Intelligent control system and method for steel pipe process waiting time based on industrial Internet of Things

Abstract

The present invention discloses an intelligent control system and method for steel pipe process waiting time based on the industrial Internet of Things, which relates to the technical field of steel pipe intelligent manufacturing data analysis. The present invention forms an industrial data combination through an acquisition device; after preprocessing based on the industrial data combination, a steel pipe transportation execution knowledge base is constructed, and several groups of underlying data are formed based on the steel pipe transportation execution knowledge base; based on the several groups of underlying data, whether the transportation decision of the RGV trolley is executed is judged, and during the execution of the transportation decision of the RGV trolley, whether there is any change in the process plan data issued by the upper system is monitored in real time; when there is a change in the process plan data, a three-dimensional warehouse shelf intelligent control decision is formed based on the material data corresponding to each process and the storage data corresponding to each material data. The present invention is suitable for steel pipe manufacturing enterprises that build a production control system with the Internet of Things as the hub, and is particularly suitable for steel pipe manufacturing enterprises with a multi-process collaborative production layout.

Description

Technical Field

[0001] The present invention relates to the technical field of steel pipe intelligent manufacturing data analysis technology, and specifically to a steel pipe process waiting time intelligent control system and method based on the Industrial Internet of Things. Background Art

[0002] In the steel pipe manufacturing sector, a multi-process collaborative production model with three-dimensional warehousing as a transit point has gradually become a mainstream choice for companies building new plants or renovating and expanding existing ones. These companies use three-dimensional warehouses to connect the material flow of various automated production units (such as procurement and batching, pipe welding, cold drawing, heat treatment, and finishing), forming a production layout with the warehouse as the core and processes linked. Each process must independently complete specialized processing tasks while also relying on the material supply and quality output of upstream and downstream processes. This demonstrates the characteristics of "asynchronous production, synchronous linkage." This means that the subsequent process must dynamically adjust its production plan based on the real-time output status of the previous process (such as material specifications, quality grades, and output rhythm) to ensure coordinated and unified production rhythms throughout the entire process.

[0003] Traditional production control models, such as those that rely heavily on manual professional judgment or set production intervals on equipment, can ensure production, but they also have obvious shortcomings:

[0004] Static duration settings are not adaptable enough to specific scenarios. Current solutions mostly preset fixed waiting times based on historical experience. Although these can serve as a benchmark for capacity analysis, they are difficult to cope with real-time production fluctuations. For example, when the upstream cold drawing process temporarily slows down due to equipment failure, pipe jams, or other reasons, if the downstream heat treatment process still operates at full capacity according to the static beat, it is very likely to cause a backlog of materials to be processed or idling of processing equipment, forming a "loose at the front and tight at the back" production fault. It will also lead to ineffective occupation of the RGV cart by the downstream system. When a lag occurs, this delayed manual intervention or rigid system execution mode is particularly inefficient in flexible production scenarios with small batches and high varieties.

[0005] Lack of collaborative mechanism driven by a single parameter. Traditional systems only focus on the capacity parameters of a single process (such as equipment operation, remaining processing pipes, processing time, RGV unloading status, etc.), and lack coupling analysis of the total factor production status, resulting in vertical collaborative faults (upstream and downstream processes), horizontal linkage lags (processes and three-dimensional warehouses), equipment status fragmentation, etc. At the same time, changes in processes will inevitably affect the mobilization of materials. How to minimize material consumption and regulate storage equipment is also one of the problems existing in the current collaborative system. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent control system and method for steel pipe process waiting time based on industrial Internet of Things to solve the problems raised in the prior art.

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solution: an intelligent control method for waiting time of steel pipe process based on industrial Internet of Things, the method comprising:

[0008] The industrial data combination is formed through the acquisition device, specifically including the process planning data issued by the upper system, the material data corresponding to each process, and the storage data corresponding to each material data;

[0009] After preprocessing based on the industrial data combination, a steel pipe transportation execution knowledge base is constructed, and several groups of underlying data are formed based on the steel pipe transportation execution knowledge base;

[0010] Determine whether the RGV transportation decision is executed based on several sets of underlying data. During the RGV transportation decision execution process, monitor in real time whether there are any changes in the process plan data issued by the upper system.

[0011] When there are changes in the process plan data, intelligent control decisions for the three-dimensional warehouse shelves are made based on the material data corresponding to each process and the storage data corresponding to each material data.

[0012] According to the above technical solution, the process planning data issued by the upper system includes the processing parameters of each process, the real-time status data of the process supporting equipment, and the material application data of the process configuration;

[0013] The storage data corresponding to each material data includes the specific shelf type and shelf number of each material data in the three-dimensional warehouse.

[0014] According to the above technical solution, the steel pipe transportation execution knowledge base includes a waiting time knowledge base, an upstream and downstream process in and out of the warehouse, a process time constraint base, and a historical production execution base;

[0015] The waiting time knowledge base is used to store production equipment status data and logistics equipment status data. The production equipment status data includes the production status of each process, the waiting status of each process, and the waiting time of each process; the logistics equipment status data includes the RGV trolley number, the sending time of the RGV trolley application, the arrival time of the RGV trolley, and the waiting time of the RGV trolley;

[0016] The upstream and downstream process in and out warehouse is used to store the upstream and downstream process contract number, plan number, real-time in and out warehouse quantity under a certain process, and the number of remaining pipe rows;

[0017] The process time constraint library is used to constrain production conditions, specifically including time constraints and capacity constraints, which are used to determine the maximum waiting time of the RGV and the minimum number of waiting roots in the downstream production process;

[0018] The historical production execution library is used to store the historical records of specification matching of upstream and downstream processes, record the equipment parameter configuration cases of pipes with different specifications in each process, form a mapping table between specifications and equipment parameters; and mark the waiting time caused by specification mismatch in each process.

[0019] According to the above technical solution, the process of determining whether the RGV vehicle's transportation decision is executed based on several sets of underlying data includes:

[0020] Determine the maximum waiting time for the RGV trolley to operate, determine the best time to request the RGV trolley based on the maximum waiting time for the RGV trolley to operate, and determine whether to execute the RGV trolley's transportation decision, specifically including:

[0021] Define the completion time of the current process for:

[0022]

[0023] in, is the current system time; The production time of the current process; The standard production time for a single pipe row is calculated by multiplying the unit production time of each pipe row of the current process by the required quantity.

[0024] Construct the state function of the RGV car:

[0025]

[0026] in, The estimated departure time of the RGV vehicle; The remaining transportation time of the RGV vehicle;

[0027] Build the IoT system process to apply for RGV car time Bidirectional time constraints:

[0028]

[0029] in, The RGV vehicle execution loss time includes the route travel time and loading and unloading time. The route travel time is calculated based on the average speed under historical data, and the loading and unloading time is determined based on the average loading and unloading time under historical data.

[0030] If exists , determine the transportation decision of the RGV car and mark its transportation; if there is , the transportation decision of the RGV car is not executed;

[0031] When the RGV trolley's transportation decision is determined, the RGV trolley's request time is formed based on the maximum waiting time of the RGV trolley.

[0032] According to the above technical solution, when the process plan data changes, the intelligent control decision of the three-dimensional warehouse shelves is formed based on the material data corresponding to each process and the storage data corresponding to each material data, including:

[0033] Changes in the process plan data include: a failure in the current process, resulting in a delay, recording the delay time; the system actively adjusts and changes the planned time of the next process, and records the difference between the changed time and the original planned time;

[0034] Set a change time value. If a fault occurs in the current process and a delay occurs, the change time value is equal to the delay time. When the system actively adjusts, if the change time is earlier than the original plan time, the change time value is equal to the absolute value of the difference between the change time and the original plan time. If the change time is later than the original plan time, the change time value is equal to the negative of the absolute value of the difference between the change time and the original plan time.

[0035] Obtain the delay time of the downstream process when the upstream process changes the time value under the historical data to form a record data group ,in Refers to the variable time value that occurs in the upstream process; Refers to the delay time of the downstream process; the delay time of the downstream process refers to the difference between the original completion time of the downstream process and the actual completion time of the downstream process, taking the absolute value; based on several sets of record data sets A linear fitting function is formed to construct a functional relationship between the time value of the change in the upstream process and the delay time of the downstream process;

[0036] Obtain the material data corresponding to the current process and the next process and the storage data corresponding to each material data. If the material data corresponding to the current process and the next process are on the same shelf, output the delay time of the downstream process based on the change time value of the current process, form a new call time of the material shelf required in the downstream process based on the delay time of the downstream process, obtain the time difference between the required material shelf and the new call time, and compare it with the shelf call threshold set in the system. If it is less than the shelf call threshold set in the system, mark the shelf of the currently required material and do not execute the shelf homing decision.

[0037] The intelligent control system for waiting time of steel pipe process based on industrial Internet of Things includes:

[0038] The acquisition and processing module is used to form an industrial data combination through the acquisition device, specifically including the process planning data issued by the upper system, the material data corresponding to each process, and the storage data corresponding to each material data;

[0039] The steel pipe transportation execution module builds a steel pipe transportation execution knowledge base after preprocessing based on the industrial data combination, and forms several groups of underlying data based on the steel pipe transportation execution knowledge base;

[0040] The RGV decision module determines whether to execute the RGV transportation decision based on several sets of underlying data. During the execution of the RGV transportation decision, it monitors in real time whether there are any changes in the process plan data issued by the upper-level system.

[0041] The intelligent control module forms intelligent control decisions for the three-dimensional warehouse shelves based on the material data corresponding to each process and the storage data corresponding to each material data when there are changes in the process plan data.

[0042] According to the above technical solution, the process planning data issued by the upper system includes the processing parameters of each process, the real-time status data of the process supporting equipment, and the material application data of the process configuration;

[0043] The storage data corresponding to each material data includes the specific shelf type and shelf number of each material data in the three-dimensional warehouse.

[0044] According to the above technical solution, the steel pipe transportation execution knowledge base includes a waiting time knowledge base, an upstream and downstream process in and out of the warehouse, a process time constraint base, and a historical production execution base;

[0045] The waiting time knowledge base is used to store production equipment status data and logistics equipment status data. The production equipment status data includes the production status of each process, the waiting status of each process, and the waiting time of each process; the logistics equipment status data includes the RGV trolley number, the sending time of the RGV trolley application, the arrival time of the RGV trolley, and the waiting time of the RGV trolley;

[0046] The upstream and downstream process in and out warehouse is used to store the upstream and downstream process contract number, plan number, real-time in and out warehouse quantity under a certain process, and the number of remaining pipe rows;

[0047] The process time constraint library is used to constrain production conditions, specifically including time constraints and capacity constraints, which are used to determine the maximum waiting time of the RGV and the minimum number of waiting roots in the downstream production process;

[0048] The historical production execution library is used to store the historical records of specification matching of upstream and downstream processes, record the equipment parameter configuration cases of pipes with different specifications in each process, form a mapping table between specifications and equipment parameters; and mark the waiting time caused by specification mismatch in each process.

[0049] According to the above technical solution, the process of determining whether the RGV vehicle's transportation decision is executed based on several sets of underlying data includes:

[0050] Determine the maximum waiting time for the RGV trolley to operate, determine the best time to request the RGV trolley based on the maximum waiting time for the RGV trolley to operate, and determine whether to execute the RGV trolley's transportation decision, including:

[0051] Get the production time of the process and single row tube standard production time , combined with the current system time , determine the completion time of the current process ;

[0052] Get the status of RGV trolley in real time;

[0053] Determine the time to apply for RGV car in the process of building the Internet of Things system Bidirectional time constraints;

[0054] If exists , determine the transportation decision of the RGV car and mark its transportation; if there is , the transportation decision of the RGV car is not executed;

[0055] in, The estimated departure time of the RGV vehicle; The RGV vehicle execution loss time includes the route travel time and loading and unloading time. The route travel time is calculated based on the average speed under historical data, and the loading and unloading time is determined based on the average loading and unloading time under historical data.

[0056] When the RGV trolley's transportation decision is determined, the RGV trolley's request time is formed based on the maximum waiting time of the RGV trolley.

[0057] According to the above technical solution, the intelligent control module includes:

[0058] The change time analysis unit is used to set the change time value. If a fault occurs in the current process and a delay occurs, the change time value is equal to the delay time. When the system actively adjusts, if the change time is earlier than the original plan time, the change time value is equal to the difference between the change time and the original plan time, taking the absolute value. If the change time is later than the original plan time, the change time value is equal to the difference between the change time and the original plan time, taking the negative value of the absolute value.

[0059] The intelligent control unit outputs the delay time of the downstream process based on the change time value of the current process, forms a new call time for the material shelf required in the downstream process based on the delay time of the downstream process, obtains the time difference between the required material shelf and the new call time, and compares it with the shelf call threshold set in the system. If it is less than the shelf call threshold set in the system, the current required material shelf is marked and the shelf return decision is not executed.

[0060] Compared with the existing technology, the present invention has the following beneficial effects: In the process of building an Internet of Things system for steel pipe production, the present invention utilizes the interconnectedness of processes, real-time situation perception, and key equipment process control. By introducing the concept of dynamic rhythm, the present invention adopts learning and analysis based on historical data and cross-process data to achieve dynamic adjustment of waiting time, improve production material transfer efficiency, maximize equipment efficiency, reduce "blocking points", and ultimately ensure controllable production rhythm. Specifically, the present invention uses a dynamic optimization method for waiting time based on cross-process data to replace the traditional static time setting. By integrating historical production data with real-time production status, it achieves intelligent adjustment of waiting time between processes, significantly improving production material transfer efficiency, effectively addressing real-time production anomalies such as equipment failures and output fluctuations, and reducing the risk of "uneven production" faults. It also constructs a full-factor production status coupling analysis mechanism, breaking through the collaborative limitations of single-parameter drive, eliminating vertical process coordination faults and horizontal process and three-dimensional warehouse linkage lags, achieving real-time linkage and global optimization of upstream and downstream processes and warehousing logistics, and reducing material and equipment consumption and waste of time and resources. It is suitable for steel pipe manufacturing companies that build a production control system with the Internet of Things as the hub, and is especially suitable for steel pipe manufacturing companies with a multi-process collaborative production layout. It is suitable for new factories or expanded production lines with the characteristics of "asynchronous production and synchronous linkage". It can effectively solve the full-process rhythm coordination and resource optimization problems in multi-variety flexible production scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a schematic diagram of the steps of the method for intelligently controlling the waiting time of a steel pipe process based on the Industrial Internet of Things of the present invention;

[0062] Figure 2 This is a structural schematic diagram of the intelligent control system for steel pipe process waiting time based on the industrial Internet of Things of the present invention. DETAILED DESCRIPTION

[0063] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0064] Example: Figure 1 As shown, the present invention provides an intelligent control method for steel pipe process waiting time based on industrial Internet of Things, the method comprising:

[0065] The industrial data combination is formed through the acquisition device, specifically including the process planning data issued by the upper system, the material data corresponding to each process, and the storage data corresponding to each material data;

[0066] After preprocessing based on the industrial data combination, a steel pipe transportation execution knowledge base is constructed, and several groups of underlying data are formed based on the steel pipe transportation execution knowledge base;

[0067] Determine whether the RGV transportation decision is executed based on several sets of underlying data. During the RGV transportation decision execution process, monitor in real time whether there are any changes in the process plan data issued by the upper system.

[0068] When there are changes in the process plan data, intelligent control decisions for the three-dimensional warehouse shelves are made based on the material data corresponding to each process and the storage data corresponding to each material data.

[0069] The process planning data issued by the upper system includes the processing parameters of each process, the real-time status data of the process supporting equipment, and the material application data of the process configuration; the storage data corresponding to each material data includes the specific shelf type and shelf number of each material data in the three-dimensional warehouse.

[0070] Specifically, the collection device can refer to the use of IoT sensors to collect the real-time status of equipment such as cold drawing machines, heat treatment furnaces, finishing equipment (straightening, flaw detection, sawing, flattening chains), auxiliary equipment, and RGVs, such as temperature, operating conditions, rack status, working status, and occupancy status. By acquiring planning information issued by the upper-level system, the processing parameters of each current process, such as specifications, number of rows, steel type, contract, order, process requirements, packaging requirements, and planned quantity, are obtained, while also obtaining material data corresponding to each process. The current system's processing rhythm, such as material application time and execution information, is also obtained. At the same time, in data preprocessing, data standardization is required to eliminate the dimensional influence between parameters and to bring the data to a small, specific measurement scale. This can be achieved by normalizing the input and target parameters, which can help improve the model's convergence speed and accuracy. Administrators can set and select this option.

[0071] The steel pipe transportation execution knowledge base includes a waiting time knowledge base, an upstream and downstream process in and out of the warehouse, a process time constraint base, and a historical production execution base;

[0072] The waiting time knowledge base is used to store production equipment status data and logistics equipment status data. The production equipment status data includes the production status of each process, the waiting status of each process, and the waiting time of each process; the logistics equipment status data includes the RGV trolley number, the sending time of the RGV trolley application, the arrival time of the RGV trolley, and the waiting time of the RGV trolley;

[0073] The upstream and downstream process in and out warehouse is used to store the upstream and downstream process contract number, plan number, real-time in and out warehouse quantity under a certain process, and the number of remaining pipe rows;

[0074] The process time constraint library is used to constrain production conditions, specifically including time constraints and capacity constraints, which are used to determine the maximum waiting time of the RGV and the minimum number of waiting roots in the downstream production process;

[0075] The historical production execution library is used to store the historical records of specification matching of upstream and downstream processes, record the equipment parameter configuration cases of pipes with different specifications in each process, form a mapping table between specifications and equipment parameters; and mark the waiting time caused by specification mismatch in each process.

[0076] The determination of whether the RGV vehicle's transportation decision is executed based on several sets of underlying data includes:

[0077] Determine the maximum waiting time for the RGV trolley to operate, determine the best time to request the RGV trolley based on the maximum waiting time for the RGV trolley to operate, and determine whether to execute the RGV trolley's transportation decision, specifically including:

[0078] Define the completion time of the current process for:

[0079]

[0080] in, is the current system time; The production time of the current process; The standard production time for a single row of pipes is calculated by multiplying the unit production time by the required quantity for each pipe row of the same specification in the current process. This means that during the algorithm execution, the production time for each pipe is calculated based on the number of pipe rows of the same specification and their execution time. This is obtained by multiplying the quantity by the unit time when acquiring materials.

[0081] Construct the state function of the RGV car:

[0082]

[0083] in, The estimated departure time of the RGV vehicle; The remaining transportation time of the RGV trolley; that is, if the RGV trolley is in an idle state, , ; If in transit, The remaining transportation time, ;

[0084] Build the IoT system process to apply for RGV car time Bidirectional time constraints:

[0085]

[0086] in, The RGV vehicle execution loss time includes the route travel time and loading and unloading time. The route travel time is calculated based on the average speed under historical data, and the loading and unloading time is determined based on the average loading and unloading time under historical data.

[0087] If exists , determine the transportation decision of the RGV car and mark its transportation; if there is , the transportation decision of the RGV car is not executed;

[0088] When the RGV trolley's transportation decision is determined, the RGV trolley's request time is formed based on the maximum waiting time of the RGV trolley.

[0089] When the process plan data changes, the intelligent control decision of the three-dimensional warehouse shelves is formed based on the material data corresponding to each process and the storage data corresponding to each material data, including:

[0090] Specifically, an automated high-bay warehouse generally includes structural components such as incoming storage, inspection, palletizing, storage, outgoing storage, pallet storage, defective product storage, and miscellaneous items. During planning, it's not necessary to include every one of these areas within the warehouse. Areas can be rationally divided and increased or decreased based on the user's process characteristics and requirements. In terms of shelf design, due to the need to consider the warehouse's floor area and space utilization, a wide variety of shelf types are used, such as beam racks, bracket racks, and mobile racks. These types are selected based on the unit's dimensions, weight, and other relevant factors. When an RGV cart retrieves goods, the shelf descends to dock with the cart, completing the material transfer process. After docking, the shelf ascends and returns to its original position, awaiting the next RGV cart's arrival. Therefore, during the shelf's movement, the system's built-in shelf call threshold is typically set based on the time it takes the shelf to ascend and descend.

[0091] Changes in the process plan data include: a failure in the current process, resulting in a delay, recording the delay time; the system actively adjusts and changes the planned time of the next process, and records the difference between the changed time and the original planned time;

[0092] Set a change time value. If a fault occurs in the current process and a delay occurs, the change time value is equal to the delay time. When the system actively adjusts, if the change time is earlier than the original plan time, the change time value is equal to the absolute value of the difference between the change time and the original plan time. If the change time is later than the original plan time, the change time value is equal to the negative of the absolute value of the difference between the change time and the original plan time.

[0093] Obtain the delay time of the downstream process when the upstream process changes the time value under the historical data to form a record data group ,in Refers to the variable time value that occurs in the upstream process; Refers to the delay time of the downstream process; the delay time of the downstream process refers to the difference between the original completion time of the downstream process and the actual completion time of the downstream process, taking the absolute value; based on several sets of record data sets A linear fitting function is formed to construct a functional relationship between the time value of the change in the upstream process and the delay time of the downstream process;

[0094] Obtain the material data corresponding to the current process and the next process and the storage data corresponding to each material data. If the material data corresponding to the current process and the next process are on the same shelf, output the delay time of the downstream process based on the change time value of the current process, form a new call time of the material shelf required in the downstream process based on the delay time of the downstream process, obtain the time difference between the required material shelf and the new call time, and compare it with the shelf call threshold set in the system. If it is less than the shelf call threshold set in the system, mark the shelf of the currently required material and do not execute the shelf homing decision.

[0095] like Figure 2 As shown, an intelligent control system for waiting time of steel pipe process based on industrial Internet of Things is also provided, which includes:

[0096] The acquisition and processing module 101 is used to form an industrial data combination through the acquisition device, specifically including the process plan data issued by the upper system, the material data corresponding to each process, and the storage data corresponding to each material data;

[0097] The steel pipe transportation execution module 102 constructs a steel pipe transportation execution knowledge base after preprocessing based on the industrial data combination, and forms several groups of underlying data based on the steel pipe transportation execution knowledge base;

[0098] The RGV decision module 103 determines whether the RGV transportation decision is executed based on several sets of underlying data. During the RGV transportation decision execution process, it monitors in real time whether there are any changes in the process plan data issued by the upper system.

[0099] The intelligent control module 104 forms intelligent control decisions for the three-dimensional warehouse shelves based on the material data corresponding to each process and the storage data corresponding to each material data when there are changes in the process plan data.

[0100] The process planning data issued by the upper system includes the processing parameters of each process, the real-time status data of the process supporting equipment, and the material application data of the process configuration;

[0101] The storage data corresponding to each material data includes the specific shelf type and shelf number of each material data in the three-dimensional warehouse.

[0102] The steel pipe transportation execution knowledge base includes a waiting time knowledge base, an upstream and downstream process in and out of the warehouse, a process time constraint base, and a historical production execution base;

[0103] The waiting time knowledge base is used to store production equipment status data and logistics equipment status data. The production equipment status data includes the production status of each process, the waiting status of each process, and the waiting time of each process; the logistics equipment status data includes the RGV trolley number, the sending time of the RGV trolley application, the arrival time of the RGV trolley, and the waiting time of the RGV trolley;

[0104] The upstream and downstream process in and out warehouse is used to store the upstream and downstream process contract number, plan number, real-time in and out warehouse quantity under a certain process, and the number of remaining pipe rows;

[0105] The process time constraint library is used to constrain production conditions, specifically including time constraints and capacity constraints, which are used to determine the maximum waiting time of the RGV and the minimum number of waiting roots in the downstream production process;

[0106] The historical production execution library is used to store the historical records of specification matching of upstream and downstream processes, record the equipment parameter configuration cases of pipes with different specifications in each process, form a mapping table between specifications and equipment parameters; and mark the waiting time caused by specification mismatch in each process.

[0107] The determination of whether the RGV vehicle's transportation decision is executed based on several sets of underlying data includes:

[0108] Determine the maximum waiting time for the RGV trolley to operate, determine the best time to request the RGV trolley based on the maximum waiting time for the RGV trolley to operate, and determine whether to execute the RGV trolley's transportation decision, including:

[0109] Get the production time of the process and single row tube standard production time , combined with the current system time , determine the completion time of the current process ;

[0110] Get the status of RGV trolley in real time;

[0111] Determine the time to apply for RGV car in the process of building the Internet of Things system Bidirectional time constraints;

[0112] If exists , determine the transportation decision of the RGV car and mark its transportation; if there is , the transportation decision of the RGV car is not executed;

[0113] in, The estimated departure time of the RGV vehicle; The RGV vehicle execution loss time includes the route travel time and loading and unloading time. The route travel time is calculated based on the average speed under historical data, and the loading and unloading time is determined based on the average loading and unloading time under historical data.

[0114] When the RGV trolley's transportation decision is determined, the RGV trolley's request time is formed based on the maximum waiting time of the RGV trolley.

[0115] The intelligent control module includes:

[0116] The change time analysis unit is used to set the change time value. If a fault occurs in the current process and a delay occurs, the change time value is equal to the delay time. When the system actively adjusts, if the change time is earlier than the original plan time, the change time value is equal to the difference between the change time and the original plan time, taking the absolute value. If the change time is later than the original plan time, the change time value is equal to the difference between the change time and the original plan time, taking the negative value of the absolute value.

[0117] The intelligent control unit outputs the delay time of the downstream process based on the change time value of the current process, forms a new call time for the material shelf required in the downstream process based on the delay time of the downstream process, obtains the time difference between the required material shelf and the new call time, and compares it with the shelf call threshold set in the system. If it is less than the shelf call threshold set in the system, the current required material shelf is marked and the shelf return decision is not executed.

[0118] Finally, it should be noted that the above descriptions are merely preferred embodiments 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 aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. An intelligent control method for waiting time of steel pipe process based on industrial Internet of Things, characterized by: The method includes: The industrial data combination is formed through the acquisition device, specifically including the process planning data issued by the upper system, the material data corresponding to each process, and the storage data corresponding to each material data; After preprocessing based on the industrial data combination, a steel pipe transportation execution knowledge base is constructed, and several groups of underlying data are formed based on the steel pipe transportation execution knowledge base; Determine whether the RGV transportation decision is executed based on several sets of underlying data. During the RGV transportation decision execution process, monitor in real time whether there are any changes in the process plan data issued by the upper system. When there are changes in process planning data, intelligent control decisions for the three-dimensional warehouse shelves are made based on the material data corresponding to each process and the storage data corresponding to each material data; The steel pipe transportation execution knowledge base includes a waiting time knowledge base, an upstream and downstream process in and out of the warehouse, a process time constraint base, and a historical production execution base; The determination of whether the RGV vehicle's transportation decision is executed based on several sets of underlying data includes: Determine the maximum waiting time for the RGV trolley to operate, determine the best time to request the RGV trolley based on the maximum waiting time for the RGV trolley to operate, and determine whether to execute the RGV trolley's transportation decision, specifically including: Define the completion time of the current process for: ； in, is the current system time; The production time of the current process; The standard production time for a single pipe row is calculated by multiplying the unit production time of each pipe row of the current process by the required quantity. Construct the state function of the RGV car: ； in, The estimated departure time of the RGV vehicle; The remaining transportation time of the RGV vehicle; Build the IoT system process to apply for RGV car time Bidirectional time constraints: ； in, The RGV vehicle execution loss time includes the route travel time and loading and unloading time. The route travel time is calculated based on the average speed under historical data, and the loading and unloading time is determined based on the average loading and unloading time under historical data. If exists , determine the transportation decision of the RGV car and mark its transportation; if there is , the transportation decision of the RGV car is not executed; When the RGV trolley's transportation decision is determined, the RGV trolley's request time is formed based on the maximum waiting time of the RGV trolley.

2. The method for intelligently controlling the waiting time of steel pipe processing based on the Industrial Internet of Things according to claim 1 is characterized in that: The process planning data issued by the upper system includes the processing parameters of each process, the real-time status data of the process supporting equipment, and the material application data of the process configuration; The storage data corresponding to each material data includes the specific shelf type and shelf number of each material data in the three-dimensional warehouse.

3. The method for intelligently controlling the waiting time of steel pipe processing based on the Industrial Internet of Things according to claim 2 is characterized in that: The waiting time knowledge base is used to store production equipment status data and logistics equipment status data. The production equipment status data includes the production status of each process, the waiting status of each process, and the waiting time of each process; the logistics equipment status data includes the RGV trolley number, the sending time of the RGV trolley application, the arrival time of the RGV trolley, and the waiting time of the RGV trolley; The upstream and downstream process in and out warehouse is used to store the upstream and downstream process contract number, plan number, real-time in and out warehouse quantity under a certain process, and the number of remaining pipe rows; The process time constraint library is used to constrain production conditions, specifically including time constraints and capacity constraints, which are used to determine the maximum waiting time of the RGV and the minimum number of waiting roots in the downstream production process; The historical production execution library is used to store the historical records of specification matching of upstream and downstream processes, record the equipment parameter configuration cases of pipes with different specifications in each process, form a mapping table between specifications and equipment parameters; and mark the waiting time caused by specification mismatch in each process.

4. The method for intelligently controlling the waiting time of steel pipe processing based on the Industrial Internet of Things according to claim 3 is characterized in that: When the process plan data changes, the intelligent control decision of the three-dimensional warehouse shelves is formed based on the material data corresponding to each process and the storage data corresponding to each material data, including: Changes in the process plan data include: a failure in the current process, resulting in a delay, recording the delay time; the system actively adjusts and changes the planned time of the next process, and records the difference between the changed time and the original planned time; Set a change time value. If a fault occurs in the current process and a delay occurs, the change time value is equal to the delay time. When the system actively adjusts, if the change time is earlier than the original plan time, the change time value is equal to the absolute value of the difference between the change time and the original plan time. If the change time is later than the original plan time, the change time value is equal to the negative of the absolute value of the difference between the change time and the original plan time. Obtain the delay time of the downstream process when the upstream process changes the time value under the historical data to form a record data group ,in Refers to the variable time value that occurs in the upstream process; Refers to the delay time of the downstream process; the delay time of the downstream process refers to the difference between the original completion time of the downstream process and the actual completion time of the downstream process, taking the absolute value; based on several sets of record data sets A linear fitting function is formed to construct a functional relationship between the time value of the change in the upstream process and the delay time of the downstream process; Obtain the material data corresponding to the current process and the next process and the storage data corresponding to each material data. If the material data corresponding to the current process and the next process are on the same shelf, output the delay time of the downstream process based on the change time value of the current process, form a new call time of the material shelf required in the downstream process based on the delay time of the downstream process, obtain the time difference between the required material shelf and the new call time, and compare it with the shelf call threshold set in the system. If it is less than the shelf call threshold set in the system, mark the shelf of the currently required material and do not execute the shelf homing decision.

5. An intelligent control system for steel pipe process waiting time based on the industrial Internet of Things, used to implement the intelligent control method for steel pipe process waiting time based on the industrial Internet of Things as claimed in claim 1, characterized in that: The system includes: The acquisition and processing module is used to form an industrial data combination through the acquisition device, specifically including the process planning data issued by the upper system, the material data corresponding to each process, and the storage data corresponding to each material data; The steel pipe transportation execution module builds a steel pipe transportation execution knowledge base after preprocessing based on the industrial data combination, and forms several groups of underlying data based on the steel pipe transportation execution knowledge base; The RGV decision module determines whether to execute the RGV transportation decision based on several sets of underlying data. During the execution of the RGV transportation decision, it monitors in real time whether there are any changes in the process plan data issued by the upper-level system. The intelligent control module forms intelligent control decisions for the three-dimensional warehouse shelves based on the material data corresponding to each process and the storage data corresponding to each material data when there are changes in the process plan data.

6. The intelligent control system for steel pipe process waiting time based on industrial Internet of Things according to claim 5 is characterized by: The process planning data issued by the upper system includes the processing parameters of each process, the real-time status data of the process supporting equipment, and the material application data of the process configuration; The storage data corresponding to each material data includes the specific shelf type and shelf number of each material data in the three-dimensional warehouse.

7. The intelligent control system for waiting time of steel pipe process based on industrial Internet of Things according to claim 5 is characterized by: The steel pipe transportation execution knowledge base includes a waiting time knowledge base, an upstream and downstream process in and out of the warehouse, a process time constraint base, and a historical production execution base; The waiting time knowledge base is used to store production equipment status data and logistics equipment status data. The production equipment status data includes the production status of each process, the waiting status of each process, and the waiting time of each process; the logistics equipment status data includes the RGV trolley number, the sending time of the RGV trolley application, the arrival time of the RGV trolley, and the waiting time of the RGV trolley; The upstream and downstream process in and out warehouse is used to store the upstream and downstream process contract number, plan number, real-time in and out warehouse quantity under a certain process, and the number of remaining pipe rows; The process time constraint library is used to constrain production conditions, specifically including time constraints and capacity constraints, which are used to determine the maximum waiting time of the RGV and the minimum number of waiting roots in the downstream production process; The historical production execution library is used to store the historical records of specification matching of upstream and downstream processes, record the equipment parameter configuration cases of pipes with different specifications in each process, form a mapping table between specifications and equipment parameters; and mark the waiting time caused by specification mismatch in each process.

8. The intelligent control system for steel pipe process waiting time based on industrial Internet of Things according to claim 7 is characterized by: The determination of whether the RGV vehicle's transportation decision is executed based on several sets of underlying data includes: Determine the maximum waiting time for the RGV trolley to operate, determine the best time to request the RGV trolley based on the maximum waiting time for the RGV trolley to operate, and determine whether to execute the RGV trolley's transportation decision, including: Get the production time of the process and single row tube standard production time , combined with the current system time , determine the completion time of the current process ; Get the status of RGV trolley in real time; Determine the time to apply for RGV car in the process of building the Internet of Things system Bidirectional time constraints; If exists , determine the transportation decision of the RGV car and mark its transportation; if there is , the transportation decision of the RGV car is not executed; in, The estimated departure time of the RGV vehicle; The RGV vehicle execution loss time includes the route travel time and loading and unloading time. The route travel time is calculated based on the average speed under historical data, and the loading and unloading time is determined based on the average loading and unloading time under historical data. When the RGV trolley's transportation decision is determined, the RGV trolley's request time is formed based on the maximum waiting time of the RGV trolley.

9. The method for intelligently controlling the waiting time of steel pipe processing based on the Industrial Internet of Things according to claim 8 is characterized in that: The intelligent control module includes: The change time analysis unit is used to set the change time value. If a fault occurs in the current process and a delay occurs, the change time value is equal to the delay time. When the system actively adjusts, if the change time is earlier than the original plan time, the change time value is equal to the difference between the change time and the original plan time, taking the absolute value. If the change time is later than the original plan time, the change time value is equal to the difference between the change time and the original plan time, taking the negative value of the absolute value. The intelligent control unit outputs the delay time of the downstream process based on the change time value of the current process, forms a new call time for the material shelf required in the downstream process based on the delay time of the downstream process, obtains the time difference between the required material shelf and the new call time, and compares it with the shelf call threshold set in the system. If it is less than the shelf call threshold set in the system, the current required material shelf is marked and the shelf return decision is not executed.

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