Industrial digital twin optimization process evaluation method and system

Abstract

The present invention provides an industrial digital twin optimization process evaluation method and system, including: step S1: obtaining a simulation business material data set and simulation business parameters; step S2: inputting the obtained simulation business material data set and simulation business parameters into a hot rolling heating furnace logistics process optimization verification model to perform heating furnace production process optimization simulation calculations, and obtain a slab heating production assignment plan and a slab production scheduling plan; step S3: displaying, summarizing and analyzing the simulation results and heating furnace simulation process data through the industrial digital twin system; wherein, the simulation business material data set includes: slab out-of-furnace sequence, slab information already in the furnace, and slab information to be entered into the furnace; the simulation business parameters include: heating furnace specification parameters and heating furnace simulation setting parameters.

Description

Technical Field

[0001] The present invention relates to the field of industrial digital twin technology, and in particular, to an industrial digital twin optimization process evaluation method and system. Background Art

[0002] At present, the production organization of hot rolling heating furnaces is usually based on the consistency of steel loading and steel withdrawal order, and the slabs are evenly distributed to the furnace, basically following the principle of first-loaded first-out. In order to balance the cross-loading of cold and hot slabs, sometimes the resources of hot slabs are not fully utilized, and the heating furnace process data may also be missing due to various reasons.

[0003] Patent document CN116240369A (application number: 202310276611.8) discloses a method and system for correcting billet tracking information in a heating furnace. This billet tracking and correction method for a heating furnace only tracks and corrects the heating furnace process for producing billets in a short period of time. It cannot simulate the heating production organization process for large-scale billet rolling plans, and cannot meet the simulation requirements of the complete heating process of the heating furnace production organization model. Summary of the Invention

[0004] In view of the defects in the prior art, the purpose of the present invention is to provide an industrial digital twin optimization process evaluation method and system.

[0005] An industrial digital twin optimization process evaluation method provided by the present invention includes:

[0006] Step S1: Acquire a simulation business material data set and simulation business parameters;

[0007] Step S2: Input the obtained simulation business material data set and simulation business parameters into the hot rolling heating furnace logistics process optimization verification model to perform heating furnace production process optimization simulation calculation to obtain the slab heating production assignment plan and slab production scheduling plan;

[0008] Step S3: Display, summarize and analyze the simulation results and heating furnace simulation process data through the industrial digital twin system;

[0009] The simulation business material data set includes: slab out-of-furnace sequence, slab information already in the furnace, and slab information to be put into the furnace;

[0010] The simulation business parameters include: heating furnace specification parameters and heating furnace simulation setting parameters;

[0011] The hot rolling heating furnace logistics process optimization verification model solves the optimization simulation of the asynchronous steel loading, steel extraction and heating production organization mode including hot and cold loading of hot rolling by creating hot and cold loading of heating furnace based on the transition of slab time in furnace and slab assignment based on the shortest slab time in furnace.

[0012] Preferably, the step S1 includes: capturing a simulation business material data set and simulation business parameters based on the industrial digital twin system;

[0013] The information of the slab to be put into the furnace in the simulation business material data set includes: slab number, slab temperature, slab specification and steel type;

[0014] The information of the slabs in the furnace in the simulation business material data set includes: slab number, slab temperature, slab specification, steel type, slab furnace number, furnace order and specific position in the furnace;

[0015] The heating furnace specifications include: furnace length, furnace width, maximum furnace entry position, and steel extraction laser point position;

[0016] The heating furnace simulation setting parameters include: simulation optimization type, whether to open the furnace, heating furnace type, standard heating furnace time, front transition time interval, and the number of materials in the rear transition.

[0017] Preferably, step S2 includes:

[0018] Step S2.1: Acquire and structure the simulation business material data set and simulation business parameters, and obtain the slab assignment plan through the hot and cold loading algorithm based on the transition time of the slab in the furnace;

[0019] Step S2.2: Using a local search and steel charging optimization algorithm, based on the slab discharge sequence and the obtained slab assignment plan, assign the slabs to heating furnaces according to the slab attributes and discharge requirements, recalculate the slab in and out of the furnace time, optimize the slab assignment plan, and obtain the optimized slab assignment plan;

[0020] Step S2.3: Based on the optimized slab assignment plan, the heating furnace logistics simulation model is used to combine the heating furnace information, the information of the slabs already in the furnace, and the information of the slabs waiting to enter the furnace. The operations performed by the heating furnace at different times and the positions of the slabs at different times are calculated. The production logistics process of the slabs in the heating furnace is simulated to obtain a complete slab heating furnace production scheduling plan.

[0021] Step S2.4: Evaluate the obtained complete slab heating furnace production scheduling plan. If the currently obtained complete slab heating furnace production scheduling plan does not meet the preset requirements, use domain movement to obtain a new slab assignment plan. Repeat steps S2.3 to S2.4 until the currently obtained complete slab heating furnace production scheduling plan meets the preset requirements.

[0022] Step S2.5: Based on the evaluated complete slab heating furnace production scheduling plan that meets the preset requirements, a simulation is performed using the industrial digital twin system to obtain the corresponding simulation results and the simulated slab production logistics process for each material, including the target heating furnace, charging, steel extraction sequence, time, and location generated by the slab production scheduling plan.

[0023] Among them, the heating furnace logistics simulation model is achieved by creating a hot rolling heating furnace production logistics simulation evaluation method that conforms to the working principle of the heating furnace and corresponds to the actual heating furnace equipment and factory layout, thereby simulating the heating production logistics process of the specified slab assignment scheme in the computer system.

[0024] Preferably, the step S2.1 includes:

[0025] Based on the influence of the slabs already in the furnace and the slabs in the next rolling unit on the furnace time of the slabs currently waiting to enter the furnace, the slabs expected to enter the warm charging and hot rolling furnaces are divided into the initial furnace material section, the front transition section, the hot and cold charging section, and the rear transition section according to the furnace entry order and the expected furnace time. The furnace time of the slabs in the front transition section shows a downward trend, while the furnace time of the slabs in the rear transition section shows an upward trend. The furnace time of the slabs in the hot and cold charging section is the standard furnace time set according to the heating mode.

[0026] Calculate the length of the front transition section, hot and cold charging section and rear transition section of each hot charging heating furnace according to the set heating mode;

[0027] Calculate the steel charging and drawing assignment plan for the slabs expected to enter the hot charging furnace transition section based on the length of the front transition section;

[0028] Calculate the allocation of slabs to the hot and cold loading sections of each heating furnace based on the length of the hot and cold loading sections, formulate a batching plan for the hot and cold loading sections and sort them in sequence, and obtain the slab loading and unloading assignment plan for the hot and cold loading sections;

[0029] Obtain the slab and its steel loading and extraction assignment plan for the rear transition section based on the length of the rear transition section;

[0030] The three-stage scheme is combined to form a slab charging and withdrawing assignment scheme based on the shortest furnace time of the heating mode.

[0031] Preferably, the step S2.2 includes:

[0032] The local search uses real number coding, and the values ​​at different positions represent the heating furnace selected for each slab. The estimated furnace loading time for each slab is calculated based on the current code, hot rolling related parameters, and slab related parameters.

[0033] Assume the rolling cycle is The time for the first slab to reach the reference position of the discharge roller is , then The time for the slab to reach the reference position of the discharge roller for

[0034]

[0035] Assume that The slabs are assigned to In a heating furnace, the reference position of the discharge roller is , No. The central axis position of a heating furnace is , the width of the heating furnace is , No. The length of the slab is , then The distance of the slab out of the furnace for:

[0036]

[0037] Assume that the running speed of the discharge roller is , then Virtual tapping time of slab for

[0038]

[0039] Assume that the time for pushing steel into the heating furnace is , the time for drawing steel out of the furnace is , No. The standard heating time of a slab is , then Virtual furnace entry time of slab for

[0040]

[0041] The loading time calculated according to the above method also needs to be corrected. Based on the fact that the time interval between the loading of two adjacent slabs into the same heating furnace should not be less than the time it takes to push steel into the heating furnace, the calculated loading time is corrected to obtain the estimated loading time of all slabs and form a new slab assignment plan.

[0042] Preferably, step S2.4 includes: evaluating the obtained complete slab heating furnace production scheduling plan based on the slab heating time constraint, slab position constraint, heating furnace action interlock constraint and rolling rhythm constraint. When any one or more constraints are not met, it is evaluated that the currently obtained complete slab heating furnace production scheduling plan does not meet the preset requirements.

[0043] An industrial digital twin optimization process evaluation system provided by the present invention includes:

[0044] Module M1: Obtain simulation business material data set and simulation business parameters;

[0045] Module M2: Input the obtained simulation business material data set and simulation business parameters into the hot rolling heating furnace logistics process optimization verification model to perform heating furnace production process optimization simulation calculations, and obtain the slab heating production assignment plan and slab production scheduling plan;

[0046] Module M3: Display, summarize and analyze simulation results and heating furnace simulation process data through the industrial digital twin system;

[0047] The simulation business material data set includes: slab out-of-furnace sequence, slab information already in the furnace, and slab information to be put into the furnace;

[0048] The simulation business parameters include: heating furnace specification parameters and heating furnace simulation setting parameters;

[0049] The hot rolling heating furnace logistics process optimization verification model solves the optimization simulation of the asynchronous steel loading, steel extraction and heating production organization mode including hot and cold loading of hot rolling by creating hot and cold loading of heating furnace based on the transition of slab time in furnace and slab assignment based on the shortest slab time in furnace.

[0050] Preferably, the module M1 includes: capturing simulation business material data sets and simulation business parameters based on the industrial digital twin system;

[0051] The information of the slab to be put into the furnace in the simulation business material data set includes: slab number, slab temperature, slab specification and steel type;

[0052] The information of the slabs in the furnace in the simulation business material data set includes: slab number, slab temperature, slab specification, steel type, slab furnace number, furnace order and specific position in the furnace;

[0053] The heating furnace specifications include: furnace length, furnace width, maximum furnace entry position, and steel extraction laser point position;

[0054] The heating furnace simulation setting parameters include: simulation optimization type, whether to open the furnace, heating furnace type, standard heating furnace time, front transition time interval, and the number of materials in the rear transition.

[0055] Preferably, the module M2 includes:

[0056] Module M2.1: Acquire and structure the simulation business material data set and simulation business parameters, and obtain the slab assignment plan through the hot and cold loading algorithm based on the slab's time in the furnace;

[0057] Module M2.2: Using a local search and steel charging optimization algorithm, based on the slab discharge sequence and the obtained slab assignment plan, assigns the slabs to heating furnaces according to slab properties and discharge requirements, recalculates the slab in and out of the furnace time, optimizes the slab assignment plan, and obtains the optimized slab assignment plan;

[0058] Module M2.3: Based on the optimized slab assignment plan, the heating furnace logistics simulation model is used to combine the heating furnace information, the information of slabs already in the furnace, and the information of slabs waiting to enter the furnace. The model calculates the operations performed by the heating furnace at different times and the positions of each slab at different times. This model simulates the production logistics process of the slabs in the heating furnace, thereby obtaining a complete slab heating furnace production scheduling plan.

[0059] Module M2.4: Evaluate the obtained complete slab heating furnace production scheduling plan. If the currently obtained complete slab heating furnace production scheduling plan does not meet the preset requirements, use domain movement to obtain a new slab assignment plan, and repeatedly trigger modules M2.3 to M2.4 until the currently obtained complete slab heating furnace production scheduling plan meets the preset requirements.

[0060] Module M2.5: Based on the evaluated complete slab heating furnace production scheduling plan that meets the preset requirements, simulation is performed through the industrial digital twin system to obtain the corresponding simulation results and the slab production logistics process generated by the slab production scheduling plan, including the simulated target heating furnace, charging and steel extraction sequence, time, and location for each material.

[0061] Among them, the heating furnace logistics simulation model is achieved by creating a hot rolling heating furnace production logistics simulation evaluation method that conforms to the working principle of the heating furnace and corresponds to the actual heating furnace equipment and factory layout, thereby simulating the heating production logistics process of the specified slab assignment scheme in the computer system.

[0062] Preferably, the module M2.1 includes:

[0063] Based on the influence of the slabs already in the furnace and the slabs in the next rolling unit on the furnace time of the slabs currently waiting to enter the furnace, the slabs expected to enter the warm charging and hot rolling furnaces are divided into the initial furnace material section, the front transition section, the hot and cold charging section, and the rear transition section according to the furnace entry order and the expected furnace time. The furnace time of the slabs in the front transition section shows a downward trend, while the furnace time of the slabs in the rear transition section shows an upward trend. The furnace time of the slabs in the hot and cold charging section is the standard furnace time set according to the heating mode.

[0064] Calculate the length of the front transition section, hot and cold charging section and rear transition section of each hot charging heating furnace according to the set heating mode;

[0065] Calculate the steel charging and drawing assignment plan for the slabs expected to enter the hot charging furnace transition section based on the length of the front transition section;

[0066] Calculate the allocation of slabs to the hot and cold loading sections of each heating furnace based on the length of the hot and cold loading sections, formulate a batching plan for the hot and cold loading sections and sort them in sequence, and obtain the slab loading and unloading assignment plan for the hot and cold loading sections;

[0067] Obtain the slab and its steel loading and extraction assignment plan for the rear transition section based on the length of the rear transition section;

[0068] The three-stage scheme is combined to form a slab charging and withdrawal assignment scheme based on the shortest furnace time in the heating mode;

[0069] The module M2.2 includes:

[0070] The local search uses real number coding, and the values ​​at different positions represent the heating furnace selected for each slab. The estimated furnace loading time for each slab is calculated based on the current code, hot rolling related parameters, and slab related parameters.

[0071] Assume the rolling cycle is The time for the first slab to reach the reference position of the discharge roller is , then The time for the slab to reach the reference position of the discharge roller for

[0072]

[0073] Assume that The slabs are assigned to In a heating furnace, the reference position of the discharge roller is , No. The central axis position of a heating furnace is , the width of the heating furnace is , No. The length of the slab is , then The distance of the slab out of the furnace for:

[0074]

[0075] Assume that the running speed of the discharge roller is , then Virtual tapping time of slab for

[0076]

[0077] Assume that the time for pushing steel into the heating furnace is , the time for drawing steel out of the furnace is , No. The standard heating time of a slab is , then Virtual furnace entry time of slab for

[0078]

[0079] The charging time calculated in the above way needs to be corrected. Based on the fact that the time interval between the charging of two adjacent slabs into the same heating furnace should not be less than the time it takes for the steel to be pushed into the heating furnace, the calculated charging time is corrected to obtain the estimated charging time of all slabs and form a new slab assignment plan.

[0080] The module M2.4 includes: evaluating the obtained complete slab heating furnace production scheduling plan based on the slab heating time constraint, slab position constraint, heating furnace action interlock constraint and rolling rhythm constraint. When any one or more constraints are not met, it is evaluated that the currently obtained complete slab heating furnace production scheduling plan does not meet the preset requirements.

[0081] Compared with the prior art, the present invention has the following beneficial effects:

[0082] 1. This invention establishes a simulation optimization and evaluation system that studies slabs of different temperature ranges, feeds different heating furnaces, and each heating furnace steps at different speeds, allowing the last slab to be discharged first. This system achieves simulation results such as the appropriate steel charging and steel withdrawal crossover ratios recommended by the simulation under asynchronous charging conditions in an industrial digital twin system.

[0083] 2. The hot and cold packaging production mode that is inconvenient to verify in the actual hot rolling heating production of steel will be realized by applying the hot rolling heating furnace logistics process optimization simulation technology in the industrial digital twin system, so as to realize the new production mode of asynchronous steel loading and withdrawal such as hot and cold packaging, and simulate production verification in the hot rolling heating production logistics simulation system, so as to improve the technological innovation and management efficiency of the new production organization mode and reduce the verification cost of new production organization such as hot and cold packaging production. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0085] Figure 1 Flowchart of the process evaluation methodology for industrial digital twin optimization. DETAILED DESCRIPTION

[0086] The present invention will be described in detail below with reference to specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those skilled in the art, several changes and improvements can be made without departing from the scope of the present invention. These all fall within the scope of protection of the present invention.

[0087] Example 1

[0088] According to an industrial digital twin optimization process evaluation method provided by the present invention, Figure 1 Shown, including:

[0089] Step 1: Prepare the business material dataset required for the production organization case for simulation verification in the industrial digital twin system;

[0090] The business material data set includes: 1) information about slabs to be put into the furnace, including key attributes such as slab number, slab temperature, slab specifications, and steel type; 2) slab production sequence requirements for the rolling plan; and 3) information about the initial slabs already in the heating furnace before the simulation begins, including key attributes such as slab number, slab temperature, slab specifications, and steel type. Information also includes: the furnace number, order in the furnace, and specific position of the slabs in the furnace.

[0091] Step 2: In the industrial digital twin system, capture the specifications and other parameter information of each heating furnace in the production line where the simulation case is located, and configure the heating furnace business simulation setting parameters based on this. Among them, the heating furnace specification parameters include: furnace length, furnace width, maximum furnace entry position, and steel extraction laser point position; the heating furnace simulation setting parameters include: simulation optimization type, whether to open the furnace, heating furnace type, standard heating furnace duration, front transition duration interval, and the number of materials in the back transition;

[0092] Step 3: Input the simulation business material data and simulation business setting parameters into the hot rolling heating furnace logistics process optimization verification model, perform heating furnace production process optimization simulation calculation, and obtain the slab heating production assignment plan and slab production scheduling plan;

[0093] Specifically, the step 3 includes:

[0094] Step 3.1: Acquire and structure simulation business material data, rolling plan slab production sequence requirements, and heating furnace business simulation parameters. For heating furnaces set to warm or hot charging mode, the minimum heating time of the slab in the furnace is shorter than the heating time of cold charging furnaces heating room-temperature slabs. The actual slab in-furnace time is significantly affected by factors such as slabs already in the furnace, the hot rolling production rhythm, and rolling unit switching. For heating furnaces set to cold charging mode, the minimum heating time of the slab in the furnace is longer, and the actual slab in-furnace time is relatively less affected by other factors. Therefore, this step proposes a hot and cold charging algorithm based on the transition of slab in-furnace time. This algorithm considers the impact of slabs already in the furnace and the slabs of the next rolling unit on the furnace time of the slab currently waiting to enter the furnace. The slabs expected to enter the warm charging and hot rolling furnaces are divided into the initial furnace material segment, the front transition segment, the hot and cold charging segment, and the back transition segment according to the furnace entry sequence and the expected furnace time. The ideal time of the slab in the furnace in the front transition section shows a downward trend, while the ideal time of the slab in the furnace in the rear transition section shows an upward trend. The default time of the slab in the furnace in the hot and cold packaging sections is the standard time of the heating mold setting.

[0095] According to the set heating mode, the length of the front transition section, hot and cold charging section, and rear transition section of each hot charging heating furnace are calculated respectively.

[0096] Then, each heating furnace calculates the acceleration of the front transition section heating time in the furnace as set by the simulation plan, calculates the length of the front transition section, and calculates the slabs expected to enter the hot charging furnace transition section and their steel loading and withdrawal assignment plan; according to the standard heating time set by the heating mode, calculates the slabs allocated to the hot and cold charging sections of each heating furnace, formulates the batching plan of the hot and cold charging sections and sorts them in sequence, and obtains the steel loading and withdrawal assignment plan of the hot and cold charging sections; according to the rear transition section parameters set by the simulation plan, calculates the length of the hot charging rear transition section and obtains the steel loading and withdrawal assignment plan of the slabs in the rear transition section; and merges the three-section plans to form a steel loading and withdrawal assignment plan for the slabs with the shortest furnace time based on the heating mode.

[0097] Step 3.2: Using the local search and steel charging optimization algorithm, according to the rolling plan slab production sequence and the previously obtained slab assignment plan, the slab attributes and furnace discharge requirements are assigned to the heating furnace, and the slab in and out of the furnace time is recalculated. The assignment plan is optimized to obtain a complete slab scheduling plan;

[0098] The local search uses real number coding, and the values ​​at different positions represent the heating furnace selected for each slab. Based on the current code, hot rolling related parameters, and slab related parameters, the estimated furnace loading time for each slab is calculated. Assume that the rolling cycle is The time for the first slab to reach the reference position of the discharge roller is , then The time when the slab reaches the reference position of the discharge roller for

[0099]

[0100] Assume that The slabs are assigned to In a heating furnace, the reference position of the discharge roller is , No. The central axis position of a heating furnace is , the width of the heating furnace is , No. The length of the slab is , then The distance of the slab out of the furnace for

[0101]

[0102] Assume that the running speed of the discharge roller is , then Virtual tapping time of slab for

[0103]

[0104] Assume that the time for pushing steel into the heating furnace is , the time for drawing steel out of the furnace is , No. The standard heating time of a slab is , then Virtual furnace entry time of slab for

[0105]

[0106] The loading time calculated using the above method needs to be corrected. The time interval between two adjacent slabs in the same heating furnace should be no less than the time it takes to push the steel into the furnace. This correction method can be used to obtain the estimated loading time for all slabs and form a new slab assignment plan.

[0107] Step 3.3: Using the heating furnace logistics simulation model, based on the slab loading and unloading assignment plan, complete the heating furnace logistics production process simulation. The simulation includes the complete heating furnace production process of pushing steel into the furnace, heating stepping, and unloading steel from the furnace for each slab, with the mutual constraints between the heating furnaces and the slabs within the furnaces. This step can be broken down into the following steps:

[0108] The slab loading and unloading assignment plan is initialized as a table of pending events in chronological order.

[0109] Calculate the earliest execution time of steel extraction / steel pushing / stepping of each heating furnace in the pending events according to the rules.

[0110] Process the event with the shortest execution time. A heating furnace performs three operations during slab heating: pushing, stepping, and withdrawing. Each operation has a duration, broken down into two states: start and end. Therefore, the events in the heating furnace logistics simulation model include push start, push end, stepping start, stepping end, withdrawal start, and withdrawal end.

[0111] During the production process, the heating furnace must adhere to production constraints such as slab heating duration, slab position, and interlocking of heating furnace actions. Different operations are executed while meeting these constraints, ensuring that different slabs complete the heating process. Different events are processed sequentially. Here, a strategy based on minimum event execution time is used to determine the order in which different events are processed. The handling methods for different events are also determined based on the heating furnace's operating mechanism.

[0112] For the push operation, it is necessary to determine whether there is sufficient space in the current furnace for the slab to enter. If there is sufficient space in the current furnace, the earliest possible start time for the push operation is calculated according to the following formula. If there is insufficient space in the current furnace, the operation is returned as infeasible and the next calculation is performed.

[0113] For stepping operations, it is necessary to determine whether the slab closest to the furnace outlet has not reached its limit position. If the slab has not reached its limit position, the earliest possible start time for the stepping operation is calculated according to the following formula. If the slab has reached its limit position, the calculation returns as infeasible and waits for the next calculation.

[0114] For steel extraction, it is necessary to determine whether the slab closest to the furnace outlet is the one that should be extracted and whether it has reached the minimum extraction position. If these two conditions are met, the earliest possible start time for steel extraction is calculated for that furnace; otherwise, the function returns "infeasible" and waits for the next calculation.

[0115] If there are still events to be processed, repeat 2.

[0116] If there are no pending events, the heating furnace logistics process simulation is completed, and the slab heating schedule and production process plan are output.

[0117] Step 3.4: The production logistics process also requires evaluation using an objective evaluation function. The evaluation criteria are derived from the heating furnace production constraints, including: slab heating duration, slab position, heating furnace motion interlocking, and rolling cadence. After evaluation, determine whether the current iteration is complete. If further optimization is required, use domain movement to obtain a new slab assignment plan. Repeat Step 3.3 to obtain the slab production scheduling plan.

[0118] Step 3.5: After the evaluation, no iteration is required, and the optimization simulation calculation process of the heating furnace production process is completed. The slab optimization simulation results formed by the slab assignment plan and the slab production logistics process of the simulated target heating furnace, loading, steel extraction sequence, time, and position of each material generated by the slab production scheduling plan are output.

[0119] Step 4: Display, summarize and analyze the simulation results and detailed heating logistics simulation process data in the industrial digital twin system.

[0120] Specifically, step 4 includes: displaying the final simulation scheme of heating furnace steel charging and steel extraction of the simulation case output by the optimization verification model of the heating furnace logistics process in the industrial digital twin system, as well as the heating furnace slab heating scheduling and production process scheme. The steel charging and steel extraction schemes of the simulation scheme are displayed graphically, and the simulation results and the heating logistics simulation process are analyzed. The case simulation data can be saved in different versions according to different setting parameters, and the simulation results under different setting parameters can be queried, analyzed and compared, so that users can select appropriate setting parameters and simulation results from them to guide similar heating production organization in the future. For example: the differences in simulation plans before and after optimization can be displayed graphically through comparison of steel extraction intervals, comparison of steel extraction time differences between adjacent slabs, comparison of steel charging and steel extraction processes, and comparison of slab in furnace time.

[0121] The present invention also provides an industrial digital twin optimization process evaluation system, which can be implemented by executing the process steps of the industrial digital twin optimization process evaluation method. That is, those skilled in the art can understand the industrial digital twin optimization process evaluation method as a preferred implementation of the industrial digital twin optimization process evaluation system.

[0122] Those skilled in the art will appreciate that, in addition to implementing the system, device, and various modules provided by the present invention in purely computer-readable program code, it is entirely possible to implement the same program in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, and the like by logically programming the method steps. Therefore, the system, device, and various modules provided by the present invention can be considered a hardware component, and the modules included therein for implementing various programs can also be considered structures within the hardware component; the modules for implementing various functions can also be considered both software programs for implementing the method and structures within the hardware component.

[0123] The above describes specific embodiments of the present invention. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art may make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. The embodiments of this application and the features in the embodiments may be combined with each other in any manner unless there is a conflict.

Claims

1. An industrial digital twin optimization process evaluation method, characterized in that: include: Step S1: Acquire a simulation business material data set and simulation business parameters; Step S2: Input the obtained simulation business material data set and simulation business parameters into the hot rolling heating furnace logistics process optimization verification model to perform heating furnace production process optimization simulation calculation to obtain the slab heating production assignment plan and slab production scheduling plan; Step S3: Display, summarize and analyze the simulation results and heating furnace simulation process data through the industrial digital twin system; The simulation business material data set includes: slab out-of-furnace sequence, slab information already in the furnace, and slab information to be put into the furnace; The simulation business parameters include: heating furnace specification parameters and heating furnace simulation setting parameters; The hot rolling furnace logistics process optimization verification model solves the optimization simulation of the asynchronous steel loading, drawing and heating production organization mode including hot and cold loading of hot rolling by creating hot and cold loading of heating furnace based on the transition of slab time in the furnace and slab assignment based on the shortest slab time in the furnace; The step S2 comprises: Step S2.1: Acquire and structure the simulation business material data set and simulation business parameters, and obtain the slab assignment plan through the hot and cold loading algorithm based on the transition time of the slab in the furnace; Step S2.2: Using a local search and steel charging optimization algorithm, based on the slab discharge sequence and the obtained slab assignment plan, assign the slabs to heating furnaces according to the slab attributes and discharge requirements, recalculate the slab in and out of the furnace time, optimize the slab assignment plan, and calculate the optimized slab assignment plan; Step S2.3: Based on the optimized slab assignment plan, the heating furnace logistics simulation model is used to combine the heating furnace information, the information of the slabs already in the furnace, and the information of the slabs waiting to enter the furnace. The operations performed by the heating furnace at different times and the positions of the slabs at different times are calculated. The production logistics process of the slabs in the heating furnace is simulated to obtain a complete slab heating furnace production scheduling plan. Step S2.4: Evaluate the obtained complete slab heating furnace production scheduling plan. If the currently obtained complete slab heating furnace production scheduling plan does not meet the preset requirements, use domain movement to obtain a new slab assignment plan. Repeat steps S2.3 to S2.4 until the currently obtained complete slab heating furnace production scheduling plan meets the preset requirements. Step S2.5: Based on the evaluated complete slab heating furnace production scheduling plan that meets the preset requirements, simulation is performed through the industrial digital twin system to obtain the corresponding simulation results and the simulated target heating furnace, loading, steel extraction sequence, time, and location of each material generated by the slab production scheduling plan. The slab production logistics process.

2. The industrial digital twin optimization process evaluation method according to claim 1, characterized in that: The step S1 includes: capturing a simulation business material data set and simulation business parameters based on the industrial digital twin system; The information of the slab to be put into the furnace in the simulation business material data set includes: slab number, slab temperature, slab specification and steel type; The information of the slabs in the furnace in the simulation business material data set includes: slab number, slab temperature, slab specification, steel type, slab furnace number, furnace order and specific position in the furnace; The heating furnace specifications include: furnace length, furnace width, maximum furnace entry position, and steel extraction laser point position; The heating furnace simulation setting parameters include: simulation optimization type, whether to open the furnace, heating furnace type, standard heating furnace time, front transition time interval, and the number of materials in the rear transition.

3. The industrial digital twin optimization process evaluation method according to claim 1, characterized in that: The heating furnace logistics simulation model is achieved by creating a hot rolling heating furnace production logistics simulation evaluation method that conforms to the working principle of the heating furnace and corresponds to the actual heating furnace equipment and factory layout, thereby simulating the heating production logistics process of a specified slab assignment scheme in a computer system.

4. The industrial digital twin optimization process evaluation method according to claim 1, characterized in that: The step S2.1 includes: Based on the influence of the slabs already in the furnace and the slabs in the next rolling unit on the furnace time of the slabs currently waiting to enter the furnace, the slabs expected to enter the warm charging and hot rolling furnaces are divided into the initial furnace material section, the front transition section, the hot and cold charging section, and the rear transition section according to the furnace entry order and the expected furnace time. The furnace time of the slabs in the front transition section shows a downward trend, while the furnace time of the slabs in the rear transition section shows an upward trend. The furnace time of the slabs in the hot and cold charging section is the standard furnace time set according to the heating mode. Calculate the length of the front transition section, hot and cold charging section and rear transition section of each hot charging heating furnace according to the set heating mode; Calculate the steel charging and drawing assignment plan for the slabs expected to enter the hot charging furnace transition section based on the length of the front transition section; Calculate the allocation of slabs to the hot and cold loading sections of each heating furnace based on the length of the hot and cold loading sections, formulate a batching plan for the hot and cold loading sections and sort them in sequence, and obtain the slab loading and unloading assignment plan for the hot and cold loading sections; Obtain the slab and its steel loading and extraction assignment plan for the rear transition section based on the length of the rear transition section; The three-stage scheme is combined to form a slab charging and withdrawing assignment scheme based on the shortest furnace time of the heating mode.

5. The industrial digital twin optimization process evaluation method according to claim 1, characterized in that: The step S2.2 includes: The local search uses real number coding, and the values ​​at different positions represent the heating furnace selected for each slab. The estimated furnace loading time for each slab is calculated based on the current code, hot rolling related parameters, and slab related parameters. Assume the rolling cycle is The time for the first slab to reach the reference position of the discharge roller is , then The time for the slab to reach the reference position of the discharge roller for Assume that The slabs are assigned to In a heating furnace, the reference position of the discharge roller is , No. The central axis position of a heating furnace is , the width of the heating furnace is , No. The length of the slab is , then The distance of the slab out of the furnace for: Assume that the running speed of the discharge roller is , then Virtual tapping time of slab for Assume that the time for pushing steel into the heating furnace is , the time for drawing steel out of the furnace is , No. The standard heating time of a slab is , then Virtual furnace entry time of slab for The loading time calculated according to the above method also needs to be corrected. Based on the fact that the time interval between the loading of two adjacent slabs into the same heating furnace should not be less than the time it takes to push steel into the heating furnace, the calculated loading time is corrected to obtain the estimated loading time of all slabs and form a new slab assignment plan.

6. The industrial digital twin optimization process evaluation method according to claim 1, characterized in that: The step S2.4 includes: evaluating the obtained complete slab heating furnace production scheduling plan based on the slab heating time constraint, slab position constraint, heating furnace action interlock constraint and rolling rhythm constraint. When any one or more constraints are not met, it is evaluated that the currently obtained complete slab heating furnace production scheduling plan does not meet the preset requirements.

7. An industrial digital twin optimization process evaluation system, characterized in that: include: Module M1: Obtain simulation business material data set and simulation business parameters; Module M2: Input the obtained simulation business material data set and simulation business parameters into the hot rolling heating furnace logistics process optimization verification model to perform heating furnace production process optimization simulation calculations, and obtain the slab heating production assignment plan and slab production scheduling plan; Module M3: Display, summarize and analyze simulation results and heating furnace simulation process data through the industrial digital twin system; The simulation business material data set includes: slab out-of-furnace sequence, slab information already in the furnace, and slab information to be put into the furnace; The simulation business parameters include: heating furnace specification parameters and heating furnace simulation setting parameters; The hot rolling furnace logistics process optimization verification model solves the optimization simulation of the asynchronous steel loading, drawing and heating production organization mode including hot and cold loading of hot rolling by creating hot and cold loading of heating furnace based on the transition of slab time in the furnace and slab assignment based on the shortest slab time in the furnace; The module M2 includes: Module M2.1: Acquire and structure the simulation business material data set and simulation business parameters, and obtain the slab assignment plan through the hot and cold loading algorithm based on the slab's time in the furnace; Module M2.2: Using a local search and steel charging optimization algorithm, based on the slab discharge sequence and the obtained slab assignment plan, assigns the slabs to heating furnaces according to slab properties and discharge requirements, recalculates the slab in and out of the furnace time, optimizes the slab assignment plan, and obtains the optimized slab assignment plan; Module M2.3: Based on the optimized slab assignment plan, the heating furnace logistics simulation model is used to combine the heating furnace information, the information of slabs already in the furnace, and the information of slabs waiting to enter the furnace. The model calculates the operations performed by the heating furnace at different times and the positions of each slab at different times. This model simulates the production logistics process of the slabs in the heating furnace, thereby obtaining a complete slab heating furnace production scheduling plan. Module M2.4: Evaluate the obtained complete slab heating furnace production scheduling plan. If the currently obtained complete slab heating furnace production scheduling plan does not meet the preset requirements, use domain movement to obtain a new slab assignment plan, and repeatedly trigger modules M2.3 to M2.4 until the currently obtained complete slab heating furnace production scheduling plan meets the preset requirements. Module M2.5: Based on the evaluated complete slab heating furnace production scheduling plan that meets the preset requirements, simulation is performed through the industrial digital twin system to obtain the corresponding simulation results and the slab production logistics process of the simulated target heating furnace, loading, steel extraction sequence, time, and location of each material generated by the slab production scheduling plan.

8. The industrial digital twin optimization process evaluation system according to claim 7, characterized in that: The module M1 includes: capturing simulation business material data sets and simulation business parameters based on the industrial digital twin system; The information of the slab to be put into the furnace in the simulation business material data set includes: slab number, slab temperature, slab specification and steel type; The information of the slabs in the furnace in the simulation business material data set includes: slab number, slab temperature, slab specification, steel type, slab furnace number, furnace order and specific position in the furnace; The heating furnace specifications include: furnace length, furnace width, maximum furnace entry position, and steel extraction laser point position; The heating furnace simulation setting parameters include: simulation optimization type, whether to open the furnace, heating furnace type, standard heating furnace time, front transition time interval, and the number of materials in the rear transition.

9. The industrial digital twin optimization process evaluation system according to claim 7, characterized in that: The heating furnace logistics simulation model is achieved by creating a hot rolling heating furnace production logistics simulation evaluation method that conforms to the working principle of the heating furnace and corresponds to the actual heating furnace equipment and factory layout, thereby simulating the heating production logistics process of a specified slab assignment scheme in a computer system.

10. The industrial digital twin optimization process evaluation system according to claim 7, characterized in that: The module M2.1 includes: Based on the influence of the slabs already in the furnace and the slabs in the next rolling unit on the furnace time of the slabs currently waiting to enter the furnace, the slabs expected to enter the warm charging and hot rolling furnaces are divided into the initial furnace material section, the front transition section, the hot and cold charging section, and the rear transition section according to the furnace entry order and the expected furnace time. The furnace time of the slabs in the front transition section shows a downward trend, while the furnace time of the slabs in the rear transition section shows an upward trend. The furnace time of the slabs in the hot and cold charging section is the standard furnace time set according to the heating mode. Calculate the length of the front transition section, hot and cold charging section and rear transition section of each hot charging heating furnace according to the set heating mode; Calculate the steel charging and drawing assignment plan for the slabs expected to enter the hot charging furnace transition section based on the length of the front transition section; Calculate the allocation of slabs to the hot and cold loading sections of each heating furnace based on the length of the hot and cold loading sections, formulate a batching plan for the hot and cold loading sections and sort them in sequence, and obtain the slab loading and unloading assignment plan for the hot and cold loading sections; Obtain the slab and its steel loading and extraction assignment plan for the rear transition section based on the length of the rear transition section; The three-stage scheme is combined to form a slab charging and withdrawing assignment scheme based on the shortest furnace time of the heating mode.

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