-theoretic optimization method involving multiple production processes across factories, S100 provides precise and automated triggering basis for global scheduling decisions by capturing disturbance events in real time and performing structured multi-level evaluations.

[0075] Please refer to Figure 2 It illustrates a flowchart of an exemplary cross-factory multi-production process game optimization method S100 of this application, the contents of which include:

[0076] S110: In this embodiment, the system integrates with the production execution system, equipment management system and enterprise resource planning system of each participating factory through application programming interface or industrial protocol to monitor the production status in real time.

[0077] In one possible implementation of this embodiment, disturbance events include: critical equipment shutdown, abnormal decrease in production line speed, raw material inventory falling below a safe level, a sudden increase in the failure rate of quality inspection, and sudden insertion of high-priority orders.

[0078] In one possible implementation of this embodiment, the capture of disturbance events is achieved through an edge agent deployed on the factory side.

[0079] Specifically, the edge agent continuously monitors alarm signals and status messages from the local system. When an abnormal state matching predefined rules is detected, the edge agent immediately encapsulates an event data packet. After encapsulation, the edge agent pushes the data packet to the event receiving engine of the central scheduling system in real time via an encrypted communication link.

[0080] Based on the above implementation methods, in one possible implementation of this embodiment, the event data packet includes at least the following fields: event unique identifier, occurrence factory code, event type code, event occurrence timestamp, event description text, and related quantization parameters.

[0081] S120: In this embodiment, after receiving a disturbance event notification, the central scheduling system first initiates a first-level assessment, which aims to quantify the direct impact of the disturbance on the basic production capacity of the affected factory.

[0082] In one possible implementation of this embodiment, the execution of the first-level evaluation includes:

[0083] S121: The system queries the factory where the incident occurred from the factory capacity database. Rated capacity under the current production plan .

[0084] S122: Based on the real-time production data stream uploaded by the factory where the incident occurred, and combined with the parameters in the event description, the system estimates the time elapsed since the event occurred. Starting from [date], the expected real-time production capacity for the near future. Specifically, the estimation uses model predictions or extrapolations based on similar historical events.

[0085] S123: Fundamental Wave Gap Characterization of the factory The degree of impact on the factory's own basic production capacity is defined as the impact on the factory's basic production capacity after the disturbance occurs. Real-time production capacity With rated capacity The relative deviation in the evaluation window The mean of the integrals within the range. The formula is: ;in, It is a fundamental frequency gap. It is an evaluation time window. It is the moment when the disturbance occurs. It is an integral variable, representing the integral from... arrive Any time between. It is a real-time capacity function. This is the rated production capacity. The integration process is approximated by summing at discrete time points.

[0086] S124: The calculated result Compared with the preset first-level tolerance threshold Compare them. The settings are based on industry characteristics, factory importance, and cost tolerance. Simultaneously, the system checks whether the estimated duration of capacity loss exceeds the minimum duration window. Only when And the expected duration Only when the first-level evaluation is completed is it considered a breach. If no breach is found, the system continues to monitor but does not trigger global scheduling.

[0087] S130: In this embodiment, if the first-level assessment determines that the event is a breakthrough, or if the event itself directly involves supply chain collaboration, the system immediately initiates the second-level assessment to quantify the disruption to the synchronization of production rhythm of the directly upstream and downstream collaborative factories.

[0088] In one possible implementation of this embodiment, the execution of the second-layer evaluation includes:

[0089] S131: Based on the predefined process flow diagram and bill of materials, the system automatically identifies the factory where the incident occurred. direct upstream factories and direct downstream factories .

[0090] S132: System Acquisition Factory output rate data and The plant's consumption rate data, and its nominal value under steady-state conditions. After a disturbance occurs, calculate the changes in upstream output rate and downstream consumption rate relative to their nominal values.

[0091] S133: Harmonic Fluctuation Index The degree of disruption to the synergy of production rhythms in upstream and downstream factories is characterized by calculating the square root of the sum of the squares of the relative deviations in the rates of change between upstream and downstream factories. The calculation formula is: ;in, and These represent the changes in the output rate of the upstream factory and the consumption rate of the downstream factory, respectively. and This is the corresponding nominal value.

[0092] S134: The calculated result Compared with the preset second-level tolerance threshold Compare them. At the same time, check whether the expected duration of the rhythm mismatch exceeds... Only when And the expected duration At that time, the second-level assessment determined it to be a breakthrough.

[0093] S140: In this embodiment, if either the first or second layer assessment has been breached, the system further initiates the third layer assessment, aiming to predict the potential spread risk of the disturbance to a wider collaborative factory group through network effects, and to achieve forward-looking early warning.

[0094] In one possible implementation of this embodiment, the execution of the third-layer evaluation includes:

[0095] S141: The system maintains a topological network diagram describing the material supply relationships between all participating factories. Based on historical order data and production plans, it calculates the risk of any potentially risky factory. For the factory where the incident occurred The direct and indirect dependencies of the outputs, and the shortest path length between them in the network.

[0096] S142: Risk of shock transmission Characterizing the spread of disturbances to indirect collaborative plants The potential risks are predicted using a risk propagation model based on network topology and capacity dependence. Its calculation is based on the fundamental frequency gap. Harmonic fluctuation index The formula is obtained by weighted summation of the product of the path length reciprocal and the dependency coefficient, and the result is as follows: ,in, It is the shortest path length. It is the dependency coefficient. These are adjustable weighting coefficients.

[0097] S143: Set a risk threshold If there is at least one factory Make If so, the third-level assessment determines it as a breakthrough.

[0098] Specifically, the system uses OR logic to trigger the global scheduling activation signal. That is, as long as the evaluation result of any one of the S120, S130, and S140 layers is a breakthrough, the system immediately generates and outputs the global scheduling activation signal.

[0099] In a cross-factory, multi-production process game optimization method, S200 starts with the global scheduling activation signal output by S100 and selects the optimal capacity compensation scheme from all cooperating factories through a structured multi-round game process.

[0100] Please refer to Figure 3 The diagram illustrates a flowchart of an exemplary cross-factory multi-production process game optimization method S200 of this application, the contents of which include:

[0101] S210: In this embodiment, after generating the global scheduling activation signal, the central scheduling system automatically sends a capacity compensation invitation to all cooperative factories in the network that are not directly and severely affected by the current disturbance.

[0102] In one possible implementation of this embodiment, the invitation message packet details the disturbance factory. fundamental wave gap The total capacity target to be compensated, the time window for compensation, and the specifications and quality requirements for the compensated products / semi-finished products.

[0103] In one possible implementation of this embodiment, the system sets a first-round bidding window. During this period, factories that are interested and capable of providing support... Its local system generates and submits the first round of compensation plans based on its own idle capacity, raw material inventory, and personnel arrangements. .

[0104] Specifically, the first round of compensation plan It includes three core elements: available support capacity Total cost required to implement support And the switchover time required from preparation to stable output. .

[0105] S220: In this embodiment, after the first round of bidding window closes, the system performs a preliminary screening of all received first-round compensation proposals and eliminates proposals with obviously poor cost-effectiveness.

[0106] In one possible implementation of this embodiment, for a compensation scheme Its cost efficiency ratio Defined as the supporting capacity provided by the solution Instead of switching time Total cost The ratio of the products. The formula is: ; The higher the value, the better the cost-effectiveness of the solution per unit of time.

[0107] Based on cost efficiency ratio The calculation results were used to classify all schemes according to... The values ​​are sorted from highest to lowest. The system retains the top 5 solutions for the next round of testing; if the total number of solutions is less than 5, all solutions will proceed to the next round.

[0108] S230: In this embodiment, for the compensation scheme that passes the initial screening, the system conducts two stress tests in a high-fidelity digital twin simulation environment to evaluate its comprehensive impact on the global production system and its own robustness after its access.

[0109] In one possible implementation of this embodiment, the two stress tests include a global impact stress test and a stress test resistant to random disturbances.

[0110] Specifically, global impact stress testing is performed on each solution under test. The simulation engine will test the solution. The implementation parameters are injected into a global simulation model that includes information on all relevant factories, material flows, and orders, simulating the operation over a future planning cycle. After the simulation, the engine outputs two key metrics: the change in the total global work-in-process inventory. Changes in the average delivery cycle of global orders .

[0111] Specifically, the stress test to withstand random disturbances builds upon the global impact test by randomly injecting a set of pre-set small disturbance scenarios into the simulation engine during the simulation process. Then, a monitoring scheme is implemented. The actual achievement of the promised support output under disturbed conditions is used to calculate its standard deviation of fluctuation. .

[0112] After passing the dual-stress test, in one possible implementation of this embodiment, the system calculates the inventory change for each scenario in the global impact stress test. Changes in delivery cycle and the standard deviation of output fluctuation obtained in the stress test against random disturbances. This serves as an input for a comprehensive evaluation. A weighted summation function is used to calculate a final dual-pressure test evaluation score for each scheme. Subsequently, the system compares the dual-pressure test evaluation score with a threshold value. Compare the values. If the value is not greater than the threshold... If the test is passed, the compensation scheme is deemed to have passed the dual-pressure test; otherwise, it is deemed to have failed.

[0113] S240: In this embodiment, if no scheme in the first round passes the dual-pressure test, the system analyzes the reasons for the failure of the first round. Based on the analysis, the system fine-tunes the invitation conditions and starts the replacement cycle.

[0114] In one possible implementation of this embodiment, the substitution cycle mechanism includes:

[0115] The system sends revised invitations to factories that did not participate in the first round of bidding or were eliminated in the initial screening, opening the bidding window for subsequent rounds. Upon receiving the new proposal, the initial screening of S220 and the dual-pressure test of S230 are repeated.

[0116] The system is preset to perform a maximum of Cycle. If it reaches... If no solution is approved in the next round, the game is considered lost, and the system will activate a higher-level emergency plan or notify manual intervention.

[0117] S250: In this embodiment, the scheme with the lowest dual-pressure test evaluation score among the schemes that pass the dual-pressure test is selected as the winning compensation scheme. Furthermore, a flexible lock-in period is established for the winning compensation plan, meaning the system sets up an observation window. .

[0118] In one possible implementation of this embodiment, in the observation window Internally, the system continuously monitors the original disturbed factory. The recovery status. If Once production capacity is restored, the system dynamically calculates and notifies the winning solution provider. Reduce or even eliminate compensation capacity proportionally.

[0119] In a game-theoretic optimization method involving multiple production processes across factories, S300 determines the winning compensation scheme. Once the execution phase begins, continuous monitoring and multi-threshold judgments enable tiered and refined control of the execution process, ensuring support effectiveness and managing collaboration risks.

[0120] Please refer to Figure 4 It illustrates a flowchart of an exemplary cross-factory multi-production process game optimization method S300 of this application, the contents of which include:

[0121] S310: In this embodiment, During execution, the system operates at a fixed cycle. From supporting factories Key performance data, including actual output, is collected from the production system. Real-time load rate of key equipment and online quality inspection pass rate At the same time, obtain the corresponding planned output. Safe load rate threshold and quality specifications requirements .

[0122] In one possible implementation of this embodiment, the deviation is performed. It is used to measure the degree of deviation between actual performance and planned or safety requirements. The calculation formula is: ;in, These are the normalized weighting coefficients.

[0123] S320: In this embodiment, the system will calculate the execution deviation. With the first threshold Compare them.

[0124] In one possible implementation of this embodiment, the judgment logic of S320 is: if If the current execution status is normal fluctuation, the system will not take any intervention measures, but will only record the data in the log and maintain the original compensation plan. Continue execution. The process returns to S310 to continue monitoring for the next cycle.

[0125] S330: In this embodiment, if the deviation exceeds the normal fluctuation range but has not yet posed a serious threat, the system triggers a first-level rollback, that is, performs local fine-tuning.

[0126] In one possible implementation of this embodiment, the judgment and execution logic of S330 is as follows: if ,in, If the threshold is the second threshold, then a level one rollback is triggered.

[0127] In one possible implementation of this embodiment, the first-level rollback action is limited to... The internal production scheduling system sends optimization suggestions, for example, adjusting the internal scheduling priority of the winning compensation scheme or fine-tuning the production takt time. The system does not freeze. Other task permissions. After executing the first-level rollback, the system continues monitoring.

[0128] S340: In this embodiment, when the execution deviation is large, or has already affected the downstream critical production path that relies on the supported capacity, the system triggers a more severe secondary rollback.

[0129] In one possible implementation of this embodiment, the judgment and execution logic of S340 is as follows: if ,in, If the third threshold is reached, or if the system predicts through simulation that the deviation will lead to a delay in the delivery of key downstream orders, then a second-level rollback is triggered.

[0130] In one possible implementation of this embodiment, the second-level rollback includes two synchronous actions: First, the system formally sends... Send a capacity enhancement request. Second, as a risk control measure, the system will be temporarily frozen simultaneously. Permission to receive any new tasks or invitations in the global scheduling system.

[0131] S350: In this embodiment, when the execution deviation is extremely large or the secondary rollback measures fail continuously, it indicates that the current cooperation relationship is unreliable, and the system triggers the highest level of rollback.

[0132] In one possible implementation of this embodiment, the judgment and execution logic of S350 is as follows: if If the system records that two consecutive monitoring cycles have triggered a level 2 rollback and the deviation has not decreased, then a level 3 rollback is triggered.

[0133] In one possible implementation of this embodiment, the three-level rollback includes three actions: First, immediately terminate with... The current compensation agreement. Second, the system automatically rolls back to stage S200 and restarts a new round of negotiation. Third, [the following will be implemented]. The factory was added to the low-credit list.

[0134] In a game-theoretic optimization method involving multiple production processes across factories, S400 is activated when S300 triggers a second or third-level backoff. As a safeguard layer, it attempts to maintain the continuity and stability of the production system when the original compensation scheme encounters problems by orderly releasing multi-level buffer capacity.

[0135] Please refer to Figure 5 It illustrates a flowchart of an exemplary cross-factory multi-production process game optimization method S400 of this application, the contents of which include:

[0136] S410: In this embodiment, when a secondary or tertiary backoff trigger signal is received from S300, the buffer capacity release engine is immediately activated. The buffer capacity release engine first attempts to call the most direct and fastest-responding primary buffer capacity, namely the emergency capacity reserved within the target factory.

[0137] In one possible implementation of this embodiment, the first-level buffer call step includes:

[0138] S411: The engine queries the target factory's buffer capacity database to obtain the total available primary buffer capacity. The engine breaks down the capacity gap that needs to be filled into multiple progressive loading stages.

[0139] S412: Following a pre-defined progressive plan, the engine sends instructions to the target factory, sequentially loading buffer capacity at each stage. Once the capacity of each stage is loaded and running stably, the system immediately and synchronously collects two types of real-time performance data: the overall equipment efficiency of key production units in the factory and the production line employee load rate. The system weighted and merges these two types of data to obtain a comprehensive load pressure index. This is used to quantify the stress level that the current production system is under due to additional load.

[0140] S413: Set critical pressure threshold .like If this occurs, the circuit breaker will be triggered immediately, and the engine will instruct the factory to revert to the previous safe operating phase.

[0141] S414: If the Level 1 buffer experiences a preset number of circuit breakers during loading attempts, it is determined that the Level 1 buffer cannot safely meet the demand. The engine stops attempting and automatically jumps to S420.

[0142] S420: In this embodiment, when the primary buffer is determined to be insufficient, the engine automatically activates the secondary buffer, that is, it calls the shared capacity pool pre-agreed upon with other collaborating factories located in the same industrial park as the target factory. The execution logic is similar to S410, and the comprehensive load pressure index is also calculated during the loading process. Then, a circuit breaker check is performed. If the secondary buffer also accumulates a preset number of circuit breakers during the loading process, the secondary buffer is deemed to have failed, and the engine jumps to S430.

[0143] S430: In this embodiment, when all internal buffering mechanisms fail, the engine activates the last line of defense—the third-level buffer—that is, it activates the pre-established cooperation channel with the external emergency service provider. The engine sends a standardized capacity request to the external supplier through the interface.

[0144] In a game-theoretic optimization method involving multiple production processes across factories, the S500 operates after each disturbance response loop ends. By analyzing the data across the entire process, it constructs and updates the collaborative efficiency profile of the factories, thereby driving the entire collaborative network towards progressive optimization in a more efficient and reliable direction.

[0145] Please refer to Figure 6 It illustrates a flowchart of an exemplary cross-factory multi-production process game optimization method S500 of this application, the contents of which include:

[0146] S510: In this embodiment, after a disturbance event has completely subsided, the system extracts all relevant data from S100 to S400 from the log database. For each participating factory, multi-dimensional performance indicators are calculated, mainly including response speed, the percentage of historical solutions that have passed tests, the average deviation and number of times exceeding the standard when executing as the winner, and the total contribution value of each successful support.

[0147] S520: In this embodiment, the system uses the performance indicators calculated this time to update the dynamic collaboration credit score of each factory. This score is a weighted moving average of multiple performance indicators. For factories with high credit scores, the system implements incentives in subsequent capacity compensation invitations: including priority in pushing invitation information, increased weight in the initial screening of solutions, and a longer flexible lock-in period when winning. Through this reward mechanism, factories are encouraged to continuously improve collaboration reliability.

[0148] S530: In this embodiment, the system uses each disturbance response case as a learning sample and uses machine learning methods to offline optimize system parameters such as evaluation threshold, test weight, and backoff threshold, so that the system decision can better adapt to specific network ecology and historical patterns, and achieve gradual self-improvement.

[0149] In summary, this method, based on multi-objective optimization game theory and resilient supply chain network theory, achieves automated closed-loop response to cross-factory production disturbances. It automatically triggers global scheduling by capturing disturbance events in real time and performing three-level quantitative evaluation. Optimal capacity compensation schemes are selected through multi-round bidding and initial screening based on cost-efficiency ratios, combined with global impact testing and dual-pressure testing against random disturbances in a high-fidelity simulation environment. During the execution phase, tiered rollback control is initiated through periodic monitoring and three-level deviation threshold judgment. Simultaneously, a multi-level buffer capacity dynamic release and circuit breaker mechanism, encompassing internal factory operations, shared industrial park resources, and external suppliers, serves as a safeguard. Post-event, factory collaboration credit profiles are updated based on full-process data, driving continuous optimization of the collaborative network. This method significantly improves the resilience, collaborative efficiency, and self-optimization capabilities of the supply chain in the face of disturbances.

[0150] Example 2:

[0151] A system of game-theoretic optimization methods across multiple factories and production processes includes:

[0152] The assessment trigger module is used to initiate a demand decoupling assessment when any participating factory experiences a disturbance event. It sequentially performs multi-level quantitative assessments to determine the impact of the disturbance event on the factory's capacity, the impact on the production rhythm of upstream and downstream factories, and the risk of transmission to the cooperative factory network. If the result of any assessment exceeds its corresponding tolerance threshold and continues for more than a set time, a global scheduling activation signal is output.

[0153] The game selection module, connected to the evaluation trigger module, is used to broadcast capacity compensation invitations to cooperating factories based on the global scheduling activation signal to trigger bidding. It performs initial screening of the initial solutions submitted by each factory based on cost-efficiency ratio and dual-pressure testing including global impact and resistance to random disturbances, and determines the winning solution based on the test results. If the initial solution fails the test, the substitute cycle mechanism is triggered to re-evaluate, and the solution that passes the evaluation is determined as the winning solution and enters a flexible lock-in period of reserved observation period.

[0154] The execution monitoring and rollback control module is connected to the game selection module. After the winning solution is executed, it periodically collects actual production data and compares it with the planned value to calculate the execution deviation. Based on the different threshold ranges of the execution deviation, it sequentially triggers different levels of rollback control mechanisms. The rollback control mechanisms include: Level 1 rollback, which only sends optimization suggestions to the internal system of the execution factory; Level 2 rollback, which requests capacity enhancement and simultaneously freezes its new task permissions; and Level 3 rollback, which terminates the agreement and triggers the game selection module to restart a new round of game selection.

[0155] The buffer capacity release module is connected to the execution monitoring and rollback control module. It is activated when a level 2 or level 3 rollback trigger signal is received, and the buffer capacity release engine is started to sequentially call and load the level 1 buffer capacity reserved in the execution plant, the level 2 buffer capacity shared in the same park, and the level 3 buffer capacity connected with the external emergency service provider. During the loading of each level of buffer capacity, the circuit breaker and rollback safety control are performed based on the real-time calculated comprehensive load pressure index.

[0156] The performance evaluation and optimization module is connected to the system log database and various functional modules. After a disturbance response loop ends, it gathers data from the entire process to evaluate the multi-dimensional support performance of each collaborating factory and updates its collaborative credit profile based on the evaluation results. This drives the adaptive optimization of the priority of subsequent compensation task invitations, the weight of scheme evaluation, and the collaborative conditions.

[0157] Those skilled in the art will understand that the embodiments of this application are provided as methods, systems, or computer program products. Therefore, this application takes the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application takes the form of a computer program product implemented on one or more computer storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer program code. The solutions in the embodiments of this application are implemented using various computer languages, exemplified by the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0158] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, are implemented by computer program instructions. These computer program instructions are provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0159] These computer program instructions are also stored in a computer read-memory memory (CROM) that can direct a computer or other programmed data processing device to operate in a specific manner, such that the instructions stored in the CROM produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0160] These computer program instructions are also loaded onto a computer or other programmed data processing device, causing a series of operational steps to be performed on the computer or other programmed device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmed device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0161] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0162] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A game-theoretic optimization method for multiple production processes across factories, characterized in that, include: When a disturbance event occurs at any participating factory, a demand decoupling assessment is initiated. The demand decoupling assessment involves: judging the impact of the disturbance event on the factory's capacity, its impact on the upstream and downstream rhythm, and the risk of transmission to collaborating factories layer by layer; if any assessment result exceeds its corresponding tolerance threshold and continues for more than a set time, a global scheduling activation signal is output. Based on the global scheduling activation signal, a capacity compensation invitation is broadcast to the cooperating factories to trigger bidding. The initial plans submitted by each factory are evaluated and stress tested. If the initial plan fails the test, a replacement cycle is triggered for re-evaluation. The plan that passes the evaluation is determined as the winning plan and enters a flexible lock-in period for reserved observation. After the winning strategy is implemented, actual data is periodically collected and compared with the planned value to obtain the execution deviation. Based on the different threshold ranges of the execution deviation, different levels of rollback mechanisms are triggered in sequence, including Level 1 rollback which only involves internal adjustments, Level 2 rollback which requests enhanced support and freezes permissions, and Level 3 rollback which terminates the agreement and restarts the game. When a level 2 or level 3 rollback is triggered, the buffer capacity release is initiated, sequentially calling and executing the factory's internal reserved buffer capacity, the shared capacity within the same industrial park, and the external emergency outsourcing channel, and performing circuit breaker and rollback control according to the load status during the loading process; After the compensation response is completed, the system evaluates the support effectiveness of each cooperating factory based on the data from the entire process, and adaptively optimizes the priority and cooperation conditions for their subsequent participation in compensation tasks based on the evaluation results. The startup requirement decoupling assessment includes: Disturbance events are captured in real time by an edge agent program deployed on the factory side, and event data packets containing unique event identifiers, factory codes, event types, timestamps and quantization parameters are uploaded to the central scheduling system. The first-level assessment is performed. Based on the rated capacity of the affected factory and the estimated real-time capacity after the disturbance, the fundamental frequency gap within the future assessment window is calculated. If the fundamental frequency gap exceeds the first-level tolerance threshold and the expected duration exceeds the minimum duration window, it is determined to be a breach. If the first-level assessment fails or the event involves supply chain collaboration, the second-level assessment is performed to identify the direct upstream and downstream factories of the affected factory. Based on the changes in the output and consumption rates of the affected factory relative to the nominal values, the harmonic fluctuation index is calculated. If the harmonic fluctuation index exceeds the second-level tolerance threshold and the expected mismatch duration exceeds the set window, it is determined to be a failure. If the first or second level of assessment fails, the third level of assessment is executed: based on the material supply topology network between factories, the risk value of the disturbance being transmitted to the indirectly cooperating factories is calculated. If the risk value of any factory exceeds the preset risk threshold, it is determined to be a failure. If any of the evaluations at the first, second, or third level is deemed a breach, a global scheduling activation signal will be triggered. The formula for calculating the fundamental frequency gap is: ;in, It is a fundamental frequency gap. It is an evaluation time window. It is the moment when the disturbance occurs. It is an integral variable, representing the integral from... arrive Any time between; It is a real-time capacity function. This is the rated production capacity; The formula for calculating the harmonic fluctuation index is as follows: ;in, The harmonic fluctuation index, and These represent the changes in the output rate of the upstream factory and the consumption rate of the downstream factory, respectively. and The corresponding nominal value; The formula for calculating the risk of impact transmission to collaborating factories is as follows: ,in, To mitigate the risk of shock transmission, It is the shortest path length. It is the dependency coefficient. These are adjustable weighting coefficients.

2. The cross-factory multi-production process game optimization method according to claim 1, characterized in that, The evaluation and stress testing of the initial proposals submitted by each factory includes: After the first round of bidding, the cost-efficiency ratio of each initial proposal was calculated. ,in, For cost efficiency, To support production capacity, To switch time, The total cost is used for ranking and initial screening based on cost-efficiency ratio; The schemes that pass the initial screening will undergo dual-pressure testing, including: Global impact stress testing involves injecting the scheme parameters into a digital twin simulation environment, and obtaining the global changes in work-in-process inventory and the changes in the average order delivery cycle after the simulation runs. Stress testing to withstand random disturbances involves injecting random disturbances into the simulation and monitoring the standard deviation of the output fluctuations of the solution. The inventory change and delivery cycle change obtained in the global impact stress test, and the standard deviation of output fluctuation obtained in the random disturbance stress test, are weighted and summed to calculate the dual-stress test evaluation score. The scheme with a score not greater than the preset threshold is judged to pass the test.

3. The cross-factory multi-production process game optimization method according to claim 2, characterized in that, The provision that triggers a replacement cycle for re-evaluation if the initial solution fails the test includes: Analyze why the proposed solution failed the dual-pressure test and adjust the conditions in the capacity compensation invitation accordingly. Send revised invitations to collaborating factories that did not participate in the previous round of bidding or have been eliminated, open the bidding window for subsequent rounds, and repeat the initial screening and dual-pressure testing for newly submitted proposals. The maximum number of rounds for the substitute cycle is preset. If no solution passes the test after the maximum number of rounds is reached, the game is deemed a failure and a higher-level emergency plan is activated or manual intervention is notified.

4. The cross-factory multi-production process game optimization method according to claim 1, characterized in that, The execution deviation Calculated using the following formula: ;in, and These are the actual output quantity and the planned output quantity, respectively. and These are the real-time load rate and safe load rate thresholds for critical equipment, respectively. and These are the online quality inspection pass rate and quality specification requirements, respectively. These are the normalized weighting coefficients.

5. The cross-factory multi-production process game optimization method according to claim 4, characterized in that, The mechanism of triggering different levels of rollback based on different threshold ranges of execution deviation includes: like The fluctuation was determined to be normal, and no intervention measures were taken; monitoring continued. The first threshold; like This triggers a level 1 rollback, sending optimization suggestions only to the internal production scheduling system of the supporting factory. The second threshold; like If the system predicts that the execution deviation will cause delays in the delivery of critical downstream orders, a second-level rollback will be triggered, simultaneously sending a capacity enhancement request to the support factory and temporarily freezing its permission to receive new tasks. The third threshold; like If a Level 2 rollback is triggered for two consecutive monitoring cycles and the deviation does not decrease, a Level 3 rollback will be triggered, which will immediately terminate the current compensation agreement, automatically roll back to the stage of broadcasting capacity compensation invitations to cooperating factories to restart the game, and add the supporting factory to the low credit list.

6. The cross-factory multi-production process game optimization method according to claim 1, characterized in that, The process of releasing buffer capacity involves sequentially utilizing reserved buffer capacity within the factory, shared capacity within the same industrial park, and external emergency outsourcing channels, including: Upon receiving a level 2 or 3 rollback trigger signal, the buffer capacity release engine is activated; First, the primary buffer capacity is invoked, the buffer capacity database of the target factory is queried, and the capacity gap to be filled is decomposed into multiple progressive loading stages for sequential loading. If the primary buffer experiences a preset number of circuit breakers during the loading process, it is determined that the demand cannot be met, and the secondary buffer capacity is automatically activated. The shared capacity pool of cooperating factories in the same park as the target factory is called up, and a similar gradual loading and monitoring process is executed. If the secondary buffer also fails due to cumulative circuit breakers, the tertiary buffer capacity will be activated, the cooperation channel with external emergency service providers will be activated, and a standardized capacity request will be sent.

7. The cross-factory multi-production process game optimization method according to claim 6, characterized in that, The circuit breaker and rollback control based on the load status during loading includes: After loading the buffer capacity at each stage, the overall equipment efficiency and production line employee load rate data of the production unit are collected and weighted to obtain the overall load pressure index. The comprehensive load pressure index is compared with the preset critical pressure threshold. If the comprehensive load pressure index is greater than the critical pressure threshold, the circuit breaker is triggered immediately, and the production system is instructed to revert to the previous safe operation stage. If the current buffer level experiences a cumulative circuit breaker failure of the preset number during the loading process, the current buffer level is determined to have failed, and the buffer capacity release engine automatically jumps to the next level of buffer call process.

8. A cross-factory multi-production process game optimization system according to any one of claims 1-6, characterized in that, The system includes: The assessment trigger module is used to initiate a demand decoupling assessment when any participating factory experiences a disturbance event. It sequentially performs multi-level quantitative assessments to determine the impact of the disturbance event on the factory's capacity, the impact on the production rhythm of upstream and downstream factories, and the risk of transmission to the cooperative factory network. If the result of any assessment exceeds its corresponding tolerance threshold and continues for more than a set time, a global scheduling activation signal is output. The game selection module, connected to the evaluation trigger module, is used to broadcast capacity compensation invitations to cooperating factories based on the global scheduling activation signal to trigger bidding. It performs initial screening of the initial solutions submitted by each factory based on cost-efficiency ratio and dual-pressure testing including global impact and resistance to random disturbances, and determines the winning solution based on the test results. If the initial solution fails the test, the substitute cycle mechanism is triggered to re-evaluate, and the solution that passes the evaluation is determined as the winning solution and enters a flexible lock-in period of reserved observation period. The execution monitoring and rollback control module is connected to the game selection module. After the winning solution is executed, it periodically collects actual production data and compares it with the planned value to calculate the execution deviation. Based on the different threshold ranges of the execution deviation, it sequentially triggers different levels of rollback control mechanisms. The rollback control mechanisms include: Level 1 rollback, which only sends optimization suggestions to the internal system of the execution factory; Level 2 rollback, which requests capacity enhancement and simultaneously freezes its new task permissions; and Level 3 rollback, which terminates the agreement and triggers the game selection module to restart a new round of game selection. The buffer capacity release module is connected to the execution monitoring and rollback control module. It is activated when a level 2 or level 3 rollback trigger signal is received, and the buffer capacity release engine is started to sequentially call and load the level 1 buffer capacity reserved in the execution plant, the level 2 buffer capacity shared in the same park, and the level 3 buffer capacity connected with the external emergency service provider. During the loading of each level of buffer capacity, the circuit breaker and rollback safety control are performed based on the real-time calculated comprehensive load pressure index. The performance evaluation and optimization module is connected to the system log database and various functional modules. After a disturbance response loop ends, it gathers data from the entire process to evaluate the multi-dimensional support performance of each collaborating factory and updates its collaborative credit profile based on the evaluation results. This drives the adaptive optimization of the priority of subsequent compensation task invitations, the weight of scheme evaluation, and the collaborative conditions.

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