Scheduling system and method for sandwich production line based on Internet of Things

Abstract

The invention provides a sandwich production line scheduling system and method based on the Internet of Things, and the method comprises the steps: obtaining the detailed information of a to-be-processed order from a production line database, and enabling the detailed information to comprise a delivery urgency degree, a production difficulty level, and a customer importance index; performing initial weight calculation on the multi-dimensional business factors in the detailed information by adopting an optimization algorithm to obtain a comprehensive priority score of each order; performing preliminary sorting processing on all orders according to the comprehensive priority score, simulating a production line resource sharing path by integrating a path optimization algorithm, and determining a preliminary production sequence arrangement; and for a complex order part in the final sequence, obtaining predicted occupation time length data of the complex order part, and if the time length causes subsequent simple order production delay, inserting buffer period allocation to obtain a complete scheduling plan.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a sandwich production line scheduling system and method based on the Internet of Things. BACKGROUND

[0002] In modern manufacturing, intelligent scheduling of production lines and order coordination is a crucial research area that directly affects production efficiency, customer satisfaction, and overall operational cost control. In the fast-paced and diverse production lines of the food processing industry, such as sandwich production, achieving efficient execution of multiple orders with limited resources is a key factor in industry competition. Research in this area not only affects internal resource utilization but also plays a decisive role in the stability of the supply chain and market response speed. However, many current production coordination methods often struggle to adapt to dynamic environmental resource competition and conflict problems when faced with complex and changing order demands. Existing solutions focus more on pre-set rules or fixed priority sorting methods, lacking comprehensive consideration of multiple factors in the production process, especially in matching order characteristics with real-time production line status. This deficiency leads to resource waste and production delays, making it difficult to effectively handle urgent orders or unexpected situations. Focusing on technical difficulties, the core challenge of order coordination lies in balancing multi-dimensional priority factors. Different orders may involve the urgency of delivery time, customer importance, and differences in production difficulty, which often contradict each other and cannot be simply sorted by a single standard. For example, an order with urgent delivery time may need to be prioritized, but if it has high production difficulty, it will occupy more equipment and manpower, affecting the progress of other orders. This conflict of multi-dimensional factors further complicates resource allocation, as the shared nature of resources on the production line means that adjustments to one order can trigger a chain reaction, causing chaos in the overall scheduling. Therefore, designing a dynamic coordination mechanism for multi-dimensional priority factors in a shared production resource environment is a key issue in improving the efficiency of sandwich production lines. Taking a specific scenario as an example, on a production line, two orders are received simultaneously, one is a large customer order with urgent delivery time but complex production steps, and the other is a small order with simple production but low customer level. If the large customer order is prioritized, the production line may be occupied for a long time, delaying the small order; if the small order is prioritized, the time requirement of the large customer may not be met. This conflict between resource allocation and priority balance directly affects production efficiency and customer satisfaction. This problem needs to be solved, and research on how to reasonably allocate resources and resolve priority conflicts in the case of multiple orders in parallel will be an important breakthrough direction for optimizing production processes. SUMMARY

[0003] The present application provides a sandwich production line scheduling system and method based on the Internet of Things, mainly comprising: obtain detailed information of the to-be-processed order from a production line database, the detailed information including a delivery urgency level, a production difficulty level and a customer importance index; perform initial weight calculation on multi-dimensional business factors in the detailed information by using an optimization algorithm to obtain a comprehensive priority score of each order; perform preliminary sorting processing on all orders according to the comprehensive priority score, and simulate a production line resource sharing path by using a path optimization algorithm to determine a preliminary production sequence arrangement; obtain resource occupation time period data in the preliminary production sequence arrangement, perform simulation running on the production difficulty level of each order, and if resource competition in the time period exceeds a preset business threshold, adjust the positions of adjacent orders to obtain an optimized sequence version; extract potential conflict point information from the optimized sequence version, compare the delivery urgency level difference and the production difficulty level difference of adjacent orders, apply a fine-tuning weight by using a dynamic balance mechanism, and determine a final sequence after conflict resolution; for a complex order part in the final sequence, obtain its predicted occupation time length data, and if the time length causes production delay of a subsequent simple order, insert a buffer time period allocation to obtain a complete scheduling plan; monitor a real-time state of the production line according to the complete scheduling plan, and if time requirements are not met due to insertion of a sudden order, re-apply the path optimization algorithm to an affected paragraph to obtain an updated scheduling plan.

[0004] The technical scheme provided by the embodiment of the application can have the following beneficial effects: The application discloses a production line order scheduling optimization method, and aims at a unique business scenario problem in a multi-order production environment, that is, how to consider multi-dimensional factors such as a delivery urgency level, a production difficulty level and a customer importance index, and fuse processing of resource competition conflict, potential production delay and time dissatisfaction caused by insertion of a sudden order and other logically associated challenges to realize efficient sequence arrangement. The application obtains order detailed information from a database, calculates an initial weight by using an optimization algorithm to obtain a comprehensive priority score, performs preliminary sorting and fuses a path optimization to simulate a resource sharing path, then adjusts order positions according to resource occupation time periods, extracts conflict points and applies a dynamic balance fine-tuning weight to resolve differences, inserts a buffer time period for a complex order, and monitors sudden situations in real time to re-optimize an affected paragraph, so that the above problems are effectively solved. The overall technical effect lies in improving production line resource utilization, reducing production delay, ensuring delivery requirements, and improving overall scheduling flexibility and efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0005] Fig. 1A flow chart of a sandwich production line scheduling system and method based on the Internet of Things.

[0006] Fig. 2 A schematic diagram of a sandwich production line scheduling system and method based on the Internet of Things.

[0007] Fig. 3 Another schematic diagram of a sandwich production line scheduling system and method based on the Internet of Things. DETAILED DESCRIPTION

[0008] The technical solutions in the embodiments of the present application will be described clearly and in detail below with reference to the drawings in the embodiments of the present application. The described embodiments are only some of the embodiments of the present application.

[0009] As Figs. 1-3 , the sandwich production line scheduling system and method based on the Internet of Things can specifically include: S101, obtaining detailed information of a to-be-processed order from a production line database, the detailed information including a delivery urgency, a production difficulty level, and a customer importance index.

[0010] The detailed records of the to-be-processed order obtained from the production line database include the delivery urgency, the production complexity, and the customer priority index, and are arranged into a structured data set through a data extraction tool to obtain a preliminary order information set. For the preliminary order information set, a preset classification rule is used to classify the delivery urgency, and if the delivery urgency of an order is higher than a preset threshold, the order is marked as a high-priority order to determine a high-priority order list. According to the high-priority order list, the production complexity and the production equipment state are analyzed, and an order is allocated to a suitable equipment resource through a matching algorithm, and if the equipment state shows that the equipment is occupied, a standby resource is queried to obtain a resource allocation scheme. Through the resource allocation scheme, the estimated completion time of each order is calculated in combination with the delivery time limit and the production cycle length, and if the estimated completion time exceeds the delivery time limit, the resource occupation ratio is adjusted to determine an optimized time arrangement. The optimized time arrangement is obtained, and the order processing state is sorted in combination with the customer priority index and the customer satisfaction value, and if the customer priority index of an order is higher, the production order of the order is adjusted preferentially to obtain a final processing sequence. According to the final processing sequence, the delivery delay risk and the order batch size are monitored, and the production progress is tracked through a real-time data updating tool, and if the delay risk is found to increase, standby resource scheduling is triggered to judge the stability of the production process. Through the stability judgment result of the production process, the production resource allocation and the equipment state data are continuously updated, the information after each adjustment is stored through a record saving mechanism to obtain a complete order processing log.

[0011] For example, when extracting order data from the production line database, the order information can be organized into a structured dataset through data extraction tools. Assuming a factory has 100 pending orders, the data contains fields such as delivery date, required production process, and customer information, after extraction, a table containing order number, delivery urgency, production complexity, and customer priority indicators is formed. This way facilitates subsequent analysis, ensuring data integrity and consistency.

[0012] For example, for the classification of delivery urgency, a threshold can be preset, such as orders with a delivery date within 5 days are marked as high urgency. Assuming order A has a delivery date of 3 days, exceeding the threshold, it is marked as a high-priority order, while order B has a delivery date of 10 days, it is classified as a normal priority. This classification helps quickly filter out orders that need urgent processing, improving delivery efficiency.

[0013] For example, when analyzing the production complexity and equipment status of high-priority orders, resources can be assigned to orders through matching algorithms. Assuming order A requires complex processes, device 1 is suitable but is in an occupied state, then query backup device 2, find it idle and match successfully. This resource allocation scheme avoids production bottlenecks and improves device utilization.

[0014] For example, when calculating the estimated completion time based on delivery time limits and production cycles, if order A has a production cycle of 2 days and a delivery limit of 3 days, the estimated completion time meets the requirements. But order C's estimated completion time is 6 days, which is 4 days beyond the delivery limit, so adjust the resource occupancy ratio and increase device investment to ensure time optimization. This adjustment can effectively reduce the risk of delay.

[0015] For example, when ordering order processing status according to customer priority indicators, assuming order A's customer priority is 9 (full score 10) and order B's is 5, the production sequence of order A is adjusted first. This ordering mechanism ensures the satisfaction of high-value customers and improves enterprise reputation.

[0016] For example, when monitoring delivery delay risks and order batch sizes, if order A's production progress shows an increased risk of delay, trigger backup resource scheduling through real-time data update tools to ensure production process stability. This dynamic monitoring can respond to unexpected problems in a timely manner and reduce losses.

[0017] For example, after determining the stability of the production process, continuously update resource allocation and equipment status data, and store adjustment information through a record saving mechanism. Assuming that a log file containing time, order number, and resource changes is generated after each adjustment, it is convenient for subsequent tracing and optimization. This mechanism improves the transparency and controllability of management.

[0018] S102, using an optimization algorithm to calculate the initial weight of the multi-dimensional business factors in the detailed information, and obtaining the comprehensive priority score of each order.

[0019] By classifying and organizing the order information, and according to the business factors and data dimensions, the preliminary processing of the multi-aspect information of each order is carried out by using the preset scoring standard, and the initial factor combination data is obtained. According to the initial factor combination data, combined with the weight analysis method, the score of each order is calculated, and the comprehensive evaluation result is determined. Through the comprehensive evaluation result, the basis for order sorting is obtained, and the sequence is arranged according to the priority of the high and low, the order of processing is judged, if the priority of a certain order is higher than the preset threshold, it is marked as an urgent processing object, and the sorted order list is obtained. According to the sorted order list, combined with the business association and decision basis, the resource matching of the urgent processing object is carried out, the current resource state is queried by using the information integration tool, if the resource state shows insufficient, the standby resource calling is triggered, and the resource allocation scheme is determined. Through the resource allocation scheme, according to the order information and factor combination, the processing period of each order is calculated, the expected processing time is obtained, if the expected processing time exceeds the limit condition, the resource allocation proportion is adjusted, and the optimized time planning is obtained. According to the optimized time planning, combined with information integration and business association, the order processing progress is monitored, the state change is tracked through the real-time data update tool, whether there is a delay risk is judged, and the final execution state record is obtained.

[0020] For example, when classifying and organizing the order information, starting from the business factors such as delivery time, order size and customer demand, combined with the data dimensions such as historical completion record and current resource state, the preliminary processing of each order is carried out by using the preset scoring standard. Assuming that the delivery time of a certain order is only 3 days, and the average processing period is 5 days, the order will be marked as a key object in the initial factor combination data because it scores higher in the urgency dimension. This classification method helps to quickly identify key orders and provides clear basis for subsequent processing.

[0021] For example, for the weight analysis of the initial factor combination data, different weights can be assigned to different business factors, such as 40% for delivery urgency, 30% for customer importance, and 30% for production complexity.

[0022] In one possible implementation, a certain order scores 8 points in the urgency dimension, 6 points in customer importance, and 5 points in production complexity. Through weighted calculation, the comprehensive evaluation result is 7.1 points, which is higher than the preset threshold of 6.5 points, so it is marked as an urgent processing object. This method can effectively quantify the order priority and ensure the rationality of resource allocation.

[0023] For example, when ordering based on comprehensive evaluation results, orders with higher scores can be prioritized. Suppose there are 5 orders with scores of 7.1, 6.8, 6.2, 5.9, and 5.5, the first two orders are listed in the urgent processing list. This ordering provides a clear direction for subsequent resource matching, avoiding low-priority orders occupying critical resources.

[0024] For example, for resource matching of urgent processing objects, the current production line status can be queried through the information integration tool. Suppose a certain urgent order requires a specific device, and the device is already occupied, the system will automatically call a backup resource, such as another device of the same type, to ensure that the order is started on time. This way can improve resource utilization efficiency and reduce waiting time.

[0025] For example, when calculating the processing cycle, the order size and resource status estimation time can be combined. Suppose an order needs to produce 1000 products, and the current device processes 200 products per hour, the estimated processing time is 5 hours, if it exceeds the delivery limit by 3 hours, additional equipment or adjustment of shifts is required to optimize time planning. This adjustment can effectively shorten the risk of delay.

[0026] For example, when monitoring the progress of order processing, real-time data update tools can be used to track the status of each link. Suppose a device failure is found during the production of an order, the system will immediately alert and adjust resource allocation to avoid delay expansion. This real-time monitoring method can timely find problems and ensure smooth production flow.

[0027] For example, the generation of final execution status records can aggregate data from all processing links to form a complete log. Suppose an order has undergone 3 resource adjustments from classification to completion, and each adjustment reason and result is recorded for subsequent analysis and optimization. This recording method helps to improve management transparency and provides a reference for future decision-making.

[0028] S103, according to the comprehensive priority score, all orders are preliminarily sorted and processed, and the path optimization algorithm is simulated to determine the preliminary production sequence arrangement.

[0029] According to the results of the comprehensive priority and score calculation, the order sequence is preliminarily arranged, the position of each order is adjusted through the preset sorting rule, and a preliminary business sorting list is obtained. According to the preliminary business sorting list, a path optimization algorithm is used to simulate the calculation of the production line resources and the shared path, and a preliminary scheme of resource allocation is obtained. According to the preliminary scheme of resource allocation, combined with the data of the shared path and the production sequence, the position of each order on the production line is dynamically adjusted, and the optimized sequence planning is determined. For the optimized sequence planning, if the position of a certain order is inconsistent with the business sorting list, the relevant data is queried through the information processing tool to determine whether the adjustment condition is met, and the adjusted production sequence is obtained. According to the adjusted production sequence, combined with the restriction conditions of resource allocation and shared path, the resource occupation of each order is checked through the data matching tool, and the final resource allocation result is obtained. For the final resource allocation result, if it is detected that the resource occupation of a certain order exceeds the preset threshold, the resource reallocation process is triggered, and the final production execution order is determined. According to the final production execution order, the progress state of each order on the production line is monitored through the real-time data updating tool, and the complete execution record is obtained.

[0030] For example, when preliminarily arranging the order sequence, the orders can be arranged from high to low according to the comprehensive priority score to form an initial list. Assuming that there are 10 orders with scores ranging from 8.5 to 4.2, the order with the highest score will be placed at the top, and the order with the lowest score will be placed at the end. This way ensures that high-priority orders are given priority in subsequent processes. For the adjustment of the sorting rule, the special business needs can be considered, such as some orders involving key customers, even if the scores are not high, the positions of these orders can be manually improved to form a preliminary business sorting list.

[0031] For example, for the preliminary business sorting list, when simulating the production line resources and shared paths using the path optimization algorithm, the load situation of each production line and the dependency relationship between orders can be analyzed first. Assuming that there are 3 shared paths on a production line, corresponding to different equipment groups, and through simulation calculation it is found that the load of a certain path is too high, then the orders will be preferentially allocated to the path with lower load to obtain a preliminary scheme of resource allocation. This simulation can effectively balance the distribution of resources.

[0032] For example, when dynamically adjusting the position of the order to optimize the sequence planning, the order can be rearranged in combination with the production sequence data and the shared path restrictions. Assuming that a certain order is originally scheduled to be produced on the first line, but the equipment maintenance time of the first line conflicts, then the order is adjusted to the second line to ensure smooth overall process. For the case where the position is inconsistent with the business list, the data is queried through the information processing tool, such as the delivery deadline of the order and the resource status, and if the deadline is urgent, the order is adjusted preferentially to obtain the adjusted production sequence.

[0033] For example, when checking resource occupation, the data matching tool can be used to compare the resource demand of each order with the actual allocation. Assuming that an order requires 2 devices, but only 1 is currently available, it needs to be recorded and marked as to be solved, forming the final resource allocation result. If it is detected that the resource occupation exceeds the preset threshold, such as an order occupying a device for more than 8 hours, the re-allocation process is triggered, and the standby resource is called first to determine the final production execution sequence.

[0034] For example, when monitoring the progress of the order, the real-time data updating tool can track the completion of each link. Assuming that an order in the processing link takes longer than expected, the tool will immediately feedback, which facilitates timely adjustment of resources to ensure overall progress. This way can improve process transparency and form a complete execution record.

[0035] S104, obtain resource occupation period data in the preliminary production sequence arrangement, simulate running for the production difficulty level of each order, and if the resource competition in the period exceeds the preset business threshold, adjust the position of adjacent orders to obtain an optimized sequence version.

[0036] Obtain resource occupation data in the preliminary sequence, record the resource usage of each order in different periods, match the resource occupation and period information through a data processing tool to determine the resource allocation state of each order in the production arrangement. According to the resource allocation state, analyze the data simulated for the production difficulty and level, and divide the difficulty level of each order according to the preset classification standard to obtain the difficulty analysis result. Through the difficulty analysis result, combined with the limitation conditions of resource competition and business threshold, if the resource competition in a period exceeds the preset business threshold, the resource occupation data is recalculated through the information processing module to determine whether the adjustment mechanism is triggered. According to the judgment result of the adjustment mechanism, for the position adjustment requirement of adjacent orders, the data sorting tool is used to update the order sequence locally to obtain the adjusted production arrangement scheme. Obtain the adjusted production arrangement scheme, combined with the latest data of resource occupation and period information, check the rationality of resource allocation of each order in the optimized version through the information comparison tool to determine the final sequence optimization result. According to the final sequence optimization result, for the comprehensive data of production difficulty and resource competition, the real-time monitoring tool is used to track the resource use dynamics of each order in the production process to obtain a complete execution record.

[0037] For example, in obtaining resource occupation data in the preliminary sequence, the data processing tool can record the resource usage of each order in different time periods. Assuming that a production workshop has 5 orders, each order uses equipment resources in early, mid, and late periods within a day, and the data record shows that order A occupies 2 equipment in the early period, 1 equipment in the mid period, and no equipment in the late period, while order B occupies 1 equipment all day. Through such detailed recording, the time period distribution of resource allocation can be clearly understood, laying a foundation for subsequent analysis.

[0038] For example, for the analysis of production difficulty and level simulation data, a preset classification standard can be used to divide the difficulty level. Assuming that the difficulty level is divided into high, medium, and low levels, the standard is based on the number of order processing procedures and equipment complexity. Order A has 8 procedures and involves precision equipment, and is rated as high difficulty; order B has 3 procedures and simple equipment, and is rated as low difficulty. This classification method helps to identify the priority direction of resource allocation.

[0039] For example, when combining resource competition and business threshold limit conditions, if the resource competition in a period exceeds the threshold, the information processing module can be used to recalculate the data. Assuming that the business threshold is that at most 3 equipment runs simultaneously per period, and in the early period there are 4 orders that require a total of 5 equipment, which exceeds the threshold by 2, at this time the adjustment mechanism is triggered, and part of the orders are redistributed to the mid period. This mechanism ensures balanced resource usage and avoids overloading in a certain period.

[0040] For example, for the adjustment needs of adjacent order positions, a data sorting tool can be used for local update. Assuming that order C and order D are adjacent in the preliminary sequence, but the high difficulty characteristics of order C cause a lack of equipment in the current period, and through the tool, order C is adjusted to the next period and exchanges positions with low difficulty order E to form a new sequence. This local adjustment can optimize the rationality of production arrangement.

[0041] For example, in checking the rationality of resource allocation in the optimized version, an information comparison tool can be used to verify the data. Assuming that order A is allocated to the mid period after adjustment, but there are already 2 orders occupying all equipment in that period, and through comparison it is found that there is a resource conflict, which needs to be further adjusted to the late period. This verification method ensures that the resource allocation of each order meets the actual conditions.

[0042] For example, by tracking resource usage dynamics during the production process through real-time monitoring tools, a complete execution record can be obtained. Suppose order B's processing time is suddenly extended by one hour due to a temporary equipment malfunction; the monitoring tool will immediately record this and prompt adjustments to the scheduling of subsequent orders. This dynamic tracking helps to respond promptly to unexpected situations and ensure smooth production processes. Through the above multi-faceted analysis and adjustments, from resource usage records to difficulty classification and dynamic monitoring, each link is closely connected, forming a complete production scheduling optimization plan. This approach not only improves resource utilization efficiency but also flexibly responds to various changes in production, ensuring orders are completed on time.

[0043] S105. Extract potential conflict point information from the optimized sequence version, compare the differences in delivery urgency and production difficulty between adjacent orders, apply fine-tuning weights using a dynamic balancing mechanism, and determine the final sequence after conflict resolution.

[0044] Conflict point data is extracted from the optimized sequence. For the location information of each conflict point, the corresponding order comparison records are obtained through data filtering tools to determine the specific order combinations involving delivery urgency and production difficulty. Based on the order combination data, a preset priority determination rule is applied to determine the urgency and urgency level of each order. If the urgency level of an order is higher than that of adjacent orders, its position is adjusted first, resulting in a preliminary adjusted sequence arrangement. Using the preliminary adjusted sequence arrangement, the information processing module performs stratified calculations on the differences in production difficulty and difficulty level to determine if there is a significant imbalance. If so, a secondary fine-tuning mechanism is triggered to obtain a further optimized sequence version. For the further optimized sequence version, the location changes of each order are recorded using data analysis tools to determine the adjusted weight allocation results, based on the dynamic balancing and fine-tuning weight allocation strategy. Based on the adjusted weight allocation results, and considering the final sequence and arrangement adjustment needs, the location information of all orders is updated using data integration tools to obtain a sequence scheme that meets business objectives. Using the above sequence scheme, to address the subsequent monitoring needs of conflict point count and order comparison, a real-time data acquisition tool is used to track the status changes of each order during execution, determine whether there are any new potential conflicts, and obtain complete execution data records.

[0045] For example, when extracting conflict point data from an optimized sequence, data filtering tools can be used to analyze the location information of each conflict point individually. Suppose a production workshop has a sequence of 10 orders, with 3 locations marked as conflict points, involving orders X, Y, and Z. By extracting comparison records of these orders using tools, it can be found that orders X and Y compete for critical equipment at the same time, while order Z is prioritized due to its higher delivery urgency. This approach helps to quickly locate problematic order combinations, providing a basis for subsequent adjustments.

[0046] For example, in applying rules for determining delivery urgency and priority, urgency levels can be preset to three levels, with the highest level requiring delivery within 48 hours. Assume order X has a delivery deadline of 24 hours, representing the highest urgency, while the adjacent order Y has a delivery deadline of 72 hours, representing a lower urgency. According to the rules, order X is prioritized for an earlier delivery time, ensuring its production is not delayed. This priority adjustment mechanism effectively guarantees the delivery time of critical orders.

[0047] For example, in the initial adjusted sequence arrangement, the tiered calculation based on differences in production difficulty and difficulty level can categorize difficulty into three levels: high, medium, and low. Suppose order X is high-difficulty, involving multiple processing steps, while order Y is low-difficulty, requiring only simple assembly. If the two are adjacent and resources are unevenly allocated, the information processing module will trigger a secondary fine-tuning, swapping the high-difficulty order X with another low-difficulty order to avoid equipment overload. This tiered calculation and fine-tuning can balance production load.

[0048] For example, after obtaining a further optimized sequence version, the allocation strategy for dynamic balancing and fine-tuning weights can be recorded using data analysis tools to document changes in the position of each order. Suppose that after adjustment, the weight of order X increases from 0.8 to 1.0, indicating a higher priority, while the weight of order Y decreases to 0.5. This method of recording weights helps to clarify the adjustment logic and ensures more rational resource allocation.

[0049] For example, when updating order location information using a data integration tool to address final sequence and scheduling adjustments, assuming order X moves to the first time slot and order Y moves to the third time slot after the adjustment, the tool will generate and save a new sequence plan. This integrated update ensures that all order locations align with business objectives, avoiding omissions caused by manual adjustments.

[0050] For example, when monitoring conflict points and comparing orders, real-time data acquisition tools can track order execution status. Suppose order X is delayed by one hour due to temporary equipment downtime, the tool will immediately record and mark potential conflict risks. This real-time monitoring method can promptly identify problems and provide data support for dynamic adjustments.

[0051] S106. For the complex order portion in the final sequence, obtain its estimated duration data. If the duration causes a delay in the production of subsequent simple orders, insert a buffer period allocation to obtain a complete scheduling plan.

[0052] For complex orders in the final sequence, data acquisition tools are used to obtain their duration information. Combined with production log records, this is used to determine if time conflicts exist, resulting in duration distribution data. Based on this distribution data, for the production sequence of simple orders, if the duration of complex orders causes production delays, a time analysis module identifies the affected order combinations and determines the specific delay period range. For this delay period range, combined with available resource data for buffer periods, an allocation calculation tool is used to rationally divide the buffer periods, resulting in a targeted time allocation scheme. Based on this time allocation scheme, to address production sequence adjustment needs, a scheduling update tool rearranges the positions of complex and simple orders, determining the adjusted production sequence version. For this production sequence version, considering the potential risk of time conflicts, a data comparison tool verifies the start and end times of each order to determine if new time overlap issues exist, resulting in a verified sequence arrangement. Based on this verified sequence arrangement, to ensure the completeness of the scheduling arrangement, an information integration module uniformly records the time allocation and production sequence of all orders, resulting in the final complete scheduling plan.

[0053] For example, when acquiring information on the processing time of complex orders using data collection tools, the actual processing time and waiting time for each order can be extracted from the production logs. Suppose a production workshop has five complex orders, one of which takes six hours to process, far exceeding the average of three hours for the other orders. Log records show that two hours of waiting time on critical equipment caused delays in subsequent orders. This data collection method helps identify time bottlenecks and provides a basis for subsequent adjustments.

[0054] For example, when adjusting the production sequence of simple orders, if complex orders cause delays, the time analysis module can identify the affected order combinations. Suppose complex order A takes 8 hours, causing subsequent simple orders B and C to be delayed by 2 hours and 3 hours respectively, the analysis module will mark the delay period as 2 PM to 5 PM. In this way, the problematic time period can be accurately located, laying the foundation for resource reallocation.

[0055] For example, when allocating buffer periods based on available resource data, a calculation tool can be used to split the buffer periods. Assuming there is a 4-hour buffer period in the workshop, the tool can allocate 2 hours for supplementary processing of complex order A, and the remaining 2 hours to simple orders B and C, based on lag levels. This allocation method can make reasonable use of idle resources and alleviate time pressure.

[0056] For example, when adjusting production sequence requirements, the scheduling update tool rearranges order positions. Assuming complex order A is moved to an earlier time slot, simpler orders B and C are postponed to later time slots, ensuring a smooth overall production sequence. This adjustment method prioritizes ensuring the completion deadlines of critical tasks.

[0057] For example, when verifying the risk of time conflicts in production sequence versions, a data comparison tool can be used to check the start and end times of each order. Suppose that after adjustment, complex order A ends at 10:00 AM, and simple order B starts at 10:30 AM; the tool confirms there are no overlap issues. This verification method helps avoid new time conflicts and ensures the feasibility of the sequence arrangement.

[0058] For example, regarding the integration of information to ensure the completeness of scheduling arrangements, the information integration module can record the time slot allocation and sequence of all orders in a unified manner. Assuming that complex order A is scheduled in the first time slot in the final plan, and simple orders B and C are scheduled in the second and third time slots respectively, the module will generate and save the complete plan. This integration method improves the transparency and efficiency of the plan, facilitating subsequent tracking. Through the above analysis and examples, it can be seen that each link from data collection to final plan integration is closely linked, jointly ensuring the rationality and efficiency of the production sequence. The implementation methods for each topic revolve around how to optimize time allocation and resource utilization to ensure the coordinated arrangement of complex and simple orders.

[0059] S107. Monitor the real-time status of the production line according to the complete scheduling plan. If a sudden order insertion causes the time requirement to be unmet, reapply the path optimization algorithm to the affected segment to obtain an updated scheduling plan.

[0060] Real-time monitoring tools continuously collect data on the production line status. For the time slots in the scheduling plan, the execution progress of each order is obtained to determine if there are any deviations from the preset time requirements, resulting in status tracking results. Based on these results, if a sudden order insertion causes the time slots to not meet requirements, a data filtering module locates the affected segments, determining the specific scheduling segment range and obtaining information on the affected order combinations. For these affected order combinations, a path optimization algorithm is used to recalculate the scheduling segments, obtaining an adjusted time allocation scheme and determining the new order execution order. Based on this new order execution order, a data verification tool compares each time slot in the adjusted scheme to determine if there are any new time conflicts, obtaining verified scheduling segment data. For this verified scheduling segment data, an information integration tool seamlessly integrates the adjusted scheme with the unaffected scheduling plan to obtain a complete updated scheduling arrangement. Based on this updated scheduling arrangement, a data distribution module distributes the adjusted time schedule and order execution order to the production line status monitoring system to determine the final execution basis.

[0061] For example, when continuously collecting data on production line status using real-time monitoring tools, attention can be paid to the deviation between the actual execution time and the planned time for orders. Suppose there are 10 orders being processed in a production workshop, and the monitoring tool detects that the processing time for one order has exceeded the scheduled time by 2 hours, affecting subsequent arrangements. By collecting real-time data on equipment operating status and worker operations, it can be quickly determined that the delay may be due to a temporary equipment malfunction. This approach helps to promptly grasp the dynamics of the production line and provides a basis for subsequent adjustments.

[0062] For example, when a sudden order insertion causes scheduling discrepancies, the data filtering module can quickly pinpoint the affected time period. Suppose an urgent order causes a delay in the planned production period from 2 PM to 4 PM, the filtering module will identify three orders within this time period and analyze their upstream and downstream dependencies. This method of identification clearly defines the scope of adjustments, avoiding unnecessary changes to the entire schedule.

[0063] For example, when recalculating scheduling segments using a path optimization algorithm, time can be reallocated based on order priority and equipment availability. Assuming that one of the three affected orders has the highest priority, the algorithm will schedule it in the earliest available time slot, such as 1 PM to 2 PM, while postponing the other orders to later slots. This approach balances urgent needs with the stability of the original plan.

[0064] For example, regarding the new order execution order, the data validation tool can compare the time schedule one by one to determine if there are any overlaps or conflicts. Suppose that after the adjustment, one order ends at 11:00 AM and the next order starts at 11:15 AM, the tool will confirm that there is sufficient buffer between the two to avoid resource contention. This validation method ensures the feasibility of the adjustment plan.

[0065] For example, when seamlessly integrating adjusted plans with unaffected schedules, information integration tools can merge the updated time slots with other parts of the original plan. Assuming the adjustment only affects the afternoon session, the tool will retain the morning schedule unchanged, updating only the affected portion to create a complete arrangement. This integration method maintains the integrity of the plan.

[0066] For example, when the updated schedule is distributed to the production line status monitoring system via the data distribution module, accurate information transmission can be ensured. Suppose that in the adjusted plan, an order is rescheduled to 8 PM; the module will synchronize this information to relevant equipment and personnel terminals to ensure error-free execution. This distribution method improves the efficiency and accuracy of instruction transmission.

[0067] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. The present invention has been described in detail with reference to preferred embodiments. Those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A scheduling system and method for a sandwich production line based on the Internet of Things, characterized in that, The method includes: Retrieve detailed information about pending orders from the production line database, including delivery urgency, production difficulty level, and customer importance indicators; An optimization algorithm is used to calculate the initial weights of the multi-dimensional business factors in the detailed information to obtain the comprehensive priority score for each order; All orders are initially sorted based on the comprehensive priority score, and a path optimization algorithm is incorporated to simulate the resource-sharing path of the production line to determine the initial production sequence arrangement. Obtain resource occupancy time period data from the preliminary production sequence arrangement, simulate the production difficulty level of each order, and if resource competition exceeds a preset business threshold during the time period, adjust the positions of adjacent orders to obtain an optimized sequence version. Information on potential conflict points is extracted from the optimized sequence version. By comparing the differences in delivery urgency and production difficulty between adjacent orders, a dynamic balancing mechanism is used to apply fine-tuning weights to determine the final sequence after conflict resolution. For the complex order portion in the final sequence, obtain its estimated duration data. If the duration causes a delay in the production of subsequent simple orders, insert a buffer period allocation to obtain a complete scheduling plan. The production line status is monitored in real time according to the complete scheduling plan. If a sudden order insertion causes the time requirement to be unmet, the path optimization algorithm is reapplied to the affected segment to obtain an updated scheduling plan.

2. The IoT-based sandwich production line scheduling system and method according to claim 1, characterized in that, The process involves retrieving detailed information about pending orders from the production line database. This detailed information includes delivery urgency, production difficulty level, and customer importance indicators, including: Detailed records of pending orders are obtained from the production line database, including delivery urgency, production complexity, and customer priority indicators. These records are then organized into a structured dataset using data extraction tools to obtain a preliminary set of order information. For the initial set of order information, the delivery urgency is classified according to preset classification rules. If the delivery urgency of an order is higher than the preset threshold, it is marked as a high-priority order, and a list of high-priority orders is determined. Based on the high-priority order list, analyze the production complexity and production equipment status, and allocate orders to appropriate equipment resources through a matching algorithm. If the equipment status shows that it is occupied, query the spare resources to obtain a resource allocation plan. By using a resource allocation plan, combined with delivery time constraints and production cycle length, the estimated completion time for each order is calculated. If the estimated completion time exceeds the delivery time constraint, the resource utilization ratio is adjusted to determine the optimized time schedule. The optimized schedule is obtained, and the order processing status is sorted by customer priority indicators and customer satisfaction values. If a certain order has a high customer priority indicator, its production order is adjusted first to obtain the final processing sequence. Based on the final processing sequence, monitor the risk of delivery delays and the order batch size, track production progress through real-time data update tools, and if an increase in delay risk is detected, trigger the scheduling of backup resources to determine the stability of the production process. Based on the stability assessment results of the production process, the production resource allocation and equipment status data are continuously updated, and a record-keeping mechanism is used to store the information after each adjustment, resulting in a complete order processing log.

3. The IoT-based sandwich production line scheduling system and method according to claim 1, characterized in that, The optimization algorithm is used to calculate the initial weights of the multi-dimensional business factors in the detailed information to obtain a comprehensive priority score for each order, including: By classifying and organizing order information, and using preset scoring criteria to preliminarily process the multi-faceted information of each order based on business factors and data dimensions, initial factor combination data is obtained. Based on the initial combination of factors data, and combined with the weighting analysis method, the business factors of each order are scored to determine the comprehensive evaluation result; Based on the comprehensive evaluation results, the order sorting criteria are obtained. The orders are arranged in sequence according to their priority scores to determine the order of processing. If the priority score of an order is higher than a preset threshold, it is marked as an urgent processing object, and the sorted order list is obtained. Based on the sorted order list, combined with business relationships and decision-making criteria, resources are matched for urgent processing objects. Information integration tools are used to query the current resource status. If the resource status shows insufficient resources, backup resources are called up to determine the resource allocation plan. By using the resource allocation scheme, the processing cycle of each order is calculated based on the order information and factor combination to obtain the estimated processing time. If the estimated processing time exceeds the limit, the resource allocation ratio is adjusted to obtain the optimized time plan. Based on the optimized timeline, combined with information integration and business linkage, the order processing progress is monitored, and status changes are tracked through real-time data update tools to determine whether there is a risk of delay and obtain the final execution status record.

4. The IoT-based sandwich production line scheduling system and method according to claim 1, characterized in that, The preliminary sorting of all orders based on the comprehensive priority score, and the incorporation of a path optimization algorithm to simulate resource-sharing paths on the production line to determine the preliminary production sequence arrangement, includes: Based on the results of comprehensive priority and score calculation, the order sequence is initially arranged, and the position of each order is adjusted according to the preset sorting rules to obtain a preliminary business sorting list; For the initial business sequence list, a path optimization algorithm is used to simulate and calculate production line resources and shared paths to obtain a preliminary resource allocation plan; Based on the preliminary resource allocation plan, and combined with data from shared paths and production sequences, the position of each order on the production line is dynamically adjusted to determine the optimized sequence planning; For the optimized sequence planning, if the position of a certain order is inconsistent with the business sorting list, the relevant data is queried through the information processing tool to determine whether the adjustment conditions are met and to obtain the adjusted production sequence. Based on the adjusted production sequence, and taking into account the constraints of resource allocation and shared paths, the resource usage of each order is checked using a data matching tool to obtain the final resource allocation result; If the resource usage of a certain order exceeds a preset threshold, the resource reallocation process is triggered to determine the final production execution order based on the final resource allocation result. Based on the final production execution sequence, the progress of each order on the production line is monitored through real-time data update tools to obtain complete execution records.

5. The IoT-based sandwich production line scheduling system and method according to claim 1, characterized in that, The process of obtaining resource occupancy time period data in the preliminary production sequence arrangement, simulating the production difficulty level of each order, and adjusting the positions of adjacent orders to obtain an optimized sequence version if resource competition exceeds a preset business threshold during the time period includes: Obtain resource usage data from the initial sequence, record the resource usage of each order in different time periods, and match resource usage with time period information using data processing tools to determine the resource allocation status of each order in the production schedule; Based on the resource allocation status, the data simulated for production difficulty and level are analyzed, and the difficulty level of each order is divided according to the preset classification criteria to obtain the results of the difficulty analysis. Based on the results of the difficulty analysis, combined with the constraints of resource competition and business thresholds, if the resource competition exceeds the preset business threshold within a certain period, the information processing module will recalculate the resource usage data to determine whether to trigger the adjustment mechanism. Based on the judgment results of the adjustment mechanism, and in response to the position adjustment needs of adjacent orders, a data sorting tool is used to locally update the order sequence to obtain the adjusted production arrangement plan; Obtain the adjusted production schedule plan, combine it with the latest data on resource usage and time period information, and use information comparison tools to verify the rationality of resource allocation for each order in the optimized version, and determine the final sequence optimization result; Based on the final sequence optimization results, and considering the comprehensive data on production difficulty and resource competition, a real-time monitoring tool is used to track the dynamic resource usage of each order during the production process, resulting in a complete execution record.

6. The IoT-based sandwich production line scheduling system and method according to claim 1, characterized in that, The step of extracting potential conflict point information from the optimized sequence version, comparing the differences in delivery urgency and production difficulty between adjacent orders, applying fine-tuning weights using a dynamic balancing mechanism, and determining the final sequence after conflict resolution includes: Data related to the number of conflict points are extracted from the optimized sequence. For the location information of each conflict point, the corresponding order comparison records are obtained through data filtering tools to determine the specific order combinations involving urgent delivery and production difficulty. Based on the order combination data above, and considering the relevant information on delivery urgency and urgency level, a preset priority determination rule is adopted. If the urgency level of a certain order is higher than that of the adjacent orders, its position is adjusted first to obtain the preliminary adjusted sequence arrangement. Based on the above preliminary adjustment of the sequence arrangement, the information processing module is used to perform hierarchical calculations on the differences in production difficulty and difficulty level to determine whether there is a significant imbalance. If so, a secondary fine-tuning mechanism is triggered to obtain a further optimized sequence version. Obtain the further optimized sequence version mentioned above. For the allocation strategy of dynamic balancing and fine-tuning weights, record the position changes of each order through data analysis tools to determine the adjusted weight allocation results. Based on the adjusted weight allocation results, and in response to the need for adjustments to the final sequence and arrangement, a data integration tool is used to update the location information of all orders to obtain a sequence plan that meets the business objectives. Using the above sequence scheme, to address the subsequent monitoring needs of conflict point count and order comparison, a real-time data acquisition tool is used to track the status changes of each order during execution, determine whether there are any new potential conflicts, and obtain complete execution data records.

7. The IoT-based sandwich production line scheduling system and method according to claim 1, characterized in that, For the complex order portion of the final sequence, its estimated duration is obtained. If the duration causes delays in the production of subsequent simple orders, a buffer period is inserted to obtain a complete scheduling plan, including: For complex orders in the final sequence, data acquisition tools are used to obtain their duration information, which is then combined with production log records to determine whether there are time conflicts and obtain duration distribution data. Based on the above duration distribution data, for the production sequence of simple orders, if the duration of complex orders causes production delays, the time analysis module is used to identify the affected order combinations and determine the specific delay period range. Based on the aforementioned lag period range and combined with the available resource data of the buffer period, an allocation calculation tool is used to reasonably divide the buffer period and obtain a targeted time period allocation scheme for insertion. Based on the above time allocation scheme, and in response to the need to adjust the production sequence, the positions of complex and simple orders are rearranged using a scheduling update tool to determine the adjusted production sequence version. For the above production sequence version, and considering the potential risk of time conflicts, the start and end times of each order are verified using data comparison tools to determine whether there are any new time overlap issues, and the verified sequence arrangement is obtained. Based on the verified sequence arrangement, and to ensure the completeness of the scheduling arrangement, the information integration module records the time slot allocation and production sequence of all orders in a unified manner to obtain the final complete scheduling plan.

8. The IoT-based sandwich production line scheduling system and method according to claim 1, characterized in that, The process involves monitoring the real-time status of the production line according to the complete scheduling plan. If a sudden order insertion causes the time requirement to be unmet, the path optimization algorithm is reapplied to the affected segment to obtain an updated scheduling plan, including: The production line status is continuously collected by real-time monitoring tools. Based on the time arrangement in the scheduling plan, the current execution progress of each order is obtained, and it is determined whether there is any deviation from the preset time requirement, so as to obtain the status tracking result. Based on the status tracking results above, if it is found that a sudden order insertion causes the time schedule to not meet the requirements, the affected segment is located through the data filtering module to determine the specific scheduling segment range and obtain the affected order combination information; For the aforementioned affected order combination information, the scheduling segment is recalculated using a path optimization algorithm to obtain an adjusted time allocation scheme and determine the new order execution order; Based on the new order execution order, a data verification tool is used to compare the time arrangements in the adjustment plan one by one to determine whether there are any new time conflicts, and to obtain the verified scheduling segment data. For the above-verified scheduling segment data, the adjustment plan is seamlessly connected with the unaffected scheduling plan through information integration tools to obtain the complete updated scheduling arrangement; Based on the updated scheduling arrangement, the data distribution module will send the adjusted schedule and order execution order to the production line status monitoring system to determine the final execution basis.

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