Production management system and method for anti-corrosion pipeline
By constructing a dimensionless normalization model and a multi-factor linear weighted model, the problem of insufficient identification of order levels and production line status in the traditional scheduling system was solved, differentiated control and optimized scheduling were achieved in the production process of anti-corrosion pipelines, and production efficiency and stability were improved.
Patent Information
- Application Number
- CN202510857766.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-25
- Publication Date
- 2025-09-26
AI Technical Summary
Traditional production scheduling systems lack comprehensive identification of order levels, production line loads, and energy status, resulting in no obvious difference in scheduling strategies between high-level orders and ordinary orders. This makes it impossible to achieve differentiated and precise control, and it is difficult to simultaneously take into account the complex coupling relationship between quality and energy consumption.
By constructing a dimensionless normalization model and a multi-factor linear weighted model, a priority evaluation mechanism is established, multi-parameter scheduling control instructions are generated, and the scheduling strategy is optimized based on performance feedback data. The parameters are optimized using the gradient descent method to build an intelligent scheduling system.
It achieves automatic grading of order levels and improved scheduling accuracy, ensures the process requirements of high-value pipelines, improves production line stability and production efficiency, and has industrial feasibility and real-time performance.
Smart Images
Figure CN120706816A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of intelligent manufacturing, and in particular to a production management system and method for anti-corrosion pipelines. Background Art
[0002] Currently, in fields such as petrochemicals and marine engineering, anti-corrosion pipelines are critical infrastructure, and their production quality and delivery efficiency directly affect the project cycle and safety and stability. Traditional production scheduling management methods often have the following technical limitations: Traditional scheduling systems are based on static process specifications and fixed processes, and lack comprehensive identification of order levels, production line loads, and energy status. This results in no significant difference in scheduling strategies between high-level orders (such as long-life deep-sea pipelines) and ordinary orders, making it impossible to achieve differentiated and precise control. In addition, in the production process of anti-corrosion pipelines, there is a complex coupling relationship between quality and energy consumption. Traditional methods lack a unified regulation and optimization model, making it difficult to take into account multiple target variables at the same time, resulting in one-sided or even ineffective optimization results. Summary of the Invention
[0003] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.
[0004] In view of the above problems in the prior art, the present invention is proposed.
[0005] To solve the above technical problems, the present invention provides the following technical solutions: a production management method for anti-corrosion pipelines, comprising: Acquire data related to orders and production line operations, including static information and dynamic data; Establish a priority assessment mechanism to classify orders and assess their processing priorities based on the acquired data; Generate scheduling control instructions containing multi-parameter control content based on order level and production line status; After executing the scheduling instruction, collecting performance feedback data related to the execution result; Based on the feedback data, the scheduling strategy or parameter setting is optimized, and the optimization results are used for subsequent production scheduling.
[0006] As a preferred solution of the production management method for anti-corrosion pipelines of the present invention, the establishment process of the priority evaluation mechanism is as follows: S101: Construct a dimensionless normalized model, including: the proportion of order life requirements to the highest acceptable standard; the intensity of fluctuations in current material costs compared to baseline costs; and the current degree of quality achievement. S102: Based on the different contributions of the above three dimensions to different enterprise goals, weight factors are introduced to construct a multi-factor linear weighted model. The model outputs the priority of the current order in production line scheduling. The model formula is:
[0007] in, Indicates that the anti-corrosion life requirements are extracted from the order database. Indicates the maximum reference life value, Indicates the actual cost of the raw materials used in the current order, represents the base cost of raw materials, Indicates the quality defect rate of the current order batch.
[0008] As a preferred solution of the production management method of an anti-corrosion pipeline described in the present invention, the minimum score line for high-value orders is defined. Highest scoring lines with low value orders ; like :This order has high performance requirements or strict quality requirements or the raw materials are in a high-risk state, and the order level is marked as S1; like : Indicates that the order value is low and the cost sensitivity is high, and the order level is marked as S2; like :For intermediate-level orders, production is scheduled normally according to the production line status benchmark model, and the order level is marked as S3.
[0009] As a preferred solution of the production management method of an anti-corrosion pipeline described in the present invention, when confirming the production line status, the physical status of the production line is parameterized and a status index model is constructed. The main indicators include: equipment availability Cache occupancy rate and energy load index ; The formulas for each indicator are defined as follows: ; ,A three-dimensional matching decision table is constructed based on the status indicator model representing the ,production line status and the order level.
[0010] As a preferred solution of the production management method for anti-corrosion pipelines described in the present invention, the generation of multi-parameter regulation control instructions includes the following steps: S201: Determine a minimum rate deviation term to control quality fluctuations. This term represents the normalized deviation between the currently set coating rate and the ideal rate. Introduce a temperature offset term to achieve defect rate control. This term represents the deviation between the current curing temperature and the standard process temperature. Consider an energy load fluctuation term to balance system energy consumption. This term is used to constrain energy allocation changes to avoid excessive interference with the existing load distribution. S202: Based on minimizing the rate deviation term, the temperature offset term, and the energy load fluctuation term, a multi-objective weighted optimization function is constructed as the control core. The objective function formula is: ; in, Indicates the coating rate, Indicates the standard rate, Indicates the maximum rate, Indicates the curing temperature, Indicates the standard process temperature, represents the energy allocation adjustment amount, 、 、 Represent the weight coefficients of different items respectively; S203: Solve the objective function based on parameter constraints to obtain final control parameters, where the parameter constraints include: the impact of equipment availability on the rate upper limit; the temperature lower limit increase driven by the defect rate; and power fluctuation control.
[0011] As a preferred solution of the production management method of an anti-corrosion pipeline described in the present invention, the performance feedback data includes: quality data, that is, actual defect rate , Energy efficiency data is the actual / theoretical energy consumption ratio , Timeliness data refers to production delay time ; Define comprehensive deviation indicators based on feedback data: ; in, represents the expected defect rate of the original scheduling instruction; represents the maximum tolerable defect rate, Indicates the standard order cycle; when Greater than the set threshold , then the optimization mechanism is triggered.
[0012] As a preferred solution of the production management method for anti-corrosion pipelines described in the present invention, the optimization mechanism includes: A loss function is constructed based on the square difference between the actual defect rate and the expected value, as well as the square difference of the energy consumption ratio from the ideal value of 1. The loss function represents the overall error or performance deviation caused by the current scheduling parameters R and T. Based on the constructed loss function, the gradient descent method is used to optimize the parameters and obtain new scheduling parameters.
[0013] The production management system applied to the above-mentioned production management method of the anti-corrosion pipeline includes the following functional modules: The data acquisition module is used to collect order data and production line status data; the priority assessment module builds a multi-factor scoring model based on the collected data and completes order level classification; the production line status modeling module is used to evaluate equipment availability, cache rate and power load in real time to generate status indicators; The strategy matching and fusion module is used to build a three-dimensional strategy matching table and realize strategy fusion based on Euclidean distance. The multi-objective scheduling optimization module builds the scheduling objective function, solves the optimal control parameters and considers dynamic constraints. As well as the instruction execution and production line control module, which receives and issues control instructions to the production line control system to execute production scheduling; the performance feedback collection module, which collects the defect rate, energy efficiency ratio and delay time after scheduling execution in real time; the strategy evaluation and adaptive optimization module, which implements gradient optimization based on the deviation and loss function model and updates the scheduling parameters.
[0014] The present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned production management system and method for anti-corrosion pipelines when executing the computer program.
[0015] The present invention also discloses a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned production management system and method for anti-corrosion pipelines are implemented.
[0016] Beneficial effects of the present invention: 1. This invention introduces dimensionless indicators such as life cycle ratio, raw material cost fluctuation, and quality achievement, and calculates scheduling priorities based on a linear weighted model to achieve automatic order classification and adapt to the process requirements of high-value pipelines. It also integrates equipment availability, buffer area occupancy, and power load to achieve real-time identification of complex production line status and fuzzy strategy matching, effectively improving scheduling accuracy and production line stability.
[0017] 2. The present invention constructs a performance feedback model that integrates quality defect rate, energy efficiency ratio and delivery delay, and triggers an adaptive optimization mechanism based on the comprehensive deviation index to realize a continuously evolving intelligent scheduling system. It constructs a loss function containing partial derivative estimation terms, and converts the feedback error into the update direction and amplitude of the scheduling parameters to ensure that the optimization strategy has industrial feasibility and real-time performance. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them: Figure 1 This is a schematic diagram of the overall process of a production management method for anti-corrosion pipelines proposed by the present invention. DETAILED DESCRIPTION
[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.
[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.
[0021] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.
[0022] Reference Figure 1 , as one embodiment of the present invention, provides a production management system and method for anti-corrosion pipelines, the method comprising: Step 1: Obtain data related to orders and production line operations, including static information and dynamic data.
[0023] Step 2: Establish a priority evaluation mechanism to classify and evaluate the order processing priorities based on the acquired data. The establishment process of the priority evaluation mechanism is as follows: S101: Construct a dimensionless normalized model, including: the proportion of order life requirements to the highest acceptable standard; the intensity of fluctuations in current material costs compared to baseline costs; and the current degree of quality achievement. S102: Based on the different contributions of the above three dimensions to different enterprise goals, weight factors are introduced to construct a multi-factor linear weighted model. The model outputs the priority of the current order in production line scheduling. The model formula is:
[0024] in, Indicates that the anti-corrosion life requirements are extracted from the order database. Indicates the maximum reference life value, Indicates the actual cost of the raw materials used in the current order, represents the base cost of raw materials, Indicates the quality defect rate of the current order batch.
[0025] Define the minimum score for high-value orders Highest scoring lines with low value orders ; like :This order has high performance requirements or strict quality requirements or the raw materials are in a high-risk state, and the order level is marked as S1; like : Indicates that the order value is low and the cost sensitivity is high, and the order level is marked as S2; like :For intermediate-level orders, production is scheduled normally according to the production line status benchmark model, and the order level is marked as S3.
[0026] Ultimately, this hierarchical mechanism achieves a strong correlation between order value evaluation and dynamic scheduling decisions, providing a basis for realizing data-driven scheduling in closed-loop control.
[0027] Step 3: Generate scheduling control instructions containing multi-parameter control content based on order level and production line status. When confirming the production line status, parameterize the physical status of the production line and build a status indicator model. The main indicators include: equipment availability Cache occupancy rate and energy load index ; The formulas for each indicator are defined as follows: ; ,A three-dimensional matching decision table is constructed based on the status indicator model representing the ,production line status and the order level.
[0028] The three-dimensional matching decision table is as follows:
[0029] It is also important to note that if the current production line status is =80%, =75%, 1.05, the status is close to: Interval A: ≥90%, ≤70% (biased towards low load); Interval B: <70%, >80% (tends to be high load) Calculate the proximity weight (using Euclidean distance for example): ; ; After normalization ; . Final strategy = acceleration mode × + Quality Priority Mode× The control parameters are also integrated proportionally, such as: Coating rate R = + ; Curing temperature T = + .
[0030] The generation of multi-parameter control instructions includes the following steps: S201: Determine a minimum rate deviation term to control quality fluctuations. This term represents the normalized deviation between the currently set coating rate and the ideal rate. Introduce a temperature offset term to achieve defect rate control. This term represents the deviation between the current curing temperature and the standard process temperature. Consider an energy load fluctuation term to balance system energy consumption. This term is used to constrain energy allocation changes to avoid excessive interference with the existing load distribution. S202: Based on minimizing the rate deviation term, the temperature offset term, and the energy load fluctuation term, a multi-objective weighted optimization function is constructed as the control core. The objective function formula is: ; in, Indicates the coating rate, Indicates the standard rate, Indicates the maximum rate, Indicates the curing temperature, Indicates the standard process temperature, represents the energy allocation adjustment amount, 、 、 Represents the weight coefficients of different items respectively, in different modes; the weight coefficients are different, such as when the strategy mode is quality priority ( =0.6, =0.3, = 0.1), while the accelerated production mode ( =0.3, =0.4, =0.3).
[0031] S203: Solve the objective function based on parameter constraints to obtain final control parameters, where the parameter constraints include: the impact of equipment availability on the rate upper limit; the temperature lower limit increase driven by the defect rate; and power fluctuation control.
[0032] Specifically, constraint 1: the impact of device availability on the upper limit of the rate ( ); The first constraint is the production rate and the current equipment availability of the production line Hook, reflecting the direct impact of equipment health status on scheduling rate. For example, when a coating line fails ( =66%), even if the optimization result is biased towards high output, the system will automatically Limit to 66% of the maximum value to avoid equipment overload or idle capacity; Constraint 2: Defect rate driven lower temperature limit increase ( ); The second constraint is the defect rate of quality inspection feedback Logically bound to the temperature setting. This is a quality compensation factor, indicating that the curing temperature should be increased by 0.8°C for every 1% increase in defect rate. This setting ensures that the system automatically increases the curing temperature if quality declines, providing a corrective effect on critical orders.
[0033] Constraint 3: Power Fluctuation Control The third constraint is used to control the energy allocation change range to real-time power load As a benchmark, only Adjust within 10% to avoid causing instability to the power system, especially during peak energy consumption periods.
[0034] Step 4: After executing the scheduling instruction, collect performance feedback data related to the execution result; the performance feedback data includes: quality data, i.e. actual defect rate , Energy efficiency data is the actual / theoretical energy consumption ratio , Timeliness data refers to production delay time ; Define comprehensive deviation indicators based on feedback data: ; in, Indicates the expected defect rate of the original scheduling instruction (e.g. 0.3%); Indicates the maximum tolerable defect rate (such as 2%), Indicates the standard order cycle (e.g. 48h); when Greater than the set threshold , then the optimization mechanism is triggered Step 5: Based on the feedback data, optimize the scheduling strategy or parameter settings, and use the optimization results for subsequent production scheduling. The optimization mechanism includes: A loss function is constructed based on the square difference between the actual defect rate and the expected value, as well as the square difference of the energy consumption ratio from the ideal value of 1. The loss function represents the overall error or performance deviation caused by the current scheduling parameters R and T. Specifically, the loss function can be expressed as: ; This loss function comprehensively considers the execution deviation of both quality and energy efficiency. The smaller the value, the more reasonable the current parameter setting is, and vice versa, adjustment is needed. Based on the constructed loss function, the gradient descent method is used to optimize the parameters and obtain new scheduling parameters.
[0035] The optimization formula is expressed as follows: ; is the learning rate.
[0036] In addition, this embodiment also discloses a production management system for anti-corrosion pipelines, which is applied to the above-mentioned production management method for anti-corrosion pipelines. The system includes the following functional modules: The data acquisition module is used to collect order data and production line status data; the priority assessment module builds a multi-factor scoring model based on the collected data and completes order level classification; the production line status modeling module is used to evaluate equipment availability, cache rate and power load in real time to generate status indicators; The strategy matching and fusion module is used to build a three-dimensional strategy matching table and realize strategy fusion based on Euclidean distance. The multi-objective scheduling optimization module builds the scheduling objective function, solves the optimal control parameters and considers dynamic constraints. As well as the instruction execution and production line control module, which receives and issues control instructions to the production line control system to execute production scheduling; the performance feedback collection module, which collects the defect rate, energy efficiency ratio and delay time after scheduling execution in real time; the strategy evaluation and adaptive optimization module, which implements gradient optimization based on the deviation and loss function model and updates the scheduling parameters.
[0037] This embodiment also provides a computer device, which is suitable for a production management method for anti-corrosion pipelines, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a production management method for anti-corrosion pipelines as proposed in the above embodiment.
[0038] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.
[0039] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements a production management method for anti-corrosion pipelines as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.
[0040] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.
Claims
1. A production management method for anti-corrosion pipelines, characterized in that: include: Obtain data related to orders and production line operations, including order data and production line status data; Establish a priority assessment mechanism to classify orders and assess their processing priorities based on the acquired data; Generate scheduling control instructions containing multi-parameter control content based on order level and production line status; After executing the scheduling instruction, collecting performance feedback data related to the execution result; Based on the feedback data, the scheduling strategy or parameter setting is optimized, and the optimization results are used for subsequent production scheduling.
2. The production management method for anti-corrosion pipelines according to claim 1, characterized in that: The establishment process of the priority evaluation mechanism is as follows: S101: Construct a dimensionless normalized model, including: the proportion of order life requirements to the highest acceptable standard; the intensity of fluctuations in current material costs compared to baseline costs; and the current degree of quality achievement. S102: Based on the different contributions of the above three dimensions to different enterprise goals, weight factors are introduced to construct a multi-factor linear weighted model. The model outputs the priority of the current order in production line scheduling. The model formula is: ； in, Indicates that the anti-corrosion life requirements are extracted from the order database. Indicates the maximum reference life value, Indicates the actual cost of the raw materials used in the current order, represents the base cost of raw materials, Indicates the quality defect rate of the current order batch.
3. The production management method of an anti-corrosion pipeline according to claim 2, characterized in that: Define the minimum score for high-value orders Highest scoring lines with low value orders ; like :This order has high performance requirements or strict quality requirements or the raw materials are in a high-risk state, and the order level is marked as S1; like : Indicates that the order value is low and the cost sensitivity is high, and the order level is marked as S2; like :For intermediate-level orders, production is scheduled normally according to the production line status benchmark model, and the order level is marked as S3.
4. The production management method of an anti-corrosion pipeline according to claim 3, characterized in that: When confirming the status of the production line, the physical status of the production line is parameterized and a status indicator model is constructed. The main indicators include: equipment availability Cache occupancy rate and energy load index ; The formulas for each indicator are defined as follows: ； ,A three-dimensional matching decision table is constructed based on the status indicator model representing the ,production line status and the order level.
5. The production management method of an anti-corrosion pipeline according to claim 4, characterized in that: The generation of multi-parameter control instructions includes the following steps: S201: Determine a minimum rate deviation term to control quality fluctuations. This term represents the normalized deviation between the currently set coating rate and the ideal rate. Introduce a temperature offset term to achieve defect rate control. This term represents the deviation between the current curing temperature and the standard process temperature. Consider an energy load fluctuation term to balance system energy consumption. This term is used to constrain energy allocation changes to avoid excessive interference with the existing load distribution. S202: Based on minimizing the rate deviation term, the temperature offset term, and the energy load fluctuation term, a multi-objective weighted optimization function is constructed as the control core. The objective function formula is: ； in, Indicates the coating rate, Indicates the standard rate, Indicates the maximum rate, Indicates the curing temperature, Indicates the standard process temperature, represents the energy allocation adjustment amount, 、 、 Represent the weight coefficients of different items respectively; S203: Solve the objective function based on parameter constraints to obtain final control parameters, where the parameter constraints include: the impact of equipment availability on the rate upper limit; the temperature lower limit increase driven by the defect rate; and power fluctuation control.
6. The production management method of an anti-corrosion pipeline according to claim 5, characterized in that: The performance feedback data includes: quality data, i.e., actual defect rate , Energy efficiency data is the actual / theoretical energy consumption ratio , Timeliness data refers to production delay time ; Define comprehensive deviation indicators based on feedback data: ； in, represents the expected defect rate of the original scheduling instruction; represents the maximum tolerable defect rate, Indicates the standard order cycle; when Greater than the set threshold , then the optimization mechanism is triggered.
7. The production management method for anti-corrosion pipelines according to claim 6, characterized in that: The optimization mechanism includes: A loss function is constructed based on the square difference between the actual defect rate and the expected value, as well as the square difference of the energy consumption ratio from the ideal value of 1. The loss function represents the overall error or performance deviation caused by the current scheduling parameters R and T. Based on the constructed loss function, the gradient descent method is used to optimize the parameters and obtain new scheduling parameters.
8. A production management system for anti-corrosion pipelines, applied to the production management method for anti-corrosion pipelines according to any one of claims 1 to 7, characterized in that: The system includes the following functional modules: The data collection module is used to collect order data and production line status data; the priority assessment module builds a multi-factor scoring model based on the collected data and completes order level classification; the production line status modeling module is used to evaluate equipment availability, cache rate and power load in real time to generate status indicators; The strategy matching and fusion module is used to build a three-dimensional strategy matching table and realize strategy fusion based on Euclidean distance. The multi-objective scheduling optimization module builds the scheduling objective function, solves the optimal control parameters and considers dynamic constraints. As well as the instruction execution and production line control module, which receives and issues control instructions to the production line control system to execute production scheduling; the performance feedback collection module, which collects the defect rate, energy efficiency ratio and delay time after scheduling execution in real time; the strategy evaluation and adaptive optimization module, which implements gradient optimization based on the deviation and loss function model and updates the scheduling parameters.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the production management method of anti-corrosion pipelines according to any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the production management method of an anti-corrosion pipeline according to any one of claims 1 to 7 are implemented.