Pipe production task management method and system

Abstract

The invention discloses a pipe production task management method and system, and belongs to the technical field of pipe production, and in the pipe production task management method and system, factors such as order priority, equipment state and raw material supply are analyzed through an intelligent algorithm, and the production schedule is automatically optimized, so that production resources are utilized more reasonably and efficiently. Resource waste can be avoided, idle time in the production process is shortened, and therefore production efficiency and the productivity utilization rate are improved. The urgency degree and the delivery date of the order are analyzed through an intelligent algorithm, and the enterprise can preferentially arrange the production of the urgent order to ensure that the customer demand is met in time. Meanwhile, production tasks are reasonably distributed according to the running state of the equipment and the raw material supply condition, and the use efficiency of the equipment and the utilization rate of raw materials are improved. The dynamic adjustment mechanism monitors the production progress in real time and automatically adjusts the production plan according to the actual situation, so that the system can rapidly adjust the production plan and distribute the tasks to other devices, and continuity and on-time delivery of the production tasks are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of pipe production, and in particular relates to a pipe production task management method and system. Background Art

[0002] Pipe production literally refers to the process of manufacturing and processing various pipe materials. This includes a series of production activities from the selection of raw materials, processing and molding, quality testing to the packaging and storage of finished products. Pipe production task management includes the following aspects: Task planning: According to order requirements and production capacity, formulate production plans, clarify production tasks and schedules. Resource allocation: Rationally allocate human, material, equipment and other resources to ensure the efficient execution of production tasks. Progress monitoring: Track production progress to ensure that various tasks are carried out according to plan, and timely discover and solve problems in production. Quality control: Ensure that every link in the production process meets quality standards and prevent unqualified products from flowing into the next stage. Cost control: Monitor production costs and reduce production costs by optimizing processes and improving efficiency. Employee management: Train and guide production personnel to improve their skills and work efficiency to ensure the smooth completion of production tasks. Through effective pipe production task management, enterprises can ensure the orderly progress of production activities, improve product quality, reduce costs, and meet market demand.

[0003] However, in the existing technology, production plans often rely on manual formulation and lack automation and intelligence. This may lead to insufficient optimization of production plans and unreasonable resource allocation, thus affecting production efficiency and capacity utilization. At the same time, there may be many wastes in the production process, such as excessively long production cycles, unreasonable resource allocation, inefficient process flows, etc. These wastes not only reduce production efficiency, but also increase production costs. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for managing pipe production tasks in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: a method for managing pipe production tasks, comprising the following steps:

[0006] S1: Determine the objectives of the pipe production task: including market demand research, company strategy considerations and order situation analysis, establish specific production goals, including pipe varieties, specifications, quantities and delivery dates, and ensure that the selected tasks are executable under existing resource conditions;

[0007] S2: Develop a production plan. The enterprise clarifies the specific requirements of the production task, breaks down the overall task into multiple batches, and allocates them to different production units;

[0008] S3: At the same time, a detailed production schedule is formulated to ensure that production activities proceed in an orderly manner according to the established plan;

[0009] S4: Production preparation This stage is the preparation of various resources before production, including the purchase of raw materials, equipment maintenance and commissioning, and personnel skills training, to ensure that all necessary conditions and resources are ready when production activities begin;

[0010] S5: Perform production execution, using artificial intelligence algorithms to automatically optimize production scheduling based on order priority, equipment status, raw material supply and other factors. At the same time, use a dynamic adjustment mechanism to monitor production progress in real time, automatically adjust production plans according to actual conditions to respond to emergencies, and collect production data. Through big data analysis technology, bottlenecks and improvement points in the production process are discovered;

[0011] S6: After the task is completed, the actual output is compared with the planned output, and quality inspection is carried out to inspect and accept the finished products to ensure that the delivered products meet customer requirements. The entire production task is summarized to provide reference for future production activities.

[0012] S7: Carry out continuous improvement: As a closed link in the production management cycle, continuous improvement involves collecting feedback from various aspects, analyzing problems that arise during the production process, and formulating improvement measures based on the analysis results to improve the overall level of production management.

[0013] In a preferred embodiment, in step S1, the enterprise first collects and analyzes market demand information, including customer preferences, competitor situations, and market trends, through market research. Then, the collected information is comprehensively considered in combination with the company's strategic goals, such as market expansion, brand building, or cost control. At the same time, the enterprise also needs to analyze the order situation in detail, including the urgency of the order, special customer requirements, etc. Based on these analyses, the enterprise establishes specific production goals and specifies the variety, specification, quantity, and delivery date of the pipe. To ensure the feasibility of the task, the enterprise also needs to evaluate existing resources, such as equipment capacity, raw material inventory, technical level, etc., to ensure that the selected task can be executed under the existing resource conditions.

[0014] In a preferred embodiment, in step S2, during the production planning stage, the enterprise needs to clarify the specific requirements of the production task and break down the overall task into multiple batches. This process includes determining the production sequence, production quantity and time schedule for each batch. The enterprise needs to consider factors such as order priority, delivery date and production line capacity, and reasonably allocate tasks to different production units, such as workshops and teams. In addition, emergency plans need to be formulated to deal with possible resource shortages or equipment failures.

[0015] In a preferred embodiment, in step S3, when formulating the production schedule, the enterprise needs to formulate a detailed schedule for each production batch according to the production plan. This includes determining the start and end time of each production step, as well as the inspection and acceptance time of key nodes. The production schedule needs to take into account the actual operation of the production line, such as equipment maintenance time, employee rest time, etc., to ensure that production activities are carried out in an orderly manner according to the established plan. At the same time, the schedule should have a certain degree of flexibility to adapt to the uncertainty in the production process.

[0016] In a preferred embodiment, in step S4, the enterprise needs to purchase the required raw materials in advance to ensure that the quality and quantity of the raw materials meet the production requirements. At the same time, the production equipment is comprehensively inspected, debugged and maintained to ensure that the equipment operates normally. In addition, the production personnel are trained in skills to improve their operational proficiency and problem-solving ability. The purpose of this stage is to ensure that all necessary conditions and resources are ready before the start of production activities.

[0017] In a preferred embodiment, in step S5, using an artificial intelligence algorithm to automatically optimize the production schedule according to factors such as order priority, equipment status, and raw material supply specifically includes:

[0018] Step 1: Data collection and preprocessing, collect data: collect order information (order quantity, delivery date, priority), equipment status (availability, efficiency, maintenance record), raw material supply status (inventory, procurement cycle), etc.

[0019] Preprocess data: clean data, remove invalid and erroneous information, and unify data formats.

[0020] Step 2: Define the scheduling goals, objective function: define the optimization goals, such as minimizing total delivery delays, maximizing equipment utilization, minimizing production costs, etc.

[0021] Constraints: including order delivery time, equipment capacity limitations, raw material inventory limitations, etc.

[0022] Step 3: Build a production scheduling model:

[0023] The formula for minimizing total delivery delay is:

[0024] Where Z is the total delivery delay, Cj is the delivery date of order j, ActualDeliveryDate j is the actual delivery date of order j, Pj is the priority of order j;

[0025] The formula for maximizing equipment utilization is:

[0026] Where Z is the total equipment utilization, UsedTime i is the actual usage time of device i, Di is the available time of device i;

[0027] The formula for minimizing production cost is:

[0028] Where Z is the total production cost, T j is the production time of order j, CostPerUnitTime is the production cost per unit time;

[0029] Step 4: Algorithm implementation:

[0030] Calculation formula:

[0031] Device selection: Device(j) = argmini∈Devices(Tj / Di)

[0032] Material allocation: Material(j) = argmaxk∈Materials(Rk-Qjk)

[0033] The optimization objective of minimizing the total delay is calculated as:

[0034] Z=∑j(Cj-ActualDeliveryDatej)×Pj;

[0035] Step 5: Output of production scheduling results:

[0036] Generate schedule: Generate a detailed production schedule based on the algorithm results.

[0037] Confirm scheduling: The scheduling results are reviewed and confirmed by management.

[0038] In a preferred embodiment, in step S5, the dynamic adjustment mechanism includes:

[0039] Step 1: Real-time monitoring

[0040] Data collection: Real-time collection of data such as production progress, equipment status, and raw material consumption.

[0041] Monitoring indicators: Define key performance indicators (KPIs), such as production efficiency, equipment failure rate, order on-time delivery rate, etc.

[0042] Step 2: Anomaly Detection

[0043] Rule definition: Set anomaly detection rules, such as equipment downtime exceeding thresholds, order delay warnings, etc.

[0044] Anomaly Identification: Identify potential anomalies through real-time data analysis.

[0045] Step 3: Dynamic Adjustment

[0046] Adjustment strategy: Define adjustment strategies, such as rescheduling, resource reallocation, overtime arrangements, etc.

[0047] Calculation formula:

[0048] Equipment failure adjustment:

[0049] NewSchedule=Reoptimize(CurrentSchedule,FaultyDevices)

[0050] Rush Order Insertion:

[0051] NewSchedule=InsertOrder(CurrentSchedule,UrgentOrder)

[0052] Step 4: Adjust execution

[0053] Execute adjustments: Execute adjustments to the production plan based on the adjustment strategy.

[0054] Notify relevant personnel: Inform relevant personnel of the adjustment content through system notification or direct communication.

[0055] Step 5: Adjustment effect evaluation

[0056] Evaluation indicators: Evaluate the adjusted production efficiency, cost, customer satisfaction and other indicators.

[0057] Feedback loop: Collect adjusted data to optimize adjustment strategies and intelligent scheduling systems.

[0058] In a preferred embodiment, in step S5, using big data analysis technology to find bottlenecks and improvement points in the production process specifically includes the following steps:

[0059] S1. Data collection: The company first collected the following production data: equipment operating time, equipment downtime and its reasons, production batch time cycle, raw material consumption, product quality test results, employee working time records and order fulfillment;

[0060] S2. Data processing: Clean and format the collected data to ensure data consistency and accuracy. For example, unify dates and times in different formats and remove invalid or erroneous data records.

[0061] S3. Data analysis: Trend analysis: Analyzing the data of equipment operation time, it is found that the number of equipment downtime is increasing; Correlation analysis: Checking the correlation between equipment downtime and product quality issues, it is found that there is a significant correlation between the two;

[0062] S4. Bottleneck identification: By analyzing the time cycle of the production batch, it is found that a specific production step takes too long, which may be a production bottleneck.

[0063] S5. Finding improvement points: Through data analysis, the company found that insufficient equipment maintenance was the main reason for the decline in efficiency. At the same time, the bottleneck step in the production process was identified, which needed to be redesigned and optimized.

[0064] In a preferred embodiment, in step S6, by comparing the actual output with the planned output, the reasons for the difference are analyzed and improvement measures are formulated. At the same time, strict quality inspection is carried out on the finished products to ensure that the delivered products meet customer requirements. After acceptance, the products can be delivered to customers. In addition, the enterprise also needs to summarize the entire production task, record the lessons learned, and provide reference for future production activities;

[0065] In step S7, by collecting opinions and suggestions from customers, production personnel, management personnel, etc., the problems that occur in the production process are analyzed and the root causes are found. Based on the analysis results, the enterprise formulates practical improvement measures, such as optimizing the process flow, improving equipment efficiency, strengthening employee training, etc., to improve the overall level of production management. The purpose of this stage is to ensure that the production system is continuously optimized to adapt to changes in the market and technology.

[0066] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0067] 1. In the present invention, the order priority, equipment status, raw material supply and other factors are analyzed by intelligent algorithms, and the production schedule is automatically optimized, so that production resources can be used more reasonably and efficiently. It can avoid waste of resources and reduce idle time in the production process, thereby improving production efficiency and capacity utilization. By analyzing the urgency and delivery date of orders by intelligent algorithms, enterprises can give priority to the production of urgent orders to ensure that customer needs are met in a timely manner. At the same time, according to the operating status of the equipment and the supply of raw materials, production tasks are reasonably allocated to improve the use efficiency of equipment and the utilization rate of raw materials.

[0068] 2. In the present invention, the dynamic adjustment mechanism monitors the production progress in real time and automatically adjusts the production plan according to the actual situation, so that the production plan can quickly respond to external changes, such as equipment failure, order changes and other emergencies. This flexibility and adaptability helps enterprises better cope with market fluctuations and changes in customer demand, and improves the market competitiveness of enterprises. When equipment fails, the system can quickly adjust the production plan and assign tasks to other equipment to ensure the continuity and on-time delivery of production tasks.

[0069] 3. In the present invention, through big data analysis technology, bottlenecks and improvement points in the production process are found, and enterprises can take targeted measures, such as optimizing the process flow, adjusting equipment configuration, etc., to improve production efficiency and product quality. At the same time, this analysis can also help enterprises identify and reduce waste in the production process and reduce production costs. By analyzing production data, enterprises can find that a certain production step takes too long, resulting in low production efficiency. By adjusting the process flow and optimizing the equipment configuration, the production efficiency of this step can be improved, thereby improving the overall production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a schematic diagram of the process principle of the present invention;

[0071] Figure 2 It is a schematic diagram of the principle of automatically optimizing the production scheduling process in the present invention. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0073] Reference Figure 1-2 ,

[0074] A pipe production task management method and system, comprising the following steps:

[0075] S1: Determine the objectives of the pipe production task: including market demand research, company strategy considerations and order situation analysis, establish specific production goals, including pipe varieties, specifications, quantities and delivery dates, and ensure that the selected tasks are executable under existing resource conditions;

[0076] S2: Develop a production plan. The enterprise clarifies the specific requirements of the production task, breaks down the overall task into multiple batches, and allocates them to different production units;

[0077] S3: At the same time, a detailed production schedule is formulated to ensure that production activities proceed in an orderly manner according to the established plan;

[0078] S4: Production preparation This stage is the preparation of various resources before production, including the purchase of raw materials, equipment maintenance and commissioning, and personnel skills training, to ensure that all necessary conditions and resources are ready when production activities begin;

[0079] S5: Perform production execution, using artificial intelligence algorithms to automatically optimize production scheduling based on order priority, equipment status, raw material supply and other factors. At the same time, use a dynamic adjustment mechanism to monitor production progress in real time, automatically adjust production plans according to actual conditions to respond to emergencies, and collect production data. Through big data analysis technology, bottlenecks and improvement points in the production process are discovered;

[0080] S6: After the task is completed, the actual output is compared with the planned output, and quality inspection is carried out to inspect and accept the finished products to ensure that the delivered products meet customer requirements. The entire production task is summarized to provide reference for future production activities.

[0081] S7: Carry out continuous improvement: As a closed link in the production management cycle, continuous improvement involves collecting feedback from various aspects, analyzing problems that arise during the production process, and formulating improvement measures based on the analysis results to improve the overall level of production management.

[0082] In step S1, the company first collects and analyzes market demand information through market research, including customer preferences, competitor situations, and market trends. Then, the collected information is comprehensively considered in combination with the company's strategic goals, such as market expansion, brand building, or cost control. At the same time, the company also needs to analyze the order situation in detail, including the urgency of the order, special customer requirements, etc. Based on these analyses, the company establishes specific production goals and specifies the variety, specification, quantity, and delivery date of the pipes. To ensure the feasibility of the task, the company also needs to evaluate existing resources, such as equipment capacity, raw material inventory, technical level, etc., to ensure that the selected task can be executed under the existing resource conditions.

[0083] In step S2, during the production planning stage, the company needs to clarify the specific requirements of the production task and break down the overall task into multiple batches. This process includes determining the production sequence, production quantity and time schedule for each batch. The company needs to consider factors such as order priority, delivery date and production line capacity, and reasonably allocate tasks to different production units, such as workshops and work teams. In addition, contingency plans need to be formulated to deal with possible resource shortages or equipment failures.

[0084] In step S3, when formulating the production schedule, the company needs to develop a detailed schedule for each production batch based on the production plan. This includes determining the start and end time of each production step, as well as the inspection and acceptance time of key nodes. The production schedule needs to take into account the actual operation of the production line, such as equipment maintenance time, employee rest time, etc., to ensure that production activities proceed in an orderly manner according to the established plan. At the same time, the schedule should have a certain degree of flexibility to adapt to the uncertainty in the production process.

[0085] In step S4, the company needs to purchase the required raw materials in advance to ensure that the quality and quantity of the raw materials meet the production needs. At the same time, the production equipment is comprehensively inspected, debugged and maintained to ensure that the equipment operates normally. In addition, the production staff is trained to improve their operational proficiency and problem-solving ability. The purpose of this stage is to ensure that all necessary conditions and resources are ready before the start of production activities.

[0086] In step S5, the production schedule is automatically optimized based on order priority, equipment status, raw material supply and other factors using artificial intelligence algorithms, including:

[0087] Step 1: Data collection and preprocessing, collect data: collect order information (order quantity, delivery date, priority), equipment status (availability, efficiency, maintenance record), raw material supply status (inventory, procurement cycle), etc.

[0088] Preprocess data: clean data, remove invalid and erroneous information, and unify data formats.

[0089] Step 2: Define the scheduling goals, objective function: define the optimization goals, such as minimizing total delivery delays, maximizing equipment utilization, minimizing production costs, etc.

[0090] Constraints: including order delivery time, equipment capacity limitations, raw material inventory limitations, etc.

[0091] Step 3: Build a production scheduling model:

[0092] The formula for minimizing total delivery delay is:

[0093] Where Z is the total delivery delay, Cj is the delivery date of order j, ActualDeliveryDate j is the actual delivery date of order j, Pj is the priority of order j;

[0094] The formula for maximizing equipment utilization is:

[0095] Where Z is the total equipment utilization, UsedTime i is the actual usage time of device i, Di is the available time of device i;

[0096] The formula for minimizing production cost is:

[0097] Where Z is the total production cost, T j is the production time of order j, CostPerUnitTime is the production cost per unit time;

[0098] Step 4: Algorithm implementation:

[0099] Calculation formula:

[0100] Device selection: Device(j) = argmini∈Devices(Tj / Di)

[0101] Material allocation: Material(j) = argmaxk∈Materials(Rk-Qjk)

[0102] The optimization objective of minimizing the total delay is calculated as:

[0103] Z=∑j(Cj-ActualDeliveryDatej)×Pj;

[0104] Step 5: Output of production scheduling results:

[0105] Generate schedule: Generate a detailed production schedule based on the algorithm results.

[0106] Confirm scheduling: The scheduling results are reviewed and confirmed by management.

[0107] In step S5, the dynamic adjustment mechanism includes:

[0108] Step 1: Real-time monitoring

[0109] Data collection: Real-time collection of data such as production progress, equipment status, and raw material consumption.

[0110] Monitoring indicators: Define key performance indicators (KPIs), such as production efficiency, equipment failure rate, order on-time delivery rate, etc.

[0111] Step 2: Anomaly Detection

[0112] Rule definition: Set anomaly detection rules, such as equipment downtime exceeding thresholds, order delay warnings, etc.

[0113] Anomaly Identification: Identify potential anomalies through real-time data analysis.

[0114] Step 3: Dynamic Adjustment

[0115] Adjustment strategy: Define adjustment strategies, such as rescheduling, resource reallocation, overtime arrangements, etc.

[0116] Calculation formula:

[0117] Equipment failure adjustment:

[0118] NewSchedule=Reoptimize(CurrentSchedule,FaultyDevices)

[0119] Rush Order Insertion:

[0120] NewSchedule=InsertOrder(CurrentSchedule,UrgentOrder)

[0121] Step 4: Adjust execution

[0122] Execute adjustments: Execute adjustments to the production plan based on the adjustment strategy.

[0123] Notify relevant personnel: Inform relevant personnel of the adjustment content through system notification or direct communication.

[0124] Step 5: Adjustment effect evaluation

[0125] Evaluation indicators: Evaluate the adjusted production efficiency, cost, customer satisfaction and other indicators.

[0126] Feedback loop: Collect adjusted data to optimize adjustment strategies and intelligent scheduling systems.

[0127] In step S5, the bottlenecks and improvement points in the production process are found through big data analysis technology, which specifically includes the following steps:

[0128] S1. Data collection: The company first collected the following production data: equipment operating time, equipment downtime and its reasons, production batch time cycle, raw material consumption, product quality test results, employee working time records and order fulfillment;

[0129] S2. Data processing: Clean and format the collected data to ensure data consistency and accuracy. For example, unify dates and times in different formats and remove invalid or erroneous data records.

[0130] S3. Data analysis: Trend analysis: Analyzing the data of equipment operation time, it is found that the number of equipment downtime is increasing; Correlation analysis: Checking the correlation between equipment downtime and product quality issues, it is found that there is a significant correlation between the two;

[0131] S4. Bottleneck identification: By analyzing the time cycle of the production batch, it is found that a specific production step takes too long, which may be a production bottleneck.

[0132] S5. Finding improvement points: Through data analysis, the company found that insufficient equipment maintenance was the main reason for the decline in efficiency. At the same time, the bottleneck step in the production process was identified, which needed to be redesigned and optimized.

[0133] In step S6, by comparing the actual output with the planned output, the reasons for the difference are analyzed and improvement measures are formulated. At the same time, strict quality inspection is carried out on the finished products to ensure that the delivered products meet customer requirements. After acceptance, the products can be delivered to customers. In addition, the company also needs to summarize the entire production task, record the lessons learned, and provide reference for future production activities.

[0134] In step S7, by collecting opinions and suggestions from customers, production personnel, management personnel, etc., the problems that occur in the production process are analyzed and the root causes are found. Based on the analysis results, the enterprise formulates practical improvement measures, such as optimizing process flow, improving equipment efficiency, strengthening employee training, etc., to improve the overall level of production management. The purpose of this stage is to ensure that the production system is continuously optimized to adapt to changes in the market and technology.

[0135] A pipe production task management system includes a memory, a processor, and a pipe production task management method program stored on the processor. When the pipe production task management method program is run by the processor, the steps described in the above embodiment are executed.

[0136] In the present invention, the production schedule is automatically optimized by analyzing factors such as order priority, equipment status and raw material supply through intelligent algorithms, so that production resources can be used more reasonably and efficiently. This optimization can avoid waste of resources and reduce idle time in the production process, thereby improving production efficiency and capacity utilization. For example, by analyzing the urgency and delivery date of orders through intelligent algorithms, enterprises can give priority to the production of urgent orders to ensure that customer needs are met in a timely manner. At the same time, according to the operating status of the equipment and the supply of raw materials, production tasks are reasonably allocated to improve the use efficiency of equipment and the utilization rate of raw materials.

[0137] In the present invention, the dynamic adjustment mechanism monitors the production progress in real time and automatically adjusts the production plan according to the actual situation, so that the production plan can quickly respond to external changes, such as equipment failure, order changes and other emergencies. This flexibility and adaptability helps enterprises better cope with market fluctuations and changes in customer demand, and improves the market competitiveness of enterprises. When equipment fails, the system can quickly adjust the production plan and assign tasks to other equipment to ensure the continuity and on-time delivery of production tasks.

[0138] In the present invention, through big data analysis technology, bottlenecks and improvement points in the production process are discovered, and enterprises can take targeted measures, such as optimizing the process flow, adjusting equipment configuration, etc., to improve production efficiency and product quality. At the same time, this analysis can also help enterprises identify and reduce waste in the production process and reduce production costs. For example, by analyzing production data, enterprises can find that a certain production step takes too long, resulting in low production efficiency. By adjusting the process flow and optimizing the equipment configuration, the production efficiency of this step can be improved, thereby improving the overall production efficiency.

[0139] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0140] The above description enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for managing pipe production tasks, characterized in that: The steps include: S1: Determine the objectives of the pipe production task: including market demand research, company strategy considerations and order situation analysis, establish specific production goals, including pipe varieties, specifications, quantities and delivery dates, and ensure that the selected tasks are executable under existing resource conditions; S2: Develop a production plan. The enterprise clarifies the specific requirements of the production task, breaks down the overall task into multiple batches, and allocates them to different production units; S3: At the same time, a detailed production schedule is formulated to ensure that production activities proceed in an orderly manner according to the established plan; S4: Production preparation This stage is the preparation of various resources before production, including the purchase of raw materials, equipment maintenance and commissioning, and personnel skills training, to ensure that all necessary conditions and resources are ready when production activities begin; S5: Perform production execution, using artificial intelligence algorithms to automatically optimize production scheduling based on order priority, equipment status, and raw material supply factors. At the same time, a dynamic adjustment mechanism is used to monitor production progress in real time, and production plans are automatically adjusted according to actual conditions to respond to emergencies. At the same time, production data is collected and big data analysis technology is used to discover bottlenecks and improvement points in the production process. S6: After the task is completed, the actual output is compared with the planned output, and quality inspection is carried out to inspect and accept the finished products to ensure that the delivered products meet customer requirements. The entire production task is summarized to provide reference for future production activities. S7: Carry out continuous improvement: As a closed link in the production management cycle, continuous improvement involves collecting feedback from various aspects, analyzing problems that arise during the production process, and formulating improvement measures based on the analysis results to improve the overall level of production management.

2. A method for managing pipe production tasks according to claim 1, characterized in that: In step S1, the enterprise first collects and analyzes market demand information through market research, including customer preferences, competitor situations and market trends; then, the enterprise comprehensively considers the collected information in combination with the company's strategic goals; at the same time, the enterprise also needs to analyze the order situation in detail, including the urgency of the order and the special requirements of the customer.

3. A method for managing pipe production tasks according to claim 1, characterized in that: In step S2, during the production planning stage, the enterprise needs to clarify the specific requirements of the production task and break down the overall task into multiple batches; this process includes determining the production sequence, production quantity and time schedule for each batch; the enterprise needs to consider the order priority, delivery date and production line capacity factors, and reasonably allocate tasks to different production units.

4. A method for managing pipe production tasks according to claim 1, characterized in that: In step S3, when formulating the production schedule, the enterprise needs to formulate a detailed schedule for each production batch according to the production plan; this includes determining the start and end time of each production step, as well as the inspection and acceptance time of key nodes; The production schedule needs to take into account the actual operation of the production line.

5. A method for managing pipe production tasks according to claim 1, characterized in that: In step S4, the enterprise needs to purchase the required raw materials in advance to ensure that the quality and quantity of the raw materials meet the production needs; at the same time, the production equipment is comprehensively inspected, debugged and maintained to ensure the normal operation of the equipment; in addition, the production personnel are trained in skills to improve their operational proficiency and problem-solving ability; the purpose of this stage is to ensure that all necessary conditions and resources are ready before the start of production activities.

6. A method for managing pipe production tasks according to claim 1, characterized in that: In step S5, the production schedule is automatically optimized based on order priority, equipment status, and raw material supply factors using an artificial intelligence algorithm, specifically including: Step 1: Data collection and preprocessing, collect data: collect order information, equipment status, raw material supply; Preprocess data: clean data, remove invalid and erroneous information, and unify data formats; Step 2: Define the production scheduling goal, objective function: define the optimization goal, including minimizing total delivery delay, maximizing equipment utilization, and minimizing production cost; Constraints: including order delivery time, equipment capacity limitations, and raw material inventory limitations; Step 3: Build a production scheduling model: The formula for minimizing total delivery delay is: Where Z is the total delivery delay, Cj is the delivery date of order j, ActualDeliveryDate j is the actual delivery date of order j, Pj is the priority of order j; The formula for maximizing equipment utilization is: Where Z is the total equipment utilization, UsedTime i is the actual usage time of device i, Di is the available time of device i; The formula for minimizing production cost is: Where Z is the total production cost, T j is the production time of order j, CostPerUnitTime is the production cost per unit time; Step 4: Algorithm implementation: Calculation formula: Device selection: Device(j) = argmini∈Devices(Tj / Di) Material allocation: Material(j) = argmaxk∈Materials(Rk-Qjk) The formula for minimizing the total delay of the optimization objective is: Z=∑j(Cj-ActualDeliveryDatej)×Pj; Step 5: Output of production scheduling results: Generate schedule: Generate detailed production schedule based on algorithm results; Confirm scheduling: The scheduling results are reviewed and confirmed by management.

7. A method for managing pipe production tasks according to claim 1, characterized in that: In step S5, the dynamic adjustment mechanism includes: Step 1: Real-time monitoring Data collection: real-time collection of production progress, equipment status, and raw material consumption data; Monitoring indicators: define key performance indicators, including production efficiency, equipment failure rate, and order delivery rate on time; Step 2: Anomaly Detection Rule definition: set anomaly detection rules, including equipment downtime exceeding thresholds and order delay warnings; Anomaly identification: Identify potential anomalies through real-time data analysis; Step 3: Dynamic Adjustment Adjustment strategy: define adjustment strategies, including rescheduling, resource reallocation, and overtime arrangements; Calculation formula: Equipment failure adjustment: NewSchedule=Reoptimize(CurrentSchedule,FaultyDevices) Rush Order Insertion: NewSchedule=InsertOrder(CurrentSchedule,UrgentOrder) Step 4: Adjust execution Execution adjustment: Execute the adjustment of production plan according to the adjustment strategy; Notify relevant personnel: Inform relevant personnel of the adjustment content through system notification or direct communication; Step 5: Adjustment effect evaluation Evaluation indicators: evaluate the adjusted production efficiency, cost, and customer satisfaction indicators; Feedback loop: Collect adjusted data to optimize adjustment strategies and intelligent scheduling systems.

8. A method for managing pipe production tasks according to claim 1, characterized in that: In step S5, the bottlenecks and improvement points in the production process are found through big data analysis technology, which specifically include the following steps: S1. Data collection: The company first collected the following production data: equipment operating time, equipment downtime and its reasons, production batch time cycle, raw material consumption, product quality test results, employee working time records and order fulfillment; S2. Data processing: Clean and format the collected data to ensure data consistency and accuracy; S3. Data analysis: Trend analysis: Analyzing the data of equipment operation time, it is found that the number of equipment downtime is increasing; Correlation analysis: Checking the correlation between equipment downtime and product quality issues, it is found that there is a significant correlation between the two; S4. Bottleneck identification: By analyzing the time cycle of the production batch, it is found that a specific production step takes too long, which may be a production bottleneck; S5. Identify areas for improvement: Through data analysis, the company discovered that insufficient equipment maintenance was the main cause of declining efficiency. At the same time, it identified the bottleneck steps in the production process, which needed to be redesigned and optimized.

9. A method for managing pipe production tasks according to claim 1, characterized in that: In step S6, by comparing the actual output with the planned output, the reasons for the difference are analyzed and improvement measures are formulated; at the same time, strict quality inspection is carried out on the finished products to ensure that the delivered products meet customer requirements; the products can be delivered to customers only after acceptance; in addition, the enterprise needs to summarize the entire production task, record the lessons learned, and provide reference for future production activities; In step S7, by collecting opinions and suggestions from customers, production personnel, and management personnel, problems arising in the production process are analyzed and the root causes are found; based on the analysis results, the enterprise formulates practical improvement measures.

10. A pipe production task management system, characterized by: The system includes a memory, a processor, and a pipe production task management method program stored on the processor. When the pipe production task management method program is run by the processor, the steps according to any one of claims 1 to 9 are executed.

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