Flange production cycle simulation and prediction method and system, program product and medium

Abstract

Provided in the present application are a flange production cycle simulation and prediction method and system, a program product and a medium, relating to the field of production cycle measurement. By means of constructing an initial node and a plurality of production lines in a prediction model and subdividing each production line into a plurality of production nodes connected in series, a simulation model architecture consistent with an actual production process is formed. By means of transferring state information such as remaining raw material quantities and remaining output demands among the production nodes, a constraint and coordination mechanism among the process steps is established, such that the production rhythm of a subsequent process step can be dynamically adjusted accordingly. By means of introducing the concepts of an intermediate production line and a production end node, the start time of intermediate lines is dynamically adjusted, and the material consumption and output conditions of related production nodes are synchronously updated. Such a scheduling method can clearly reflect product dependencies and material constraints among the process steps, thus achieving temporal optimization and takt synchronization of production activities. The accuracy of flange production cycle simulation and prediction can be improved.

Description

A method, system, program, product, and medium for simulating and predicting flange production cycles. Technical Field

[0001] This application relates to the field of production cycle measurement, and more particularly to a method, system, program product and medium for simulating and predicting flange production cycle. Background Technology

[0002] With the continuous development of industrialization, the market demand for flanges is also constantly increasing. Flanges are important components connecting pipes, valves, containers, and other equipment. How to quickly and accurately predict the production cycle of flanges, and thus rationally arrange production plans, has become an urgent problem for flange manufacturers to solve.

[0003] Currently, flange manufacturers typically use production management software such as MRP to predict production cycles. This type of software estimates the completion time of production tasks based on bills of materials and production routes, taking into account material requirements and inventory.

[0004] However, flange production involves multiple complex processes with intricate constraints between different stages. Factors such as capacity balance, material limitations, and equipment status at each stage significantly impact the production cycle. Related production management software, such as MRP, struggles to fully consider the dynamic effects of these factors, making it difficult to accurately predict and quantify the constraint relationships between processes. This ultimately leads to significant discrepancies between forecasts and actual production. Consequently, production planning becomes inefficient, delivery times are extended, and the company's production management and market responsiveness are negatively affected. Summary of the Invention

[0005] This application provides a method, system, program product, and medium for simulating and predicting flange production cycles, which can improve the accuracy of simulating and predicting flange production cycles.

[0006] In the first aspect, this application provides a flange production cycle simulation and prediction method, including: inputting demand information and preparation information into a flange production cycle prediction model to obtain the predicted production cycle; the flange production cycle prediction model includes: an initial node, multiple production lines, each production line including multiple production nodes connected in series, each production node corresponding to a buffer, each production line representing a process step in flange production, and each production node being limited to a time length.

[0007] For the initial node, the demand information and preparation information are broken down into sub-demand information and sub-preparation information corresponding to each production line;

[0008] For the first production node in the production line, it is necessary to obtain the corresponding sub-requirement information and sub-preparation information from the initial node.

[0009] For the first production node of the initial production line; calculate the initial output and raw materials used within the time period based on the corresponding sub-preparation information; store the initial output, remaining raw materials, and remaining initial output requirements in the corresponding buffer; the initial production line corresponds to the first process step of flange production; the remaining raw materials are determined based on the corresponding sub-preparation information and raw materials used; the remaining initial output requirements are determined based on the initial output and the corresponding sub-requirement information.

[0010] For subsequent production nodes of the initial production line, the remaining raw materials and remaining initial output requirements are obtained from the buffer of the previous node. The initial output and raw materials used within the time length are calculated based on the corresponding sub-preparation information. The initial output, remaining raw materials, and remaining initial output requirements are stored in the corresponding buffer.

[0011] Define the end production node of the production line as the node where the remaining initial output demand is zero under the production of the corresponding production node, and arrange all the production nodes of the initial production line on the time axis in sequence.

[0012] For production nodes in intermediate production lines, calculate the intermediate output and required middleware within the time length based on the corresponding sub-preparation information. Based on the type and quantity of required middleware, select a specific production node for the corresponding production line and move the intermediate production line after the specific production node on the timeline. Update the buffers of the specific production node and subsequent production nodes in the corresponding production line, reducing the stored middleware output to the required middleware quantity. Store the middleware output and remaining middleware output requirements in the corresponding buffers. Required middleware refers to the intermediate output or initial output from the production nodes of previous production lines. The type of intermediate output stored in the buffer of a specific production node must match the required middleware type, and the quantity of stored intermediate output must be greater than the quantity of required middleware.

[0013] The final production node is defined as the production node whose output satisfies the demand information stored in the corresponding buffer; the predicted production cycle is the time length between the first production node of the initial production line and the final production node.

[0014] By adopting the above technical solution, and constructing initial nodes, multiple production lines, and subdividing each production line into multiple serial production nodes in the prediction model, a simulation model architecture that closely matches the actual production process is formed. Different production lines represent different process routes or parallel operations in flange production; each production node corresponds to an independent processing step, with a defined processing time and a buffer for storing raw materials and output materials, simulating the material flow, caching, and consumption processes between processes. This architecture can realistically reproduce the entire production process, detailing the time and resource consumption of production activities, and helping to improve the accuracy of predictions. By transmitting status information such as remaining raw materials and remaining output requirements between production nodes, a constraint and coordination mechanism between processes is established, enabling the next process to dynamically adjust its production rhythm, shorten waiting time between processes, and reduce material backlog. The concepts of intermediate production lines and ending production nodes are introduced. Based on the intermediate production line's demand for intermediate components from the preceding lines, the start time of the intermediate line is dynamically adjusted, and the material consumption and output status of relevant production nodes are updated synchronously. This scheduling method clearly demonstrates the sequential and parallel relationships of each process in time, intuitively reflecting product dependencies and material constraints between processes, thus achieving time-series optimization and cycle time synchronization in production activities. This calculation method fully considers the dynamic evolution of production activities, determining task completion time nodes through simulation and avoiding estimation errors caused by simply adding process times. Furthermore, using delivery requirements as a criterion for production completion improves the accuracy of flange production cycle simulation and prediction.

[0015] In conjunction with some embodiments of the first aspect, in some embodiments, the final production end node is defined as the production node whose final output satisfies the demand information stored in the corresponding buffer; before the step of predicting the production cycle as the time length between the first production node of the initial production line and the final production end node, the method further includes:

[0016] Several rest periods are determined based on the rest arrangements in the actual production process; each rest period includes a start time and an end time.

[0017] Several rest periods are inserted into the timeline. For each inserted rest period, the start and end times of all production nodes after the end time of the rest period are shifted backward by the duration of the rest period.

[0018] By adopting the above technical solution, and by obtaining information on equipment maintenance and personnel rest arrangements during actual production, a series of rest periods on the timeline are determined and inserted into the original simulation and prediction process. This allows for the dynamic reflection of the actual impact of equipment and personnel rest on the production rhythm during production cycle simulation, making the prediction results closer to actual production.

[0019] In conjunction with some embodiments of the first aspect, in some embodiments, the steps of calculating the initial output and raw materials used within a time length based on the corresponding sub-preparation information are specifically as follows: determining several rest periods based on the rest arrangements in the actual production process;

[0020] Determine the effective duration within the time frame; the effective duration is the total time frame minus the rest periods within the time frame.

[0021] Calculate the initial output and raw materials used within the effective time frame based on the corresponding sub-preparation information.

[0022] By adopting the above technical solution, the effective production time within each time period can be calculated by obtaining rest schedules. Based on the effective time, the consumption of raw materials and product output can be quantified more accurately, avoiding the problem of overestimating actual production capacity and making the prediction results closer to the actual production.

[0023] In conjunction with some embodiments of the first aspect, in some embodiments, before the steps of calculating the initial output and raw materials used within a time length based on the corresponding sub-preparation information, the method further includes: refreshing the sub-preparation information at preset time intervals;

[0024] Determine whether the quantity of available raw materials in the sub-preparation information is not less than the quantity of raw materials used;

[0025] If the amount of raw materials used is not less than the amount of raw materials used, execute the step of storing the initial output, remaining raw materials, and remaining initial output requirements into the corresponding buffer.

[0026] If the quantity of available raw materials is less than the quantity of raw materials to be used, proceed to the step of determining whether the quantity of available raw materials in the preparation information is not less than the quantity of raw materials to be used.

[0027] By adopting the above technical solution, the latest status of raw material inventory can be obtained in real time, dynamically incorporating factors such as supplier delivery progress and procurement arrival cycles, making raw material conditions a dynamic variable affecting production cycle forecasting. By comparing raw material demand and inventory in real time, raw material availability can be determined, avoiding the problem of inflated forecasts caused by material constraints on production capacity. Output simulations for subsequent production nodes will only be conducted when raw materials are sufficient, making the forecast results more reliable.

[0028] In conjunction with some embodiments of the first aspect, in some embodiments, the requirements information includes the type, quantity, and delivery deadline of the flange.

[0029] By adopting the above technical solutions, the flange type information can help quickly identify the required raw material types, processing routes, etc. The flange quantity information directly determines the scale of the production task and is an important basis for determining the raw material procurement quantity and rationally allocating production capacity. The delivery deadline is included in the demand information to guide production scheduling optimization and process coordination.

[0030] In conjunction with some embodiments of the first aspect, in some embodiments, after inputting demand information and preparation information into the flange production cycle prediction model to obtain the predicted production cycle, the method further includes: if the predicted production cycle exceeds the delivery deadline, adjusting the relevant parameters in the flange production cycle prediction model, and inputting the demand information and preparation information into the flange production cycle prediction model according to the adjusted relevant parameters to obtain a new predicted production cycle.

[0031] By adopting the above technical solution, when the predicted production cycle exceeds the delivery deadline, the relevant parameters in the flange production cycle prediction model are adjusted. While maintaining the model framework, key elements are optimized to unlock production potential and shorten the production cycle. Simultaneously, the prediction model is run again after parameter adjustment to obtain a new predicted production cycle. By comparing the old and new prediction results, the effectiveness of the parameter adjustment is evaluated, and the parameters are further fine-tuned. This process is iterated multiple times until the production cycle meets the delivery deadline. Thus, dynamic optimization of production organization is achieved while ensuring delivery, thereby achieving the goal of shortening the cycle.

[0032] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of storing the initial output, remaining raw materials, and remaining initial output requirements into the corresponding buffer, the method further includes: determining the unqualified products in the initial output according to preset quality inspection rules; removing the unqualified products from the corresponding buffer and updating the corresponding buffer.

[0033] By adopting the above technical solutions, non-conforming products can be isolated from the production process and material flow, which can reflect the impact of quality status on the production rhythm in real time, making the prediction results closer to the actual production.

[0034] Secondly, this application provides a flange production cycle simulation and prediction system, which includes: one or more processors and a memory; the memory is coupled to one or more processors, and the memory is used to store computer program code, which includes computer instructions, and the one or more processors call the computer instructions to cause the flange production cycle simulation and prediction system to perform the method described in the first aspect and any possible implementation thereof.

[0035] Thirdly, this application provides a computer program product containing instructions that, when run on a flange production cycle simulation and prediction system, cause the flange production cycle simulation and prediction system to perform the method described in the first aspect and any possible implementation thereof.

[0036] Fourthly, this application provides a computer-readable storage medium including instructions that, when executed on a flange production cycle simulation and prediction system, cause the flange production cycle simulation and prediction system to perform the method described in the first aspect and any possible implementation thereof.

[0037] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0038] 1. By constructing initial nodes, multiple production lines, and subdividing each production line into multiple serial production nodes in the prediction model, a simulation model architecture that closely matches the actual production process is formed. Different production lines represent different process routes or parallel operations in flange production; each production node corresponds to an independent processing step, with a defined processing time and a buffer for storing raw materials and output materials, simulating the material flow, caching, and consumption processes between processes. This architecture can realistically reproduce the entire production process, detailing the time and resource consumption of production activities, thus improving prediction accuracy. By transmitting status information such as remaining raw materials and remaining output requirements between production nodes, a constraint and coordination mechanism between processes is established, enabling the next process to dynamically adjust its production rhythm, shorten waiting time between processes, and reduce material backlog. The concepts of intermediate production lines and ending production nodes are introduced. Based on the intermediate production line's demand for intermediate components from the preceding lines, the start time of the intermediate line is dynamically adjusted, and the material consumption and output status of related production nodes are updated synchronously. This scheduling method clearly demonstrates the sequential and parallel relationships of each process in time, intuitively reflecting product dependencies and material constraints between processes, thus achieving time-series optimization and cycle time synchronization in production activities. This calculation method fully considers the dynamic evolution of production activities, determining task completion time nodes through simulation and avoiding estimation errors caused by simply adding process times. Furthermore, using delivery requirements as a criterion for production completion improves the accuracy of flange production cycle simulation and prediction.

[0039] 2. By obtaining data on equipment maintenance and personnel rest schedules during actual production, a series of rest periods on the timeline are identified and inserted into the existing simulation and prediction process. This allows for dynamic reflection of the actual impact of equipment and personnel rest on production rhythm during production cycle simulation, making the prediction results more closely resemble actual production.

[0040] 3. It can obtain the latest status of raw material inventory in real time, dynamically incorporating factors such as supplier delivery progress and procurement arrival cycles, making raw material conditions a dynamic variable affecting production cycle forecasting. By comparing raw material demand and inventory in real time, it can determine raw material availability and avoid the problem of inflated forecasts caused by production capacity being limited by material constraints. Output simulations for subsequent production nodes will only be conducted when raw materials are sufficient, making the forecast results more reliable. Attached Figure Description

[0041] Figure 1 is a flowchart illustrating a flange production cycle simulation and prediction method in an embodiment of this application.

[0042] Figure 2 is a schematic diagram of an exemplary application scenario of the flange production cycle simulation and prediction method in the embodiments of this application;

[0043] Figure 3 is a schematic diagram of another exemplary application scenario of the flange production cycle simulation and prediction method in the embodiments of this application;

[0044] Figure 4 is another flowchart illustrating the flange production cycle simulation and prediction method in this application embodiment;

[0045] Figure 5 is a schematic diagram of an exemplary hardware structure of the flange production cycle simulation and prediction system in an embodiment of this application. Detailed Implementation

[0046] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0047] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0048] Please refer to Figure 1, which is a flowchart illustrating a flange production cycle simulation and prediction method in an embodiment of this application.

[0049] S101. Input the demand information and preparation information into the flange production cycle prediction model to obtain the predicted production cycle. The flange production cycle prediction model includes: initial node, multiple production lines, each production line includes multiple production nodes connected in series, each production node has a corresponding buffer, each production line represents a process step in flange production, and each production node is limited to a time length.

[0050] Demand information refers to the various requirements for flange products in customer orders, such as product model, specifications, quantity, delivery date, etc., which are used to represent the goals and constraints of the production task. Of course, these constraints refer to the general constraints.

[0051] Preparation information refers to the production resource status that an enterprise prepares to fulfill flange orders, including available personnel, equipment, materials, and process parameters. It represents the execution conditions and capabilities of the production task. It's important to note that preparation information includes not only actual, observable production resources such as personnel, equipment, and materials, but also some virtual, calculated indicators. These indicators are estimated based on historical data and experience, reflecting the efficiency and consumption levels of production resources. For example, the output rate per unit time per unit of equipment is the average number of flanges that a certain type of equipment can process within a given time period; another example is the material consumption quota per unit product, which is the average quantity of various materials required to produce one flange product, reflecting the material utilization level and cost of the production process. These virtual, calculated indicators are key performance parameters summarized and refined by enterprises from long-term production practices, and they have significant reference value for evaluating and predicting flange production cycles.

[0052] In some embodiments, the requirements information includes the type, quantity, and delivery deadline of the flange.

[0053] It is evident that flange type information can help quickly identify the required raw material types and processing routes. Flange quantity information directly determines the scale of the production task and is an important basis for determining the raw material procurement quantity and rationally allocating production capacity. Incorporating delivery deadlines into demand information can guide production scheduling optimization and process coordination.

[0054] The flange production cycle prediction model is introduced below:

[0055] S102. For the initial node, the demand information and preparation information are broken down into sub-demand information and sub-preparation information corresponding to each production line.

[0056] After obtaining the total demand and preparation information for flange orders, it is necessary to break it down into individual process steps or production lines so that each process can formulate a production execution plan based on its assigned task requirements and resource status. Specifically, at the initial node of the flange production cycle forecasting model, the demand information for the entire flange order, such as the total order quantity and delivery date, is broken down into sub-demand information for each production line, such as the number of parts to be processed and their specifications allocated to the process represented by each line. At the same time, the company's total preparation information, such as the total number of available personnel, the number of equipment units, and material inventory, is also broken down into sub-preparation information for each line, such as the number of available personnel, equipment, and materials allocated to each line.

[0057] In some embodiments, based on the process characteristics represented by each production line, the total demand and total preparation information are split into sub-information for each line according to a pre-set allocation ratio of demand information.

[0058] It should be noted that in some embodiments, the initial node also needs to take time into account, so the predicted production cycle is the length of time between the initial node and the final production end node.

[0059] In some other embodiments, the initial node does not need to consider time, so the predicted production cycle is the time length between the first production node of the initial production line and the final production node. In this embodiment, the initial node and the first production node of the production line are synchronized on the time axis. Of course, the above two embodiments can be selected according to the actual situation, and no limitation is made here.

[0060] A buffer is a data structure that stores data on the flow of materials between production nodes, and is usually implemented using a series of variables or objects.

[0061] S103. For the first production node of the production line, it is necessary to obtain the corresponding sub-requirement information and sub-preparation information from the initial node.

[0062] It should be noted that in some embodiments where the initial node does not require time consideration, the timing of the first production node of each production line obtaining the corresponding sub-requirement and sub-preparation information from the initial node is not synchronized. This time difference mainly stems from the following reasons: the start-up times of different production lines vary, or some production lines depend on the output of other lines to begin operation. Therefore, the initial node calculates and provides the corresponding sub-requirement and sub-preparation information separately based on the specific circumstances and dependencies of each production line. Only when this information is ready will the first production node of the relevant production line obtain it.

[0063] Of course, in some embodiments where the initial node needs to be considered, the time for the first production node of each production line to obtain the corresponding sub-requirement information and sub-preparation information from the initial node can also be synchronized.

[0064] Specifically, this step requires starting from the first node of the production line, the initial node, and obtaining the input requirements for that node, including the types and quantities of raw materials and components needed, as well as the preparation requirements for production resources such as equipment and personnel. This information will serve as the basis and starting point for subsequent production node scheduling optimization.

[0065] In some embodiments, the first production node of the production line obtains the corresponding sub-requirement information and sub-preparation information from the initial node, which means that the production node also has sub-requirement information and sub-preparation information from the initial node.

[0066] S104. For the first production node of the initial production line; calculate the initial output and raw materials used within the time length based on the corresponding sub-preparation information; store the initial output, remaining raw materials, and remaining initial output requirements in the corresponding buffer. The initial production line corresponds to the first process step of flange production; the remaining raw materials are determined based on the corresponding sub-preparation information and raw materials used, and the remaining initial output requirements are determined based on the initial output and the corresponding sub-requirement information.

[0067] In one exemplary embodiment, the typical production process of a carbon steel flange includes forging, turning, drilling, milling, heat treatment, surface treatment, inspection, and marking. In this embodiment, there are a total of 8 production lines, with each process step corresponding to one production line.

[0068] The initial production line represents the first process step in producing a product, such as forging. The initial output is, for example, a rough disc-shaped forging, using raw materials such as carbon steel. The remaining raw materials represent the quantity of raw materials that have not yet been used after the completion of this node, such as carbon steel. The remaining initial output requirement represents the quantity of initial products that still need to be completed after this node to meet customer demand, such as a rough disc-shaped forging.

[0069] Please refer to Figure 2, which is a schematic diagram of an exemplary application scenario of the flange production cycle simulation and prediction method in this application embodiment;

[0070] In the diagram, production nodes A1 to An represent all production nodes corresponding to forging. Production node A1 is the first production node, production nodes A2 to An-1 are subsequent production nodes, and production node An is the final production node.

[0071] In some embodiments, firstly, based on the initial node's sub-preparation information, such as the number of devices and processing efficiency, the estimated quantity of initial products that the node can complete within the planned time frame (e.g., one working day), i.e., the initial output, and the quantity of raw materials consumed, are estimated. Then, the initial output, surplus raw materials, and remaining initial product demand are stored in a buffer. The surplus raw materials are obtained by subtracting the usage from the raw material quantity in the sub-preparation information, and the remaining initial output demand is obtained by subtracting the initial output of this node from customer demand. These data will serve as input for the next node.

[0072] In some embodiments, the method further includes: S1041, determining non-conforming products in the initial output according to preset quality inspection rules;

[0073] Pre-defined quality inspection rules refer to a set of quality judgment standards and methods that an enterprise formulates in advance based on product characteristics, regulations and standards, customer requirements, etc., such as the estimated pass rate. Non-conforming products refer to the portion of the output that does not meet the various indicator limits and conditions stipulated in the quality inspection rules.

[0074] S1042. Remove the non-conforming products from the corresponding buffer and update the corresponding buffer.

[0075] In some embodiments, the system calculates the number of defective products based on the pass rate and removes the number of qualified products from the corresponding output buffer.

[0076] It is evident that isolating non-conforming products from the production process and material flow can reflect the impact of quality status on production rhythm in real time, making the prediction results closer to the actual production situation.

[0077] S105. For subsequent production nodes of the initial production line, obtain the remaining raw materials and remaining initial output requirements from the buffer of the previous node, calculate the initial output and raw materials used within the time length based on the corresponding sub-preparation information, and store the initial output, remaining raw materials, and remaining initial output requirements into the corresponding buffer.

[0078] It should be noted that the role of subsequent production nodes is: the initial number of products that can be completed at a given time level within the planned time frame (such as a working day).

[0079] In some embodiments, for each subsequent node, this step first retrieves three key data points from the buffer corresponding to the previous node: the available raw material inventory of the node, i.e., the remaining raw materials of the previous node; the initial product quantity that the node needs to produce, i.e., the remaining initial output requirement of the previous node; and the initial output quantity actually completed by the previous node. Then, based on the node's own sub-preparation information, such as the number of equipment and processing efficiency, similar to S104, the initial output and raw materials used by the node within a specified time are calculated, and the new results are updated in the buffer corresponding to this node for use by the next node.

[0080] By transmitting status information such as remaining raw materials and remaining output requirements between various production nodes, a sub-constraint and coordination mechanism between processes is established.

[0081] S106. Define the end production node of the production line as the node where the remaining initial output demand is zero under the production of the corresponding production node, and arrange all the production nodes of the initial production line on the time axis in sequence.

[0082] The end of production node refers to the last production node in the initial production line that can fulfill the customer order requirements. It should be noted that the customer order requirements here are the requirements after being broken down (initial output requirements). For example, if the customer order requirements are xx carbon steel flanges, then the initial output requirements here are rough disc-shaped forgings.

[0083] It should be noted that the flange production cycle prediction model does not pre-determine the number of flange process steps or the length of the production cycle. Therefore, the model is designed with a flexible structure that can accommodate an unlimited number of production lines and nodes. The system checks each node in the initial production line until it finds a node where production is completed, ensuring that the initial output demand is fully met—that is, the remaining initial output demand for that node is reduced to zero. This node is defined as the end production node, representing the end point of the initial production line; subsequent nodes will be canceled or deleted. After determining the end node, the system arranges all remaining nodes in the initial production line according to the process flow sequence on a left-to-right time axis, with the horizontal width of each node representing its processing time. This visually displays the total duration of the initial production line, as well as the sequence and time allocation of each node.

[0084] S107. For production nodes in intermediate production lines, calculate the intermediate output and required middleware within the time length based on the corresponding sub-preparation information. Based on the type and quantity of required middleware, select a specific production node in the corresponding production line and move the intermediate production line after the specific production node on the time axis. Update the buffers of the specific production node and subsequent production nodes in the corresponding production line to reduce the stored middleware output to the required middleware quantity. Store the middleware output and remaining middleware output requirements in the corresponding buffers. The required middleware is the intermediate output or initial output of the production nodes in the previous production line. The type of intermediate output stored in the buffer of the specific production node is consistent with the required middleware type, and the quantity of stored intermediate output is greater than the quantity of required middleware.

[0085] Intermediate production lines represent the subsequent technological steps in producing a product, such as turning, drilling, milling, heat treatment, surface treatment, inspection, and marking. Intermediate output represents the quantity of intermediate products produced by a specific node in an intermediate production line within a specified time period; for example, for turning, the intermediate output is a precisely sized disc. Demand intermediate parts represent the type and quantity of intermediate products required by a specific node in the initial production line; for example, for turning, the demand intermediate parts are the rough, disc-shaped forgings produced in the preceding forging process. A specific production node refers to a node in the initial production line that can provide the required intermediate products to the intermediate production lines.

[0086] In some embodiments, for each node of the intermediate production line, the quantity of intermediate products that can be produced within a specified time period, as well as the intermediate component demand for a certain node of the initial production line, are first calculated based on its sub-preparation information. Then, a suitable specific node is selected on the initial production line so that it can meet the intermediate component demand of the intermediate production line. To balance materials, the material inventory of the specific node and its subsequent nodes needs to be updated, reducing the intermediate output of the specific node by the amount of intermediate components required. Finally, the entire intermediate production line is shifted to after the specific node, and the new intermediate output, remaining intermediate output demand, and other data are written to the buffer of the corresponding node.

[0087] Please refer to Figure 2, which is a schematic diagram of an exemplary application scenario of the flange production cycle simulation and prediction method in this application embodiment;

[0088] In the diagram, production nodes B1 to Bn represent all production nodes corresponding to the drilling process. Production node B1 is the first production node, production nodes B2 to Bn-1 are subsequent production nodes, and production node Bn is the final production node. Production nodes N1 to Nn represent all production nodes corresponding to the identifier. Production node N1 is the first production node, production nodes N2 to Nn-1 are subsequent production nodes, and production node Nn is the final production node.

[0089] S108. Define the final production node as the production node whose final output meets the demand information stored in the corresponding buffer; predict the production cycle as the time length between the first production node of the initial production line and the final production node.

[0090] In some embodiments, the final production end node refers to the node where, after inserting an intermediate production line, the customer order demand is fully met. It should be noted that the customer order demand here refers to the original demand, such as: carbon steel flange.

[0091] In some embodiments, the system checks the buffer inventory data of all nodes on all production lines until it finds a node where, upon completion of production at that node, the quantity of products meets customer demand; that is, the final output of that node equals customer demand. This node is marked as the final production end node, representing the endpoint of all production activities. Simultaneously, the time difference between this node and the initial node is calculated as the predicted production cycle. A complete production scheduling plan from raw materials to finished products is thus generated.

[0092] As can be seen, by constructing initial nodes, multiple production lines, and subdividing each production line into multiple serial production nodes in the prediction model, a simulation model architecture that closely matches the actual production process is formed. Different production lines represent different technological routes or parallel processes in flange production; each production node corresponds to an independent processing step, with a defined processing time and a buffer for storing raw materials and output materials, simulating the material flow, caching, and consumption processes between processes. This architecture can realistically reproduce the entire production process, detailing the time and resource consumption of production activities, thus improving prediction accuracy. By transmitting status information such as remaining raw materials and remaining output requirements between production nodes, a constraint and coordination mechanism between processes is established, enabling the next process to dynamically adjust its production rhythm, shorten waiting time between processes, and reduce material backlog. The concepts of intermediate production lines and ending production nodes are introduced. Based on the intermediate production line's demand for intermediate components from the preceding lines, the start time of the intermediate line is dynamically adjusted, and the material consumption and output status of relevant production nodes are updated synchronously. This scheduling method clearly demonstrates the sequential and parallel relationships of each process in time, intuitively reflecting product dependencies and material constraints between processes, thus achieving time-series optimization and cycle time synchronization in production activities. This calculation method fully considers the dynamic evolution of production activities, determining task completion time nodes through simulation and avoiding estimation errors caused by simply adding process times. Furthermore, using delivery requirements as a criterion for production completion improves the accuracy of flange production cycle simulation and prediction.

[0093] In some embodiments, after step S108, the method further includes: S109, if the predicted production cycle exceeds the delivery deadline, adjusting the relevant parameters in the flange production cycle prediction model, inputting the demand information and preparation information into the flange production cycle prediction model according to the adjusted relevant parameters, and obtaining a new predicted production cycle.

[0094] Among them, relevant parameters refer to key variables used in the prediction model to characterize production features, such as the number of personnel and the number of equipment.

[0095] In some embodiments, the system automatically extracts the delivery date field from the order information and compares it with the predicted end date of the production cycle. If the predicted completion date is later than the customer's requested delivery date, it indicates that the predicted production cycle has exceeded the delivery deadline and cannot be delivered on time. To eliminate the supply-demand time lag, the relevant parameters of the forecasting model must be adjusted to better align with the actual production rhythm. After adjustment, the order demand information and the latest preparation information are input into the model again to obtain a new predicted production cycle. Through parameter tuning and iterative forecasting, the gap between the predicted cycle and the delivery date can be continuously narrowed until the delivery date requirement is met. This mechanism enables dynamic matching of delivery date and production capacity, improving the reliability of order commitments.

[0096] It should be noted that the flange production cycle prediction model is not an actual production scheduling plan, but rather a reference for users.

[0097] As can be seen, when the predicted production cycle exceeds the delivery deadline, adjusting the relevant parameters in the flange production cycle prediction model optimizes key elements of the model while maintaining the model framework, tapping into production potential and shortening the production cycle. Simultaneously, after parameter adjustment, the prediction model is run again to obtain a new predicted production cycle. By comparing the old and new prediction results, the effectiveness of the parameter adjustment is evaluated, and the parameters are further fine-tuned. This process is iterated multiple times until the production cycle meets the delivery deadline, thereby achieving dynamic optimization of production organization while ensuring delivery, and ultimately achieving the goal of shortening the cycle.

[0098] In actual use, production equipment requires regular downtime for maintenance, and employees also need scheduled rest periods. However, the above-described embodiment did not consider the impact of equipment and employee rest on production task completion time when simulating and predicting production cycles. This leads to a certain deviation between the predicted production cycle and the actual situation, limiting its guiding significance in actual production scheduling.

[0099] Two methods are provided below to solve the above problem. The first method is suitable for situations where the time limit for production nodes is relatively short.

[0100] In some embodiments, the method further includes the following steps prior to step S108:

[0101] 1) Determine several rest periods based on the actual rest arrangements during the production process; each rest period includes a start time and an end time.

[0102] Among them, rest arrangements refer to the planned rest time for employees determined in the actual production process based on factors such as laws and regulations, company policies, and production needs. A rest period refers to a complete rest time interval, including the start and end times.

[0103] 2) Insert several rest periods into the timeline. For each inserted rest period, the start and end times of all production nodes after the end time of the rest period are shifted backward by the duration of the rest period.

[0104] Please refer to Figure 3, which is a schematic diagram of another exemplary application scenario of the flange production cycle simulation and prediction method in this application embodiment;

[0105] In the diagram, rest periods are included between production nodes A1 and A2, and between production nodes A2 and A3, etc. It's important to note that Figure 3 illustrates an ideal scenario for ease of understanding. In actual production, rest periods may occur within production nodes, dividing a single production node into multiple segments. This segmentation reflects the work patterns in a real production environment, where rest periods may interrupt continuous production activities.

[0106] After obtaining several rest periods, to integrate these rest periods into the overall production schedule, they need to be inserted into the existing scheduling timeline. Specifically, for each rest period to be inserted, its position on the timeline is located, and its start and end times are marked on the corresponding scale. Simultaneously, since inserting a rest period consumes a certain amount of time, all production activities after the rest period ends must be shifted. Therefore, for all production nodes after the rest period's end time, their original start and end times need to be postponed by a duration equal to the rest period's length. This postponement ensures that, after considering the rest factor, the start and end times of subsequent production activities do not conflict with the rest periods.

[0107] As can be seen, by obtaining information on equipment maintenance and personnel rest arrangements during actual production, a series of rest periods on the timeline can be identified and inserted into the original simulation and prediction process. This allows the actual impact of equipment and personnel rest on the production rhythm to be dynamically reflected during production cycle simulation, making the prediction results closer to actual production.

[0108] The second method is suitable for situations where the time limit for production nodes is relatively long.

[0109] In some embodiments, the step S104, which calculates the initial output and raw materials used within the time period based on the corresponding sub-preparation information, specifically involves:

[0110] S1041. Determine several rest periods based on the rest arrangements in the actual production process;

[0111] S1042. Determine the effective duration within the time length; the effective duration is the time length minus the rest period within the time length.

[0112] Effective time refers to the actual time that can be used for production, that is, the remaining time after deducting rest time from the total time.

[0113] In some embodiments, the time span covered by the scheduling scheme is first defined, i.e., the length of time between the start and end dates. Then, all rest periods included within this time span are identified. Finally, the total time span is subtracted from the cumulative duration of the rest periods to obtain the effective time span. The sum of the effective time span and the rest periods should equal the total scheduled time span. Only by deducting the impact of rest periods can the actual production capacity of the enterprise be accurately assessed.

[0114] S1043. Calculate the initial output and raw materials used within the effective time period based on the corresponding sub-preparation information.

[0115] It is evident that by obtaining rest schedules and calculating the effective production time within each time period, the consumption of raw materials and product output can be quantified more accurately based on the effective time, thus avoiding the problem of overestimating actual production capacity and making the prediction results closer to actual production.

[0116] In practical applications, flange production requires various raw materials. However, a company's raw material inventory levels are constrained by multiple factors, including procurement funds and supplier delivery times, making it impossible to maintain sufficient inventory at all times. The above example assumes that there are sufficient raw materials in the warehouse and does not consider the impact of raw material shortages on the production cycle. In real-world applications, the arrival time of raw materials is likely to become a key factor affecting the flange production cycle.

[0117] Please refer to Figure 4, which is another flowchart illustrating the flange production cycle simulation and prediction method in this application embodiment;

[0118] Before step S104, which involves calculating the initial output and raw materials used within the time period based on the corresponding sub-preparation information, the method further includes:

[0119] S401. Refresh the sub-preparation information according to the preset time interval;

[0120] Among them, the preset time interval refers to a fixed time interval set in advance according to the needs of production management.

[0121] S402. Determine whether the quantity of available raw materials in the sub-preparation information is not less than the quantity of raw materials used;

[0122] S403. If the output is not less than the raw materials used, execute the steps in step S104 to calculate the initial output and raw materials used within the time length based on the corresponding sub-preparation information.

[0123] S404. If the amount of raw material used is less than the required amount, proceed to step S402.

[0124] As can be seen, the latest status of raw material inventory can be obtained in real time, and factors such as supplier delivery progress and procurement arrival cycles can be dynamically considered, making raw material conditions a dynamic variable affecting production cycle forecasting. By comparing raw material demand and inventory in real time, raw material availability can be determined, avoiding the problem of inflated forecasts caused by production capacity being limited by material constraints. Only when raw materials are sufficient will output simulations for subsequent production nodes be conducted, making the forecast results more reliable.

[0125] The following describes an exemplary flange production cycle simulation and prediction system 500 provided in an embodiment of this application. Figure 5 is a schematic diagram of an exemplary hardware structure of the flange production cycle simulation and prediction system 500 provided in an embodiment of this application.

[0126] In some embodiments, the flange production cycle simulation and prediction system 500 is a computer device or includes a computer device in the flange production cycle simulation and prediction system 500. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0127] Those skilled in the art will understand that the structure shown in Figure 5 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or may combine certain components, or may have different component arrangements.

[0128] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0129] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0130] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0131] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for simulating and predicting flange production cycles, characterized in that, include: Input the demand and preparation information into the flange production cycle prediction model to obtain the predicted production cycle. The flange production cycle prediction model includes: an initial node, multiple production lines, each production line including multiple production nodes connected in series, each production node corresponding to a buffer zone, each production line representing a process step in flange production, and each production node being limited to a time length. For the initial node, the demand information and the preparation information are broken down into sub-demand information and sub-preparation information corresponding to each production line; For the first production node of the production line, it is necessary to obtain the corresponding sub-requirement information and sub-preparation information from the initial node; For the first production node of the initial production line; calculate the initial output and raw materials used within the time length based on the corresponding sub-preparation information; store the initial output, remaining raw materials, and remaining initial output requirements in the corresponding buffer; the initial production line corresponds to the first process step of flange production; the remaining raw materials are determined based on the corresponding sub-preparation information and the raw materials used; the remaining initial output requirements are determined based on the initial output and the corresponding sub-requirement information. For subsequent production nodes of the initial production line, the remaining raw materials and the remaining initial output requirements are obtained from the buffer of the previous node, and the initial output and the raw materials used within the time length are calculated according to the corresponding sub-preparation information; the initial output, the remaining raw materials, and the remaining initial output requirements are stored in the corresponding buffer. The end production node of the production line is defined as the node where the remaining initial output demand is zero under the production of the corresponding production node. All production nodes of the initial production line are arranged on the time axis in sequence. For production nodes in intermediate production lines, the intermediate output and required middleware within the specified time period are calculated based on the corresponding sub-preparation information. Based on the type and quantity of the required middleware, a specific production node in the corresponding production line is selected, and the intermediate production line is moved to a position after the specific production node on the time axis. The buffers of the specific production node and subsequent production nodes in the corresponding production line are updated to reduce the stored middleware output to the required middleware quantity. The middleware output and remaining middleware output requirements are stored in the corresponding buffers. The required middleware is the intermediate output or initial output from the production nodes of the previous production line. The type of intermediate output stored in the buffer of the specific production node is consistent with the required middleware type, and the quantity of stored intermediate output is greater than the quantity of required middleware. The final production node is defined as the production node whose output in the corresponding buffer satisfies the required information; the predicted production cycle is the time length between the first production node of the initial production line and the final production node.

2. The method according to claim 1, characterized in that, The definition of the final production end node as the production node whose output in the corresponding buffer satisfies the demand information; before the step of predicting the production cycle as the time length between the first production node of the initial production line and the final production end node, the method further includes: Several rest periods are determined based on the rest arrangements in the actual production process; each rest period includes a start time and an end time. Several rest periods are inserted into the time axis, wherein for each inserted rest period, the start and end times of all production nodes after the end time of the rest period are extended by the duration of the rest period.

3. The method according to claim 1, characterized in that, The step of calculating the initial output and raw materials used within the time period based on the corresponding sub-preparation information is as follows: Several rest periods are determined based on the rest arrangements in the actual production process; Determine the effective duration within the specified time length; the effective duration is the specified time length minus the rest period within the specified time length. The initial output and raw materials used within the effective time period are calculated based on the corresponding sub-preparation information.

4. The method according to claim 1, characterized in that, Before the step of calculating the initial output and the raw materials used within the time length based on the corresponding sub-preparation information, the method further includes: The sub-preparation information is refreshed at preset time intervals; Determine whether the quantity of available raw materials in the sub-preparation information is not less than the quantity of raw materials to be used; If the amount is not less than the amount of raw materials used, then execute the step of storing the initial output, remaining raw materials, and remaining initial output requirements into the corresponding buffer. If the quantity of available raw materials is less than the quantity of raw materials to be used, proceed with the step of determining whether the quantity of available raw materials in the sub-preparation information is not less than the quantity of raw materials to be used.

5. The method according to claim 1, characterized in that, The required information includes the type, quantity, and delivery deadline of the flanges.

6. The method according to claim 5, characterized in that, After the step of inputting demand information and preparation information into the flange production cycle prediction model to obtain the predicted production cycle, the method further includes: If the predicted production cycle exceeds the delivery deadline, the relevant parameters in the flange production cycle prediction model are adjusted, and the demand information and the preparation information are input into the flange production cycle prediction model according to the adjusted relevant parameters to obtain a new predicted production cycle.

7. The method according to claim 1, characterized in that, After the step of storing the initial output, remaining raw materials, and remaining initial output requirements into the corresponding buffer, the method further includes: According to the preset quality inspection rules, the unqualified products in the initial output are determined; Remove the non-conforming products from the corresponding buffer and update the corresponding buffer.

8. A flange production cycle simulation and prediction system, characterized in that, The flange production cycle simulation and prediction system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the flange production cycle simulation and prediction system to perform the method as described in any one of claims 1-7.

9. A computer program product containing instructions, characterized in that, When the computer program product is run on the flange production cycle simulation and prediction system, the flange production cycle simulation and prediction system performs the method as described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is run on the flange production cycle simulation and prediction system, the flange production cycle simulation and prediction system performs the method as described in any one of claims 1-7.

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