Intelligent warehousing scheduling method and system for electronic tray

Abstract

The application provides an intelligent warehouse scheduling method and system of an electronic tray, which is applied to the field of management data processing; the explicit attribute, process attribute and associated attribute of the electronic tray are uniformly modeled as a state sequence evolving with time, so that the scheduling decision can dynamically perceive the change of the availability of the tray, thereby effectively avoiding the mismatch problem caused by state lag; the scheduling path is screened through the introduction of time constraints, sequence constraints and matching constraints, a feasible solution space is constructed, and the process adaptability and executability of the scheduling scheme are significantly improved; on this basis, the resource competition relationship is established by combining the demand urgency, supply capacity and cross-unit allocation cost, global resource coordination and distribution among multiple production units are realized, and the rhythm interruption and state failure problems are avoided in advance through the risk judgment mechanism.

Description

Technical Field

[0001] This invention relates to the field of management data processing, and in particular to an intelligent warehouse scheduling method and system for electronic material trays. Background Technology

[0002] In the field of electronics manufacturing, electronic trays are characterized by high-frequency back-and-forth movement and cross-regional transfer between warehousing and production. Especially in multi-variety mixed production mode, the trays not only serve as storage carriers, but also contain implicit process constraint information.

[0003] However, in actual operation, the state of electronic material trays has obvious "implicit evolution" characteristics, such as the exposure time after opening, the correlation of work-in-process, and the reuse of cross-work orders. These states are difficult to be completely described in real time by a single system, which makes the scheduling system rely on incomplete information when making decisions. Summary of the Invention

[0004] This invention aims to solve the problem of how to achieve global coordination and real-time optimization scheduling of resource competition among multiple production units in the intelligent warehousing scheduling of electronic material trays, under the premise of considering the implicit process constraints and dynamic evolution of the state of the material trays. It provides an intelligent warehousing scheduling method and system for electronic material trays.

[0005] The present invention employs the following technical means to solve the technical problem: This invention provides an intelligent warehouse scheduling method for electronic material trays, comprising: Based on the attribute vector pre-established for the electronic material tray by the warehousing terminal, an evolution state sequence of the electronic material tray over time is constructed, wherein the attribute vector specifically includes display attributes, process attributes and related attributes; Determine whether the evolution state sequence meets the preset availability conditions of the production unit; If not, the constraint information preset by the production unit is identified. Based on the constraint information, scheduling paths that cannot meet the availability conditions are removed from the evolution state sequence, and a corresponding feasible solution space is generated. Based on the production demand of the production unit for the electronic material tray, the resource competition relationship of the warehousing terminal is established. Through the resource competition relationship, the scheduling scheme of the electronic material tray is constructed. The constraint information specifically includes time constraints, sequence constraints, and matching constraints. The resource competition relationship specifically includes the urgency of demand of each production unit, material tray supply capacity, and cross-unit allocation cost. Determine whether the scheduling scheme causes any production unit to experience a preset risk, wherein the preset risks specifically include cycle interruption risk and material tray status failure risk; If this does not occur, a multi-objective scheduling index for the electronic material tray is generated through the warehousing terminal. Based on the multi-objective scheduling index, a candidate scheduling scheme is generated within the feasible solution space. The resource allocation is then globally coordinated in conjunction with the resource competition relationship, and the scheduling cycle of the electronic material tray is continuously optimized. Specifically, the multi-objective scheduling index includes production continuity index, resource utilization rate index, and state loss index. The global coordination of instructions specifically includes material tray inbound / outbound tasks, material handling equipment scheduling instructions, and production unit material supply instructions.

[0006] Furthermore, after the step of identifying the preset constraint information of the production unit, removing scheduling paths that cannot meet the availability conditions from the evolutionary state sequence based on the constraint information, and generating the corresponding feasible solution space, the method further includes: Based on the feasible solution space, the resource contention intensity corresponding to each scheduling path is identified, and the resource contention intensity is mapped to a time interval during the execution process to generate corresponding resource occupancy segments. Based on the superposition of the resource occupancy segments, a time overlap relationship diagram is formed. Specifically, the resource contention intensity refers to the degree of demand conflict of each production unit for the same material tray and the tightness of material tray supply. Determine whether the time overlap graph can characterize the potential conflict level of different paths during execution; If possible, a corresponding path coupling index is constructed based on the time overlap relationship diagram. The feasible solution space is restructured using the path coupling index to generate several independent scheduling subsets. Based on the scheduling subsets, the effective weights of each scheduling path are dynamically adjusted. Specifically, the path coupling index is used to characterize the influence range of any scheduling path on other paths. The structural reorganization specifically includes splitting highly coupled paths and preferentially combining low-coupling paths.

[0007] Furthermore, the step of establishing a resource competition relationship among the warehousing terminals based on the production unit's production demand for the electronic material tray, and constructing a scheduling scheme for the electronic material tray through the resource competition relationship, further includes: Based on the demand parameters of the production needs, an association mapping relationship between each production unit is constructed. According to the association mapping relationship, the resource allocation tendency parameters preset by the warehousing terminal are introduced to generate a preliminary scheduling structure. The demand parameters specifically include production progress, number of processes to be completed, and current material availability. Determine whether there are any scheduled tasks to be executed in the preliminary scheduling structure; If so, then based on the associated mapping relationship, the resource reallocation information of the preliminary scheduling structure is detected. Through the resource reallocation information, the chain adjustment risk corresponding to each scheduling path is constructed. Based on the chain adjustment risk, the multi-tray combination task of the electronic material tray is dynamically adjusted.

[0008] Furthermore, the step of generating candidate scheduling schemes within the feasible solution space based on the multi-objective scheduling index further includes: Based on each scheduling path in the feasible solution space, a corresponding response index vector is constructed. According to the response index vector, several seed paths are selected from each scheduling path, and expansion and combination are performed around the seed paths to generate a set of scheduling candidate schemes. The response index vector is specifically used to characterize the performance characteristics of each scheduling path under different objectives. Determine whether the set of solutions is concentrated in a locally similar region; If so, a preset structural disturbance mechanism is introduced to optimize the scheduling of some scheduling paths in the local similar regions. Based on the optimization results, representative non-dominated solution combinations in each scheduling path are dynamically retained. Through the non-dominated solution combinations, the coverage of the candidate scheduling scheme in the feasible solution space is enhanced. Specifically, the scheduling optimization includes replacement, insertion, and order adjustment.

[0009] Furthermore, the step of determining whether the evolutionary state sequence satisfies the preset availability conditions of the production unit also includes: Based on the set of condition determination rules corresponding to the available conditions, key state components are extracted from the evolutionary state sequence to form a corresponding set of state features. Specifically, the key state components include time-related components, environmental cumulative components, and associated constraint components. Determine whether the set of state features can be mapped and matched with the set of conditional judgment rules; If possible, an availability confirmation identifier is generated for the electronic material tray based on the mapping matching result. The constraint information of the electronic material tray is constructed through the availability confirmation identifier. The calling scope of the electronic material tray is dynamically divided based on the constraint information. The constraint information specifically includes constraint type and constraint boundary.

[0010] Furthermore, the step of determining whether the scheduling scheme causes a preset risk to any production unit also includes: Based on the risk factors preset in the warehousing terminal, the risk distribution sequence of the production unit over time is detected, wherein the risk factors specifically include material supply interruption risk, waiting backlog risk and status failure risk; Determine whether the risk distribution sequence will be propagated to other production units through scheduling relationships; If so, the identified risk transmission path is locked, the starting node and the ending node of the risk transmission path are marked, and the target production unit set affected by the risk is dynamically divided according to the starting node and the ending node. The target production unit set specifically includes the direct impact area and the indirect impact area.

[0011] Furthermore, the step of constructing the evolution state sequence of the electronic material tray over time based on the attribute vector pre-established for the electronic material tray by the warehousing terminal also includes: Based on the preset influencing factors of the warehousing terminal, the changing trend of the evolutionary state sequence is identified. The influencing factors specifically include endogenous driving factors and external driving factors, and the changing trend specifically includes monotonic decay, stage transition and periodic change. Determine whether the changing trend can map the process logic of the warehousing terminal; If possible, then the nodes of the evolutionary state sequence are extracted, the decision nodes that have scheduling influence in the trend of the change trend are identified, and the scheduling calculation efficiency of the evolutionary state sequence is dynamically compressed based on the decision nodes.

[0012] The present invention also provides an intelligent warehouse scheduling system for electronic material trays, comprising: The construction module is used to construct the evolution state sequence of the electronic material tray over time based on the attribute vector pre-established for the electronic material tray by the warehousing terminal. The attribute vector specifically includes display attributes, process attributes, and related attributes. The judgment module is used to determine whether the evolution state sequence meets the preset availability conditions of the production unit; The execution module is used to identify the preset constraint information of the production unit if not, remove scheduling paths that cannot meet the availability conditions from the evolution state sequence according to the constraint information, generate the corresponding feasible solution space, establish the resource competition relationship of the warehousing terminal according to the production demand of the production unit for the electronic material tray, and construct the scheduling scheme of the electronic material tray through the resource competition relationship. The constraint information specifically includes time constraints, sequence constraints and matching constraints, and the resource competition relationship specifically includes the urgency of demand of each production unit, material tray supply capacity and cross-unit allocation cost. The second judgment module is used to determine whether the scheduling scheme causes any production unit to have a preset risk, wherein the preset risk specifically includes cycle interruption risk and material tray status failure risk; The second execution module is used to generate multi-objective scheduling indicators for the electronic material tray through the warehousing terminal if no such result is obtained. Based on the multi-objective scheduling indicators, candidate scheduling schemes are generated in the feasible solution space. The resource allocation is coordinated globally by combining the resource competition relationship, and the scheduling cycle of the electronic material tray is optimized in a rolling manner. The multi-objective scheduling indicators specifically include production continuity indicators, resource utilization indicators, and state loss indicators. The global coordination of instructions specifically includes material tray inbound and outbound tasks, material handling equipment scheduling instructions, and production unit material supply instructions.

[0013] Furthermore, it also includes: The generation module is used to identify the resource contention intensity corresponding to each scheduling path based on the feasible solution space, map the resource contention intensity to a time interval during the execution process, generate corresponding resource occupancy segments, and form a time overlap relationship diagram based on the superposition of the resource occupancy segments. Specifically, the resource contention intensity refers to the degree of demand conflict of each production unit for the same material tray and the tightness of material tray supply. The third judgment module is used to determine whether the time overlap relationship graph can characterize the potential conflict degree of different paths during execution; The third execution module is used to construct a corresponding path coupling index based on the time overlap relationship graph if possible. Through the path coupling index, the feasible solution space is restructured to generate several independent scheduling subsets. Based on the scheduling subsets, the effective weights of each scheduling path are dynamically adjusted. The path coupling index is specifically used to characterize the influence range of any scheduling path on other paths. The structural reorganization specifically includes splitting highly coupled paths and preferentially combining low-coupling paths.

[0014] Furthermore, the execution module also includes: The generation unit is used to construct the association mapping relationship between each production unit based on the demand parameters of the production demand, and to introduce the resource allocation tendency parameters preset by the warehousing terminal according to the association mapping relationship to generate a preliminary scheduling structure. The demand parameters specifically include production progress, number of processes to be completed and current material availability. The judgment unit is used to determine whether there are any scheduling tasks to be executed in the preliminary scheduling structure. An execution unit is configured to, if so, detect the resource reallocation information of the preliminary scheduling structure based on the associated mapping relationship, construct the chain adjustment risk corresponding to each scheduling path through the resource reallocation information, and dynamically adjust the multi-tray combination task of the electronic material tray based on the chain adjustment risk.

[0015] This invention provides an intelligent warehouse scheduling method and system for electronic material trays, which has the following beneficial effects: This invention models the explicit, process, and associated attributes of electronic material trays into a time-evolving state sequence, enabling scheduling decisions to dynamically perceive changes in tray availability. This effectively avoids mismatch problems caused by state lag. By introducing time constraints, sequence constraints, and matching constraints to filter scheduling paths and construct a feasible solution space, the invention significantly improves the process adaptability and executability of scheduling schemes. Furthermore, it establishes resource competition relationships based on demand urgency, supply capacity, and cross-unit allocation costs, achieving global resource coordination and allocation among multiple production units. Simultaneously, a risk assessment mechanism proactively avoids cycle time interruptions and state failures. Moreover, multi-objective scheduling indicators drive candidate scheme generation. Combined with rolling optimization and instruction-level global coordination, the invention achieves continuous dynamic adjustment of the scheduling process, thereby improving production continuity, resource utilization efficiency, and tray lifespan in complex, multi-constraint environments, and enhancing the overall stability and intelligence of the system. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating an embodiment of the intelligent warehouse scheduling method for electronic material trays according to the present invention. Figure 2 This is a structural block diagram of an embodiment of the intelligent warehousing and scheduling system for electronic material trays of the present invention. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The realization of the purpose, functional features, and advantages of the invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings.

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Reference Appendix Figure 1 The intelligent warehousing scheduling method for electronic trays in one embodiment of the present invention includes: S1: Based on the attribute vector pre-established for the electronic material tray by the warehousing terminal, construct the evolution state sequence of the electronic material tray over time, wherein the attribute vector specifically includes display attributes, process attributes and related attributes; S2: Determine whether the evolution state sequence meets the preset availability conditions of the production unit; S3: If not, identify the preset constraint information of the production unit, remove scheduling paths that cannot meet the availability conditions from the evolution state sequence according to the constraint information, generate the corresponding feasible solution space, establish the resource competition relationship of the warehousing terminal according to the production demand of the production unit for the electronic material tray, and construct the scheduling scheme of the electronic material tray through the resource competition relationship. The constraint information specifically includes time constraints, sequence constraints and matching constraints, and the resource competition relationship specifically includes the urgency of demand of each production unit, material tray supply capacity and cross-unit allocation cost. S4: Determine whether the scheduling scheme causes any production unit to experience a preset risk, wherein the preset risk specifically includes cycle interruption risk and material tray status failure risk; S5: If not, a multi-objective scheduling index for the electronic material tray is generated through the warehousing terminal. Based on the multi-objective scheduling index, a candidate scheduling scheme is generated in the feasible solution space. The resource allocation is coordinated globally by combining the resource competition relationship, and the scheduling cycle of the electronic material tray is optimized in a rolling manner. The multi-objective scheduling index specifically includes production continuity index, resource utilization index, and state loss index. The global coordination of instructions specifically includes material tray inbound and outbound tasks, material handling equipment scheduling instructions, and production unit material supply instructions.

[0020] In this embodiment, the system constructs an evolutionary state sequence of electronic material trays over time based on pre-established attribute vectors for the warehousing terminal. These attribute vectors specifically include display attributes, process attributes, and association attributes. The system then determines whether this evolutionary state sequence meets the pre-set availability conditions of the production unit and executes the corresponding steps accordingly. For example, when the system determines that the evolutionary state sequence of a certain electronic material tray meets the pre-set availability conditions of the production unit, the system considers that the tray's process attributes or in-process relationships are within acceptable limits within the current and foreseeable time window, qualifying it for scheduling and production use. The system then performs appropriate adjustments to the electronic material tray. The system uses a confirmation tag and includes the material in the scheduling candidate resource pool. Simultaneously, it combines the material's state margin (such as remaining available time) and spatial location to generate a corresponding scheduling segment, clarifying its target production unit and expected supply time. Furthermore, based on the urgency of each production unit's needs and resource competition, it determines the material's priority allocation direction and incorporates it into the candidate scheduling scheme construction process. This is combined and optimized with other material trays to form an overall scheduling structure. For example, if the system determines that the evolution sequence of a certain electronic material tray over time cannot meet the pre-set availability conditions of a production unit, the system will consider the material tray ineligible for scheduling and production use. The system will then identify the pre-set availability conditions of the production unit. Constraint information, specifically time constraints, sequence constraints, and matching constraints, is used to eliminate scheduling paths that cannot meet availability conditions from the evolutionary state sequence based on different constraints, generating a corresponding feasible solution space. Based on the production unit's production demand for the electronic material tray, resource competition relationships are established at the warehousing terminal. These resource competition relationships specifically include the urgency of each production unit's demand, the tray's supply capacity, and cross-unit allocation costs. Through these resource competition relationships, a scheduling scheme for the electronic material tray is constructed. The system effectively avoids scheduling paths that do not meet process requirements or pose potential risks by identifying electronic material trays that do not meet availability conditions and filtering them in conjunction with time constraints, sequence constraints, and matching constraints. The system addresses the issue of misplaced pallets in production, thereby improving the accuracy and reliability of scheduling decisions from the outset. This reduces production anomalies caused by incorrect materials, excessive usage time, or process mismatches. Furthermore, by eliminating infeasible paths and constructing a feasible solution space, the scheduling process shifts from "blind selection of all materials" to "optimal selection after constraint convergence," significantly reducing the complexity of the solution space and improving the efficiency of subsequent scheduling calculations. It also quantifies the resource contention relationships between different production units, enhancing the system's ability to coordinate multi-source demand conflicts. The system then determines whether the scheduling scheme leads to pre-defined risks for any production unit. These pre-defined risks include cycle time interruption risk and pallet status failure risk, and executes corresponding steps accordingly.For example, when the system determines that the scheduling scheme has indeed caused a pre-defined risk to a certain production unit, the system will consider that the scheduling scheme can no longer guarantee production continuity or the effectiveness of the pallet status during the current or predicted execution process. This may lead to material supply interruptions, cycle time disruptions, or pallet failure due to timeouts, environmental exposure, or other factors, thereby adversely affecting production stability. The system will locate the key scheduling segment that triggers the risk, identify the corresponding pallet, production unit, and time interval, and mark it as a high-risk node. At the same time, it will implement restrictive measures on the relevant pallets, such as suspending allocation or lowering priority, from the feasible... The system filters alternative pallets or backup scheduling paths from the solution space to eliminate sources of risk and partially restructures the original scheduling plan. This includes adjusting the supply sequence, advancing or delaying delivery times, or reallocating pallets to other production units with lower loads. For example, if the system determines that the scheduling plan does not cause a pre-defined risk to the production unit, it considers the plan to still ensure production continuity or pallet status validity. The system then generates multi-objective scheduling indicators for the electronic pallet through the warehouse terminal. These indicators include production continuity indicators, resource utilization indicators, and status loss indicators. Based on these multi-objective scheduling indicators, candidate scheduling schemes are generated within the feasible solution space. Resource allocation is then globally coordinated based on resource competition relationships. This global coordination specifically includes tasks for pallet in / out, scheduling instructions for handling equipment, and material supply instructions for production units. The scheduling cycle of the electronic pallet is continuously optimized. Assuming the scheduling schemes do not have pre-existing risks, the system introduces production continuity indicators, resource utilization indicators, and state loss indicators to construct a multi-objective scheduling system. This ensures that scheduling decisions are no longer limited to a single optimization objective, but rather that while ensuring stable production operation, resource utilization efficiency and pallet state management are considered, thereby improving the comprehensiveness and scientific nature of the overall decision-making. Furthermore, by generating candidate scheduling schemes within the feasible solution space based on multi-objective scheduling indicators and globally coordinating them with resource competition relationships, resource allocation among different production units becomes more balanced and reasonable, effectively avoiding resource congestion or idleness caused by local optima. Through unified scheduling of pallet in / out, handling equipment, and material supply instructions, collaborative linkage between the warehousing and production execution layers is achieved, improving the overall system operating efficiency. Moreover, by continuously optimizing the scheduling cycle, the system can continuously adjust its scheduling strategy according to real-time state changes, enhancing its adaptability to dynamic environments. ;

[0021] It should be noted that, based on the constraints preset by the production unit, scheduling paths that cannot meet the availability conditions are removed from the evolutionary state sequence, generating a corresponding feasible solution space. Based on the production unit's production demand for the electronic material tray, a resource competition relationship is established among the warehousing terminals. Through this resource competition relationship, a scheduling scheme for the electronic material tray is constructed, specifically as follows: The first step is the convergence processing of "unavailable states." After identifying the constraints of the production unit (such as time constraints, sequence constraints, and matching constraints), the system maps these constraints to the evolution state sequence of the electronic reels and verifies each possible scheduling path one by one. For example, if a reel is in stock but its maximum availability time is approaching after being opened, it does not meet the time constraint. Similarly, if a batch of reels must be used first, paths that do not meet the sequence constraint will be eliminated. Or, if a reel is only compatible with specific production line equipment, paths that do not meet the matching constraint will also be excluded. Through this process, the system compresses the originally complex scheduling paths containing a large number of invalid combinations into a set of "feasible solution spaces" that satisfy all constraints. For example, when an SMT production line is about to enter a critical placement process, the system will automatically eliminate all reel paths that cannot be delivered within the specified time or do not meet the device requirements of the process, retaining only candidate paths that can supply materials in a timely and compliant manner. Based on this, the system further establishes resource competition relationships according to the actual needs of each production unit for electronic material trays, and constructs scheduling schemes accordingly. Specifically, when multiple production units simultaneously request the same type of material tray, the system comprehensively analyzes the urgency of each unit's demand (such as whether a line stop is imminent), current inventory supply capacity, and the cost required for cross-unit allocation (such as handling distance or equipment occupation), thereby quantifying the competitive relationship between different demands. For example, if production line A will run out of material within 10 minutes, while production line B still has a 30-minute buffer, the system will prioritize allocating available material trays to production line A. However, if production line A is far away and the allocation cost is too high, while the demand of production line B is also gradually increasing, the system may reallocate resources through a balancing strategy. In this process, the system does not simply "give to whoever is in a hurry," but rather constructs the optimal scheduling scheme based on a global perspective, enabling material trays to flow dynamically and rationally among multiple production units, thereby ensuring that critical production lines do not stop while also taking into account overall resource utilization efficiency.

[0022] It should be added that the warehousing terminal generates multi-objective scheduling indicators for the electronic material trays. Based on these indicators, candidate scheduling schemes are generated within the feasible solution space. The resource allocation is then globally coordinated according to the resource competition relationship, and the scheduling cycle of the electronic material trays is continuously optimized. Specifically: The system first generates multi-objective scheduling indicators for electronic pallets based on the warehousing terminal, quantifying different dimensions of operational objectives. For example, the production continuity indicator measures whether material supply will affect the production line cycle time, the resource utilization rate indicator measures the efficiency of warehousing equipment and handling resources, and the state loss indicator assesses the time consumption and failure risk of pallets during the scheduling process. Based on these indicators, the system performs multi-dimensional evaluation of each scheduling path within the previously converged feasible solution space, and generates a set of candidate scheduling schemes by combining, adjusting, or rearranging paths. These candidate schemes are not simply ranked results, but rather scheduling structures that reflect different optimization focuses. For example, some schemes focus more on reducing handling costs, while others prioritize ensuring uninterrupted material supply to critical production lines. Based on this, the system coordinates candidate solutions globally by considering resource competition relationships, transforming abstract scheduling results into specific execution-level instructions. These instructions include pallet inbound / outbound arrangements, task allocation for handling equipment (such as AGVs or conveyor systems), and material supply sequence control for production units. For example, when two production lines simultaneously require the same type of pallet, the system may prioritize delivering the pallet closer to the line with higher urgency, while simultaneously arranging another handling device to replenish materials for the other line from a remote location, thus achieving optimal overall efficiency. During execution, the system does not determine the scheduling all at once but continuously updates the scheduling cycle through a rolling optimization mechanism: as pallet status changes, production line demand fluctuates, or equipment status is updated, the system dynamically adjusts subsequent scheduling plans. For example, when a pallet's available time is shortened due to delays, the system will automatically lower its priority or replace it with another pallet in the next scheduling cycle, thereby ensuring production continuity while continuously optimizing resource allocation and pallet utilization efficiency.

[0023] In this embodiment, after step S3 of identifying the preset constraint information of the production unit, removing scheduling paths that cannot meet the availability conditions from the evolutionary state sequence based on the constraint information, and generating the corresponding feasible solution space, the method further includes: S301: Based on the feasible solution space, identify the resource competition intensity corresponding to each scheduling path, map the resource competition intensity to a time interval during the execution process, generate corresponding resource occupancy segments, and form a time overlap relationship diagram based on the superposition of the resource occupancy segments. Specifically, the resource competition intensity refers to the degree of demand conflict of each production unit for the same material tray and the tightness of material tray supply. S302: Determine whether the time overlap relationship graph can characterize the potential conflict degree of different paths during execution; S303: If possible, construct a corresponding path coupling index based on the time overlap relationship diagram, and restructure the feasible solution space using the path coupling index to generate several independent scheduling subsets. Based on the scheduling subsets, dynamically adjust the effective weights of each scheduling path. The path coupling index is specifically used to characterize the influence range of any scheduling path on other paths. The structural reorganization specifically includes splitting highly coupled paths and preferentially combining low-coupling paths.

[0024] In this embodiment, based on the feasible solution space, the system identifies the resource competition intensity corresponding to each scheduling path. Specifically, the resource competition intensity refers to the degree of demand conflict for the same material tray by each production unit and the tightness of material tray supply. Different resource competition intensities are mapped to time intervals during execution, generating corresponding resource occupancy segments. Based on the superposition of these resource occupancy segments, a time overlap graph is formed. The system then determines whether this time overlap graph can characterize the potential conflict degree of different scheduling paths during execution, and executes the corresponding steps accordingly. For example, if the system determines that the time overlap graph cannot characterize the potential conflict degree of different scheduling paths during execution, the system considers that the time mapping model constructed from these resource occupancy segments still has insufficient information representation. That is, the overlap relationship of time intervals alone cannot fully reflect the actual conflict situation between scheduling paths. The system will then enhance the existing resource occupancy segments by introducing more dimensions of constraint information, such as handling path conflict relationships, equipment type occupancy relationships, and key resource locking ranges, thereby constructing a multi-dimensional resource association description. Simultaneously, based on the original time overlap relationship graph, a composite relationship structure including time, space, and resource type is generated to more comprehensively characterize the mutual influence between scheduling paths. The system also re-evaluates the coupling degree between scheduling paths, identifies key areas where potential conflicts are not covered, and splits, rearranges, or introduces buffer time for related scheduling paths to reduce conflict risk. For example, when the system determines that the time overlap relationship graph can characterize the potential conflict degree of different scheduling paths during execution, the system will consider that the time mapping model constructed by these resource occupancy segments can reflect the real conflict situation between scheduling paths. Based on the time overlap relationship graph, the system will construct a corresponding path coupling index. The path coupling index is specifically used to characterize the influence range of any scheduling path on other paths. Through the path coupling index, the feasible solution space is restructured. The restructuring includes splitting highly coupled paths and preferentially combining low-coupling paths to generate several independent scheduling subsets. Based on these scheduling subsets, the effective weight of each scheduling path is dynamically adjusted.The system constructs a path coupling index through a time overlap graph, elevating the analysis from a simple "whether there is a conflict" judgment to a quantitative analysis of the "scope and intensity of conflict impact." This index accurately reflects the degree of influence of a scheduling path on other paths in terms of time, resource consumption, and execution order, thus providing a more targeted decision-making basis for subsequent scheduling optimization. This avoids relying solely on local information for coarse-grained adjustments. Simultaneously, based on path coupling, the system restructures the feasible solution space, splitting highly coupled paths and prioritizing the combination of low-coupling paths. This effectively reduces mutual interference between scheduling paths, decomposing the originally complex and intertwined scheduling relationships into several relatively independent scheduling subsets. Furthermore, based on these subsets, the effective weights of each scheduling path are dynamically adjusted, allowing scheduling decisions to adaptively adjust based on the coupling relationships between paths. By increasing the priority of low-conflict paths and decreasing the impact weight of high-conflict paths, the system can achieve a dynamic balance in resource allocation while ensuring overall coordination, thereby further improving warehouse scheduling efficiency and production continuity.

[0025] It should be noted that, based on the aforementioned time overlap graph, a corresponding path coupling index is constructed. Using this index, the feasible solution space is structurally reorganized to generate several independent scheduling subsets. Based on these subsets, the effective weights of each scheduling path are dynamically adjusted, specifically as follows: First, the "time overlap relationship diagram" is further transformed from a structured conflict representation into a computable impact intensity model. Based on the degree of overlap, duration, and frequency of resource reuse of each resource occupation segment on the time axis in this relationship diagram, the system constructs a path coupling index to quantify the impact range of any scheduling path on other paths. For example, when two scheduling paths share the same handling equipment or the same type of electronic tray within the same time window, their coupling degree will increase significantly. If there is only a short-term, low-resource-density overlap, the coupling degree will be relatively low. Through this index, the system can upgrade from "whether there is a conflict" to a more refined characterization of "how strong the conflict is and how wide the impact is," thereby providing a higher-resolution decision-making basis for subsequent optimization. Building upon this, the system restructures the feasible solution space using path coupling, splitting highly coupled paths by decoupling them in terms of time or resources, preventing them from concentrating on the same critical resources. Simultaneously, it prioritizes combining low-coupling paths to form multiple independent or weakly dependent scheduling subsets. For example, in an electronics manufacturing scenario, paths A and B, sharing the same AGV and with highly overlapping times, are identified as highly coupled paths and split for scheduling. Paths C and D, serving different production lines and with non-conflicting resource usage, are combined into the same scheduling subset. In this way, the originally complex and intertwined global scheduling problem is transformed into multiple relatively independent small-scale scheduling problems, significantly reducing the overall optimization complexity. Finally, after forming multiple scheduling subsets, the system dynamically adjusts the effective weights of each scheduling path based on the conflict density within the subsets and the external coupling relationship. For example, paths running in low-coupling subsets have less resource contention pressure and higher execution stability, so their weights are appropriately increased to improve their scheduling priority. Paths located at the high-coupling split boundary, on the other hand, have higher potential conflict risks, so their weights are correspondingly reduced or their execution is dynamically delayed. Through this weight redistribution mechanism based on structural reorganization, the system can achieve a dynamic balance of global resource allocation while ensuring local independence, thereby improving overall scheduling efficiency and execution stability.

[0026] In this embodiment, step S3, which establishes a resource competition relationship among the warehousing terminals based on the production unit's demand for the electronic material tray, and constructs a scheduling scheme for the electronic material tray through this resource competition relationship, further includes: S31: Based on the demand parameters of the production demand, construct the association mapping relationship between each production unit. According to the association mapping relationship, introduce the resource allocation tendency parameters preset by the warehousing terminal to generate a preliminary scheduling structure. The demand parameters specifically include production progress, number of processes to be completed, and current material availability. S32: Determine whether there are any scheduling tasks to be executed in the preliminary scheduling structure; S33: If so, then based on the associated mapping relationship, detect the resource reallocation information of the preliminary scheduling structure, construct the chain adjustment risk corresponding to each scheduling path through the resource reallocation information, and dynamically adjust the multi-tray combination task of the electronic material tray based on the chain adjustment risk.

[0027] In this embodiment, the system constructs an association mapping relationship between production units based on production demand parameters, specifically including production progress, the number of processes to be completed, and the current material availability. Based on this association mapping relationship, it introduces pre-set resource allocation preference parameters from the warehousing terminal to generate a preliminary scheduling structure. The system then determines whether these preliminary scheduling structures contain scheduling tasks that can be merged for execution, and executes the corresponding steps accordingly. For example, if the system determines that there are no scheduling tasks that can be merged in these preliminary scheduling structures, it assumes that there are no shared execution windows or reusable handling resources between different scheduling tasks. The system maintains the independent execution framework of the existing preliminary scheduling structure and decouples and confirms each scheduling task to avoid execution conflicts or resource mismatches caused by forced merging. Simultaneously, it re-evaluates the demand parameters of each production unit, focusing on analyzing changes in production progress, the number of processes to be completed, and fluctuations in material availability to determine if there are potential subsequent merging windows. Furthermore, without changing the current execution structure, the system introduces a buffer scheduling mechanism to fine-tune or stagger task execution times to reduce the pressure of concentrated resource occupation. For example, when the system determines that these preliminary scheduling structures do not contain shared execution windows, it considers that there are no shared execution windows or reusable handling resources between different scheduling tasks. When there are merged scheduling tasks in the structure, the system assumes that different scheduling tasks have shareable execution windows or reusable handling resources. Based on the correlation mapping between production units, the system detects resource reallocation information in these initial scheduling structures. Using this information, it constructs cascading adjustment risks for each scheduling path. Based on different cascading adjustment risks, it dynamically adjusts multi-pallet combination tasks in the electronic material tray. By identifying scheduling tasks with merged execution conditions in the initial scheduling structure, the system can shift from distributed scheduling to a collaborative scheduling mode based on shared execution windows and reusable handling resources. This improves the concentration of resource utilization and the overall efficiency of scheduling execution, reducing system overhead caused by repetitive handling and redundant scheduling. Simultaneously, by detecting resource reallocation information based on production unit correlation mapping, the system constructs cascading adjustment risks for scheduling paths, enabling it to identify potential cascading effects in advance during merged scheduling. Furthermore, by dynamically adjusting multi-pallet combination tasks based on different cascading adjustment risks, the system achieves an adaptive balance between merged scheduling and risk control. It splits, reorganizes, or prioritizes combination tasks according to the risk level, thereby improving scheduling coordination.

[0028] It should be noted that, based on the aforementioned correlation mapping relationship, the resource reallocation information of the preliminary scheduling structure is detected. Using this resource reallocation information, a chain adjustment risk corresponding to each scheduling path is constructed. Based on this chain adjustment risk, the multi-tray combination task of the electronic material tray is dynamically adjusted, specifically as follows: The system first moves from the "mergeable scheduling structure" to the "resource reallocation impact analysis" stage. Based on the correlation mapping relationship between production units, the system detects resource changes involved in the merging execution of the initial scheduling structure, focusing on identifying resource reallocation behaviors caused by task merging, such as sharing of handling equipment, overlapping pallet paths, or compression of material supply time windows. These changes are then uniformly represented as resource reallocation information. Through this information, the system can determine the degree of impact that a certain scheduling path may have on other paths after being adjusted or merged, thus providing basic data for subsequent risk modeling. Based on this, the system constructs the chain adjustment risk corresponding to each scheduling path based on resource reallocation information to characterize the cascading impact that a single scheduling change may cause. For example, when two scheduling paths share the same AGV resource due to merging, if one path is delayed, it may cause multiple subsequent paths that depend on that AGV to be delayed as a whole. This impact of "multi-path synchronous change caused by local adjustment" constitutes the chain adjustment risk. For example, in the process of electronic material tray delivery, if material tray A and material tray B were originally delivered independently, but share the same handling window after merging scheduling, the delay of path A will directly affect the material supply sequence of path B, and further affect the cycle time of the downstream chip mounting process. This chain of influence is modeled by the system as a high-level chain adjustment risk. Finally, the system dynamically adjusts the multi-pallet combination tasks of electronic material trays based on different chain adjustment risks, realizing adaptive optimization of the scheduling structure. When the chain risk of a combination task is high, the system will split it into multiple loosely coupled sub-tasks or readjust the execution order to reduce the scope of risk propagation. For combination tasks with low chain risk, priority is given to maintaining merged execution to improve resource utilization efficiency. For example, in a certain production scenario, if the system detects that the combination tasks of three material trays A, B, and C have high chain risk, it will split them into two independent combinations, "A+B" and "C", to avoid global cycle time fluctuations due to delays in a single node, thus achieving a balance between risk control and scheduling efficiency.

[0029] In this embodiment, step S5, which generates candidate scheduling schemes within the feasible solution space based on the multi-objective scheduling index, further includes: S51: Based on each scheduling path in the feasible solution space, construct a corresponding response index vector. According to the response index vector, select several seed paths from each scheduling path, expand and combine them around the seed paths, and generate a set of scheduling candidate schemes. The response index vector is specifically used to characterize the performance characteristics of each scheduling path under different objectives. S52: Determine whether the set of solutions is concentrated in a locally similar region; S53: If so, a preset structural disturbance mechanism is introduced to optimize the scheduling of some scheduling paths in the local similar region. Based on the optimization results of the scheduling optimization, representative non-dominated solution combinations in each scheduling path are dynamically retained. Through the non-dominated solution combinations, the coverage of the candidate scheduling scheme in the feasible solution space is enhanced. The scheduling optimization specifically includes replacement, insertion, and order adjustment.

[0030] In this embodiment, the system constructs corresponding response index vectors based on each scheduling path in the feasible solution space. These response index vectors specifically characterize the performance of each scheduling path under different objectives. Based on these response index vectors, several seed paths are selected from each scheduling path, and expansion and combination are performed around these seed paths to generate a set of scheduling candidate schemes. The system then determines whether this set of schemes is concentrated in a locally similar region to execute the corresponding steps. For example, if the system determines that the set of scheduling candidate schemes is not concentrated in a locally similar region, the system considers that the candidate schemes currently generated by expanding the seed paths exhibit good dispersion and coverage in the multidimensional objective space of the response index vectors. In other words, different candidate solutions exhibit significant differences in objective dimensions such as production continuity, resource utilization, and state loss. The system retains the candidate solution set and proceeds to the next stage of optimization. The response index vectors of each candidate solution undergo consistency normalization to eliminate the impact of scale differences between different objective dimensions. Simultaneously, based on this dispersion characteristic, the system further performs global ranking and screening of candidate solutions, prioritizing solutions with complementary advantages in different objective dimensions to enhance the diversity and robustness of the final scheduling decision. Furthermore, the system performs secondary correction on candidate solutions based on resource competition relationships, ensuring that they meet actual resource constraints while maintaining dispersed coverage. For example, when the system determines that a scheduling decision is needed... If the set of candidate solutions is indeed concentrated in locally similar regions, the system will assume that there are no significant differences between the different candidate solutions in the target dimension. The system will then introduce a pre-set structural perturbation mechanism to optimize the scheduling of some paths in the locally similar regions. This optimization includes replacement, insertion, and order adjustment. Based on the optimization results, representative non-dominated solution combinations from each scheduling path are dynamically retained. These non-dominated solution combinations enhance the coverage of candidate scheduling solutions in the feasible solution space. By identifying that candidate scheduling solutions are concentrated in locally similar regions, the system can promptly detect the homogenization problem of candidate solutions in the response index vector. The introduction of the structural perturbation mechanism further addresses this issue. By replacing, inserting, and adjusting the order of paths, the solution space, which was originally converging, regains its ability to expand differentiatedly. This effectively avoids the algorithm getting stuck in local optima and improves the sufficiency of solution space exploration. At the same time, by optimizing and perturbing the scheduling paths in local similar regions, the system dynamically selects and retains representative non-dominated solution combinations. This allows the system to eliminate redundant or highly repetitive candidate schemes while ensuring solution quality, thereby improving the structural diversity and optimization efficiency of the candidate scheduling scheme set. Furthermore, by expanding the coverage of candidate scheduling schemes in the feasible solution space based on non-dominated solution combinations, the system can obtain a more balanced scheduling decision basis under multi-objective constraints, enhancing the adaptability of the scheme to changes in different production scenarios.

[0031] It should be noted that a preset structural perturbation mechanism is introduced to optimize the scheduling of some scheduling paths in the locally similar regions. Based on the optimization results, representative non-dominated solution combinations from each scheduling path are dynamically retained. Through these non-dominated solution combinations, the coverage of the candidate scheduling scheme in the feasible solution space is enhanced. Specifically: Firstly, based on the discovery that candidate scheduling schemes are concentrated in locally similar regions, a pre-defined structural disturbance mechanism is introduced to regenerate and reconstruct some scheduling paths within this region for optimization. The so-called structural disturbance mechanism is not a simple random adjustment, but rather a structural transformation rule based on the scheduling path in terms of time arrangement, resource consumption, and execution order. It replaces, inserts, or rearranges the original paths in a directional manner, so as to generate new combination forms without violating feasible constraints. For example, in an electronic material tray scheduling scenario, multiple paths exhibit a similar structure of "first outbound → then transport → finally supply". The system can adjust the transport order of some paths or introduce intermediate buffer steps to create differences in time windows and resource consumption, thereby breaking the original structural homogeneity. After completing structural disturbance and scheduling optimization, the system evaluates and analyzes the optimized scheduling paths and selects representative non-dominated solution combinations based on multi-objective response index vectors. A non-dominated solution combination refers to a set of scheduling paths that are not comprehensively superior to other solutions in multiple objective dimensions such as production continuity, resource utilization, and state loss. The system retains representative solutions with advantages in different objective dimensions from the multiple paths after disturbance, while eliminating paths with high repetition or obvious dominance. For example, among three disturbance paths A, B, and C, path A is optimal in production continuity, path B performs best in resource utilization, and path C is better in state loss control. In this case, the system will retain all three as a non-dominated solution combination, rather than retaining only a single "comprehensive optimal" path. Finally, the system expands the coverage of candidate scheduling schemes in the feasible solution space by using the retained non-dominated solution combinations, so that the scheduling solutions are no longer limited to local similar regions, but extend to multiple different optimization directions, thus forming a wider distribution of candidate solutions. For example, by retaining non-dominated solution combinations with different preference directions, the system can simultaneously cover multiple scheduling strategies such as "high efficiency priority", "low loss priority" and "balanced", so that subsequent global scheduling optimization can be selected from a richer solution space, thereby improving the diversity, robustness and global optimal approximation ability of the final scheduling results.

[0032] In this embodiment, step S2, which determines whether the evolutionary state sequence meets the preset availability conditions of the production unit, further includes: S21: Based on the set of condition judgment rules corresponding to the available conditions, extract key state components from the evolutionary state sequence to form a corresponding set of state features, wherein the key state components specifically include time-related components, environmental accumulation components, and associated constraint components. S22: Determine whether the set of state features can be mapped and matched with the set of conditional judgment rules; S23: If possible, then generate an availability confirmation identifier for the electronic material tray based on the mapping matching result, construct constraint information for the electronic material tray through the availability confirmation identifier, and dynamically divide the calling scope of the electronic material tray based on the constraint information, wherein the constraint information specifically includes constraint type and constraint boundary.

[0033] In this embodiment, the system extracts key state components from the evolutionary state sequence based on the condition determination rule set corresponding to the available conditions. These key state components specifically include time-related components, environmental cumulative components, and associated constraint components, forming a corresponding set of state features. The system then determines whether these state feature sets can be mapped and matched with the condition determination rule set to execute the corresponding steps. For example, if the system determines that these state feature sets cannot be mapped and matched with the condition determination rule set, the system considers that the key state components of the current electronic material tray in the evolutionary state sequence have deviated from the preset range of available conditions for the production unit, and the system will perform anomaly component analysis on this state feature set. The system identifies key state deviations that cause mapping failures, such as time out-of-bounds errors, environmental cumulative threshold exceedances, or mismatches in correlations. Relevant components are marked, and corrective backtracking analysis is performed on the evolutionary state sequence. By combining the state change trends of adjacent time points, the current state is trend-corrected or its confidence level reassessed to reduce the impact of single-point anomalies on the overall judgment. Furthermore, the system introduces an extended rule set or a flexible judgment mechanism to supplement the original conditional judgment rule set, enabling some boundary states to regain their decidability. For example, when the system determines that these state feature sets can be mapped and matched with the conditional judgment rule set, the system considers the current electronic tray... When key state components in the evolutionary state sequence fall within the preset availability conditions of the production unit, the system generates availability confirmation identifiers for the electronic material tray based on the mapping and matching results. These identifiers then construct constraint information for the electronic material tray, including constraint types and boundaries. Based on this constraint information, the system dynamically defines the call range for the electronic material tray. Through the mapping and matching between the state feature set and the condition judgment rule set, the system can accurately identify whether the key state components of the electronic material tray are within the preset availability conditions, thereby transforming the originally scattered and continuously changing state information into structured, decidable results and improving the tray availability judgment. To ensure accuracy and consistency, and avoid misjudgments caused by fluctuations in a single state, the system generates availability confirmation identifiers based on mapping and matching results, and further constructs constraint information including constraint types and constraint boundaries. This transforms the status of electronic material trays from a binary judgment of "available / unavailable" to a refined description with hierarchical and boundary constraint characteristics. This provides a more expressive data foundation for subsequent scheduling and resource allocation. Furthermore, by dynamically dividing the calling scope of electronic material trays based on constraint information, the system can adaptively manage the usage boundaries of different material trays while meeting process constraints, enabling reasonable allocation and restricted calling of these trays across different production units.

[0034] It should be noted that, based on the mapping matching result, an availability confirmation identifier is generated for the electronic material tray. Using this availability confirmation identifier, constraint information for the electronic material tray is constructed. Based on this constraint information, the calling scope of the electronic material tray is dynamically divided, specifically as follows: First, based on the mapping and matching results between the set of state features and the set of condition judgment rules, the electronic material trays are "structuredly expressed in terms of availability". The system no longer simply gives a simple available / unavailable conclusion, but generates a corresponding availability confirmation identifier for each material tray. This identifier is used to reflect which specific conditions its current state meets, what availability level it is, and whether there are any critical usage restrictions. For example, for a material tray, if its opening time is still within the safe window, its environmental exposure has not exceeded the threshold, and it matches the target work order, the system can generate a "fully available" identifier. For material trays that are close to the time boundary but are still usable, a "restricted availability" identifier may be generated, thus providing a more granular state basis for subsequent scheduling. Based on this, the system further constructs the constraint information of the electronic material tray through the availability confirmation identifier, transforming it into a structured description containing constraint types and constraint boundaries. For example, for a "limited availability" material tray, the system can extract its remaining available time as a time constraint boundary, limiting its use to a specific time window. At the same time, combined with its applicable process or production line type, a matching constraint is formed. If batch priority use rules are involved, a sequence constraint can also be formed. For example, if an electronic material tray has 2 hours of remaining available time and is only applicable to production line A, its constraint information can be represented as "time constraint ≤ 2 hours + matching constraint = production line A only + sequence constraint = medium priority", thus giving the material tray a clear usage boundary during the scheduling process. Finally, the system dynamically divides the scope of electronic material trays based on the aforementioned constraints, precisely defining their optional usage scenarios within the scheduling system. For example, "fully available" material trays can be freely allocated among multiple production units; while "limited availability" material trays are only allowed to be preferentially allocated to nearby or urgently needed production units within a specific time window to ensure they are consumed within their validity period; for material trays with stricter constraints, they may be restricted to use in a single production unit or a specific task. Through this dynamic division mechanism, the system can achieve refined scheduling and efficient utilization of material tray resources while ensuring process compliance.

[0035] In this embodiment, step S4, which determines whether the scheduling scheme causes a preset risk to any production unit, further includes: S41: Based on the risk factors preset in the warehousing terminal, detect the risk distribution sequence of the production unit over time, wherein the risk factors specifically include material supply interruption risk, waiting backlog risk and status failure risk; S42: Determine whether the risk distribution sequence will be transmitted to other production units through scheduling relationships; S43: If so, then the identified risk transmission path is locked, the starting node and the ending node of the risk transmission path are marked, and the target production unit set affected by the risk is dynamically divided according to the starting node and the ending node. The target production unit set specifically includes the direct impact area and the indirect impact area.

[0036] In this embodiment, the system detects the risk distribution sequence of a production unit over time based on pre-set risk factors in the warehousing terminal. These risk factors specifically include supply interruption risk, waiting backlog risk, and status failure risk. The system then determines whether these risk distribution sequences will propagate to other production units through scheduling relationships, and executes corresponding steps accordingly. For example, if the system determines that the risk distribution sequence of a production unit over time will not propagate to other production units through scheduling relationships, the system considers the supply interruption risk, waiting backlog risk, or status failure risk currently existing in that production unit to be localized and negligible. If the risk is controllable and its impact is limited to the local unit or a short time window, the system will mark the risk as a local risk event and maintain the current overall scheduling scheme's global structure unchanged to avoid unnecessary global adjustments. Simultaneously, local optimization processing will be performed on the production unit, such as fine-tuning the material supply rhythm, adjusting the task sequence within the unit, or temporarily increasing resource supply to alleviate material supply interruptions or backlogs. Furthermore, for pallets with a high risk of failure, priority can be given to consumption within the unit or replacement with pallets in better condition, thereby reducing the risk level. For example, when the system determines the risk level of the production unit changing over time... The risk distribution sequence will propagate to other production units through scheduling relationships. At this point, the system will consider the risk existing in the current production unit to be uncontrollable. The system will lock the identified risk propagation path, marking the starting and ending nodes of these risk propagation paths. Based on different starting and ending nodes, the system will dynamically divide the target production unit set affected by the risk, specifically including the directly affected area and the indirectly affected area. By recognizing that the risk distribution sequence will propagate to other production units through scheduling relationships, the system can anticipate the escalation from "local anomaly" to "global risk event". By locking the path, the system can clearly identify the starting and ending nodes of the risk propagation link, thereby preventing the disorderly spread of risk in the system and improving the accuracy and traceability of risk identification. At the same time, based on the locked risk propagation path, the affected production units are dynamically divided into directly affected areas and indirectly affected areas, enabling the system to manage the impact of risks at different levels in a hierarchical manner. Through this "path locking + impact partitioning" processing mechanism, the system can establish clear impact boundaries in the early stages of risk propagation, providing a structured foundation for subsequent resource isolation, scheduling reconfiguration, and risk blocking, which helps to reduce the chain reaction caused by risk propagation.

[0037] It should be noted that the identified risk transmission path is locked, and the starting and ending nodes of the risk transmission path are marked. Based on the starting and ending nodes, the set of target production units affected by the risk is dynamically divided, specifically as follows: First, the system performs structured positioning of risk propagation. After identifying a risk that may spread through scheduling relationships, the system performs path locking on the corresponding risk transmission path. This involves fixing the key scheduling links involved in the risk propagation and marking the starting and ending nodes of the risk within these links. The starting node is typically the production unit that is the source of the risk (e.g., a unit experiencing material supply interruption or abnormal pallet status), while the ending node is the target production unit that may be affected through material allocation, equipment sharing, or scheduling dependencies. Through this path locking and node marking, the system can clearly define the direction, scope, and key node locations of risk propagation, providing a clear structural basis for subsequent intervention. For example, in a pallet-sharing path, production line A becomes the starting node due to pallet delays, while production lines B and C, which are scheduled through the same material handling resources, may become subsequent nodes in the propagation chain. If production line C is located at the end of the scheduling chain, it is marked as the ending node. Based on this, the system dynamically divides the production units affected by risks according to the path relationship between the starting and ending nodes, forming a target production unit set, which is further subdivided into direct impact zone and indirect impact zone. The direct impact zone typically includes production units that have a strong dependency relationship with the starting node, such as units that share the same pallet or critical handling resources and are closely coupled in time. The indirect impact zone includes production units that may be affected by cascading effects through multi-level scheduling, and the risk propagation has a certain delay or uncertainty. For example, if the pallet delay of production line A directly affects the material supply cycle of production line B, then B belongs to the direct impact zone. Although production line C does not directly depend on A, it is affected by the further occupation of shared resources due to the delay of B, so C is classified as an indirect impact zone. Through this partitioning mechanism, the system can adopt differentiated scheduling strategies for different impact levels to achieve more refined risk control and resource allocation.

[0038] In this embodiment, step S1, which constructs the evolution state sequence of the electronic material tray over time based on the attribute vector pre-established by the warehousing terminal for the electronic material tray, further includes: S11: Based on the preset influencing factors of the warehousing terminal, identify the changing trend of the evolutionary state sequence, wherein the influencing factors specifically include endogenous driving factors and external driving factors, and the changing trend specifically includes monotonic decay, stage transition and periodic change. S12: Determine whether the changing trend can be mapped to the process logic of the warehousing terminal; S13: If possible, then extract nodes from the evolutionary state sequence, identify decision nodes that have scheduling influence in the trend of the change trend, and dynamically compress the scheduling calculation efficiency of the evolutionary state sequence based on the decision nodes.

[0039] In this embodiment, the system identifies the changing trends of the evolutionary state sequence based on pre-set influencing factors of the warehousing terminal. These influencing factors specifically include endogenous driving factors and external driving factors. The changing trends specifically include monotonically decreasing, stage transitions, and periodic changes. The system then determines whether these changing trends can map the process logic of the warehousing terminal and executes the corresponding steps accordingly. For example, when the system determines that the changing trends of the evolutionary state sequence cannot map the process logic of the warehousing terminal, the system considers that the current state change of the electronic tray has deviated from the preset process evolution law. The system will perform anomaly identification and decomposition processing on the changing trends, analyze the reasons for the deviation, and determine whether it is caused by endogenous driving factors (such as abnormal tray state) or external driving factors (such as environmental changes or scheduling interference). Simultaneously, the system performs verification and correction on the relevant state data, such as by tracing back historical states, smoothing abnormal fluctuations, or introducing compensation estimates, to restore its basic consistency with the process logic. Furthermore, the system will perform extended updates to include newly identified change patterns within the interpretable range. For example, when the system determines that the changing trends of the evolutionary state sequence can map the warehousing terminal... Based on the process logic, the system assumes that the current state change of the electronic tray conforms to the preset process evolution law. The system extracts nodes from the evolution state sequence, identifies decision nodes with scheduling impact during the trend of change, and dynamically compresses the scheduling calculation efficiency of the evolution state sequence based on these decision nodes. By confirming that the change trend of the evolution state sequence can map the process logic of the storage terminal, the system can ensure that the tray state change is interpretable and predictable, thereby improving the credibility of state data in scheduling decisions. On this basis, node extraction helps to identify the time nodes that truly have a key impact on scheduling from the continuously changing state sequence, avoiding redundant analysis of irrelevant or low-impact states. At the same time, by extracting decision nodes with scheduling impact, the system can transform the originally high-dimensional, continuous state sequence into a discrete representation of a small number of key nodes, thereby significantly reducing the data scale required to process during scheduling calculations. Furthermore, dynamic compression of the evolution state sequence based on decision nodes allows the scheduling process to focus on the key state change stages, improving overall operating efficiency while ensuring scheduling accuracy.

[0040] It should be noted that the endogenous driving factors come from the changes in the state of the material tray itself, while the external driving factors come from scheduling behavior and environmental changes, and their influence weights are modeled respectively.

[0041] It should be added that node extraction is performed on the evolutionary state sequence to identify decision nodes with scheduling influence during the trend of change. Based on the decision nodes, the scheduling computation efficiency of the evolutionary state sequence is dynamically compressed, specifically as follows: First, the system performs "criticality extraction" on the continuously changing evolutionary state sequence. After confirming that the state changes conform to the process logic, the system no longer participates in the scheduling calculation point by point on the entire time series. Instead, it performs node extraction processing based on the changing trend (such as monotonically decaying, stage transition, or periodic changes) to identify decision nodes that have a real impact on scheduling. These nodes usually correspond to the moments when the state changes significantly or approaches the constraint boundary, such as when the remaining available time is close to the threshold, the environmental accumulation is about to exceed the limit, or the time point when the correlation changes. In this way, the system can transform the originally continuous and redundant state sequence into a set of several representative critical nodes, thereby highlighting "when a decision must be made" rather than "calculating at every moment". After extracting the decision nodes, the system dynamically compresses the evolutionary state sequence based on these nodes, retaining only the key nodes and their necessary transition information, ignoring the stable intervals that have no substantial impact on scheduling, thereby significantly reducing the data scale involved in scheduling calculations. For example, in the state evolution of an electronic material tray, its state changes slowly and is far from the failure boundary in the first hour after opening, while it approaches the usable time limit at the end of the second hour. At this time, the system only extracts the "opening time", "critical moment", and "failure boundary moment" as decision nodes, without having to calculate every minute of the entire continuous time. As another example, in the scenario of periodic environmental changes, only the nodes corresponding to the periodic peak or valley value are retained for scheduling judgment, which can effectively express the state trend. Through the above node extraction and sequence compression, the system significantly reduces the amount of computation and processing complexity while ensuring the accuracy of scheduling decisions, enabling the scheduling algorithm to respond to dynamic changes more quickly. At the same time, this approach also improves the system's scalability in scenarios with multiple material trays and multiple production units, allowing it to maintain high real-time performance and stability in large-scale scheduling environments, achieving a balance between efficiency and accuracy.

[0042] Reference Appendix Figure 2 An intelligent warehousing and scheduling system for electronic trays, as described in one embodiment of the present invention, includes: The construction module 10 is used to construct the evolution state sequence of the electronic material tray over time based on the attribute vector pre-established for the electronic material tray by the warehousing terminal, wherein the attribute vector specifically includes display attributes, process attributes and related attributes. The judgment module 20 is used to determine whether the evolution state sequence meets the preset availability conditions of the production unit; The execution module 30 is used to identify the preset constraint information of the production unit if no, and according to the constraint information, remove the scheduling path that cannot meet the availability conditions from the evolution state sequence, generate the corresponding feasible solution space, establish the resource competition relationship of the warehousing terminal according to the production demand of the production unit for the electronic material tray, and construct the scheduling scheme of the electronic material tray through the resource competition relationship. The constraint information specifically includes time constraints, sequence constraints and matching constraints, and the resource competition relationship specifically includes the demand urgency of each production unit, material tray supply capacity and cross-unit allocation cost. The second judgment module 40 is used to judge whether the scheduling scheme causes any production unit to have a preset risk, wherein the preset risk specifically includes cycle interruption risk and material tray status failure risk; The second execution module 50 is used to generate multi-objective scheduling indicators for the electronic material tray through the warehousing terminal if no such result is obtained. Based on the multi-objective scheduling indicators, candidate scheduling schemes are generated in the feasible solution space. The resource allocation is coordinated globally by combining the resource competition relationship, and the scheduling cycle of the electronic material tray is optimized in a rolling manner. The multi-objective scheduling indicators specifically include production continuity indicators, resource utilization indicators, and state loss indicators. The global coordination of instructions specifically includes material tray inbound and outbound tasks, material handling equipment scheduling instructions, and production unit material supply instructions.

[0043] In this embodiment, the system constructs an evolutionary state sequence of electronic material trays over time based on pre-established attribute vectors for the storage terminal. These attribute vectors specifically include display attributes, process attributes, and association attributes. The system then determines whether these evolutionary state sequences meet the pre-set availability conditions of the production unit and executes the corresponding steps accordingly. For example, when the system determines that the evolutionary state sequence of a certain electronic material tray meets the pre-set availability conditions of the production unit, the system considers that the tray's process attributes or in-process relationships are within acceptable limits within the current and foreseeable time window, qualifying it for scheduling and production use. The system then marks the electronic material tray with availability and includes it in the production process. The material is added to the candidate resource pool for scheduling. Simultaneously, considering the material's state margin (such as remaining available time) and spatial location, a corresponding scheduling segment is generated, specifying its target production unit and expected supply time. Furthermore, based on the urgency of each production unit's needs and resource competition, the priority allocation direction of the material is determined, and it is incorporated into the construction of candidate scheduling schemes. It is then combined and optimized with other material trays to form an overall scheduling structure. For example, if the system determines that the evolution sequence of a certain electronic material tray over time cannot meet the pre-set availability conditions of a production unit, the system considers the material tray ineligible for scheduling and production. The system will then identify the pre-set constraints for that production unit, specifically including time constraints and sequence constraints. The system uses constraints and matching rules to eliminate scheduling paths that cannot meet availability conditions from the evolutionary state sequence, generating a corresponding feasible solution space. Based on the production unit's demand for the electronic pallet, it establishes resource competition relationships for the warehousing terminal. These relationships include the urgency of each production unit's demand, pallet supply capacity, and cross-unit allocation costs. Through these resource competition relationships, a scheduling scheme for the electronic pallet is constructed. The system then determines whether the scheduling scheme causes any production unit to experience a pre-defined risk, including cycle time interruption risk and pallet status failure risk, and executes corresponding steps accordingly. For example, if the system determines that the scheduling scheme has indeed caused a pre-defined risk to a certain production unit, it will proceed accordingly. If the system determines that the scheduling scheme cannot guarantee production continuity or the effectiveness of the pallet status during the current or predicted execution process, it may cause material supply interruption, cycle disorder, or cause the pallet to fail due to timeout, environmental exposure, or other factors, thereby adversely affecting production stability. The system will locate the key scheduling segment that triggers the risk, identify the corresponding pallet, production unit, and time interval, and mark it as a high-risk node. At the same time, it will implement restrictive measures on the relevant pallets, such as suspending allocation or reducing priority, selecting alternative pallets or backup scheduling paths from the feasible solution space to eliminate the source of risk, and partially reconstruct the original scheduling scheme, including adjusting the material supply sequence, advancing or delaying the delivery time, or reallocating to other production units with lower load.For example, when the system determines that the scheduling scheme does not lead to a pre-defined risk in the production unit, it considers that the scheduling scheme can still guarantee production continuity or the effectiveness of the pallet status. The system then generates multi-objective scheduling indicators for the electronic pallet through the warehousing terminal. These indicators include production continuity indicators, resource utilization indicators, and state loss indicators. Based on these indicators, candidate scheduling schemes are generated within the feasible solution space. The system then performs global command coordination of resource allocation, considering resource competition relationships. This global command coordination includes pallet in / out tasks, material handling equipment scheduling instructions, and production unit material supply instructions, continuously optimizing the scheduling cycle of the electronic pallet.

[0044] In this embodiment, it also includes: The generation module is used to identify the resource contention intensity corresponding to each scheduling path based on the feasible solution space, map the resource contention intensity to a time interval during the execution process, generate corresponding resource occupancy segments, and form a time overlap relationship diagram based on the superposition of the resource occupancy segments. Specifically, the resource contention intensity refers to the degree of demand conflict of each production unit for the same material tray and the tightness of material tray supply. The third judgment module is used to determine whether the time overlap relationship graph can characterize the potential conflict degree of different paths during execution; The third execution module is used to construct a corresponding path coupling index based on the time overlap relationship graph if possible. Through the path coupling index, the feasible solution space is restructured to generate several independent scheduling subsets. Based on the scheduling subsets, the effective weights of each scheduling path are dynamically adjusted. The path coupling index is specifically used to characterize the influence range of any scheduling path on other paths. The structural reorganization specifically includes splitting highly coupled paths and preferentially combining low-coupling paths.

[0045] In this embodiment, based on the feasible solution space, the system identifies the resource competition intensity corresponding to each scheduling path. Specifically, the resource competition intensity refers to the degree of demand conflict for the same material tray by each production unit and the tightness of material tray supply. Different resource competition intensities are mapped to time intervals during execution, generating corresponding resource occupancy segments. Based on the superposition of these resource occupancy segments, a time overlap graph is formed. The system then determines whether this time overlap graph can characterize the potential conflict degree of different scheduling paths during execution, and executes the corresponding steps accordingly. For example, if the system determines that the time overlap graph cannot characterize the potential conflict degree of different scheduling paths during execution, the system considers that the time mapping model constructed from these resource occupancy segments still has insufficient information representation. That is, the overlap relationship of time intervals alone cannot fully reflect the actual conflict situation between scheduling paths. The system will then enhance the existing resource occupancy segments by introducing more dimensions of constraint information, such as handling path conflict relationships, equipment type occupancy relationships, and key resource locking ranges, thereby constructing a multi-dimensional resource association description. Simultaneously, based on the original time overlap relationship graph, a composite relationship structure including time, space, and resource type is generated to more comprehensively characterize the mutual influence between scheduling paths. The system also re-evaluates the coupling degree between scheduling paths, identifies key areas where potential conflicts are not covered, and splits, rearranges, or introduces buffer time to reduce conflict risk for relevant scheduling paths. For example, when the system determines that the time overlap relationship graph can characterize the potential conflict degree of different scheduling paths during execution, it considers that the time mapping model constructed from these resource occupancy segments can reflect the actual conflict situation between scheduling paths. Based on the time overlap relationship graph, the system constructs a corresponding path coupling index. The path coupling index specifically characterizes the influence range of any scheduling path on other paths. Through this path coupling index, the feasible solution space is restructured. This restructuring includes splitting highly coupled paths and preferentially combining low-coupling paths to generate several independent scheduling subsets. Based on these subsets, the effective weights of each scheduling path are dynamically adjusted.

[0046] In this embodiment, the execution module further includes: The generation unit is used to construct the association mapping relationship between each production unit based on the demand parameters of the production demand, and to introduce the resource allocation tendency parameters preset by the warehousing terminal according to the association mapping relationship to generate a preliminary scheduling structure. The demand parameters specifically include production progress, number of processes to be completed and current material availability. The judgment unit is used to determine whether there are any scheduling tasks to be executed in the preliminary scheduling structure. An execution unit is configured to, if so, detect the resource reallocation information of the preliminary scheduling structure based on the associated mapping relationship, construct the chain adjustment risk corresponding to each scheduling path through the resource reallocation information, and dynamically adjust the multi-tray combination task of the electronic material tray based on the chain adjustment risk.

[0047] In this embodiment, the system constructs an association mapping relationship between production units based on production demand parameters, specifically including production progress, the number of processes to be completed, and the current material availability. According to this association mapping relationship, resource allocation preference parameters pre-set in the warehousing terminal are introduced to generate a preliminary scheduling structure. The system then determines whether these preliminary scheduling structures contain scheduling tasks that can be merged for execution, and executes the corresponding steps accordingly. For example, if the system determines that there are no scheduling tasks that can be merged in these preliminary scheduling structures, it assumes that there are no shared execution windows or reusable handling resources between different scheduling tasks. The system maintains the independent execution framework of the existing preliminary scheduling structure and decouples each scheduling task to avoid execution conflicts or resource mismatches caused by forced merging. Simultaneously, the system also considers the production units... The demand parameters are reassessed, with a focus on analyzing changes in production progress, the number of processes to be completed, and fluctuations in material availability to determine if there are potential subsequent merging windows. Furthermore, without altering the current execution structure, the system introduces a buffer scheduling mechanism to fine-tune or stagger task execution times, reducing the pressure of concentrated resource usage. For example, when the system detects merged tasks in these initial scheduling structures, it assumes that different tasks share a common execution window or reusable material handling resources. Based on the correlation mapping between production units, the system detects resource reallocation information in these initial scheduling structures. Using this information, it constructs the chain adjustment risks corresponding to each scheduling path and dynamically adjusts the multi-tray combination tasks of the electronic material tray based on different chain adjustment risks.

[0048] In this embodiment, the second execution module further includes: The second generation unit is used to construct a corresponding response index vector based on each scheduling path in the feasible solution space, and select several seed paths from each scheduling path according to the response index vector, and expand and combine them around the seed paths to generate a set of scheduling candidate schemes. The response index vector is specifically used to characterize the performance characteristics of each scheduling path under different objectives. The second judgment unit is used to determine whether the set of solutions is concentrated in a local similar region; The second execution unit is used to introduce a preset structural disturbance mechanism if the condition is met, to optimize the scheduling of some scheduling paths in the local similar region, and to dynamically retain representative non-dominated solution combinations in each scheduling path based on the optimization results of the scheduling optimization. Through the non-dominated solution combinations, the coverage of the candidate scheduling scheme in the feasible solution space is enhanced. The scheduling optimization specifically includes replacement, insertion, and order adjustment.

[0049] In this embodiment, the system constructs corresponding response index vectors based on each scheduling path in the feasible solution space. These response index vectors specifically characterize the performance of each scheduling path under different objectives. Based on these response index vectors, several seed paths are selected from each scheduling path, and expansion and combination are performed around these seed paths to generate a set of scheduling candidate schemes. The system then determines whether this set of schemes is concentrated in a locally similar region to execute the corresponding steps. For example, if the system determines that the set of scheduling candidate schemes is not concentrated in a locally similar region, the system considers that the candidate schemes generated by expanding the seed paths exhibit good dispersion and coverage in the multi-dimensional objective space of the response index vectors. That is, different candidate schemes have significant differences in objective dimensions such as production continuity, resource utilization, and state loss. The system retains the set of candidate schemes and proceeds to the next stage of optimization processing, performing consistency normalization on the response index vectors of each candidate scheme. To eliminate the impact of scale differences between different target dimensions, the system further sorts and filters candidate solutions globally based on this dispersion characteristic, prioritizing solutions with complementary advantages in different target dimensions to enhance the diversity and robustness of the final scheduling decision. Furthermore, the system performs secondary correction on candidate solutions based on resource competition relationships, ensuring they meet actual resource constraints while maintaining dispersed coverage. For example, when the system determines that the set of scheduling candidate solutions is indeed concentrated in locally similar regions, it assumes that there are no particularly significant differences between the different candidate solutions in the target dimension. The system then introduces a pre-set structural perturbation mechanism to optimize some scheduling paths in locally similar regions. Scheduling optimization specifically includes replacement, insertion, and order adjustment. Based on the optimization results, representative non-dominated solution combinations are dynamically retained from each scheduling path. These non-dominated solution combinations enhance the coverage of candidate scheduling solutions in the feasible solution space.

[0050] In this embodiment, the determination module further includes: The extraction unit is used to extract key state components from the evolutionary state sequence based on the condition judgment rule set corresponding to the available conditions, and form a corresponding state feature set, wherein the key state components specifically include time-related components, environmental accumulation components and associated constraint components. The third judgment unit is used to determine whether the set of state features can be mapped and matched with the set of condition judgment rules; The third execution unit is used to generate an availability confirmation identifier for the electronic material tray based on the mapping matching result if possible, construct constraint information for the electronic material tray through the availability confirmation identifier, and dynamically divide the calling scope of the electronic material tray according to the constraint information. The constraint information specifically includes constraint type and constraint boundary.

[0051] In this embodiment, the system extracts key state components from the evolutionary state sequence based on the condition determination rule set corresponding to the available conditions. These key state components specifically include time-related components, environmental accumulation components, and associated constraint components, forming a corresponding set of state features. The system then determines whether these state feature sets can be mapped and matched with the condition determination rule set to execute the corresponding steps. For example, if the system determines that these state feature sets cannot be mapped and matched with the condition determination rule set, the system considers that the key state components of the current electronic material tray in the evolutionary state sequence have deviated from the preset range of available conditions for the production unit. The system performs abnormal component localization processing on the state feature set, identifying the source of the key state deviation that caused the mapping failure, such as time exceeding the limit, environmental accumulation exceeding the threshold, or mismatch in association relationships, and marks the relevant components. Simultaneously, the system updates the evolutionary state sequence... The system performs corrective backtracking analysis, combining the state change trends of adjacent time nodes to perform trend repair or confidence reassessment of the current state, in order to reduce the impact of single-point anomalies on the overall judgment. Furthermore, the system introduces an extended rule set or a flexible judgment mechanism to supplement and map the original conditional judgment rule set, enabling some boundary states to regain their decidability. For example, when the system determines that these state feature sets can be mapped and matched with the conditional judgment rule set, the system considers the key state components of the current electronic material tray in the evolution state sequence to be within the range of available conditions preset by the production unit. Based on the mapping and matching results, the system generates availability confirmation identifiers for the electronic material tray. Through these availability confirmation identifiers, the system constructs the constraint information of the electronic material tray, specifically including constraint type and constraint boundary. Based on this constraint information, the system dynamically divides the calling scope of the electronic material tray.

[0052] In this embodiment, the second determination module further includes: The detection unit is used to detect the risk distribution sequence of the production unit over time based on the risk factors preset by the warehousing terminal, wherein the risk factors specifically include the risk of material supply interruption, the risk of waiting backlog, and the risk of status failure. The fourth judgment unit is used to determine whether the risk distribution sequence will be transmitted to other production units through scheduling correlation; The fourth execution unit is used to lock the identified risk transmission path if the condition is met, mark the starting node and the ending node of the risk transmission path, and dynamically divide the target production unit set affected by the risk according to the starting node and the ending node. Specifically, the target production unit set includes a direct impact area and an indirect impact area.

[0053] In this embodiment, the system detects the risk distribution sequence of a production unit over time based on pre-defined risk factors in the warehousing terminal. These risk factors specifically include supply interruption risk, waiting backlog risk, and status failure risk. The system then determines whether these risk distribution sequences will propagate to other production units through scheduling relationships, and executes corresponding steps accordingly. For example, if the system determines that the risk distribution sequence of a production unit will not propagate to other production units through scheduling relationships, the system considers the supply interruption risk, waiting backlog risk, or status failure risk of that production unit to be localized and controllable, with its impact limited to within the unit or a short time window. The system will mark this risk as a local risk event and maintain the current overall scheduling scheme's global structure unchanged, avoiding unnecessary global adjustments. Simultaneously, local optimization is performed on the production unit. This includes fine-tuning the material supply rhythm, adjusting the task sequence within the unit, or temporarily increasing resource supply to alleviate material supply interruptions or backlogs. For pallets with a high risk of failure, priority is given to consuming them within the unit or replacing them with pallets in better condition, thereby reducing the risk level. For example, when the system determines that the risk distribution sequence of the production unit over time will propagate to other production units through scheduling relationships, the system considers the current risk of the production unit uncontrollable. The system will then lock the identified risk propagation paths, marking the starting and ending nodes of these paths. Based on different starting and ending nodes, the system dynamically divides the target production unit set affected by the risk, specifically including directly affected and indirectly affected areas. In this embodiment, the construction module also includes: The identification unit is used to identify the changing trend of the evolutionary state sequence based on the preset influencing factors of the warehousing terminal. The influencing factors specifically include endogenous driving factors and external driving factors, and the changing trend specifically includes monotonic decay, stage transition and periodic change. The fifth judgment unit is used to determine whether the changing trend can map the process logic of the warehousing terminal; The fifth execution unit is used to extract nodes from the evolutionary state sequence if possible, identify decision nodes that have scheduling influence in the trend process of the change trend, and dynamically compress the scheduling calculation efficiency of the evolutionary state sequence based on the decision nodes.

[0054] In this embodiment, the system identifies the changing trends of the evolutionary state sequence based on pre-set influencing factors of the warehousing terminal. These influencing factors specifically include endogenous driving factors and external driving factors. The changing trends specifically include monotonic decay, stage transitions, and periodic changes. The system then determines whether these changing trends can map the process logic of the warehousing terminal to execute corresponding steps. For example, when the system determines that the changing trends of the evolutionary state sequence cannot map the process logic of the warehousing terminal, the system considers that the current state change of the electronic tray has deviated from the preset process evolution law. The system will then perform anomaly identification and decomposition processing on the changing trends, analyze the reasons for the deviation, and determine whether it is due to endogenous driving factors (such as abnormal tray state) or external factors. The system is triggered by driving factors (such as environmental changes or scheduling interference), and performs verification and correction on relevant state data. For example, it restores the basic consistency with the process logic by backtracking historical states, smoothing abnormal fluctuations, or introducing compensation estimates. The system will also perform extended updates to include newly identified change patterns within the interpretable range. For example, when the system determines that the change trend of the evolution state sequence can map the process logic of the warehouse terminal, the system will consider that the current state change of the electronic tray conforms to the preset process evolution law. The system will extract nodes from the evolution state sequence, identify decision nodes with scheduling influence in the trend process, and dynamically compress the scheduling calculation efficiency of the evolution state sequence based on these decision nodes.

[0055] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent warehouse scheduling method for electronic material trays, characterized in that, Includes the following steps: Based on the attribute vector pre-established for the electronic material tray by the warehousing terminal, an evolution state sequence of the electronic material tray over time is constructed, wherein the attribute vector specifically includes display attributes, process attributes and related attributes; Determine whether the evolution state sequence meets the preset availability conditions of the production unit; If not, the constraint information preset by the production unit is identified. Based on the constraint information, scheduling paths that cannot meet the availability conditions are removed from the evolution state sequence, and a corresponding feasible solution space is generated. Based on the production demand of the production unit for the electronic material tray, the resource competition relationship of the warehousing terminal is established. Through the resource competition relationship, the scheduling scheme of the electronic material tray is constructed. The constraint information specifically includes time constraints, sequence constraints, and matching constraints. The resource competition relationship specifically includes the urgency of demand of each production unit, material tray supply capacity, and cross-unit allocation cost. Determine whether the scheduling scheme causes any production unit to experience a preset risk, wherein the preset risks specifically include cycle interruption risk and material tray status failure risk; If this does not occur, a multi-objective scheduling index for the electronic material tray is generated through the warehousing terminal. Based on the multi-objective scheduling index, a candidate scheduling scheme is generated within the feasible solution space. The resource allocation is then globally coordinated in conjunction with the resource competition relationship, and the scheduling cycle of the electronic material tray is continuously optimized. Specifically, the multi-objective scheduling index includes production continuity index, resource utilization rate index, and state loss index. The global coordination of instructions specifically includes material tray inbound / outbound tasks, material handling equipment scheduling instructions, and production unit material supply instructions.

2. The intelligent warehousing scheduling method for electronic material trays according to claim 1, characterized in that, After the step of identifying the pre-set constraint information of the production unit, removing scheduling paths that cannot meet the availability conditions from the evolutionary state sequence based on the constraint information, and generating the corresponding feasible solution space, the method further includes: Based on the feasible solution space, the resource contention intensity corresponding to each scheduling path is identified, and the resource contention intensity is mapped to a time interval during the execution process to generate corresponding resource occupancy segments. Based on the superposition of the resource occupancy segments, a time overlap relationship diagram is formed. Specifically, the resource contention intensity refers to the degree of demand conflict of each production unit for the same material tray and the tightness of material tray supply. Determine whether the time overlap graph can characterize the potential conflict level of different paths during execution; If possible, a corresponding path coupling index is constructed based on the time overlap relationship diagram. The feasible solution space is restructured using the path coupling index to generate several independent scheduling subsets. Based on the scheduling subsets, the effective weights of each scheduling path are dynamically adjusted. Specifically, the path coupling index is used to characterize the influence range of any scheduling path on other paths. The structural reorganization specifically includes splitting highly coupled paths and preferentially combining low-coupling paths.

3. The intelligent warehousing scheduling method for electronic material trays according to claim 1, characterized in that, The step of establishing a resource competition relationship for the warehousing terminal based on the production demand of the production unit for the electronic material tray, and constructing a scheduling scheme for the electronic material tray through the resource competition relationship, further includes: Based on the demand parameters of the production needs, an association mapping relationship between each production unit is constructed. According to the association mapping relationship, the resource allocation tendency parameters preset by the warehousing terminal are introduced to generate a preliminary scheduling structure. The demand parameters specifically include production progress, number of processes to be completed, and current material availability. Determine whether there are any scheduled tasks to be executed in the preliminary scheduling structure; If so, then based on the associated mapping relationship, the resource reallocation information of the preliminary scheduling structure is detected. Through the resource reallocation information, the chain adjustment risk corresponding to each scheduling path is constructed. Based on the chain adjustment risk, the multi-tray combination task of the electronic material tray is dynamically adjusted.

4. The intelligent warehousing scheduling method for electronic material trays according to claim 1, characterized in that, The step of generating candidate scheduling schemes within the feasible solution space based on the multi-objective scheduling index further includes: Based on each scheduling path in the feasible solution space, a corresponding response index vector is constructed. According to the response index vector, several seed paths are selected from each scheduling path, and expansion and combination are performed around the seed paths to generate a set of scheduling candidate schemes. The response index vector is specifically used to characterize the performance characteristics of each scheduling path under different objectives. Determine whether the set of solutions is concentrated in a locally similar region; If so, a preset structural disturbance mechanism is introduced to optimize the scheduling of some scheduling paths in the local similar regions. Based on the optimization results, representative non-dominated solution combinations in each scheduling path are dynamically retained. Through the non-dominated solution combinations, the coverage of the candidate scheduling scheme in the feasible solution space is enhanced. Specifically, the scheduling optimization includes replacement, insertion, and order adjustment.

5. The intelligent warehousing scheduling method for electronic material trays according to claim 1, characterized in that, The step of determining whether the evolution state sequence meets the preset availability conditions of the production unit further includes: Based on the set of condition determination rules corresponding to the available conditions, key state components are extracted from the evolutionary state sequence to form a corresponding set of state features. Specifically, the key state components include time-related components, environmental cumulative components, and associated constraint components. Determine whether the set of state features can be mapped and matched with the set of conditional judgment rules; If possible, an availability confirmation identifier is generated for the electronic material tray based on the mapping matching result. The constraint information of the electronic material tray is constructed through the availability confirmation identifier. The calling scope of the electronic material tray is dynamically divided based on the constraint information. The constraint information specifically includes constraint type and constraint boundary.

6. The intelligent warehousing scheduling method for electronic material trays according to claim 1, characterized in that, The step of determining whether the scheduling scheme causes a preset risk to any production unit further includes: Based on the risk factors preset in the warehousing terminal, the risk distribution sequence of the production unit over time is detected, wherein the risk factors specifically include material supply interruption risk, waiting backlog risk and status failure risk; Determine whether the risk distribution sequence will be propagated to other production units through scheduling relationships; If so, the identified risk transmission path is locked, the starting node and the ending node of the risk transmission path are marked, and the target production unit set affected by the risk is dynamically divided according to the starting node and the ending node. The target production unit set specifically includes the direct impact area and the indirect impact area.

7. The intelligent warehousing scheduling method for electronic material trays according to claim 1, characterized in that, The step of constructing the evolution state sequence of the electronic material tray over time based on the attribute vector pre-established for the electronic material tray by the warehousing terminal further includes: Based on the preset influencing factors of the warehousing terminal, the changing trend of the evolutionary state sequence is identified. The influencing factors specifically include endogenous driving factors and external driving factors, and the changing trend specifically includes monotonic decay, stage transition and periodic change. Determine whether the changing trend can map the process logic of the warehousing terminal; If possible, then the nodes of the evolutionary state sequence are extracted, the decision nodes that have scheduling influence in the trend of the change trend are identified, and the scheduling calculation efficiency of the evolutionary state sequence is dynamically compressed based on the decision nodes.

8. An intelligent warehousing and scheduling system for electronic material trays, characterized in that, include: The construction module is used to construct the evolution state sequence of the electronic material tray over time based on the attribute vector pre-established for the electronic material tray by the warehousing terminal. The attribute vector specifically includes display attributes, process attributes, and related attributes. The judgment module is used to determine whether the evolution state sequence meets the preset availability conditions of the production unit; The execution module is used to identify the preset constraint information of the production unit if not, remove scheduling paths that cannot meet the availability conditions from the evolution state sequence according to the constraint information, generate the corresponding feasible solution space, establish the resource competition relationship of the warehousing terminal according to the production demand of the production unit for the electronic material tray, and construct the scheduling scheme of the electronic material tray through the resource competition relationship. The constraint information specifically includes time constraints, sequence constraints and matching constraints, and the resource competition relationship specifically includes the urgency of demand of each production unit, material tray supply capacity and cross-unit allocation cost. The second judgment module is used to determine whether the scheduling scheme causes any production unit to have a preset risk, wherein the preset risk specifically includes cycle interruption risk and material tray status failure risk; The second execution module is used to generate multi-objective scheduling indicators for the electronic material tray through the warehousing terminal if no such result is obtained. Based on the multi-objective scheduling indicators, candidate scheduling schemes are generated in the feasible solution space. The resource allocation is coordinated globally by combining the resource competition relationship, and the scheduling cycle of the electronic material tray is optimized in a rolling manner. The multi-objective scheduling indicators specifically include production continuity indicators, resource utilization indicators, and state loss indicators. The global coordination of instructions specifically includes material tray inbound and outbound tasks, material handling equipment scheduling instructions, and production unit material supply instructions.

9. The intelligent warehousing and scheduling system for electronic trays according to claim 8, characterized in that, Also includes: The generation module is used to identify the resource contention intensity corresponding to each scheduling path based on the feasible solution space, map the resource contention intensity to a time interval during the execution process, generate corresponding resource occupancy segments, and form a time overlap relationship diagram based on the superposition of the resource occupancy segments. Specifically, the resource contention intensity refers to the degree of demand conflict of each production unit for the same material tray and the tightness of material tray supply. The third judgment module is used to determine whether the time overlap relationship graph can characterize the potential conflict degree of different paths during execution; The third execution module is used to construct a corresponding path coupling index based on the time overlap relationship graph if possible. Through the path coupling index, the feasible solution space is restructured to generate several independent scheduling subsets. Based on the scheduling subsets, the effective weights of each scheduling path are dynamically adjusted. The path coupling index is specifically used to characterize the influence range of any scheduling path on other paths. The structural reorganization specifically includes splitting highly coupled paths and preferentially combining low-coupling paths.

10. The intelligent warehousing and scheduling system for electronic trays according to claim 8, characterized in that, The execution module further includes: The generation unit is used to construct the association mapping relationship between each production unit based on the demand parameters of the production demand, and to introduce the resource allocation tendency parameters preset by the warehousing terminal according to the association mapping relationship to generate a preliminary scheduling structure. The demand parameters specifically include production progress, number of processes to be completed and current material availability. The judgment unit is used to determine whether there are any scheduling tasks to be executed in the preliminary scheduling structure; An execution unit is configured to, if so, detect the resource reallocation information of the preliminary scheduling structure based on the associated mapping relationship, construct the chain adjustment risk corresponding to each scheduling path through the resource reallocation information, and dynamically adjust the multi-tray combination task of the electronic material tray based on the chain adjustment risk.

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