Cod crisp biscuit processing order dynamic scheduling management method and system

Abstract

The application provides a cod crisp biscuit processing order dynamic scheduling management method and system, relates to the technical field of order scheduling management, and comprises the following steps: acquiring basic scheduling data; identifying process switching waiting time and structurally decomposing the process switching waiting time to generate variable structure section data; constructing a target process demand chain according to order characteristic information of a target order, and associating the target process demand chain with the variable structure section data; embedding the variable structure section data into a plurality of candidate positions of a current process link structure, and performing structure replacement calculation on each candidate position; screening a target structure replacement scheme that meets a preset condition, and performing connection offset calculation according to a planned start time of a subsequent batch and process timing constraint information; when the connection offset is within a preset offset threshold, generating an updated process link structure, and expanding the updated process link structure into an updated scheduling time axis; and the application improves the autonomy and accuracy of dynamic scheduling management.

Description

Method and System for Dynamic Scheduling Management of Cod Crispy Biscuit Processing Orders Technical Field

[0001] This invention relates to the field of order scheduling management technology, and in particular to a method and system for dynamic scheduling management of codfish crisp biscuit processing orders. Background Technology

[0002] In order management at food processing companies, existing technologies typically employ order scheduling systems based on preset rules. These systems statically arrange information such as order arrival time, batch size, and equipment availability to generate a fixed production sequence. This type of system is a basic scheduling module within a typical Enterprise Resource Planning (ERP) or Manufacturing Execution System (MES), capable of registering, sorting, and detecting basic conflicts in orders. However, its scheduling logic is mostly linear and rule-driven, lacking the ability to analyze dynamic factors such as raw material characteristics, process dependencies, and changeover costs in real time.

[0003] In the codfish crisp biscuit processing scenario, orders frequently change due to factors such as raw material shelf-life limitations and urgent replenishment needs. When an urgent order is inserted, the existing static scheduling system can only reorder orders according to rules, but it cannot assess the crucial factor of "waiting time required for the current process switch." For example, the baking stage requires a temperature stabilization process of about ten minutes when switching from a high-temperature curve to a low-temperature curve, and the system does not include this non-processing time in its feasibility assessment. As a result, the system generates scheduling instructions that are "ostensibly insertable but cannot actually be executed immediately," causing equipment to idle or previous batches to be interrupted, demonstrating the limitations of this type of static scheduling method in dynamic order processing. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for dynamic scheduling management of codfish crisp biscuit processing orders, aiming to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] Firstly, a dynamic scheduling management method for codfish crisp biscuit processing orders, the method comprising:

[0007] Obtain the target order's order characteristics, current process link structure information, and the planned start time and process sequence constraint information of subsequent batches to generate basic scheduling data;

[0008] Based on the basic scheduling data, the process switching waiting time is identified from the current process link structure information, food processing switching feature data is extracted, the switching waiting time is decomposed into a structure, and variable structure segment data including temperature adjustment, parameter stabilization, food-specific cleaning ordered sub-segments and structural coupling attributes is generated.

[0009] Based on the order characteristic information of the target order, construct the target process requirement chain, and associate the target process requirement chain with the variable structure segment data to generate structure compatibility condition data;

[0010] Based on the structural compatibility data, the variable structural segment data is embedded into multiple candidate positions of the current process link structure information, and structural replacement calculation is performed. Through triple checks of process sequence consistency, food processing key parameter continuity, and food-specific equipment load balance, structural replacement feasibility data is generated.

[0011] Based on the feasibility data of structural replacement, target structural replacement schemes that meet the preset conditions are selected, and the connection offset is calculated according to the planned start time and process sequence constraints of the target structural replacement schemes and subsequent batches, generating connection offset data.

[0012] When the connection offset data is within the preset offset threshold, an updated process link structure is generated according to the target structure replacement scheme, and the updated process link structure is expanded into an updated scheduling time axis to generate an updated scheduling result; when the connection offset data is outside the preset offset threshold, a prohibited order insertion prompt message is generated and the original scheduling result is maintained.

[0013] Preferably, based on the basic scheduling data, the process switching waiting time is identified from the current process link structure information, food processing switching feature data is extracted, and the switching waiting time is decomposed into a structured form to generate variable structure segment data containing temperature adjustment, parameter stabilization, food-specific cleaning ordered sub-segments, and structural coupling attributes, including:

[0014] Based on the basic scheduling data, extract the equipment temperature status information, process parameter switching rule information and equipment cleaning requirement information from the current process link structure information to generate food processing switching feature data;

[0015] Based on the food processing switching characteristic data, the temperature adjustment duration required for temperature state change, the parameter stabilization duration required for process parameter switching, and the preparation duration required for equipment cleaning are calculated respectively, generating multiple original sub-waiting time periods;

[0016] Based on the occurrence order and position of each original sub-waiting time period, they are arranged according to the equipment temperature recovery order, parameter convergence order, and cleaning operation order to generate an ordered sub-segment sequence with time sequence identifier;

[0017] Based on the ordered sub-segment sequence and combined with the inter-process connection relationship in the current process link structure information, the scope of each sub-segment is matched with the temporal connection relationship of adjacent processes to generate variable structure segment data containing structural coupling attributes.

[0018] Preferably, a target process requirement chain is constructed based on the order characteristic information of the target order, and the target process requirement chain is associated with variable structure segment data to generate structural compatibility condition data, including:

[0019] Based on the order characteristic information of the target order, extract the temperature curve requirements, process execution sequence requirements, and equipment occupancy requirements to generate a set of target process parameters;

[0020] Based on the target process parameter set, the execution order, parameter dependencies, and equipment dependencies of each process are combined and processed to generate target process requirement chain data;

[0021] Based on the target process demand chain data, extract the temperature characteristics, parameter sensitivity characteristics, and equipment dependence characteristics of each process, and perform structured processing to generate a target process chain structure model;

[0022] Based on the variable structure segment data and the target process chain structure model, the structure adaptation value of each variable structure segment in the target process chain is generated by matching each segment segment with the variable structure segment segment;

[0023] The embeddability of variable structural segments in the target process chain is evaluated based on the structural fit value, and structural compatibility condition data is generated to constrain replacement behavior.

[0024] Preferably, based on structural compatibility data, variable structural segment data is embedded into multiple candidate positions of the current process link structure information, and structural replacement calculation is performed. Through a triple check of process sequence consistency, continuity of key food processing parameters, and load balance of food-specific equipment, structural replacement feasibility data is generated, including:

[0025] Based on the structural compatibility data, the current process link structure information is scanned, and the position segments of each variable structure segment that meet the structural compatibility conditions are selected to generate a candidate position set.

[0026] The variable structure segment data is embedded into each candidate position in the candidate position set to construct multiple replacement process link structures;

[0027] For each replaced process link structure, perform process sequence consistency checks, process parameter continuity checks, and equipment load balance checks, and generate corresponding consistency check results;

[0028] The replacement behavior of each candidate position is evaluated based on the consistency check results, and structural replacement feasibility data containing candidate position identifiers and replacement validity is generated.

[0029] Preferably, based on the structural replacement feasibility data, target structural replacement schemes that meet preset conditions are selected, and connection offset calculations are performed based on the planned start time and process sequence constraints of the target structural replacement schemes and subsequent batches to generate connection offset data, including:

[0030] Based on the feasibility data of structural replacement, extract the replacement location identifier and structural duration of the target structural replacement scheme that meets the preset conditions, and generate target replacement structural parameter data;

[0031] Calculate the end time of the process link structure after replacement based on the target replacement structure parameter data, and generate batch start time difference data based on the planned start time of subsequent batches;

[0032] Based on the process timing constraint information of subsequent batches, the batch start time difference data is compared with the earliest and latest start times allowed for each process in the subsequent batches to generate the offset impact value of each process.

[0033] Based on the combined results of each offset influence value, connection offset data is generated to characterize the degree of impact of structural replacement on the timing of subsequent batches.

[0034] Preferably, the variable structure segment data is embedded into each candidate position in the candidate position set to construct multiple replacement process link structures, including:

[0035] Based on the candidate position set, determine the preceding and following process data corresponding to each candidate position, and generate candidate position boundary data;

[0036] Based on the candidate position boundary data, the variable structure segment data is inserted between the preceding process data and the following process data, and the local process link data is regenerated according to the execution order of the inserted process.

[0037] Based on the local process link data, extract the timing and parameter change information between the processes before and after insertion, and generate local link adjustment data;

[0038] Based on the local link adjustment data, the inserted local process link is merged with the current process link structure information to generate the replaced process link structure data.

[0039] Preferably, for each replaced process link structure, process sequence consistency checks, process parameter continuity checks, and equipment load balancing checks are performed to generate corresponding consistency check results, including:

[0040] Based on the replaced process link structure data, extract the preceding process, following process, and necessary sequence constraint information for each process, perform sequence comparison processing, and generate process sequence consistency data.

[0041] Based on the replaced process link structure data, extract the temperature parameters, execution parameters and equipment setting parameters of each process, compare the parameter change range and parameter stability requirements of adjacent processes, and generate process parameter continuity data.

[0042] Based on the replaced process link structure data, extract the equipment occupancy time period and equipment usage requirements for each process, accumulate the equipment load status in each time period, and generate equipment load balancing data.

[0043] Based on the process sequence consistency data, process parameter continuity data, and equipment load balance data, constraint satisfaction calculation is performed to generate consistency check result data.

[0044] Secondly, a dynamic scheduling management system for codfish crisp biscuit processing orders, the system comprising:

[0045] The basic scheduling data acquisition module is used to acquire the order characteristic information of the target order, the current process link structure information, and the planned start time and process sequence constraint information of subsequent batches, and generate basic scheduling data.

[0046] The variable structure segment generation module is used to identify the process switching waiting time from the current process link structure information based on the basic scheduling data, extract food processing switching feature data, decompose the switching waiting time into a structure, and generate variable structure segment data that includes temperature adjustment, parameter stability, food-specific cleaning ordered sub-segments and structural coupling attributes.

[0047] The structural compatibility condition generation module is used to construct the target process requirement chain based on the order feature information of the target order, and associate the target process requirement chain with the variable structure segment data to generate structural compatibility condition data.

[0048] The structural replacement feasibility calculation module is used to embed variable structural segment data into multiple candidate positions of the current process link structure information based on structural compatibility condition data, perform structural replacement calculation, and generate structural replacement feasibility data through triple checks of process sequence consistency, food processing key parameter continuity, and food-specific equipment load balance.

[0049] The connection offset calculation module is used to filter target structure replacement schemes that meet preset conditions based on the structural replacement feasibility data, and to perform connection offset calculation based on the target structure replacement scheme and the planned start time and process sequence constraint information of subsequent batches, and generate connection offset data.

[0050] The scheduling update module is used to generate an update process link structure based on the target structure replacement scheme when the connection offset data is within the preset offset threshold, and then expand the update process link structure into an update scheduling time axis to generate the update scheduling result.

[0051] The order insertion prohibition module is used to generate an order insertion prohibition prompt message and maintain the original scheduling result when the connection offset data is outside the preset offset threshold.

[0052] The above-described solution of the present invention has at least the following beneficial effects:

[0053] 1. Real-time analysis of dynamic scheduling and food processing process dependencies: Compared to existing static scheduling systems and CNC machining scheduling technologies, this invention focuses on the codfish crisp biscuit processing scenario. It can assess the food-specific dependencies between processes in real time based on the specific food processing characteristics of the target order, the current process flow structure, and the planned start time of subsequent batches. The system not only considers key information in the food industry such as raw material shelf life, food-specific process switching time, and equipment availability, but also dynamically adjusts the schedule according to order changes (such as near-expiration raw materials or urgent replenishment orders), ensuring more flexible and adaptable scheduling in complex food processing production environments.

[0054] 2. Precise Calculation and Optimization of Food Processing Changeover Waiting Time: Traditional static scheduling systems and CNC machining scheduling technologies neglect the unique non-processing time during food processing process changes, such as baking temperature adjustment and cleaning of food-specific equipment. The solution of this invention can accurately identify and extract the unique changeover characteristic data of food processing, decomposing the changeover waiting time into ordered sub-segments such as temperature adjustment, parameter stabilization, and food-specific cleaning. This achieves structured quantification of non-processing time and incorporates these critical times into the scheduling evaluation, thereby avoiding phenomena such as equipment idling, process interruptions, and food quality fluctuations. By comprehensively considering food-specific changeover waiting time, process sequence constraints, and equipment load, the system optimizes food production efficiency and equipment resource utilization, completely avoiding the false order insertion instructions that are "appear to be insertable but actually cannot be executed" in traditional scheduling methods.

[0055] 3. Intelligent Calculation of Food Processing Structure Replacement and Connection Offset: The dynamic scheduling method of this invention, tailored to the characteristics of food processing, accurately calculates the impact of order insertion on the timing of subsequent batches by combining the current process link and the planned time of subsequent processes. This technology, based on a single dimension of connection offset judgment, effectively avoids production timing conflicts caused by traditional scheduling methods and CNC machining multi-parameter optimization scheduling when inserting urgent orders. For example, if order insertion causes the connection offset to exceed a preset offset threshold, the system will automatically generate a prompt message prohibiting order insertion, rather than blindly executing the schedule, ensuring stable food production progress and controllable quality.

[0056] 4. Load Balancing and Conflict Elimination for Food-Specific Equipment: Existing static scheduling systems and CNC machining scheduling technologies lack load balancing mechanisms designed for the usage characteristics of food-specific equipment, easily leading to excessive idleness or overloading. This invention optimizes equipment resource scheduling by calculating the usage periods and load conditions of food-specific equipment in real time, combined with constraints specific to food processing such as equipment cleaning time and temperature adjustment cycles, thus avoiding equipment conflicts and inefficient idleness. The system adjusts in real time based on the occupancy of dedicated equipment during replacement processes, ensuring that equipment load remains within a reasonable range, thereby improving the overall efficiency and resource utilization of the food production line.

[0057] 5. Enhanced Flexibility and Response Speed ​​in Food Production Scheduling: This invention overcomes the limitations of traditional static scheduling systems and CNC machining scheduling technologies in handling urgent orders and production plan changes in the food industry by comprehensively incorporating dynamic factors unique to food processing (such as food-specific changeover waiting time, process dependencies, and dedicated equipment load) into the scheduling decision-making process. The system can quickly respond to changes in food production needs, flexibly adjust scheduling, and ensure continuous and efficient operation of the production process. For example, when encountering urgent orders for raw materials nearing their expiration date, the system can assess whether a new order can be inserted in the shortest possible time and make scheduling decisions based on food processing feasibility, avoiding scheduling errors, equipment idleness, and raw material waste that occur in traditional methods.

[0058] 6. Reduce human intervention and scheduling errors in food production: Traditional static scheduling systems and CNC machining scheduling technologies often rely on manual intervention to solve problems such as order insertion or process adjustment, which is prone to errors. The automated scheduling system of this invention, combining triple checks of process sequence consistency, continuity of key food processing parameters, and load balance of food-specific equipment, reduces the need for human intervention. The system can autonomously adjust processes, schedule specialized equipment, and verify parameters, significantly reducing the scheduling error rate, ensuring food production efficiency and product quality stability, and meeting the hygiene compliance and quality control requirements of the food industry.

[0059] In summary, this invention is specifically designed for the codfish crisp biscuit processing scenario, which greatly improves the intelligence and dynamic adaptability of food production line scheduling. It can effectively cope with various emergencies in complex food production environments, optimize resource allocation, improve production efficiency, reduce unnecessary equipment idle time, production interruptions and quality risks, and meet the needs of food production enterprises for efficient, flexible and compliant production scheduling. Attached Figure Description

[0060] Figure 1 is a flowchart of the dynamic scheduling management method for codfish crisp biscuit processing orders provided by an embodiment of the present invention. Detailed Implementation

[0061] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0062] As shown in Figure 1, an embodiment of the present invention proposes a dynamic scheduling management method for codfish crisp biscuit processing orders, the method comprising:

[0063] Obtain the target order's order characteristics, current process link structure information, and the planned start time and process sequence constraint information of subsequent batches to generate basic scheduling data;

[0064] Based on the basic scheduling data, the process switching waiting time is identified from the current process link structure information, food processing switching feature data is extracted, the switching waiting time is decomposed into a structure, and variable structure segment data including temperature adjustment, parameter stabilization, food-specific cleaning ordered sub-segments and structural coupling attributes is generated.

[0065] Based on the order characteristic information of the target order, construct the target process requirement chain, and associate the target process requirement chain with the variable structure segment data to generate structure compatibility condition data;

[0066] Based on the structural compatibility data, the variable structural segment data is embedded into multiple candidate positions of the current process link structure information, and structural replacement calculation is performed. Through triple checks of process sequence consistency, food processing key parameter continuity, and food-specific equipment load balance, structural replacement feasibility data is generated.

[0067] Based on the feasibility data of structural replacement, target structural replacement schemes that meet the preset conditions are selected, and the connection offset is calculated according to the planned start time and process sequence constraints of the target structural replacement schemes and subsequent batches, generating connection offset data.

[0068] When the connection offset data is within the preset offset threshold, an updated process link structure is generated according to the target structure replacement scheme, and the updated process link structure is expanded into an updated scheduling time axis to generate an updated scheduling result; when the connection offset data is outside the preset offset threshold, a prohibited order insertion prompt message is generated and the original scheduling result is maintained.

[0069] In this embodiment of the invention, by acquiring the order feature information of the target order, the current process link structure information, and the planned start time and process sequence constraint information of subsequent batches, relatively complete basic scheduling data can be formed at the beginning of the scheduling stage, so that subsequent scheduling judgments are based on a consistent data structure, which helps to reduce scheduling errors caused by human misunderstanding.

[0070] By identifying process changeover waiting times from basic scheduling data and extracting food processing-specific changeover characteristic data (including equipment temperature status, process parameter changeover rules, and food-specific equipment cleaning requirements), and decomposing this data into ordered sub-segments such as temperature adjustment, parameter stabilization, and food-specific cleaning, these difficult-to-quantify changeover behaviors in food processing scenarios can be presented in a structured manner. The structured variable segment data contains structural coupling attributes, which can accurately reflect the causes and scope of food processing-specific waiting times, helping to accurately assess the impact of changeovers on product quality and hygiene compliance in scheduling.

[0071] By constructing a target process requirement chain based on the order characteristic information of the target order and associating it with variable structural segment data, a correspondence can be established between the food processing-specific technological requirements of the target order and the current production line's operational structure. Through the generation and evaluation of structural fit values, the appropriate insertion position of the variable structural segment in the target process chain can be determined, thereby forming structural compatibility condition data that can be used to limit subsequent replacement behaviors. This ensures that process structure adjustments are fully adapted to the specific requirements of food processing, providing a clear basis for adjustments.

[0072] By embedding variable structure segment data into multiple candidate locations and constructing the replacement process link structure, the impact of different insertion positions on the overall link can be simulated, providing support for finding insertion positions that meet food processing requirements. Combining triple checks of process sequence consistency, continuity of key food processing parameters, and load balancing of food-specific equipment, the effectiveness of the replacement process link structure can be confirmed from multiple angles. This reduces timing conflicts, abnormal key parameter connections, or overload of specialized equipment caused by replacement, ensuring food processing quality and production efficiency.

[0073] By selecting target replacement schemes based on their feasibility and calculating the connection offset of the replacement scheme relative to subsequent batches, it is possible to promptly determine whether the structural replacement causes an offset beyond the allowable range for the start timing of subsequent batches. If the connection offset is within the allowable range, an updated process link structure can be generated and expanded into a new scheduling timeline to complete the scheduling update adapted to the current food processing needs. If the connection offset exceeds the range, a message prohibiting order insertion can be generated to avoid executing order insertion operations that cannot meet the food processing sequence constraints and quality constraints.

[0074] For example, in the processing of codfish crisp biscuits, when a batch of orders needs to be processed ahead of schedule due to the shelf life of raw materials, the processing flow of this invention can be used to determine this order insertion request. By identifying the temperature adjustment time of the current baking process, the parameter stabilization time required for recipe switching, and the cleaning requirements of food-specific equipment, these are treated as variable structural segments with structural coupling attributes. Then, based on the process requirement chain of the target order, it is assessed whether this variable structural segment can be inserted at a specific position. Subsequently, the process sequence, the connection of key food processing parameters, and the load of special equipment are checked in multiple replaced process links, and the connection offset is calculated in conjunction with the planned start time of subsequent batches to determine whether there is room for adjustment. If the connection offset is within the allowable range, the scheduling can be adjusted; if it exceeds the range, the original schedule will be maintained and a message will be displayed indicating that the order cannot be inserted. Through the above processing, process conflicts, quality fluctuations, or production delays caused by unreasonable order insertions can be effectively avoided in specific food processing scenarios.

[0075] Process link structure information refers to a structured data set formed by connecting various production stages with the entire codfish crisp biscuit processing flow as the core. This data is used to comprehensively represent the process logic, constraints, and operational status of the production process, specifically including:

[0076] Basic process information: unique identifiers for each processing step, execution sequence, and core process requirements (such as the order of baking, cooling, and shaping steps, and corresponding temperature and duration standards).

[0077] Process relationships: Pre-process dependencies (e.g., the molding process can only begin after the raw material mixing process is completed), post-process connection logic, and parameter transfer requirements across processes (e.g., the impact of the moisture content of materials in the preceding process on subsequent baking parameters).

[0078] Equipment matching information: the identification of the food-specific equipment corresponding to each process, the time period during which the equipment is occupied, and the range of equipment operating parameters (such as the oven model, usage period, and temperature control range corresponding to the baking process);

[0079] Switching constraint information: Process switching characteristics between adjacent processes, including non-processing time-related constraints such as temperature adjustment requirements, parameter stability thresholds, and cleaning standards for food-specific equipment;

[0080] Timing baseline information: the planned start time and estimated completion time of each process, as well as the connection time nodes with the preceding and following batches of orders.

[0081] In a preferred embodiment of the present invention, when the connection offset data is within a preset offset threshold, an update process link structure is generated according to the target structure replacement scheme, and the update process link structure is expanded into an update scheduling time axis to generate an update scheduling result, specifically including:

[0082] After obtaining the connection offset data, the connection offset is compared with a preset offset threshold. By determining whether the connection offset is within the preset offset threshold, it is determined whether the structural replacement meets the execution conditions. If it is confirmed to be within the allowable range, then based on the replacement position information, variable structural segment data, and original process link structure recorded in the target structural replacement scheme, the replacement part is embedded into the corresponding position according to the execution order of the processes, thereby forming a complete and time-defined update process link structure.

[0083] Based on the generated updated process chain structure, the start and end times of each process are sequentially expanded. This expansion method typically starts with the first process in the process chain and sets the end time of each process to the sum of its start time and duration. For subsequent processes, the end time of the previous process is used as the start time of the current process, and its end time is calculated using its duration. This continuous expansion method forms a complete scheduling timeline.

[0084] After obtaining the complete scheduling timeline, the execution sequence of the processes, the planned start time of each process, and the estimated completion time are output as updated scheduling results to guide the production line to execute according to the adjusted process sequence. Through this unfolding method, the production line can accurately execute the updated process based on the new schedule, avoiding production chaos caused by structural changes.

[0085] In a preferred embodiment of the present invention, when the connection offset data is outside a preset offset threshold, a "prohibit order insertion" message is generated while maintaining the original scheduling result, specifically including:

[0086] If the connection offset exceeds the upper limit of the preset offset threshold or falls below the lower limit of the threshold (e.g., the time of subsequent batches being brought forward or delayed due to order insertion exceeds the tolerable range), it is determined that the structure replacement behavior will have an unacceptable time impact on subsequent production and cannot meet the requirements of the earliest or latest start time of subsequent processes.

[0087] In this situation, triggering the order insertion prohibition module generates a message prohibiting order insertion, which includes the reason why replacement cannot be implemented, such as the expected delay exceeding the allowable range or the process dependency not being met, so that operators can make a judgment. At the same time, the original process link structure and its corresponding scheduling timeline before the order insertion are retained, so that the production process continues to execute according to the established plan, avoiding process conflicts, equipment idleness, or batch delays caused by unreasonable order insertion.

[0088] By maintaining the original scheduling results, the production line can still maintain stable operation when faced with order insertion requests that cannot meet the timing requirements, avoiding the impact on the overall scheduling caused by incorrect adjustments, and ensuring the normal operation of production rhythm and process flow.

[0089] In a preferred embodiment of the present invention, the preset offset threshold needs to be set according to the actual production environment and scheduling requirements. The preset offset threshold can be set by the production scheduler based on the equipment performance of the production line, production cycle requirements, and time tolerance of each process. Typically, the offset threshold is set in units of time (such as minutes or hours) and adjusted according to the actual capacity of the production line and process requirements.

[0090] For example, if each step in the production line has very strict time requirements, the offset threshold might be set in a smaller time unit (e.g., 5 minutes). If the production process has a higher tolerance for time fluctuations, the offset threshold can be set in a larger time unit (e.g., 30 minutes). The setting of the offset threshold should take into account the maximum load capacity of the equipment, the time stability of the process execution, and the urgency of the production task.

[0091] In a preferred embodiment of the present invention, the preset conditions refer to the basic requirements and constraints that must be met to ensure the smooth operation of the production line during production scheduling and process replacement, specifically including:

[0092] Time requirements: the earliest start time and the latest end time of each process, the time window for subsequent processes, the maximum load of the equipment, etc.

[0093] Process dependencies: The execution of certain processes depends on the completion status of preceding processes, and alternative solutions must ensure that these dependencies are not broken;

[0094] Equipment availability: Whether the equipment is available during the required time period, and whether the replacement schedule will cause equipment occupancy conflicts;

[0095] Parameter stability: Whether process changes will lead to parameter instability, such as whether fluctuations in temperature, pressure, or formulation parameters are within acceptable limits.

[0096] These preset conditions are set through rules or configuration files to ensure the continuous and stable operation of the production line.

[0097] The specific setup process is as follows:

[0098] First, time requirement settings: First, based on the specific requirements of the production task, the earliest start time and latest end time of each process need to be set. The time window for each process is usually set based on the execution time of the preceding process, equipment load capacity, and product quality requirements.

[0099] The following factors were considered during the setup:

[0100] Process dependency: Ensure that the next process can only begin after the previous process is completed to avoid time conflicts.

[0101] Equipment usage: Ensure that each process has available equipment resources within its designated time.

[0102] For example, when setting the time for a baking process, the temperature setting and equipment runtime are first ensured to match, and the earliest time the process can start is calculated, as well as when it must end to meet the requirements of subsequent processes.

[0103] Second, process dependency settings: The dependencies between processes are usually defined in the process flow diagram or scheduling diagram. For each process, its predecessor and successor processes are set. By checking the end time of the predecessor processes, the earliest start time of the current process is determined to ensure that the process sequence is not disrupted.

[0104] For example, if the batching process needs to be performed after the mixing process, the scheduling algorithm will automatically calculate the end time of the mixing process and then set the start time of the batching process to the end time of the mixing process. This process continues, adjusting the time nodes of each process to ensure compliance with production sequence requirements.

[0105] Third, equipment availability settings: Equipment availability is a crucial part of setting preset conditions. In production scheduling, each process is typically assigned to specific equipment. To ensure that equipment is not reused, its usage time window must be considered during scheduling.

[0106] In this system, equipment availability is typically pre-set in production equipment management, including idle periods, maintenance times, and scheduled tasks for each piece of equipment. Based on equipment occupancy, it is determined whether there are sufficient equipment resources to support the execution of processes. For example, if a piece of equipment is already scheduled for production, then no new processes can be scheduled for that equipment during that time period, and equipment resources will be automatically reallocated.

[0107] Fourth, parameter stability settings: Parameter stability refers to whether process replacement will lead to changes in process parameters, especially for parameters such as temperature and pressure that have a significant impact on product quality. If the replacement scheme causes fluctuations in process parameters such as temperature and humidity in a certain process to exceed the predetermined range, it may affect product quality.

[0108] In this process, process parameters such as temperature and humidity are typically set within a reasonable fluctuation range based on historical data and experience. For example, if the temperature of a certain process is set between 180°C and 5°C, and a replacement process causes temperature fluctuations exceeding this range, it will be marked as not meeting the preset conditions. By simulating changes in process parameters, it is determined whether the parameter stability requirements are met, thereby avoiding quality fluctuations caused by excessive changes in process parameters.

[0109] In addition, the preset conditions are dynamically adjusted: During implementation, the production environment, process requirements, and equipment conditions may change. Therefore, the preset conditions are not static and can be dynamically adjusted according to the actual needs of the production task. The preset conditions will be checked and updated regularly, for example, adjusting time requirements based on the urgency of the production task, or adjusting equipment availability based on equipment maintenance status.

[0110] The dynamic adjustment process can be carried out through the following steps:

[0111] Data feedback and adjustment: By collecting production data and equipment status in real time, the execution sequence and time requirements of processes can be adjusted.

[0112] Process optimization: In production, if the execution time of a certain process can be shortened or optimized, the preset conditions will be adjusted according to the new optimization results to improve production efficiency.

[0113] If the replacement scheme meets all preset conditions, the final scheduling timeline will be generated; if there are any areas that do not meet the conditions, a warning will be generated or the replacement behavior will be blocked.

[0114] In a preferred embodiment of the present invention, based on basic scheduling data, the process switching waiting time is identified from the current process link structure information, food processing switching feature data is extracted, and the switching waiting time is decomposed into a structured form to generate variable structure segment data containing temperature adjustment, parameter stabilization, food-specific cleaning ordered sub-segments, and structural coupling attributes, including:

[0115] Based on the basic scheduling data, extract the equipment temperature status information, process parameter switching rule information and equipment cleaning requirement information from the current process link structure information to generate food processing switching feature data;

[0116] Based on the food processing switching characteristic data, the temperature adjustment duration required for temperature state change, the parameter stabilization duration required for process parameter switching, and the preparation duration required for equipment cleaning are calculated respectively, generating multiple original sub-waiting time periods;

[0117] Based on the occurrence order and position of each original sub-waiting time period, they are arranged according to the equipment temperature recovery order, parameter convergence order, and cleaning operation order to generate an ordered sub-segment sequence with time sequence identifier;

[0118] Based on the ordered sub-segment sequence and combined with the inter-process connection relationship in the current process link structure information, the scope of each sub-segment is matched with the temporal connection relationship of adjacent processes to generate variable structure segment data containing structural coupling attributes.

[0119] In this embodiment of the invention, by extracting information such as equipment temperature status, process parameter switching rules, and equipment cleaning requirements from basic scheduling data, various types of waiting times that were originally implicit in the process switching can be provided with an analyzable numerical basis, which helps to fully identify switching behavior before scheduling. Decomposing the process switching time into temperature adjustment sub-segments, parameter stabilization sub-segments, and cleaning preparation sub-segments allows these waiting times from different sources to correspond to specific process attributes, thus facilitating the tracking of their impact location and scope during scheduling. Further sorting multiple sub-segments according to the temperature recovery order, parameter convergence order, and cleaning operation order gives the variable structure segments clear temporal sequence information, facilitating position matching and embedding in subsequent steps. By matching the ordered sub-segment sequence with the process connection relationships in the current process link structure, the temporal coupling between each sub-segment and adjacent processes can be determined, enabling the variable structure segment data to truly reflect the structural impact of process switching on the production link, thereby supporting subsequent calculations of replacement feasibility and structural compatibility.

[0120] In a preferred embodiment of the present invention, based on the basic scheduling data, equipment temperature status information, process parameter switching rule information, and equipment cleaning requirement information are extracted from the current process link structure information to generate food processing switching feature data, specifically including:

[0121] After obtaining the current production line's process chain structure, real-time temperature values, historical temperature adjustment records, or current temperature setpoints for corresponding equipment are read from each process node to form temperature status information. When extracting process parameter switching rules, the required setting parameters for formula changes, such as stirring time, baking temperature curves, and drying time, are read from the process formula, equipment operating requirements, or standard production line processes, and the stabilization time required for parameter switching is recorded. When extracting equipment cleaning requirements, the cleaning methods and time periods required for switching different raw materials are determined from the production line's hygiene management processes or cleaning procedures. These temperature data, parameter change requirements, and cleaning requirements from different sources are compiled and summarized to form food processing switching characteristic data, providing a basis for subsequent identification of switching waiting times.

[0122] In a preferred embodiment of the present invention, based on food processing switching characteristic data, the temperature adjustment duration required for temperature state change, the parameter stabilization duration required for process parameter switching, and the preparation duration required for equipment cleaning are calculated respectively, generating multiple original sub-waiting time periods, specifically including:

[0123] Based on the difference between the current and target temperatures of the equipment, and combined with empirical data on the equipment's heating or cooling rates, the estimated time interval required for temperature change is continuously accumulated to determine the temperature adjustment duration. For process parameter switching, by comparing the current parameter value with the parameter value required for the next process, and based on the parameter change amplitude and equipment response characteristics, the required parameter stabilization time is divided into multiple segments and accumulated to form the parameter stabilization duration. For equipment cleaning requirements, the cleaning operation steps required in the cleaning procedure are read, and the time of each step is summed to obtain the cleaning preparation duration. Finally, these three types of durations are stored as independent sub-waiting time intervals for subsequent sorting processing.

[0124] In a preferred embodiment of the present invention, based on the occurrence order and position of each original sub-waiting time period, they are arranged according to the equipment temperature recovery order, parameter convergence order, and cleaning operation order to generate an ordered sub-segment sequence with time sequence identifiers, specifically including:

[0125] After obtaining the three initial waiting time segments, the order in which each segment appears in the switchover process is determined by analyzing the process switching positions in the current process chain. Temperature adjustment is typically prioritized to ensure the equipment is within the temperature range suitable for the next process; therefore, the temperature adjustment segment is placed at the beginning of the sequence. Parameter stabilization usually occurs after the temperature reaches the executable range; therefore, the parameter stabilization segment is placed in the middle. Cleaning preparation operations are usually performed after parameter stabilization or temperature adjustment, and are therefore placed at the end of the sequence. After sorting each segment according to the above logic, each segment is labeled with its sequential number within the entire switchover process, thus forming an ordered sequence of segments with clear temporal attributes.

[0126] In a preferred embodiment of the present invention, based on the ordered sub-segment sequence and combined with the inter-process connection relationship in the current process link structure information, the scope of effect of each sub-segment is matched with the temporal connection relationship of adjacent processes to generate variable structure segment data containing structural coupling attributes, specifically including:

[0127] Based on the sorted sequence of ordered sub-segments, the duration, start position, and end position of each sub-segment are read one by one. Then, the upstream and downstream process information in the process chain structure is read, such as the end time of the preceding process and the planned start time of the subsequent process. The scope of the sub-segment's effect is compared with these time points to determine whether the sub-segment will cause delays in subsequent processes or prevent the preceding process from completing on time. Through this comparison process, the temporal coupling between each sub-segment and its corresponding process can be determined, such as whether there are overlapping, gaps, or adjacent relationships. Finally, these matching results are stored as structural coupling attributes, forming variable structure segment data together with the sub-segment information, providing a structural basis for subsequent structural replacement and feasibility assessment.

[0128] In a preferred embodiment of the present invention, a target process requirement chain is constructed based on the order feature information of the target order, and the target process requirement chain is associated with variable structure segment data to generate structure compatibility condition data, including:

[0129] Based on the order characteristic information of the target order, extract the temperature curve requirements, process execution sequence requirements, and equipment occupancy requirements to generate a set of target process parameters;

[0130] Based on the target process parameter set, the execution order, parameter dependencies, and equipment dependencies of each process are combined and processed to generate target process requirement chain data;

[0131] Based on the target process demand chain data, extract the temperature characteristics, parameter sensitivity characteristics, and equipment dependence characteristics of each process, and perform structured processing to generate a target process chain structure model;

[0132] Based on the variable structure segment data and the target process chain structure model, the structure adaptation value of each variable structure segment in the target process chain is generated by matching each segment segment with the variable structure segment segment;

[0133] The embeddability of variable structural segments in the target process chain is evaluated based on the structural fit value, and structural compatibility condition data is generated to constrain replacement behavior.

[0134] In this embodiment of the invention, by generating a target process parameter set based on the temperature curve requirements, process execution sequence requirements, and equipment occupancy requirements of the target order, the process attributes involved in the target order can be fully expressed. Combining the execution sequence, parameter dependencies, and equipment dependencies of the processes generates process requirement chain data that describes the logical relationships between processes, providing a clear link basis for subsequent structural matching. This process requirement chain is further structured into a process chain structure model, giving the temperature characteristics, parameter sensitivity characteristics, and equipment dependency characteristics between processes a clear structured representation. By matching the variable structure segment data with the process chain structure model segment by segment, the adaptability of each variable structure segment in the process chain can be identified, thus providing a quantitative or logical basis for judging the embedding capability of the variable structure segment. By combining the structure adaptation value to generate structure compatibility condition data, limiting conditions can be provided for subsequent replacement behavior, making the entire scheduling adjustment process controllable and verifiable, and reducing link conflicts caused by unreasonable insertions.

[0135] In a preferred embodiment of the present invention, based on the order feature information of the target order, the temperature curve requirement, the process execution sequence requirement, and the equipment occupancy requirement are extracted to generate a target process parameter set, specifically including:

[0136] Upon receiving a target order, the system reads information recorded in the order, including the product category, formula number, process requirements, and raw material specifications. Based on the product category and formula number, it retrieves the corresponding temperature control curve from the process database, including the target temperature and duration for the heating, isothermal, and cooling stages. It extracts the sequence of processes required for the product based on the process requirements, such as ingredient mixing, molding, baking, and cooling, and marks their dependencies. When reading equipment occupancy requirements, it determines the type of equipment needed based on the execution requirements of different processes, such as mixing equipment, molding equipment, or baking equipment, and extracts the available time slots and start / stop requirements for each piece of equipment from the equipment management system. After structuring and organizing the above temperature requirements, process sequence requirements, and equipment occupancy requirements, it generates a target process parameter set, providing a foundation for constructing the target process requirement chain.

[0137] In a preferred embodiment of the present invention, based on the target process parameter set, the execution order, parameter dependencies, and equipment dependencies of each process are combined and processed to generate target process demand chain data, specifically including:

[0138] Based on the execution order requirements of each process in the target process parameter set, the processes are sorted according to their dependencies, ensuring that any process with prerequisites does not appear before its preceding process. Based on parameter dependencies, such as processes requiring a specific temperature range to begin, these dependencies are compared with temperature curve requirements to establish dependency pairings between processes and temperature conditions. When a process requires the use of specific equipment, the process requirement is combined with the available time slots of the equipment, ensuring that each node in the process chain has corresponding equipment association information. Finally, the sorting, dependency pairings, and equipment association information are organized into a chain structure according to the process execution order, forming the target process requirement chain data, which serves as the basis for subsequently constructing the process chain structure model.

[0139] In a preferred embodiment of the present invention, based on the target process demand chain data, the temperature characteristics, parameter sensitivity characteristics, and equipment dependency characteristics of each process are extracted, and structured processing is performed to generate a target process chain structure model, specifically including:

[0140] The temperature characteristics of each process are extracted from the target process demand chain data, including the target temperature range, the allowable temperature fluctuation range, and the pretreatment time required to reach the required temperature. For parameter sensitivity characteristics, the relationship between process parameters and process quality is analyzed to determine the tolerance range of each process for parameter changes, such as the acceptable baking temperature deviation within a certain range. For equipment dependency characteristics, the fixed dependencies between processes and equipment are determined by reading the equipment usage requirements and equipment availability information for each process. After completing the above feature extraction, each feature is expressed in a structured manner, forming a process chain structure model with nodes and interrelationships, giving the process chain structural attributes that can be used for matching analysis.

[0141] In a preferred embodiment of the present invention, the variable structure segment data is matched segment by segment with the target process chain structure model to generate the structural adaptation value of each variable structure segment in the target process chain, specifically including:

[0142] The temporal attributes, duration, and structural coupling attributes of each variable structural segment are sequentially read from the variable structural segment data, and then compared item by item with the corresponding process features in the target process chain structure model. The comparison includes whether the duration of the variable structural segment affects the earliest start time and latest finish time of the corresponding process in the chain, and whether the temperature or parameter coupling attribute of the structural segment conflicts with the temperature characteristics or parameter sensitivity characteristics of the target process. Based on the degree of matching in the comparison (e.g., fully satisfied, partially satisfied, or not satisfied), a structural adaptation value is generated for each variable structural segment according to a certain level standard, providing a basis for subsequent determination of whether the variable structural segment can be embedded in the target process chain.

[0143] In a preferred embodiment of the present invention, the embeddability of the variable structural segment in the target process chain is evaluated based on the structural fit value, and structural compatibility condition data for constraining replacement behavior is generated, specifically including:

[0144] After obtaining the structural fit value for each variable structural segment, these values ​​are categorized to identify which segments fully meet the embedding requirements, which can only be embedded under specific conditions, and which cannot be embedded into the target process chain. For structural segments that meet the embedding conditions, their structural positions and embedding conditions are recorded in the structural compatibility condition data. For structural segments that partially meet the conditions, their limiting conditions, such as the maximum allowable duration or temperature fluctuation range, are analyzed and written into the structural compatibility condition data as constraints. Structural segments that do not meet the conditions are marked so that subsequent replacement steps will not attempt to embed them. The resulting structural compatibility condition data can serve as the basis for restricting the embedding behavior of variable structural segments, making the entire replacement process structurally controllable.

[0145] In a preferred embodiment of the present invention, based on structural compatibility condition data, variable structural segment data is embedded into multiple candidate positions of the current process link structure information, and structural replacement calculation is performed. Through a triple check of process sequence consistency, continuity of key food processing parameters, and load balance of food-specific equipment, structural replacement feasibility data is generated, including:

[0146] Based on the structural compatibility data, the current process link structure information is scanned, and the position segments of each variable structure segment that meet the structural compatibility conditions are selected to generate a candidate position set.

[0147] The variable structure segment data is embedded into each candidate position in the candidate position set to construct multiple replacement process link structures;

[0148] For each replaced process link structure, perform process sequence consistency checks, process parameter continuity checks, and equipment load balance checks, and generate corresponding consistency check results;

[0149] The replacement behavior of each candidate position is evaluated based on the consistency check results, and structural replacement feasibility data containing candidate position identifiers and replacement validity is generated.

[0150] In this embodiment of the invention, by scanning the current process link structure and screening for position segments that meet structural compatibility conditions, invalid or potentially conflicting positions can be reduced from entering the replacement evaluation scope, thus improving subsequent calculation efficiency. Embedding variable structural segments into multiple candidate positions to form the replaced process link structure allows for various structural adjustment results based on different insertion positions, facilitating multi-scheme comparison during dynamic scheduling. By performing process sequence consistency checks, process parameter continuity checks, and equipment load balancing checks on the replaced structure, the effectiveness of the structural replacement can be judged from three aspects: process sequence, parameter stability, and equipment usage load, avoiding new process conflicts after replacement. Generating structural replacement feasibility data based on the consistency check results provides an objective basis for subsequent selection of target replacement schemes, ensuring that order insertion adjustments are based on effectiveness verification and reducing the risk of production interruptions or process conflicts.

[0151] In a preferred embodiment of the present invention, the current process link structure information is scanned based on structural compatibility condition data to filter out the position segments of each variable structure segment that satisfy the structural compatibility conditions, and a candidate position set is generated, specifically including:

[0152] After obtaining the structural compatibility data, the embedding location requirements for the variable structural segments are compared with each process node in the current process link structure. The scanning process starts from the first process in the process link structure, reading the process type, temperature range, parameter requirements, and equipment occupancy of each process node, and checking them against the embedding conditions in the structural compatibility conditions. For example, when the structural compatibility conditions require that the variable structural segment can only be inserted between processes in a stable temperature range or with small parameter changes, the system determines whether the position can be used as a candidate embedding location by judging whether the end feature of the current process and the start feature of the next process simultaneously meet these conditions.

[0153] If the verification results show that the embedding conditions are met, the end position of this process and the start position of the next process are recorded together as candidate position segments. After the entire link scan is completed, all position segments that meet the conditions are organized into a candidate position set to provide a basis for available insertion positions for subsequent replacement operations.

[0154] In a preferred embodiment of the present invention, the replacement behavior of each candidate position is evaluated based on the consistency check results, and structural replacement feasibility data containing candidate position identifiers and replacement validity is generated, specifically including:

[0155] After completing the sequence consistency check, parameter continuity check, and equipment load balance check of the replaced process link structure, the three types of check data generated during the check process are read out respectively. For the replacement result corresponding to each candidate position, first determine whether its process sequence still meets the constraints of the preceding and following processes; if the sequence is not broken, then continue to determine whether the parameter continuity is good, that is, whether the temperature change between adjacent processes is within an acceptable range, and whether the parameter jump is within the equipment's responsive range.

[0156] If both of the above conditions are met, then check whether the equipment load is within the equipment's carrying capacity. For example, whether overlapping execution of multiple processes will lead to equipment timeouts or conflicts. If no problems are found in any of the three types of checks, the candidate location is marked as a valid replacement location, and its location identifier and validity are written into the structural replacement feasibility data. If any check result is not met, the candidate location is marked as unavailable, so that it will not be used in subsequent processes to construct replacement solutions.

[0157] In a preferred embodiment of the present invention, based on structural replacement feasibility data, target structural replacement schemes that meet preset conditions are selected, and connection offset calculations are performed based on the planned start time and process sequence constraints of the target structural replacement schemes and subsequent batches to generate connection offset data, including:

[0158] Based on the feasibility data of structural replacement, extract the replacement location identifier and structural duration of the target structural replacement scheme that meets the preset conditions, and generate target replacement structural parameter data;

[0159] Calculate the end time of the process link structure after replacement based on the target replacement structure parameter data, and generate batch start time difference data based on the planned start time of subsequent batches;

[0160] Based on the process timing constraint information of subsequent batches, the batch start time difference data is compared with the earliest and latest start times allowed for each process in the subsequent batches to generate the offset impact value of each process.

[0161] Based on the combined results of each offset influence value, connection offset data is generated to characterize the degree of impact of structural replacement on the timing of subsequent batches.

[0162] In this embodiment of the invention, by selecting replacement schemes that meet preset requirements from the replacement feasibility data, it can be ensured that subsequent calculations are based on structural results that have passed the sequence, parameter, and equipment checks, thereby avoiding redundant calculations for replacement schemes that do not meet the implementation conditions. Replacement structure parameter data generated based on the replacement location and structural duration provides a clear end time basis for the replaced process chain, providing the necessary time boundary for calculating the start time difference of subsequent batches. By comparing the replacement end time with the planned start time of subsequent batches, the impact of structural replacement on the start timing of subsequent batches can be intuitively reflected, thus forming batch start time difference data. Combined with the process sequence constraint information of subsequent batches, the batch start time difference is compared with the allowable time range of each process to determine whether the replacement behavior will exceed the time limit of subsequent processes, providing a constraint reference for maintaining production rhythm. Finally, based on the comprehensive processing of the offset impact of each process, a connection offset data that characterizes the degree of impact of the entire replacement on the subsequent batch chain can be formed, providing a direct basis for selecting whether to adopt the replacement scheme and facilitating order adjustments without disrupting the overall scheduling.

[0163] In a preferred embodiment of the present invention, based on the process timing constraint information of subsequent batches, the batch start time difference data is compared with the earliest and latest start times allowed for each process in the subsequent batches to generate the offset influence value for each process, specifically including:

[0164] After obtaining the batch start time difference data, the allowable start time interval for each process is read from the process sequence constraint information of subsequent batches, including the earliest start time and the latest start time. By comparing the batch start time difference with the two boundaries of this interval, it can be determined whether the difference falls within an acceptable range. When the batch start time difference is earlier than the earliest start time, the difference is marked as an early offset; if it is later than the latest start time, it is marked as a delayed offset; if it is within the range, it is considered to have no impact on the process.

[0165] Based on the comparison results of multiple processes, the offset type and degree of each process are recorded as offset impact values, which are used to describe the impact of the replacement behavior on the timing of subsequent processes, providing the necessary data foundation for the next step of generating an overall connection offset assessment.

[0166] In a preferred embodiment of the present invention, based on the comprehensive result of each offset influence value, connection offset data is generated to characterize the degree of impact of structural replacement on the timing of subsequent batches, specifically including:

[0167] After sequentially reading the offset impact value for each process step, it is weighted according to the type and severity of the offset impact. For example, a delayed offset that would cause the entire batch to fail to execute as planned can be assigned a higher importance, while an offset within the allowable range can be assigned a lower importance. By accumulating different offset levels, a connection offset data that reflects the overall timing impact is formed.

[0168] The connection offset data is ultimately used to determine whether the structural replacement exceeds the allowable scheduling adjustment range. By comparing it with the preset offset threshold, it can be determined whether the replacement operation can be performed, thus establishing an operable judgment standard for the practical application of dynamic scheduling.

[0169] In a preferred embodiment of the present invention, variable structure segment data is embedded into each candidate position in the candidate position set to construct multiple replacement process link structures, including:

[0170] Based on the candidate position set, determine the preceding and following process data corresponding to each candidate position, and generate candidate position boundary data;

[0171] Based on the candidate position boundary data, the variable structure segment data is inserted between the preceding process data and the following process data, and the local process link data is regenerated according to the execution order of the inserted process.

[0172] Based on the local process link data, extract the timing and parameter change information between the processes before and after insertion, and generate local link adjustment data;

[0173] Based on the local link adjustment data, the inserted local process link is merged with the current process link structure information to generate the replaced process link structure data.

[0174] In this embodiment of the invention, by determining the preceding and following processes corresponding to each candidate position based on the candidate position set, the boundary between the processes before and after the insertion of the variable structure segment can be clearly defined, providing accurate link anchors for subsequent structure insertion operations. The variable structure segment is inserted between the processes before and after based on the candidate position boundary data, and a new local process link is generated. This allows the current link to reflect the actual execution order after replacement within the local structure, helping to understand the impact range of the insertion behavior at the structural level. By extracting the timing and parameter changes in the local link, it is possible to identify situations such as time advancement, delay, and changes in process parameters that may be caused by the insertion behavior, ensuring that the adjusted link has a clear record of changes. By merging this change information with the original process link information, a complete replacement process link structure can be constructed, accurately representing the insertion behavior at the structural level and establishing a reliable foundation for subsequent consistency checks, feasibility assessments, and connection offset calculations.

[0175] In a preferred embodiment of the present invention, the preceding process data and subsequent process data corresponding to each candidate position are determined based on the candidate position set, and candidate position boundary data is generated, specifically including:

[0176] First, extract the process position and its upstream and downstream process node information for each candidate position from the candidate position set. For each candidate position, read the end time of the preceding process and the start time of the following process. The end time of the preceding process is usually determined by the sum of its execution duration and start time, while the start time of the following process is determined based on the end time of the preceding process and the process dependencies of the production line.

[0177] Then, combining the specific process node information of the candidate position, boundary data for that candidate position is generated by calculating the end time of the preceding process and the start time of the following process. The boundary data contains the maximum allowable insertion time range for that process node, that is, the time interval between the end time of the preceding process and the start time of the following process, which will serve as the time reference boundary when inserting subsequent variable structure segments.

[0178] In a preferred embodiment of the present invention, variable structure segment data is inserted between preceding and subsequent process data based on candidate position boundary data, and local process link data is regenerated according to the execution order of the inserted subsequent process, specifically including:

[0179] Based on the generated candidate position boundary data, the specific insertion position of the variable structure segment is determined, and the variable structure segment data is embedded between the preceding and following operations. At this point, it is necessary to ensure that the inserted structure segment does not cause timing conflicts between operations. Specifically, before insertion, it is necessary to check whether the execution time of the variable structure segment is compatible with the time interval between the end time of the preceding operation and the start time of the following operation.

[0180] After insertion, the data structure of the local process link is updated, and the start and end times of each process are recalculated. The inserted process will use the end time of the preceding process as its start time, and the new end time will be determined by the execution duration of the process. This process ensures that the local link data can reflect the execution order of the processes after the inserted structure segment, thereby maintaining the timing consistency of the process link.

[0181] In a preferred embodiment of the present invention, based on local process link data, timing variation information and parameter variation information between processes before and after insertion are extracted to generate local link adjustment data, specifically including:

[0182] After inserting the variable structure segment, the start and end times of the processes before and after the insertion are extracted from the local process link data, and the timing changes of the processes before and after the insertion are calculated. For example, the end time of the preceding process is delayed or advanced, and the start time of the subsequent process is advanced or delayed. For each process, the time offset caused by the insertion of the variable structure segment is calculated, and the impact of this offset on the process execution sequence, equipment usage, and the overall production timeline is recorded.

[0183] Simultaneously, it is necessary to extract parameter changes resulting from structural replacements or adjustments. For example, have temperature or pressure requirements changed? Are the parameter changes within the equipment's tolerance range? Or do they affect product quality? These results of time-series changes and parameter variations are then merged to generate local process adjustment data, serving as the basis for subsequent process updates.

[0184] In a preferred embodiment of the present invention, based on the local link adjustment data, the inserted local process link and the current process link structure information are merged to generate the replaced process link structure data, specifically including:

[0185] The local process link adjustment data is merged with the non-replacement parts of the original process link structure. First, it is confirmed that the transition between the new process and the original process link is smooth, ensuring that the insertion operation will not cause timing or parameter conflicts between the preceding and following processes. Then, based on the new local process link data, the time axis, process dependencies, and equipment load in the entire process link structure are updated.

[0186] All updated process data, adjusted process start and end times, equipment occupancy times, and replaced process sequences are integrated into the replaced process link structure data. This data reflects the updated status of the entire production link after the insertion of the variable structure segment, providing a basis for subsequent scheduling calculations and production scheduling.

[0187] In a preferred embodiment of the present invention, process sequence consistency checks, process parameter continuity checks, and equipment load balancing checks are performed on each replaced process link structure to generate corresponding consistency check results, including:

[0188] Based on the replaced process link structure data, extract the preceding process, following process, and necessary sequence constraint information for each process, perform sequence comparison processing, and generate process sequence consistency data.

[0189] Based on the replaced process link structure data, extract the temperature parameters, execution parameters and equipment setting parameters of each process, compare the parameter change range and parameter stability requirements of adjacent processes, and generate process parameter continuity data.

[0190] Based on the replaced process link structure data, extract the equipment occupancy time period and equipment usage requirements for each process, accumulate the equipment load status in each time period, and generate equipment load balancing data.

[0191] Based on the process sequence consistency data, process parameter continuity data, and equipment load balance data, constraint satisfaction calculation is performed to generate consistency check result data.

[0192] In this embodiment of the invention, by extracting the preceding, succeeding, and sequence constraint information of each process in the replaced process chain structure, it is possible to confirm whether the replacement operation disrupts the original process sequence, providing a basis for ensuring process execution dependencies. By comparing the temperature parameters, execution parameters, and equipment setting parameters of adjacent processes, it is possible to determine whether the replacement results in excessive parameter spans or failure to meet parameter stability requirements, thereby verifying the stability of parameter connections between processes. By extracting equipment occupancy periods and equipment usage demands, and calculating equipment load conditions at different time periods, it is possible to identify potential high or low load issues caused by structural replacement, providing a reference for the rational scheduling of production equipment. Finally, by processing the sequence consistency data, parameter continuity data, and load balancing data for constraint satisfaction, a validity result can be generated to indicate whether the replaced process chain structure meets execution requirements, providing a basis for selecting replacement schemes and avoiding structural replacements that may lead to conflicts or resource unavailability.

[0193] In a preferred embodiment of the present invention, based on the replaced process link structure data, the preceding process, following process, and necessary sequence constraint information of each process are extracted, and sequence comparison processing is performed to generate process sequence consistency data, specifically including:

[0194] First, the preceding and following processes for each process are extracted from the post-replacement process chain structure. A preceding process is a process that must be completed before the current process, while a following process is a process that can only be executed after the current process is completed. For each process, the execution order of its preceding and following processes is checked to ensure it meets production requirements based on the dependencies between processes.

[0195] Next, the dependencies between each process are compared to check for any violations of sequence constraints. For example, if a process's predecessor is not completed, but the current process is scheduled to execute, it is considered an inconsistency in sequence and will be marked. During the comparison process, any violation of the process sequence will trigger a warning message and be marked as a process that does not conform to the schedule.

[0196] Finally, process sequence consistency data is generated, which includes information such as whether each process meets the sequence constraints and whether there are scheduling conflicts between preceding and subsequent processes, providing a basis for evaluating the effectiveness of subsequent process links.

[0197] In a preferred embodiment of the present invention, based on the replaced process link structure data, the temperature parameters, execution parameters, and equipment setting parameters of each process are extracted. The parameter variation range and parameter stability requirements of adjacent processes are compared and processed to generate process parameter continuity data, specifically including:

[0198] First, extract the temperature control parameters, execution parameters (such as pressure, time, and formula), and equipment setting parameters (such as equipment operating speed and heating temperature) for each process in the replacement process chain structure. These parameters affect the execution effect of each process, so it is necessary to ensure that the parameter changes between adjacent processes do not exceed the tolerance range of the equipment and process.

[0199] Then, compare the parameter changes between adjacent processes. For example, if the temperature of a certain process is set to 200°C, and the next process requires a temperature of 220°C, the temperature change range needs to be calculated, and it needs to be determined whether the range is within the range that the equipment can withstand. If the temperature change range is too large, additional stabilization time or adjustment time is required to ensure process stability.

[0200] For each adjacent process, the magnitude of changes in its execution parameters and equipment parameters will be compared to check whether the process requirements are met, and process parameter continuity data will be generated, including whether there are any cases of discontinuous parameter changes or non-compliance with the specified range.

[0201] In a preferred embodiment of the present invention, based on the replaced process link structure data, the equipment occupancy time period and equipment usage demand of each process are extracted, and the load status of the equipment in each time period is accumulated to generate equipment load balancing data, specifically including:

[0202] First, for each process in the replaced process chain structure, extract the time period information of the equipment it occupies. This includes the start and end times of each process, equipment type, and equipment running time. Based on the equipment requirements of the process, combine the equipment usage requirements with the equipment occupancy time periods to generate the load information of each device in different time periods.

[0203] Next, the equipment load is accumulated. When multiple processes use the same equipment, the usage time of each process is accumulated to calculate the equipment load for each time period. For example, if two processes use the same equipment for different time periods, the total load of the equipment over the entire time period is calculated.

[0204] By assessing the load conditions of multiple devices, equipment load balancing data is generated to check for equipment overload or excessively long idle times. If the equipment load exceeds its maximum capacity, the equipment scheduler will be notified to make adjustments to ensure the efficient and stable operation of the production line.

[0205] In a preferred embodiment of the present invention, constraint satisfaction calculation is performed based on process sequence consistency data, process parameter continuity data, and equipment load balance data to generate consistency check result data, specifically including:

[0206] After obtaining process sequence consistency data, process parameter continuity data, and equipment load balancing data, these data will be comprehensively processed to assess the overall consistency of the process chain. First, based on the sequence consistency data, it will be determined whether there are any sequence conflicts between processes. Then, combined with the process parameter continuity data, it will be checked whether the parameter variations in each process are within the allowable range and whether they will affect the stability of the overall process. Finally, based on the equipment load balancing data, the usage of the equipment will be assessed to ensure that the equipment load does not exceed its maximum capacity during any given time period.

[0207] These assessment results generate consistency check data, which includes whether the sequence consistency of each process, parameter continuity, and equipment load meet preset requirements. If any inconsistencies are found, corresponding warning messages will be generated to prompt production schedulers to make adjustments.

[0208] Embodiments of the present invention also provide a dynamic scheduling management system for codfish crisp biscuit processing orders, the system comprising:

[0209] The basic scheduling data acquisition module is used to acquire the order characteristic information of the target order, the current process link structure information, and the planned start time and process sequence constraint information of subsequent batches, and generate basic scheduling data.

[0210] The variable structure segment generation module is used to identify the process switching waiting time from the current process link structure information based on the basic scheduling data, extract food processing switching feature data, decompose the switching waiting time into a structure, and generate variable structure segment data that includes temperature adjustment, parameter stability, food-specific cleaning ordered sub-segments and structural coupling attributes.

[0211] The structural compatibility condition generation module is used to construct the target process requirement chain based on the order feature information of the target order, and associate the target process requirement chain with the variable structure segment data to generate structural compatibility condition data.

[0212] The structural replacement feasibility calculation module is used to embed variable structural segment data into multiple candidate positions of the current process link structure information based on structural compatibility condition data, perform structural replacement calculation, and generate structural replacement feasibility data through triple checks of process sequence consistency, food processing key parameter continuity, and food-specific equipment load balance.

[0213] The connection offset calculation module is used to filter target structure replacement schemes that meet preset conditions based on the structural replacement feasibility data, and to perform connection offset calculation based on the target structure replacement scheme and the planned start time and process sequence constraint information of subsequent batches, and generate connection offset data.

[0214] The scheduling update module is used to generate an update process link structure based on the target structure replacement scheme when the connection offset data is within the preset offset threshold, and then expand the update process link structure into an update scheduling time axis to generate the update scheduling result.

[0215] The order insertion prohibition module is used to generate an order insertion prohibition prompt message and maintain the original scheduling result when the connection offset data is outside the preset offset threshold.

[0216] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0217] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0218] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0219] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for dynamic scheduling management of codfish crisp biscuit processing orders, characterized in that, The method includes: acquiring order feature information of the target order, current process link structure information, and planned start time and process timing constraint information of subsequent batches to generate basic scheduling data; based on the basic scheduling data, identifying process switching waiting time from the current process link structure information, extracting food processing switching feature data, and structurally decomposing the switching waiting time to generate variable structure segment data containing temperature adjustment, parameter stabilization, food-specific cleaning ordered sub-segments, and structural coupling attributes; constructing the target process demand chain based on the target order's order feature information, and associating the target process demand chain with the variable structure segment data to generate structural compatibility condition data; and embedding the variable structure segment data into the current process link structure information based on the structural compatibility condition data. For each candidate position, a structural replacement calculation is performed. This involves a triple check of process sequence consistency, continuity of key food processing parameters, and load balance of food-specific equipment to generate structural replacement feasibility data. Based on this data, target structural replacement schemes that meet preset conditions are selected. Then, connection offset calculations are performed based on the target structural replacement scheme and the planned start time and process sequence constraints of subsequent batches, generating connection offset data. When the connection offset data is within a preset offset threshold, an updated process link structure is generated based on the target structural replacement scheme. This updated process link structure is then expanded into an updated scheduling timeline, generating an updated scheduling result. When the connection offset data is outside the preset offset threshold, a "prohibit order insertion" message is generated, and the original scheduling result is maintained.

2. The method for dynamic scheduling management of codfish crisp biscuit processing orders according to claim 1, characterized in that, Based on the basic scheduling data, the process switching waiting time is identified from the current process link structure information. Food processing switching feature data is extracted, and the switching waiting time is decomposed into a structured form to generate variable structure segment data containing ordered sub-segments for temperature adjustment, parameter stabilization, food-specific cleaning, and structural coupling attributes. This includes: extracting equipment temperature status information, process parameter switching rule information, and equipment cleaning requirement information from the current process link structure information based on the basic scheduling data to generate food processing switching feature data; calculating the temperature adjustment duration required for temperature status changes, the parameter stabilization duration required for process parameter switching, and the preparation duration required for equipment cleaning based on the food processing switching feature data to generate multiple original sub-waiting time periods; arranging these original sub-waiting time periods according to their occurrence order and position, based on the equipment temperature recovery order, parameter convergence order, and cleaning operation order, to generate an ordered sub-segment sequence with time sequence identifiers; and matching the scope of each sub-segment with the time sequence connection relationship of adjacent processes based on the ordered sub-segment sequence and the inter-process connection relationship in the current process link structure information to generate variable structure segment data containing structural coupling attributes.

3. The method for dynamic scheduling management of codfish crisp biscuit processing orders according to claim 1, characterized in that, Based on the order feature information of the target order, a target process requirement chain is constructed, and the target process requirement chain is associated with variable structure segment data to generate structural compatibility condition data. This includes: extracting temperature curve requirements, process execution sequence requirements, and equipment occupancy requirements from the order feature information of the target order to generate a target process parameter set; combining the execution sequence, parameter dependencies, and equipment dependencies of each process based on the target process parameter set to generate target process requirement chain data; extracting temperature characteristics, parameter sensitivity characteristics, and equipment dependency characteristics of each process from the target process requirement chain data and performing structured processing to generate a target process chain structure model; matching the variable structure segment data with the target process chain structure model segment by segment to generate structural adaptation values ​​for each variable structure segment in the target process chain; and evaluating the embeddability of the variable structure segments in the target process chain based on the structural adaptation values ​​to generate structural compatibility condition data for constraining replacement behavior.

4. The method for dynamic scheduling management of codfish crisp biscuit processing orders according to claim 1, characterized in that, Based on structural compatibility data, variable structural segment data is embedded into multiple candidate positions in the current process link structure information. Structural replacement calculations are then performed. Through a triple check of process sequence consistency, food processing key parameter continuity, and food-specific equipment load balance, structural replacement feasibility data is generated. This includes: scanning the current process link structure information based on structural compatibility data to identify variable structural segments that meet the structural compatibility conditions, generating a candidate position set; embedding variable structural segment data into each candidate position in the candidate position set to construct multiple replacement process link structures; performing process sequence consistency checks, process parameter continuity checks, and equipment load balance checks on each replacement process link structure to generate corresponding consistency check results; and evaluating the replacement behavior of each candidate position based on the consistency check results to generate structural replacement feasibility data containing candidate position identifiers and replacement validity.

5. The method for dynamic scheduling management of codfish crisp biscuit processing orders according to claim 1, characterized in that, Based on the feasibility data of structural replacement, target structural replacement schemes that meet preset conditions are selected. Then, connection offset calculations are performed based on the planned start time and process sequence constraints of the target structural replacement schemes and subsequent batches, generating connection offset data. This includes: extracting the replacement location identifiers and structural durations from the target structural replacement schemes that meet preset conditions based on the feasibility data, generating target replacement structural parameter data; calculating the end time of the replacement process link structure based on the target replacement structural parameter data, and generating batch start time difference data based on the planned start time of subsequent batches; comparing the batch start time difference data with the earliest and latest allowed start times of each process in the subsequent batches based on the process sequence constraints, generating offset impact values ​​for each process; and generating connection offset data to characterize the degree of impact of structural replacement on the timing of subsequent batches based on the comprehensive results of the offset impact values.

6. The method for dynamic scheduling management of codfish crisp biscuit processing orders according to claim 4, characterized in that, The variable structure segment data is embedded into each candidate position in the candidate position set to construct multiple replacement process link structures. This includes: determining the preceding and following process data corresponding to each candidate position based on the candidate position set, and generating candidate position boundary data; inserting the variable structure segment data between the preceding and following process data based on the candidate position boundary data, and regenerating local process link data according to the execution order of the inserted processes; extracting the timing and parameter variation information between the inserted and preceding processes based on the local process link data, and generating local link adjustment data; and merging the inserted local process link with the current process link structure information based on the local link adjustment data to generate replacement process link structure data.

7. The method for dynamic scheduling management of codfish crisp biscuit processing orders according to claim 4, characterized in that, For each replaced process link structure, perform process sequence consistency checks, process parameter continuity checks, and equipment load balance checks to generate corresponding consistency check results. This includes: extracting the preceding and succeeding processes and necessary sequence constraints for each process based on the replaced process link structure data, performing sequence comparison processing, and generating process sequence consistency data; extracting temperature parameters, execution parameters, and equipment setting parameters for each process based on the replaced process link structure data, comparing the parameter variation amplitude and parameter stability requirements of adjacent processes, and generating process parameter continuity data; extracting equipment occupancy periods and equipment usage requirements for each process based on the replaced process link structure data, accumulating the equipment load situation in each period, and generating equipment load balance data; and performing constraint satisfaction calculation processing based on the process sequence consistency data, process parameter continuity data, and equipment load balance data to generate consistency check result data.

8. A dynamic scheduling management system for codfish crisp biscuit processing orders, characterized in that, The system, applicable to the method described in any one of claims 1 to 7, comprises: a basic scheduling data acquisition module, used to acquire order characteristic information of the target order, current process link structure information, and planned start time and process timing constraint information of subsequent batches, and generate basic scheduling data; a variable structure segment generation module, used to identify process switching waiting time from the current process link structure information based on the basic scheduling data, extract food processing switching characteristic data, perform structured decomposition of the switching waiting time, and generate variable structure segment data including temperature adjustment, parameter stability, food-specific cleaning ordered sub-segments, and structural coupling attributes; a structure compatibility condition generation module, used to construct a target process demand chain based on the order characteristic information of the target order, and associate the target process demand chain with the variable structure segment data to generate structure compatibility condition data; and a structure replacement feasibility calculation module, used to calculate the structure replacement feasibility based on the structure compatibility condition data. The system embeds variable structure segment data into multiple candidate positions in the current process link structure information, performs structure replacement calculations, and generates structure replacement feasibility data through triple checks of process sequence consistency, continuity of key food processing parameters, and load balance of food-specific equipment. The connection offset calculation module filters target structure replacement schemes that meet preset conditions based on the structure replacement feasibility data, and performs connection offset calculations based on the target structure replacement scheme, the planned start time of subsequent batches, and process sequence constraints, generating connection offset data. The scheduling update module generates an updated process link structure based on the target structure replacement scheme when the connection offset data is within a preset offset threshold, expands the updated process link structure into an updated scheduling timeline, and generates an updated scheduling result. The order insertion prohibition module generates a prohibition message and maintains the original scheduling result when the connection offset data is outside the preset offset threshold.

9. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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