A method for intelligent scheduling and control of cable protection pipe production

Abstract

This invention relates to the field of cable protection pipe manufacturing technology, and more particularly to an intelligent scheduling and control method for cable protection pipe production. The method includes: periodically acquiring the production types of several production processes within a target production scheduling range to determine the corresponding production scheduling method for each process; determining the adjustment parameters for each production process based on the corresponding production scheduling method, and identifying several key processes; determining the production early warning index for each key process based on the adjustment parameters, to determine whether to adjust the production status of each key process from a normal analysis state to an abnormal analysis state; and executing production tasks based on the target production parameters for each production process to obtain the target cable protection pipe. This invention can improve the efficiency of cable protection pipe production control and enhance the flexibility of quality management.

Description

Technical Field

[0001] The present invention relates to the technical field of cable protection pipe production, and particularly to an intelligent scheduling control method for cable protection pipe production. Background Art

[0002] Under the background of the continuous promotion of municipal power, rail transit, communication pipe networks and new urbanization construction, cable protection pipes have become the core supporting components for power cable laying and underground pipe gallery construction. Currently, cable protection pipe production lines generally adopt continuous flow line operations such as extrusion molding, cooling and shaping, fixed-length cutting, flaring molding, inspection and palletizing. The production process involves many key process parameters such as extrusion temperature, traction speed, cooling water temperature, vacuum shaping pressure, cutting length, and flaring depth. The coupling degree between each process is high and the timing constraint is strong. Any scheduling lag or parameter mismatch in any link will directly lead to problems such as production interruption, uneven wall thickness, poor flaring, and reduced production capacity.

[0003] With the gradual penetration of intelligent manufacturing and industrial Internet technologies in the plastic pipe industry, most enterprises have introduced basic production data collection, order management, and equipment monitoring systems, but there are still obvious shortcomings in the production scheduling and process coordination control levels. Existing cable protection pipe production scheduling mostly relies on manual experience or fixed timing production scheduling. The scheduling method is single and lacks a dynamic adjustment mechanism, making it difficult to adapt to frequent specification switching. In the scenario of parallel production of multiple production lines and multiple processes, each process operates independently, and the phenomenon of information islands is prominent. The rhythm between the upstream extrusion process and the downstream cutting, flaring, and palletizing processes does not match, and it is very easy to出现 situations such as accumulation of semi-finished products and unstable production quality in the upstream affecting downstream production, resulting in insufficient production control efficiency and passive response in quality control, with insufficient flexibility. Summary of the Invention

[0004] Therefore, the present invention provides an intelligent scheduling control method for cable protection pipe production to overcome the problems of single production scheduling method, insufficient production control efficiency, and insufficient flexibility in quality control in the prior art.

[0005] To achieve the above object, the present invention provides an intelligent scheduling control method for cable protection pipe production, including: Step S1, periodically obtaining the production types of several production processes within the target production scheduling range to determine the production scheduling methods corresponding to each production process, wherein the production scheduling methods include scheduling coordination methods and scheduling optimization methods; Step S2, determining the adjusted production parameters of each production process based on the production scheduling methods corresponding to each production process, and determining several key processes; Step S3: Based on the adjusted production parameters of each key process, determine the production early warning index for each key process to determine whether to adjust the production status of each key process from a normal analysis state to an abnormal analysis state. In normal analysis, based on the scheduling conflict index and scheduling difficulty index of any of the key processes, it is determined whether to optimize the production parameters of the key processes to obtain the target production parameters of the key processes. In the anomaly analysis state, the correlation early warning index of the key process is determined based on the related processes of any of the key processes, so as to determine whether to optimize the production parameters of the key process and obtain the target production parameters of the key process. Step S4: Execute production tasks based on the target production parameters of each production process to obtain the target cable protection tube.

[0006] Further, prior to step S1, the following steps are included: Real-time acquisition of production material parameters for several production processes within the target production scheduling range; Based on the comparison results between the production material parameters of each production process and the corresponding standard material parameters, the production deviation index corresponding to each production process is determined. Based on the comparison results between the production deviation index corresponding to each production process and the preset deviation index, the production type of each production process is determined, wherein the production type includes the production type to be coordinated and the production type to be optimized.

[0007] Further, in step S1, determining the production scheduling method corresponding to any of the production processes includes: If the production type of the production process is a production type to be coordinated, then the production scheduling method corresponding to the production process is determined to be a scheduling coordination method. If the production type of the production process is a production type to be optimized, then the production scheduling method corresponding to the production process is determined to be the scheduling optimization method.

[0008] Further, in step S2, determining the adjusted production parameters for any of the production processes includes: If the production scheduling method corresponding to the production process is the scheduling coordination method, then the initial production parameters of the production process are determined as the adjusted production parameters; If the production scheduling method corresponding to the production process is the scheduling optimization method, then the production adjustment coefficient is determined based on the production deviation index of the production process and the preset deviation index, and the adjusted production parameters are determined based on the production adjustment coefficient and the initial production parameters.

[0009] Furthermore, in step S2, several key processes are identified, including: If at least two of any three adjacent production processes have a production scheduling method that is optimized, then the intermediate production process among the three adjacent production processes is identified as a critical process.

[0010] Further, in step S3, determining the production early warning index for any of the key processes includes: The production early warning index of the key process is determined based on the comparison between the adjusted production parameters and the preset production parameters.

[0011] Further, in step S3, determining whether to adjust the production status of any of the key processes from a normal analysis state to an abnormal analysis state includes: Based on the comparison between the production early warning index of the key process and the preset early warning index, it is determined whether to adjust the production status of the key process from the normal analysis status to the abnormal analysis status.

[0012] Further, in step S3, during the normal analysis state, determining whether to optimize the production parameters for any of the key processes includes: In normal analysis, based on the comparison results of the scheduling conflict index of the key process with the preset conflict index, and the comparison results of the scheduling difficulty index of the key process with the preset difficulty index, it is determined whether to optimize the production parameters of the key process. The scheduling conflict index of the key process is determined based on the number of historical scheduling optimizations of the key process, and the scheduling difficulty index of the key process is determined based on the process order and the number of process constraints of the key process.

[0013] Further, in step S3, determining the correlation early warning index of the key process based on any of the related processes of the key process includes: Several related processes of the key process are determined based on a preset related process table; The correlation early warning index of the key process is determined based on the comparison results between the adjusted production parameters and the preset production parameters of each related process.

[0014] Further, in step S3, during the anomaly analysis, determining whether to optimize the production parameters for any of the key processes includes: In the anomaly analysis state, the comparison result between the correlation early warning index of the key process and the preset early warning index determines whether to optimize the production parameters of the key process.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: By periodically acquiring the production type of each production process within the target production scheduling range, this invention can dynamically match the appropriate scheduling coordination method or scheduling optimization method, ensuring that the scheduling strategy adapts to the actual operating characteristics of the production process. This avoids scheduling redundancy, scheduling lag, or scheduling failure caused by using a unified scheduling logic, improving the targeting, adaptability, and smoothness of production scheduling, and laying the foundation for subsequent precise parameter control. By matching corresponding adjusted production parameters to each production process based on the determined production scheduling method, and selecting several key processes, this invention enables the centralized allocation of production resources and optimization computing power, effectively reducing the control risks caused by blindly adjusting the entire process, while highlighting the critical links with significant impact, thus improving the efficiency and reliability of overall scheduling control. By calculating a production early warning index based on adjustments to production parameters in key processes, a quantitative assessment of the operational status of these processes is achieved. This allows for timely switching of production status from normal to abnormal when abnormal trends emerge, establishing a hierarchical intelligent scheduling mechanism. Under normal analysis conditions, parameter optimization combining scheduling conflict and difficulty indices effectively reduces resource competition and conflict resistance, achieving efficient and robust scheduling under normal operating conditions. Under abnormal analysis conditions, combining the associated early warning indices of related processes enables global correction from a process linkage perspective, preventing the spread of single-point anomalies that could lead to line-wide fluctuations. This improves the efficiency of cable protection pipe production control and enhances the flexibility of quality management. Production tasks are executed based on optimized target production parameters to complete the production of target cable protection pipes, ensuring stable operation of each process according to optimal parameters, thereby improving both production efficiency and product quality.

[0016] Furthermore, this invention, by collecting production material parameters for each production process within the target production scheduling range in real time, can comprehensively and accurately reflect the actual operating information of each production process, providing a data foundation for subsequent deviation judgment, type classification, and scheduling decisions, thereby improving the accuracy and stability of scheduling control. By quantitatively comparing real-time production material parameters with preset standard material parameters, a production deviation index is obtained, realizing a digital representation of the degree of material deviation in each production process. This accurately reflects issues such as material supply fluctuations, material specification differences, and abnormal material states, providing a quantifiable basis for classifying the production types of production processes. By comparing the production deviation index with a preset deviation index, the invention automatically classifies production types to be coordinated and production types to be optimized, achieving automatic classification of processes based on material deviation. This allows the selection of subsequent scheduling methods to align with actual production, making the scheduling logic more targeted. Processes with low deviation levels are handled through coordination, while processes with high deviation levels are handled through optimization, thereby improving scheduling control efficiency and enhancing the consistency and stability of the production process.

[0017] Furthermore, this invention achieves precise matching between scheduling mode and process status by matching scheduling coordination mode and scheduling optimization mode according to whether the production process is a production type to be coordinated or a production type to be optimized. This makes the scheduling strategy more targeted and reasonable, avoids problems such as poor adaptability and control failure caused by unified scheduling logic, and improves the intelligence level and operational stability of production scheduling control.

[0018] Furthermore, this invention, by directly using initial production parameters as adjustment parameters for processes employing a scheduling and coordination approach, simplifies the control process while ensuring production stability, reduces the volatility risk caused by frequent parameter changes, and guarantees continuous and stable operation of the process. For processes employing a scheduling optimization approach, production adjustment coefficients are calculated based on the production deviation index and a preset deviation index, and these are combined with the initial production parameters to determine the adjustment parameters. This achieves quantitative correction and precise adjustment of parameters, reducing the deviation between actual production and the standard state and ensuring product quality.

[0019] Furthermore, this invention makes a judgment by scheduling three adjacent processes, and identifies the intermediate production process that meets the conditions as the key process. It can automatically identify the nodes with significant impact in the production process, thereby improving optimization efficiency and control effect.

[0020] Furthermore, this invention calculates a production early warning index based on a comparison between adjusted production parameters and preset production parameters, enabling digital judgment of the operational status of key processes. This allows for timely identification of potential risks such as parameter deviations and abnormal trends, providing an objective basis for status switching and improving the accuracy and timeliness of early warnings. By automatically switching production statuses based on the comparison between the production early warning index and the preset early warning index, hierarchical control of normal and abnormal operating conditions is achieved. Higher-level control logic can be immediately activated when abnormal trends appear, preventing small deviations from escalating into production failures and improving the efficiency and stability of production scheduling and control.

[0021] Furthermore, this invention automatically matches several related processes corresponding to key processes through a preset related process table, thereby standardizing and rapidly determining the relationship. By comparing the production parameters adjusted according to each related process with the preset production parameters, the association early warning index of the key process is calculated, realizing a global status linkage quantitative assessment of key processes and related processes. This can comprehensively reflect the overall operational deviation and abnormal transmission risk of the process chain, and improve the comprehensiveness and accuracy of abnormal status assessment. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the intelligent scheduling and control method for cable protection pipe production according to an embodiment of the present invention. Figure 2 A logic diagram for determining the production scheduling method corresponding to any production process in an embodiment of the present invention; Figure 3 A logic diagram for determining whether to adjust the production status of any key process from a normal analysis state to an abnormal analysis state in an embodiment of the present invention; Figure 4 This is a flowchart illustrating the process of determining the associated early warning index for any key process in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0024] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0025] Please see Figure 1 The diagram shown is a flowchart illustrating the intelligent scheduling and control method for cable protection pipe production according to an embodiment of the present invention. The present invention provides an intelligent scheduling and control method for cable protection pipe production, comprising: Step S1: Periodically obtain the production type of several production processes within the target production scheduling range to determine the production scheduling method corresponding to each production process. The production scheduling method includes scheduling coordination method and scheduling optimization method. Please see Figure 2 As shown, it is a logic judgment diagram for determining the production scheduling method corresponding to any production process in an embodiment of the present invention; specifically, before step S1, it further includes: Step S01: Obtain the production material parameters of several production processes within the target production scheduling range in real time; In implementation, the production processes include, but are not limited to: batching and drying, extrusion molding, and sizing and cooling. The production material parameters are determined according to the corresponding production process. For example, for the batching and drying process, the production material parameters include the raw material moisture content, the average particle size of the raw material, and the feeding speed; for the extrusion molding process, the production material parameters include the melt temperature, the melt pressure, the pipe wall thickness, and the pipe outer diameter; for the sizing and cooling process, the production material parameters include the pipe surface temperature and the sizing vacuum degree. Specifically, corresponding sensors and control systems can be installed in each production process to collect the corresponding production material parameters in real time.

[0026] Step S02: Based on the comparison results between the production material parameters of each production process and the corresponding standard material parameters, determine the production deviation index corresponding to each production process; In implementation, each production process has corresponding standard material parameters, which are pre-stored process baseline values ​​in a database. These parameters can be determined through historical optimal operating conditions or process design values. In practical applications, each material parameter is normalized to map it to the 0-1 range, eliminating dimensional interference. For example, for any material parameter x, its corresponding normalized material parameter... , where x max x represents the maximum value of this production parameter within the industry standard production range or in actual historical production processes. min This represents the minimum value of the production parameter within the industry standard production range or in actual historical production processes. For any production process, the corresponding production material parameters SY1, SY2, ..., SY... j , ..., SY m The corresponding standard material parameters are SE1, SE2, ..., SE j SE m Then the production deviation index SP corresponding to this production process is ∑ m j=1 (abs(SY j -SE j ) / SE j ) / m, where j=1,2,…,m, m is the quantity of production material parameters for this production process, and abs() is a preset absolute value determination function.

[0027] Step S03: Based on the comparison results between the production deviation index corresponding to each production process and the preset deviation index, determine the production type of each production process, wherein the production type includes the production type to be coordinated and the production type to be optimized.

[0028] In implementation, for any production process, if the production deviation index corresponding to that process is less than the preset deviation index, it indicates that the material parameters of that process are within an acceptable range and can be recovered through adaptive coordination of process parameters. In this case, the production type of that process is determined as the production type to be coordinated. If the production deviation index corresponding to that process is greater than or equal to the preset deviation index, it indicates that the material parameters of that process are significantly deviated and difficult to recover through adaptive coordination, requiring control optimization. In this case, the production type of that process is determined as the production type to be optimized. Each production process has a corresponding preset deviation index. In practical applications, the preset deviation index can be set based on the average production deviation index calculated from the production material parameters that have passed the qualification inspection in the historical production data of the corresponding production process. Passing the qualification inspection indicates that the production material parameters of the corresponding production process meet production requirements or industry standards.

[0029] Specifically, this invention, by collecting real-time production material parameters for each production process within the target production scheduling range, comprehensively and accurately reflects the actual operating information of each production process. This provides a data foundation for subsequent deviation judgment, type classification, and scheduling decisions, improving the accuracy and stability of scheduling control. By quantitatively comparing real-time production material parameters with preset standard material parameters, a production deviation index is obtained, digitally representing the degree of material deviation in each production process. This accurately reflects issues such as material supply fluctuations, material specification differences, and abnormal material states, providing a quantifiable basis for classifying production types within each process. By comparing the production deviation index with a preset deviation index, the invention automatically classifies production types to be coordinated and production types to be optimized. This achieves automatic process classification based on material deviation, ensuring that subsequent scheduling methods align with actual production, making the scheduling logic more targeted. Processes with low deviation levels are handled through coordination, while processes with high deviation levels are handled through optimization, thereby improving scheduling control efficiency and enhancing the consistency and stability of the production process.

[0030] Specifically, in step S1, determining the production scheduling method corresponding to any of the production processes includes: If the production type of the production process is a production type to be coordinated, then the production scheduling method corresponding to the production process is determined to be a scheduling coordination method. If the production type of the production process is a production type to be optimized, then the production scheduling method corresponding to the production process is determined to be the scheduling optimization method.

[0031] Specifically, this invention achieves precise matching between scheduling mode and process status by matching scheduling coordination mode and scheduling optimization mode according to whether the production process is a production type to be coordinated or a production type to be optimized. This makes the scheduling strategy more targeted and reasonable, avoids problems such as poor adaptability and control failure caused by unified scheduling logic, and improves the intelligence level and operational stability of production scheduling control.

[0032] Step S2: Determine the adjusted production parameters for each production process based on the production scheduling method corresponding to each production process, and identify several key processes. Specifically, in step S2, determining the adjustment parameters for any of the production processes includes: If the production scheduling method corresponding to the production process is the scheduling coordination method, then the initial production parameters of the production process are determined as the adjusted production parameters; If the production scheduling method corresponding to the production process is the scheduling optimization method, then the production adjustment coefficient is determined based on the production deviation index of the production process and the preset deviation index, and the adjusted production parameters are determined based on the production adjustment coefficient and the initial production parameters.

[0033] In implementation, each production process has corresponding production parameters. For example, for the batching and drying process, the production parameters include drying temperature, drying time, and raw material stirring speed; for the extrusion molding process, the production parameters include heating temperature and extrusion speed; for the sizing and cooling process, the production parameters include cooling temperature, traction speed, and discharge temperature. The initial production parameters are the initially set production parameters. For any production process, if the production scheduling method corresponding to the production process is the scheduling coordination method, then the initial production parameters are directly determined as the adjusted production parameters. If the production scheduling method corresponding to the production process is the scheduling optimization method, then the production adjustment coefficient K = 1 + a × ((SP - HP) / HP), where SP is the production deviation index corresponding to the production process, HP is the preset deviation index, and a is the adjustment sensitivity coefficient, 0 < a ≤ 1, which can be preset by the process engineer based on the response sensitivity of the production parameters to material deviation. For example, for positively correlated parameters (such as reducing the speed if the deviation is too high), a negative sign is taken; for negatively correlated parameters (such as increasing the drying temperature if the deviation is too high), a positive sign is taken, and the product of the production adjustment coefficient and the initial production parameters is determined as the adjusted production parameter.

[0034] Specifically, this invention, by directly using initial production parameters as adjustment parameters for processes employing a scheduling and coordination approach, simplifies the control process while ensuring production stability, reduces the volatility risk caused by frequent parameter changes, and guarantees continuous and stable operation of the process. For processes employing a scheduling optimization approach, production adjustment coefficients are calculated based on the production deviation index and a preset deviation index, and the adjustment production parameters are determined in conjunction with the initial production parameters. This achieves quantitative correction and precise adjustment of parameters, reducing the deviation between actual production and standard conditions, and ensuring product quality.

[0035] Specifically, in step S2, several key processes are identified, including: If at least two of any three adjacent production processes have a production scheduling method that is optimized, then the intermediate production process among the three adjacent production processes is identified as a critical process.

[0036] In implementation, each production process is connected sequentially. If, in any three adjacent production processes (i.e., processes b-1, b, b+1), at least two of the production processes have a production scheduling optimization mode, it indicates that the production process of this group of production processes is not stable enough. Moreover, the production process in the middle position is in the core position of optimization density. Therefore, it is regarded as a key process for in-depth analysis.

[0037] Specifically, this invention uses the scheduling method of three adjacent processes to determine the intermediate production process that meets the conditions as the key process, which can automatically identify the nodes with significant impact in the production process and improve optimization efficiency and control effect.

[0038] Step S3: Based on the adjusted production parameters of each key process, determine the production early warning index for each key process to determine whether to adjust the production status of each key process from a normal analysis state to an abnormal analysis state. In normal analysis, based on the scheduling conflict index and scheduling difficulty index of any of the key processes, it is determined whether to optimize the production parameters of the key processes to obtain the target production parameters of the key processes. In the anomaly analysis state, the correlation early warning index of the key process is determined based on the related processes of any of the key processes, so as to determine whether to optimize the production parameters of the key process and obtain the target production parameters of the key process. Specifically, in step S3, determining the production early warning index for any of the key processes includes: The production early warning index of the key process is determined based on the comparison between the adjusted production parameters and the preset production parameters.

[0039] In implementation, each production parameter is normalized to map it to the 0-1 range, eliminating dimensional interference. For example, for any production parameter r, its corresponding normalized material parameter... , where r tag For the target value (preset production parameters), r tol This represents the maximum permissible deviation of the production parameter within industry standards / design requirements (usually taken as the process tolerance, such as a melt temperature tolerance of ±5℃, then r). tol =5). For any critical process, the corresponding production parameters TY1, TY2, ..., TY should be adjusted. i , ..., TY n The corresponding preset production parameters TE1, TE2, ..., TE i , ..., TE n Then the production early warning index corresponding to this production process Then the production early warning index corresponding to this production process `sqrt()` is a preset square root determination function, where `i` = 1, 2, ..., n, and `n` is the number of production parameters for the critical process. Each production process has corresponding preset production parameters. These parameters can be dynamically updated and optimized based on historical qualified production data or optimal process data, ensuring they align with actual production line conditions. A higher production warning index indicates greater consistency between the current adjusted production parameters and the preset parameters, resulting in a more stable production state. Conversely, a lower index indicates a more significant deviation between the current adjusted parameters and the preset parameters, leading to a more unstable production state and a higher risk of anomalies.

[0040] Please see Figure 3 As shown, this is a logic diagram illustrating whether to adjust the production status of any critical process from a normal analysis state to an abnormal analysis state according to an embodiment of the present invention. Specifically, in step S3, determining whether to adjust the production status of any of the critical processes from a normal analysis state to an abnormal analysis state includes: Based on the comparison between the production early warning index of the key process and the preset early warning index, it is determined whether to adjust the production status of the key process from the normal analysis status to the abnormal analysis status.

[0041] In implementation, for any critical process, if the production early warning index of that critical process is less than the preset early warning index, it indicates that the current production adjustment deviates significantly and the production status is unstable. In this case, the production status of the critical process will be adjusted from normal analysis to abnormal analysis. If the production early warning index of that critical process is greater than or equal to the preset early warning index, it indicates that the current production status is relatively stable, the deviation is within a controllable range, and the normal analysis status can be maintained. In this case, the production status of the critical process will not be adjusted from normal analysis to abnormal analysis. The actual implementers can set the preset early warning index as the average of the production parameters of each production process in the cable protection pipe production process that does not meet production requirements, based on actual conditions or historical data.

[0042] Specifically, this invention calculates a production early warning index based on a comparison between adjusted production parameters and preset production parameters, enabling digital judgment of the operational status of key processes. This allows for timely identification of potential risks such as parameter deviations and abnormal trends, providing objective evidence for status switching and improving the accuracy and timeliness of early warnings. By automatically switching production statuses based on the comparison between the production early warning index and the preset early warning index, it achieves hierarchical control of normal and abnormal operating conditions. Higher-level control logic can be immediately activated when abnormal trends appear, preventing small deviations from escalating into production failures and improving the efficiency and stability of production scheduling and control.

[0043] Specifically, in step S3, during the normal analysis state, determining whether to optimize the production parameters for any of the key processes includes: In normal analysis, based on the comparison results of the scheduling conflict index of the key process with the preset conflict index, and the comparison results of the scheduling difficulty index of the key process with the preset difficulty index, it is determined whether to optimize the production parameters of the key process. The scheduling conflict index of the key process is determined based on the number of historical scheduling optimizations of the key process, and the scheduling difficulty index of the key process is determined based on the process order and the number of process constraints of the key process.

[0044] In implementation, for any critical process, the scheduling optimization records of that critical process within a preset historical period are retrieved in real time. The preset historical period can be set to a range of 15 to 30 days. The total number of times the critical process has been optimized in the scheduling optimization records is counted as the historical scheduling optimization count of that critical process. The ratio of the historical scheduling optimization count to the total number of times the critical process has been produced within the preset historical period is determined as the scheduling conflict index. The processes are then ranked according to their execution order. For example, if the ranking value of the batching and drying process is 1, the ranking value of the extrusion molding process is 2, and the ranking value of the sizing and cooling process is 3, then the ranking factor is... Where Rk is the ranking value of the corresponding process, and Rm is the maximum ranking value. That is, the ranking factor of the upstream process (with a small Rk) is close to 1, and the ranking factor of the downstream process is close to 0. The parameter adjustment of the upstream process will affect all downstream processes, so the optimization difficulty is higher. The adjustment of the downstream process has a smaller impact range and is relatively easier. Each production process has a corresponding number of process constraints, which include, but are not limited to, temperature constraints, pressure constraints, speed constraints, vacuum constraints, and timing constraints. In practical applications, the number of process constraints corresponding to each production process can be determined by a finite number of single-variable experiments. For any critical process, the ratio of the number of process constraints of the critical process to the total number of statistical constraints is determined as the process constraint factor, and the product of the ranking factor corresponding to the critical process and the process constraint factor is determined as the scheduling difficulty index of the critical process.

[0045] Understandably, if the scheduling conflict index and scheduling difficulty index of a critical process are both less than the preset conflict index and the preset difficulty index, then the production parameters for that critical process are optimized. The product of the scheduling conflict index, the scheduling difficulty index, and the production adjustment coefficient is determined as the scheduling adjustment coefficient, and the product of the scheduling adjustment coefficient and the initial production parameters of that critical process is determined as the target production parameters for that critical process. If the scheduling conflict index of a critical process is greater than or equal to the preset conflict index, or the scheduling difficulty index is greater than or equal to the preset difficulty index, then the production parameters for that critical process are not optimized, and the adjusted production parameters are determined as the target production parameters for that critical process. In practical applications, the average scheduling conflict index of each production process is determined as the preset conflict index, and the average scheduling difficulty index of each production process is determined as the preset difficulty index.

[0046] Please see Figure 4 The diagram illustrates a flowchart of determining the correlation early warning index of any key process according to an embodiment of the present invention. Specifically, in step S3, determining the correlation early warning index of the key process based on the related processes of any of the key processes includes: Step S31: Determine several related processes of the key process based on the preset related process table; Step S32: Determine the correlation early warning index of the key process based on the comparison results of the adjusted production parameters and preset production parameters of each related process.

[0047] In implementation, associated processes refer to other production processes that have a strong coupling relationship with the current critical process. The pre-set associated process table is a mapping table pre-stored in the system database, used to quickly locate associated processes with a strong coupling relationship to the current critical process. Strong coupling relationships include upstream, downstream, and collaborative relationships. Taking the extrusion molding process as an example, the batching and drying process has an upstream relationship with the extrusion molding process, the sizing and cooling process has a downstream relationship with the extrusion molding process, and several extruders in the same workshop (sharing a mold) have collaborative relationships. For any associated process, its corresponding production parameters QY1, QY2, ..., QY are adjusted. g QY h The corresponding preset production parameters are QE1, QE2, ..., QE g QE h Then the production early warning index corresponding to the related process. Where g = 1, 2, ..., h, and h is the number of production parameters for the associated process. The average production early warning index of each associated process corresponding to the key process is determined as the associated early warning index for the key process.

[0048] Specifically, this invention automatically matches several related processes corresponding to key processes by pre-setting a related process table, thereby standardizing and rapidly determining the relationship. By comparing the production parameters adjusted according to each related process with the pre-set production parameters, the association early warning index of the key process is calculated, realizing the global status linkage quantitative assessment of key processes and related processes. This can comprehensively reflect the overall operational deviation and abnormal transmission risk of the process chain, and improve the comprehensiveness and accuracy of abnormal status assessment.

[0049] Specifically, in step S3, during the anomaly analysis, determining whether to optimize the production parameters for any of the key processes includes: In the anomaly analysis state, the comparison result between the correlation early warning index of the key process and the preset early warning index determines whether to optimize the production parameters of the key process.

[0050] In implementation, if the correlation early warning index of the key process is less than the preset early warning index, it indicates that the current production deviation is significant and the production status is unstable. In this case, the production parameters of the key process will be optimized, and the average of the production early warning index and the correlation early warning index of the key process will be determined as the early warning adjustment factor. The product of the early warning adjustment factor and the production parameters will be determined as the target production parameter. If the correlation early warning index of the key process is greater than or equal to the preset early warning index, the production parameters of the key process will not be optimized, and the production parameters of the key process will be determined as the target production parameter.

[0051] Step S4: Execute production tasks based on the target production parameters of each production process to obtain the target cable protection tube.

[0052] This invention, through periodically acquiring the production type of each production process within the target production scheduling range, can dynamically match suitable scheduling coordination or optimization methods. This ensures that the scheduling strategy adapts to the actual operating characteristics of the production processes, avoiding scheduling redundancy, lag, or failure caused by using a unified scheduling logic. This improves the targeting, adaptability, and smoothness of production scheduling, laying the foundation for subsequent precise parameter control. By matching corresponding adjusted production parameters to each production process based on a determined production scheduling method and selecting several key processes, it enables centralized allocation of production resources and optimization computing power. This effectively reduces the control risks caused by blindly adjusting all processes, while highlighting critical links with significant impact, thus improving the efficiency and reliability of overall scheduling control. By calculating a production early warning index based on adjustments to production parameters in key processes, a quantitative assessment of the operational status of these processes is achieved. This allows for timely switching of production status from normal to abnormal when abnormal trends emerge, establishing a hierarchical intelligent scheduling mechanism. Under normal analysis conditions, parameter optimization combining scheduling conflict and difficulty indices effectively reduces resource competition and conflict resistance, achieving efficient and robust scheduling under normal operating conditions. Under abnormal analysis conditions, combining the associated early warning indices of related processes enables global correction from a process linkage perspective, preventing the spread of single-point anomalies that could lead to line-wide fluctuations. This improves the efficiency of cable protection pipe production control and enhances the flexibility of quality management. Production tasks are executed based on optimized target production parameters to complete the production of target cable protection pipes, ensuring stable operation of each process according to optimal parameters, thereby improving both production efficiency and product quality.

[0053] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for intelligent scheduling and control of cable protection pipe production, characterized in that, include: Step S1: Periodically obtain the production type of several production processes within the target production scheduling range to determine the production scheduling method corresponding to each production process. The production scheduling method includes scheduling coordination method and scheduling optimization method. Step S2: Determine the adjusted production parameters for each production process based on the production scheduling method corresponding to each production process, and identify several key processes. Step S3: Based on the adjusted production parameters of each key process, determine the production early warning index for each key process to determine whether to adjust the production status of each key process from a normal analysis state to an abnormal analysis state. In normal analysis, based on the scheduling conflict index and scheduling difficulty index of any of the key processes, it is determined whether to optimize the production parameters of the key processes to obtain the target production parameters of the key processes. In the anomaly analysis state, the correlation early warning index of the key process is determined based on the related processes of any of the key processes, so as to determine whether to optimize the production parameters of the key process and obtain the target production parameters of the key process. Step S4: Execute production tasks based on the target production parameters of each production process to obtain the target cable protection tube.

2. The intelligent scheduling and control method for cable protection pipe production according to claim 1, characterized in that, Prior to step S1, the following is included: Real-time acquisition of production material parameters for several production processes within the target production scheduling range; Based on the comparison results between the production material parameters of each production process and the corresponding standard material parameters, the production deviation index corresponding to each production process is determined. Based on the comparison results between the production deviation index corresponding to each production process and the preset deviation index, the production type of each production process is determined, wherein the production type includes the production type to be coordinated and the production type to be optimized.

3. The intelligent scheduling and control method for cable protection pipe production according to claim 2, characterized in that, In step S1, determining the production scheduling method corresponding to any of the production processes includes: If the production type of the production process is a production type to be coordinated, then the production scheduling method corresponding to the production process is determined to be a scheduling coordination method. If the production type of the production process is a production type to be optimized, then the production scheduling method corresponding to the production process is determined to be the scheduling optimization method.

4. The intelligent scheduling and control method for cable protection pipe production according to claim 3, characterized in that, In step S2, determining the adjusted production parameters for any of the production processes includes: If the production scheduling method corresponding to the production process is the scheduling coordination method, then the initial production parameters of the production process are determined as the adjusted production parameters; If the production scheduling method corresponding to the production process is the scheduling optimization method, then the production adjustment coefficient is determined based on the production deviation index of the production process and the preset deviation index, and the adjusted production parameters are determined based on the production adjustment coefficient and the initial production parameters.

5. The intelligent scheduling and control method for cable protection pipe production according to claim 4, characterized in that, In step S2, several key processes are identified, including: If at least two of any three adjacent production processes have a production scheduling method that is optimized, then the intermediate production process among the three adjacent production processes is identified as a critical process.

6. The intelligent scheduling and control method for cable protection pipe production according to claim 5, characterized in that, In step S3, determining the production early warning index for any of the key processes includes: The production early warning index of the key process is determined based on the comparison between the adjusted production parameters and the preset production parameters.

7. The intelligent scheduling and control method for cable protection pipe production according to claim 6, characterized in that, In step S3, determining whether to adjust the production status of any of the key processes from a normal analysis state to an abnormal analysis state includes: Based on the comparison between the production early warning index of the key process and the preset early warning index, it is determined whether to adjust the production status of the key process from the normal analysis status to the abnormal analysis status.

8. The intelligent scheduling and control method for cable protection pipe production according to claim 7, characterized in that, In step S3, during normal analysis, determining whether to optimize the production parameters for any of the key processes includes: In normal analysis, based on the comparison results of the scheduling conflict index of the key process with the preset conflict index, and the comparison results of the scheduling difficulty index of the key process with the preset difficulty index, it is determined whether to optimize the production parameters of the key process. The scheduling conflict index of the key process is determined based on the number of historical scheduling optimizations of the key process, and the scheduling difficulty index of the key process is determined based on the process order and the number of process constraints of the key process.

9. The intelligent scheduling and control method for cable protection pipe production according to claim 8, characterized in that, In step S3, determining the correlation early warning index of the key process based on any of the related processes of the key process includes: Several related processes of the key process are determined based on a preset related process table; The correlation early warning index of the key process is determined based on the comparison results between the adjusted production parameters and the preset production parameters of each related process.

10. The intelligent scheduling and control method for cable protection pipe production according to claim 9, characterized in that, In step S3, during the anomaly analysis, determining whether to optimize the production parameters for any of the key processes includes: In the anomaly analysis state, the comparison result between the correlation early warning index of the key process and the preset early warning index determines whether to optimize the production parameters of the key process.

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