Reasonability evaluation method and system for rolling plan of hot rolled strip steel

Abstract

The invention provides a rationality evaluation method and system based on a hot rolling strip steel rolling plan provided by the embodiment of the invention, and belongs to the field of metallurgical process formulation and evaluation. The method comprises the steps that a rolling simulation model is established based on an obtained rolling plan and corresponding product information, and a simulation production result is obtained through calculation of the simulation model and serves as a judgment basis of simulation production; a historical production result recorded in the past is obtained to serve as a judgment basis of historical production; respectively calculating a simulated production risk coefficient and a historical production risk coefficient, then carrying out weighted summation on the simulated production risk coefficient and the historical risk coefficient according to the plate shape consideration weight, and calculating a plate shape problem risk coefficient of the current steel coil; and after traversing all the steel coils, presetting an evaluation standard, arranging an evaluation result and an identifier according to the risk coefficient and the evaluation standard, and outputting the evaluation result as a risk evaluation result by adopting the identifier. According to the method, the reliability of a big data analysis result is improved, and the reliability of a rationality evaluation result is ensured.

Description

Technical field:

[0001] The present invention belongs to the field of metallurgical process formulation and evaluation, and in particular relates to a method and system for evaluating the rationality of a hot-rolled strip steel rolling plan. Background technology:

[0002] Strip steel products, a crucial component of national economic development, are widely used in a variety of industries. Hot rolling is a crucial step in strip production, and the shape and quality of the hot-rolled strip directly determine the overall competitiveness of the product. Developing a rolling plan before hot-rolling strip production is crucial, on the one hand, to meet customer orders—guaranteeing delivery schedules while ensuring consistent and stable product quality—and on the other hand, to meet the company's own needs for cost reduction and efficiency improvement, namely, optimizing resource allocation, controlling production pace, and reducing inventory management costs.

[0003] Currently, there's no definitive solution or strategy for ensuring product flatness quality after rolling plans are implemented in uncertain production scenarios. For example, there's a significant degree of randomness in the customer orders received by manufacturers, and the production line itself is subject to numerous unexpected events that can impact the rolling rhythm. This means that rolling plans must be adjusted instantly as needed. In other words, rolling plans inevitably require considerable flexibility. The need to address unexpected production processes to ensure on-time delivery to customers, maintain strip flatness to prevent customer quality objections and financial losses, and meet manufacturers' own needs for cost reduction and efficiency improvement presents significant challenges in rolling plan planning. Therefore, a rationale assessment of established rolling plans is necessary.

[0004] In the existing technology, researchers generally focus on the scheduling of hot rolling plans. For example, some scholars have proposed a cloud-edge collaborative industrial Internet production scheduling framework and an automatic scheduling method for thick plate rolling plans. The above rolling plan scheduling methods all aim to improve scheduling efficiency and realize the automation of the scheduling process. They use pre-established scheduling benchmarks as constraints and adopt data-driven methods based on intelligent algorithms or expert experience as a method to achieve scheduling. However, the scheduling results, that is, the rationality evaluation of the rolling plan, are not involved. Therefore, although the above methods have improved the scheduling efficiency to a certain extent, they still cannot guarantee the plate quality of the product after the rolling plan is implemented. When the problem is serious, it will lead to unstable production and even major accidents such as scrap steel, which will affect the production efficiency of the hot rolling production line and on-time delivery. Summary of the invention:

[0005] In order to solve the above problems, the present invention provides a method and system for evaluating the rationality of a hot-rolled strip rolling plan, which realizes quantitative evaluation of the rationality of the rolling plan, that is, the risk of plate shape problems after the plan is implemented, which helps to adjust the rolling plan, avoid problems, reduce the uncontrollable plate shape quality problems caused by the flexibility of the rolling plan, improve the rationality of the rolling plan, and improve the strip rolling effect.

[0006] In order to achieve the above objectives, the technical solutions adopted in the embodiments of the present invention are as follows:

[0007] In a first aspect, an embodiment of the present invention provides a method for evaluating the rationality of a hot strip rolling plan, the method comprising the following steps:

[0008] Step S1, obtaining rolling plan information and corresponding product information to be evaluated; the rolling plan information includes the order of rolled steel coils, and the corresponding product information includes product steel grade specifications, flatness control targets, and specification transition information;

[0009] Step S2: establishing a rolling simulation model based on the rolling plan information and the corresponding product information, and obtaining simulated production results through the simulation model as a basis for judging the simulated production; obtaining historical production results recorded in the past as a basis for judging the historical production;

[0010] Step S3, initializing the steel coil serial number to set the steel coil serial number to the first coil in the rolling plan;

[0011] Step S4, for the current steel coil, calculate the simulated production risk coefficient based on the judgment basis of simulated production, flatness control target and specification transition information;

[0012] Step S5, determining whether the current steel coil is rolled for the first time; if so, assigning a simulated production weight in the flatness consideration weight to 1, and proceeding to step S7; if not, assigning a simulated production weight in the flatness consideration weight to a preset value other than 1, and proceeding to step S6;

[0013] Step S6, calculating the historical production risk coefficient of the current steel coil based on the historical production judgment basis, flatness control target, actual production situation and specification transition information;

[0014] Step S7, allocating flatness consideration weights to the simulated production risk coefficient and the historical production risk coefficient, performing a weighted summation of the simulated production risk coefficient and the historical risk coefficient according to the flatness consideration weights, and calculating the flatness problem risk coefficient of the current steel coil;

[0015] Step S8, determining whether all coils in the rolling plan have been traversed; if not, incrementing the coil number by 1 and returning to step S4; if so, proceeding to step S9;

[0016] Step S9: preset evaluation criteria, arrange evaluation results and labels according to risk factors and evaluation criteria, and output evaluation results using labels as risk assessment results.

[0017] As a preferred embodiment of the present invention, step S6 includes:

[0018] Step S61: Preset five second-category evaluation indicators, namely, whether the head flatness exceeds the limit, whether the bending roller setting exceeds the limit, whether the manual intervention amount exceeds the limit, whether the looper height fluctuation exceeds the limit, and whether the centerline offset exceeds the limit;

[0019] Step S62, calculating the risk value of whether the head flatness exceeds the limit as the first second-category evaluation index;

[0020] Step S63: Determine whether the current steel coil is a transition coil. If so, assign a historical production weight of whether the head flatness exceeds the limit, which is the first second-category evaluation index, to 100%, and proceed to step S65. If not, assign a historical production weight of whether the head flatness exceeds the limit, which is the first second-category evaluation index, to a preset value, and proceed to step S64.

[0021] Step S64, sequentially calculating the risk values of the second to fifth second-category evaluation indicators, and assigning the historical production consideration weights of the second to fifth second-category evaluation indicator risk values to preset values respectively;

[0022] Step S65 , performing weighted summation on the risk values of the second type of evaluation indicators according to the historical production consideration weights, and calculating the historical production risk coefficient of the current steel coil.

[0023] As a preferred embodiment of the present invention, the preset value in step S63 and the preset value in step S64 are the same, both being 20%.

[0024] As a preferred embodiment of the present invention, the historical production consideration weight of each second-category evaluation indicator is calculated based on the specification transition information, and the risk values of all second-category evaluation indicators are weighted and summed based on the historical production consideration weight. The calculation formula is as follows:

[0025]

[0026] In formula (3), Score data represents the historical production risk coefficient; j represents the jth indicator; n represents the total number of evaluation indicators; D j Represents the risk value corresponding to the j-th indicator; Represents the historical production consideration weight corresponding to the indicator.

[0027] As a preferred embodiment of the present invention, the specification transition information refers to the specification change of each coil relative to the previous coil in the rolling plan, and is calculated as follows:

[0028] ΔSp i =Sp i –Sp i-1 (1)

[0029] In formula (1), ΔSp i Represents the specification change corresponding to the i-th coil, Sp i and Sp i-1 They represent the specification values of the i-th and i-1-th rolls of steel in the rolling plan respectively; the specifications represented by Sp include material code, width code, thickness code, width dimension and thickness dimension.

[0030] As a preferred embodiment of the present invention, the simulation production results calculated by the simulation model include the secondary crown of the strip, equivalent flatness, edge drop, local high and low points, optimal bending roll setting, and optimal shifting roll setting;

[0031] The historical production results obtained by retrieving past records include strip head convexity, head flatness, head wedge shape, bending roll setting, bending roll setting lower limit, bending roll setting upper limit, shifting roll setting, shifting roll setting lower limit, shifting roll setting upper limit, operator leveling amount, leveling amount lower limit, leveling amount upper limit, operator bending roll amount, operation bending roll lower limit, operation bending roll upper limit, loop height fluctuation, loop quantity fluctuation lower limit and loop quantity fluctuation upper limit.

[0032] As a preferred embodiment of the present invention, when calculating the simulated production risk coefficient in step S4, several first-category evaluation indicators are preset, and the parameters in the simulated production results obtained by the simulation model are used as the basis for judging different first-category evaluation indicators. The corresponding parameters in the plate shape control target are used as the judgment criteria, and each first-category evaluation indicator is evaluated and calculated separately to obtain the risk value of each first-category evaluation indicator, and the risk values of all first-category evaluation indicators are weighted and summed, and the result is used as the simulated production risk coefficient.

[0033] As a preferred embodiment of the present invention, the number of the first-category evaluation indicators is preset to four, and the four evaluation indicator codes are represented by category M plus a serial number, M1-M4 respectively represent whether the convexity exceeds the limit, whether the equivalent flatness exceeds the limit, whether the roll gap shape is abnormal, and whether the bending roll setting reaches the limit; under each first-category evaluation indicator, the corresponding parameters in the corresponding simulation production results are used as the judgment basis, and the corresponding parameters in the corresponding plate shape control target are used as the judgment criteria, and the risk value under the evaluation indicator is calculated according to the corresponding evaluation criteria.

[0034] As a preferred embodiment of the present invention, the consideration weight of each first-category evaluation indicator is calculated based on the specification transition information, and the risk values of all first-category evaluation indicators are weighted and summed according to the consideration weight. The calculation formula is as follows:

[0035]

[0036] In formula (2), Score simu represents the simulated production risk coefficient; i represents the i-th indicator; m represents the total number of evaluation indicators; M i Represents the risk value corresponding to the i-th indicator; Represents the simulated production consideration weight corresponding to the indicator.

[0037] In a second aspect, an embodiment of the present invention further provides a rationality evaluation system for a hot-rolled strip rolling plan, the system comprising: a rolling plan acquisition module, a simulation module, a historical production data acquisition module, a simulated production risk coefficient calculation module, a plate shape consideration weight assignment module, a historical production risk coefficient calculation module, a plate shape risk coefficient calculation module, a traversal judgment module, and a rationality evaluation module; wherein,

[0038] The rolling plan acquisition module is used to obtain rolling plan information to be evaluated and corresponding product information; the rolling plan information includes the order of rolled steel coils, and the corresponding product information includes product steel grade specifications, flatness control targets and specification transition information;

[0039] The simulation module is used to establish a rolling simulation model based on rolling plan information and corresponding product information, and obtain simulated production results through simulation model calculation as a basis for judging simulated production;

[0040] The historical production data acquisition module is used to obtain historical production results recorded in the past as a basis for judging historical production;

[0041] The historical production data acquisition module is used to obtain historical production results recorded in the past as a basis for judging historical production;

[0042] The simulated production risk coefficient calculation module is used to calculate the simulated production risk coefficient for the current steel coil based on the judgment basis of simulated production, flatness control target and specification transition information;

[0043] The flatness consideration weight assignment module is used to assign a simulated production weight in the flatness consideration weight of the current steel coil that is first rolled to 1, and assign a simulated production weight in the flatness consideration weight of the current steel coil that is not first rolled to a preset value other than 1;

[0044] The historical production risk coefficient calculation module is used to calculate the historical production risk coefficient of the current steel coil based on the historical production judgment basis, flatness control target, actual production situation and specification transition information;

[0045] The flatness risk coefficient calculation module is used to allocate flatness consideration weights to the simulated production risk coefficient and the historical production risk coefficient, and perform weighted summation of the simulated production risk coefficient and the historical risk coefficient according to the flatness consideration weights to calculate the flatness problem risk coefficient of the current steel coil;

[0046] The traversal judgment module is used to judge whether all the steel coils in the rolling plan have been traversed; if so, the rationality evaluation module is started; if not, the steel coil serial number is increased by 1, and the simulation production risk coefficient calculation module is started;

[0047] The rationality evaluation module is used to preset evaluation criteria, arrange evaluation results and labels according to risk factors and evaluation criteria, and output evaluation results using labels as risk assessment results.

[0048] The solution of the embodiment of the present invention has the following beneficial effects:

[0049] The method and system for evaluating the rationality of a hot-rolled strip rolling plan provided by the embodiment of the present invention adopts a roll system-rolled product integrated plate shape setting model in terms of mechanism analysis. This model is a fast coupling model that comprehensively considers the elastic deformation behavior of the roll system and the elastic-viscoplastic deformation behavior of the rolled product. It has been tested in practice for many years of online application and has been widely praised. It ensures the reliability of the mechanism analysis results in terms of calculation speed and accuracy. The obtained rolling plan information to be evaluated and the corresponding product information include specification transition information. The present invention improves the reliability of the big data analysis results based on the similarity between the retrieval results of previous records and the corresponding products in the rolling plan. When calculating the risk coefficient of plate shape problems, the mechanism analysis results and the big data analysis results are comprehensively considered to ensure the reliability of the rationality evaluation results. The adopted technical solution is easy to deploy and has a fast calculation speed, which is conducive to multiple iterations and evaluations of the rolling plan in a short period of time, thereby providing support for the optimization of the rolling plan to achieve good plate shape.

[0050] Of course, it is not necessary to achieve all of the advantages described above simultaneously in order to implement any product or method of the present invention. Description of the drawings:

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 is a flow chart of a method for evaluating the rationality of a hot strip rolling plan according to an embodiment of the present invention;

[0053] Figure 2 4 is a schematic diagram of a method for evaluating the rationality of a hot strip rolling plan according to an embodiment of the present invention. Specific implementation method:

[0054] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations. It should be noted that the embodiments of the present invention and the features in the embodiments can also be combined with each other in the absence of conflict.

[0055] It should be noted that similar reference numerals and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined or explained in subsequent figures. In the description of the present invention, the terms "first," "second," "third," "fourth," etc. are used only to distinguish the description and are not to be understood as indicating or implying relative importance.

[0056] Based on the evaluation problem of the rationality of the rolling plan during hot rolling production of strip steel, an embodiment of the present invention provides a method and system for evaluating the rationality of the rolling plan of hot rolled strip steel. The evaluation process first obtains the rolling plan information to be evaluated; then, based on the rolling plan information, the judgment basis required for the evaluation is calculated and integrated; then, based on the judgment basis, the risk coefficient of the plate shape problem is calculated; finally, the evaluation results and process quantities are output to provide a reference for optimizing the rolling plan. The present invention comprehensively considers the results of mechanism analysis and big data analysis, and quantitatively evaluates the rationality of the hot rolling plan to quantitatively grasp the risk of plate shape problems after the implementation of the rolling plan, thereby providing a scientific basis for optimizing the rolling plan that can achieve good plate shape, and provides a feasible solution for optimizing the rolling plan that can achieve good plate shape.

[0057] like Figure 1 As shown, the rationality evaluation method of the hot strip rolling plan includes the following steps:

[0058] Step S1, obtaining rolling plan information to be evaluated and corresponding product information.

[0059] In this step, the rolling plan information includes the order of rolled steel coils; the corresponding product information includes the product steel grade and specification, flatness control target, and specification transition information. The specification transition information refers to the specification change of each coil relative to the previous coil in the rolling plan, and is calculated as follows:

[0060] ΔSp i =Sp i –Sp i-1 (1)

[0061] In formula (1), ΔSp i Represents the specification change corresponding to the i-th coil, Sp i and Sp i-1 Represents the specifications of the steel for roll i and roll i-1 in the rolling plan. The specifications represented by Sp include material code, width code, thickness code, width dimension, and thickness dimension.

[0062] Step S2: Establish a rolling simulation model based on the rolling calculation information, and obtain simulated production results through the simulation model calculation as a basis for judging the simulated production; obtain historical production results recorded in the past as a basis for judging historical production.

[0063] Among them, the simulated production results calculated by the simulation model include the secondary convexity of the strip, equivalent flatness, edge drop, local high and low points, optimal bending roll setting, and optimal shifting roll setting; the historical production results obtained by retrieving past records include the strip head convexity, head flatness, head wedge shape, bending roll setting, bending roll lower limit (bending roll setting lower limit), bending roll upper limit (bending roll setting upper limit), shifting roll setting, shifting roll lower limit (shifting roll setting lower limit), shifting roll upper limit (shifting roll setting upper limit), operator leveling amount, leveling amount lower limit, leveling amount upper limit, operator bending roll amount, operation bending roll lower limit, operation bending roll upper limit, loop height fluctuation, loop quantity fluctuation lower limit, loop quantity fluctuation upper limit.

[0064] Step S3, initializing the steel coil serial number, setting the steel coil serial number to the first coil in the rolling plan.

[0065] Step S4: For the current steel coil, the simulated production risk coefficient is calculated based on the judgment basis of the simulated production, the plate shape control target and the specification transition information.

[0066] In this step, when calculating the simulated production risk coefficient, several first-category evaluation indicators are preset, and the parameters in the simulated production results obtained by the simulation model are used as the basis for judging different first-category evaluation indicators. The corresponding parameters in the plate shape control target are used as the judgment standard. Each first-category evaluation indicator is evaluated and calculated separately to obtain the risk value of each first-category evaluation indicator, and the risk values of all first-category evaluation indicators are weighted and summed, and the result is used as the simulated production risk coefficient.

[0067] In a preferred embodiment, four first-category evaluation indicators are preset, as shown in Table 1. The four evaluation indicators are coded as category M followed by a sequence number. M1-M4 represent, respectively, whether the crown exceeds the limit, whether the equivalent flatness exceeds the limit, whether the roll gap shape is abnormal, and whether the bending and shifting roll setting reaches the limit. For each first-category evaluation indicator, the corresponding parameter in the simulated production results is used as the judgment basis, and the corresponding parameter in the flatness control target is used as the judgment standard. The risk value for that evaluation indicator is calculated according to the corresponding evaluation standard, and each risk value ranges from [0, 100].

[0068] In a preferred embodiment, as shown in Table 1, for the evaluation index M1, that is, whether the convexity exceeds the limit, the corresponding judgment basis is the secondary convexity in the simulated production result, and the corresponding judgment standard is the target convexity and convexity tolerance in the flatness control target. The corresponding evaluation standard adopts a proportional division according to the tolerance with the risk value 60 as the node; the calculated risk value increases proportionally according to the interval of the absolute deviation. The simulated production consideration weight of the M1 evaluation index is Similarly, for the evaluation index M2, that is, whether the equivalent flatness exceeds the limit, the corresponding judgment basis is the equivalent flatness in the simulated production results, and the corresponding judgment standard is the target flatness and flatness tolerance in the plate shape control target. The corresponding evaluation standard adopts the risk value 60 as the node to divide the tolerance in proportion, and the calculated risk value increases in proportion to the interval of the absolute deviation; for the evaluation index M3, that is, whether the roll gap shape is abnormal, the corresponding evaluation basis is the edge drop and local high and low points in the simulated production results, and the corresponding judgment standard is the edge drop threshold and local high point threshold defined according to the actual situation. The corresponding evaluation standard is based on the edge drop and local high and low point thresholds. The value is judged by the 0-1 formula; when calculating the risk value, the consideration weights of edge drop and local high and low points for M3 are 50% respectively, and then the edge drop and local high and low points are weighted according to the weights to obtain the risk value; for the evaluation index M4, that is, whether the bending and shifting roll settings have reached the limit, the corresponding judgment basis is the optimal bending roll of each frame and the optimal shifting roll of each frame in the simulation production results, and the corresponding judgment standard is the upper and lower limits of the bending roll of each frame and the upper and lower limits of the shifting roll of each frame defined according to the actual situation. The evaluation standard adopted is to make a 0-1 judgment according to the upper and lower limits, and the consideration weights of the bending roll and the shifting roll are 50% respectively, and then the upper and lower limits of the bending roll and the shifting roll are weighted and summed to obtain the risk value.

[0069] Table 1

[0070]

[0071] The consideration weight of each first-category evaluation indicator is calculated based on the specification transition information. The risk values of all first-category evaluation indicators are weighted and summed according to the consideration weight. The calculation formula is as follows:

[0072]

[0073] In formula (2), Score simu represents the simulated production risk coefficient; i represents the i-th indicator; m represents the total number of evaluation indicators; M i Represents the risk value corresponding to the i-th indicator; The simulated production consideration weight corresponding to the representative index. In this embodiment, m=4.

[0074] Step S5, determine whether the current steel coil is rolled for the first time; if so, assign the simulated production weight in the plate shape consideration weight to 1, and go to step S7; if not, assign the simulated production weight in the plate shape consideration weight to a preset value other than 1, and go to step S6.

[0075] In this step, the preset value may preferably be 0.5.

[0076] Step S6, calculating the historical production risk coefficient of the current steel coil based on the historical production judgment basis, flatness control target, actual production situation and specification transition information.

[0077] In this step, when calculating the historical production risk coefficient, several second-category evaluation indicators are preset, and the parameters in the historical production results obtained by retrieving past records are used as the basis for judging different second-category evaluation indicators. The corresponding parameters in the rolling plan or actual production situation are used as the judgment criteria. Each second-category evaluation indicator is evaluated and calculated to obtain the risk value of each second-category evaluation indicator, and the risk values of all second-category evaluation indicators are weighted and summed, and the result is used as the historical production risk coefficient.

[0078] In a preferred embodiment, preferably, the number of the second-category evaluation indicators is preset to five, as shown in Table 2. The five preset second-category evaluation indicators are represented by the category code D followed by a sequence number. D1-D5 are, respectively, whether the head plate shape exceeds the limit, whether the bending and shifting roll setting exceeds the limit, whether the manual intervention amount exceeds the limit, whether the looper height fluctuation exceeds the limit, and whether the centerline offset exceeds the limit. Under each second-category evaluation indicator, the corresponding parameter in the historical production results is used as the judgment basis, and the corresponding parameter in the rolling plan or actual production situation is used as the judgment standard. The risk value under the evaluation indicator is calculated according to the corresponding evaluation standard, and the value range of each risk value is [0,100].

[0079] In a preferred embodiment, as shown in Table 2, for D1 in the second type of evaluation index, that is, whether the head plate shape exceeds the limit, the corresponding judgment basis is the head convexity, head flatness and head wedge in the historical production results, and the corresponding judgment standard is the target convexity, target flatness and target wedge in the rolling plan information and the corresponding convexity tolerance, flatness tolerance and wedge tolerance. The evaluation standard adopted is to divide the risk value 60 as the node and divide it in equal proportion according to the tolerance. The weights of convexity, flatness and wedge are 33.3% each. Finally, the three aspects are divided into the following categories according to the weights: The weighted sum is calculated to obtain the risk value of the evaluation index. For the evaluation index D2, that is, whether the bending and shifting roll settings exceed the limit, the corresponding judgment basis is the bending roll settings of each frame and the shifting roll settings of each frame in the historical production results. The corresponding judgment standard is the upper and lower limits of the bending roll and the upper and lower limits of the shifting roll defined according to the actual production situation. The evaluation standard adopted is to make a 0-1 judgment according to the upper and lower limits. The weights of the bending roll and the shifting roll are each assigned 50%. The risk value of the evaluation index is calculated by weighting according to the weights. Similarly, for the evaluation index D3, that is, whether the manual intervention amount exceeds the limit, The corresponding judgment basis is the leveling amount of each frame operator and the bending amount of each frame operator in the historical production results. The corresponding judgment standard is the upper and lower limits of the leveling amount and the upper and lower limits of the operation bending roll defined according to the actual production history. The evaluation standard adopted is to make a 0-1 judgment based on the upper and lower limits of the leveling amount and the upper and lower limits of the operation bending roll. The weights of the leveling amount and the operation bending roll are respectively assigned a value of 50%, and then the weighted sum is performed according to the weight to calculate the risk value of the evaluation index; for the evaluation index D4, that is, whether the fluctuation amount of the loop height exceeds the limit, the corresponding judgment basis is the upper and lower limits of the leveling amount and the upper and lower limits of the operation bending roll defined according to the actual production history. For the high fluctuation amount, the corresponding judgment standard is the upper and lower limits of the loop amount fluctuation defined according to the actual production situation, and the evaluation standard adopted is a 0-1 judgment based on the upper and lower limits of the loop maximum fluctuation; for the evaluation indicator D5, that is, whether the center line offset exceeds the limit, the corresponding judgment basis is the center line offset in the historical production results, and the corresponding judgment standard is to define the allowable absolute offset according to the actual production situation. The evaluation standard adopted is to divide the risk value 60 as the node in proportion according to the allowable absolute offset, and the calculated risk value increases proportionally according to the interval of the absolute value of the center line offset.

[0080] Table 2

[0081]

[0082] The historical production consideration weight of each second-category evaluation indicator is calculated based on the specification transition information. The risk values of all second-category evaluation indicators are weighted and summed based on the historical production consideration weight. The calculation formula is as follows:

[0083]

[0084] In formula (3), Score data represents the historical production risk coefficient; j represents the jth indicator; n represents the total number of evaluation indicators; D j Represents the risk value corresponding to the j-th indicator; The weight corresponding to the representative indicator. Preferably, in this embodiment, n=5.

[0085] The weights considered vary depending on the product type, which includes transitional rolls and non-transitional rolls. A transitional roll refers to a product whose specifications have changed relative to the previous roll in the rolling plan; a non-transitional roll refers to a product whose specifications have not changed relative to the previous roll in the rolling plan. The first roll in the rolling plan is a non-transitional roll. Therefore, this step may specifically include:

[0086] Step S61: Preset five second-category evaluation indicators, namely, whether the head flatness exceeds the limit, whether the bending roller setting exceeds the limit, whether the manual intervention amount exceeds the limit, whether the looper height fluctuation exceeds the limit, and whether the centerline offset exceeds the limit;

[0087] Step S62, calculating the risk value of whether the head flatness exceeds the limit as the first second-category evaluation index;

[0088] Step S63, determine whether the current steel coil is a transition coil; if so, assign a historical production consideration weight of whether the head plate shape exceeds the limit as the first second-category evaluation indicator to 100%, and proceed to step S65; if not, assign a risk value as the historical production consideration weight of whether the head plate shape exceeds the limit as the first second-category evaluation indicator to a preset value, and proceed to step S64; in this step, the preset value is preferably 20%.

[0089] Step S64, calculate the risk values of the second to fifth second-category evaluation indicators in sequence, and assign the historical production consideration weights of the second to fifth second-category evaluation indicator risk values to preset values; in this step, the preset value is preferably 20%.

[0090] Step S65 , performing weighted summation on the risk values of the second type of evaluation indicators according to the historical production consideration weights, and calculating the historical production risk coefficient of the current steel coil.

[0091] Step S7, allocating flatness consideration weights to the simulated production risk coefficient and the historical production risk coefficient, performing weighted summation on the simulated production risk coefficient and the historical risk coefficient according to the flatness consideration weights, and calculating the flatness problem risk coefficient of the current steel coil.

[0092] In this step, the formula for calculating the flatness risk factor is as follows:

[0093] Score=λScore simu +(1-λ)Score data(4)

[0094] In formula (4), Score represents the risk coefficient of shape problems occurring, and λ represents the weighting factor for considering the risk coefficient of simulated production.

[0095] Preferably, for the product of the first rolling, λ = 1, and for other cases, λ = 0.5. Therefore, for the steel coil of the first rolling, that is, the steel coil ranked 1, the historical production risk coefficient does not need to be considered.

[0096] Step S8: Determine whether all steel coils in the rolling plan have been traversed; if not, increment the steel coil number by 1 and return to step S4; if so, proceed to step S9.

[0097] Step S9: Preset an evaluation criterion. According to the risk coefficient and the evaluation criterion, arrange the evaluation result and the identifier, and output the evaluation result using the identifier as the risk assessment result.

[0098] In this step, the evaluation criterion includes: when the risk coefficient of shape problems Score ≤ 40, the evaluation result is low risk, and a green identifier is used; when 40 < Score ≤ 70, the evaluation result is recommended for adjustment, and an orange identifier is used; when 70 < Score, the evaluation result is must be adjusted, and a red identifier is used. Among them, low risk means that the ranking of the corresponding product can be left unchanged; recommended for adjustment means that the ranking of the corresponding product may cause shape problems, and it is necessary to check the judgment basis in detail and consider whether it is necessary to adjust; must be adjusted means that the ranking of the corresponding product is basically certain to have shape problems and should be adjusted.

[0099] Based on the same idea, an embodiment of the present invention also provides a rationality evaluation system for a hot-rolled strip rolling plan. The system includes: a rolling plan acquisition module, a simulation module, a historical production data acquisition module, a simulated production risk coefficient calculation module, a shape consideration weight assignment module, a historical production risk coefficient calculation module, a shape risk coefficient calculation module, a traversal judgment module, and a rationality evaluation module.

[0100] Among them, the rolling plan acquisition module is used to obtain the rolling plan information to be evaluated and the corresponding product information.

[0101] The rolling plan acquisition module is used to obtain the rolling plan information to be evaluated and the corresponding product information. The rolling plan information includes the ranking of the rolled steel coils, and the corresponding product information includes the product steel grade specification, the shape control target, and the specification transition information.

[0102] The simulation module is used to establish a rolling simulation model based on the rolling plan information and the corresponding product information, and calculate the simulated production result through the simulation model as the judgment basis for simulated production.

[0103] The simulated production risk coefficient calculation module is used to calculate the simulated production risk coefficient for the current steel coil based on the judgment basis of simulated production, flatness control target and specification transition information;

[0104] The flatness consideration weight assignment module is used to assign a simulated production weight in the flatness consideration weight of the current steel coil that is first rolled to 1, and assign a simulated production weight in the flatness consideration weight of the current steel coil that is not first rolled to a preset value other than 1;

[0105] The historical production risk coefficient calculation module is used to calculate the historical production risk coefficient of the current steel coil based on the historical production judgment basis, flatness control target, actual production situation and specification transition information;

[0106] The flatness risk coefficient calculation module is used to allocate flatness consideration weights to the simulated production risk coefficient and the historical production risk coefficient, and perform weighted summation of the simulated production risk coefficient and the historical risk coefficient according to the flatness consideration weights to calculate the flatness problem risk coefficient of the current steel coil;

[0107] The traversal judgment module is used to judge whether all the steel coils in the rolling plan have been traversed; if so, the rationality evaluation module is started; if not, the steel coil serial number is increased by 1, and the simulation production risk coefficient calculation module is started;

[0108] The rationality evaluation module is used to preset evaluation criteria, arrange evaluation results and labels according to risk factors and evaluation criteria, and output evaluation results using labels as risk assessment results.

[0109] In this embodiment, each module is implemented by a processor, and a memory is appropriately added when storage is required. The processor may be, but is not limited to, a microprocessor MPU, a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components, etc. The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk storage. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0110] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode.

[0111] It should also be noted that the rationality evaluation system for the hot-rolled strip rolling plan described in this embodiment corresponds to the rationality evaluation method for the hot-rolled strip rolling plan. The description and limitation of the method are also applicable to the system and will not be repeated here.

[0112] The rationality evaluation method and system for hot strip rolling plan according to the embodiment of the present invention are used to evaluate the rationality of a rolling plan. Table 3 shows the obtained rolling plan.

[0113] Table 3

[0114]

[0115] The rationality of the rolling plan is evaluated according to steps S1 through S9 described above. This example evaluates the steel coils in a traversal manner, selecting only representative coils 10-13 for risk assessment as an example. Coil 10 is a non-transition coil (its specifications remain unchanged from the previous coil), coils 11 and 12 are transitional coils, and coil 13 is a first-time rolling operation (historical production records do not contain similar steel grades or specifications).

[0116] The simulation results obtained by simulation model calculation are shown in Table 4 and Table 5:

[0117] Table 4

[0118]

[0119] Table 5

[0120]

[0121] The historical production results obtained by searching past records are shown in Tables 6 to 8:

[0122] Table 6

[0123] Sorting within a cell Head convexity / μm Head flatness / IU Head wedge / μm Centerline offset / cm Offset limit / cm 10 62 -3 5 3 20 11 60 13 21 6 20 12 94 -18 41 29 20 13 - - - - -

[0124] Table 7

[0125]

[0126] Table 8

[0127]

[0128] The calculation results of the simulated production risk coefficient are shown in Table 9:

[0129] Table 9

[0130]

[0131] The calculation results of historical production risk coefficient are shown in Table 10:

[0132] Table 10

[0133]

[0134] After completing the calculation of the simulated production risk coefficient and the historical production risk coefficient, the two coefficients are weighted and summed to obtain the risk coefficient of flatness problems for each roll of product. The calculation results are shown in Tables 11 and 12:

[0135] Table 11

[0136] Sorting within a cell Volume 10 Volume 11 Volume 12 Volume 13 Flatness problem risk factor 0.5×2.3+0.5×6.6＝4.5 0.5×38.8+0.5×47.6＝43.2 0.5×74.8+0.5×58.5＝66.7 1×39.7+0×100＝39.7 Arrangement Evaluation Less risky Recommended adjustments Recommended adjustments Less risky

[0137] Table 12

[0138]

[0139] The risk assessment results and assessment process quantities corresponding to the rolling plan are presented in a structured manner on the screen, as shown in Table 13:

[0140] Table 13

[0141]

[0142] After obtaining the risk factor and schedule evaluation for plate shape problems, we reviewed the judgment basis in detail and decided not to make any adjustments to the original rolling plan.

[0143] Among them, F5 represents that the corresponding index of the 5th finishing rolling stand, or the relevant aspects within the index are exceeded, and F0 represents that the index or all the stands corresponding to the index are within the limit.

[0144] After the rolling plan was implemented, the measured values of the flatness of the products of coils 10-13 after rolling (mainly the statistical values of the cross-sectional shape and head flatness) were observed, and it was found that: First, coils 11 and 12 had slight flatness problems, while coils 10 and 13 basically had no flatness problems, which confirmed the evaluation results and proved the scientific nature and reliability of the evaluation method described in this embodiment.

[0145] It can be seen that the rationality evaluation method and system for the hot-rolled strip rolling plan provided by the embodiment of the present invention adopts a roll system-rolled product integrated plate shape setting model in terms of mechanism analysis. This model is a fast coupling model that comprehensively considers the elastic deformation behavior of the roll system and the elastic-viscoplastic deformation behavior of the rolled product. It has been tested and widely praised in online applications for many years, and the reliability of the mechanism analysis results is guaranteed in terms of calculation speed and accuracy. The rolling plan information to be evaluated and the corresponding product information are obtained, and the corresponding product information contains specification transition information. It is beneficial to improve: the similarity between the previous record retrieval results and the corresponding products in the rolling plan, thereby improving the reliability of the big data analysis results; when calculating the risk coefficient of plate shape problems, the mechanism analysis results and the big data analysis results are comprehensively considered to ensure the reliability of the rationality evaluation results; the adopted technical solution is easy to deploy and has a fast calculation speed, which is conducive to multiple iterations and evaluations of the rolling plan in a short period of time, thereby providing support for the optimization of the rolling plan with good plate shape.

[0146] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. It is not intended to limit the scope of the invention to be protected, but merely represents a preferred embodiment of the present invention. Those skilled in the art should understand that the scope of the invention involved in the present invention is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the inventive concept. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work shall fall within the scope of protection of the present invention.

Claims

1. A method for evaluating the rationality of a hot strip rolling plan, characterized in that: The method comprises the following steps: Step S1, obtaining rolling plan information and corresponding product information to be evaluated; the rolling plan information includes the order of rolled steel coils, and the corresponding product information includes product steel grade specifications, flatness control targets, and specification transition information; Step S2: establishing a rolling simulation model based on the rolling plan information and the corresponding product information, and obtaining simulated production results through the simulation model as a basis for judging the simulated production; obtaining historical production results recorded in the past as a basis for judging the historical production; Step S3, initializing the steel coil serial number to set the steel coil serial number to the first coil in the rolling plan; Step S4, for the current steel coil, calculate the simulated production risk coefficient based on the judgment basis of simulated production, flatness control target and specification transition information; Step S5, determining whether the current steel coil is rolled for the first time; if so, assigning a simulated production weight in the plate shape consideration weight to 1, and proceeding to step S7; If not, the simulated production weight in the plate shape consideration weight is assigned a preset value other than 1, and the process proceeds to step S6; Step S6, calculating the historical production risk coefficient of the current steel coil based on the historical production judgment basis, flatness control target, actual production situation and specification transition information; Step S7, allocating flatness consideration weights to the simulated production risk coefficient and the historical production risk coefficient, performing a weighted summation of the simulated production risk coefficient and the historical risk coefficient according to the flatness consideration weights, and calculating the flatness problem risk coefficient of the current steel coil; Step S8, determining whether all the coils in the rolling plan have been traversed; if not, incrementing the coil number by 1 and returning to step S4; If yes, proceed to step S9; Step S9: preset evaluation criteria, arrange evaluation results and labels according to risk factors and evaluation criteria, and output evaluation results using labels as risk assessment results.

2. The method according to claim 1, characterized in that The step S6 comprises: Step S61: Preset five second-category evaluation indicators, namely, whether the head flatness exceeds the limit, whether the bending roller setting exceeds the limit, whether the manual intervention amount exceeds the limit, whether the looper height fluctuation exceeds the limit, and whether the centerline offset exceeds the limit; Step S62, calculating the risk value of whether the head flatness exceeds the limit as the first second-category evaluation index; Step S63: Determine whether the current steel coil is a transition coil. If so, assign a historical production weight of whether the head flatness exceeds the limit, which is the first second-category evaluation index, to 100%, and proceed to step S65. If not, assign a historical production weight of whether the head flatness exceeds the limit, which is the first second-category evaluation index, to a preset value, and proceed to step S64. Step S64, sequentially calculating the risk values of the second to fifth second-category evaluation indicators, and assigning the historical production consideration weights of the second to fifth second-category evaluation indicator risk values to preset values respectively; Step S65 , performing weighted summation on the risk values of the second type of evaluation indicators according to the historical production consideration weights, and calculating the historical production risk coefficient of the current steel coil.

3. The method according to claim 2, characterized in that The preset value in step S63 and the preset value in step S64 are the same, both being 20%.

4. The method according to claim 1 or 2, characterized in that The historical production consideration weight of each second-category evaluation indicator is calculated based on the specification transition information. The risk values of all second-category evaluation indicators are weighted and summed based on the historical production consideration weight. The calculation formula is as follows: In formula (3), Score data represents the historical production risk coefficient; j represents the jth indicator; n represents the total number of evaluation indicators; D j Represents the risk value corresponding to the j-th indicator; Represents the historical production consideration weight corresponding to the indicator.

5. The method according to claim 1, wherein Specification transition information refers to the specification change of each coil relative to the previous coil in the rolling plan. The calculation method is as follows: ΔSp i =Sp i –Sp i-1 (1) In formula (1), ΔSp i Represents the specification change corresponding to the i-th coil, Sp i and Sp i-1 They represent the specification values of the i-th and i-1-th rolls of steel in the rolling plan respectively; the specifications represented by Sp include material code, width code, thickness code, width dimension and thickness dimension.

6. The method according to claim 1, characterized in that The simulation results obtained by the simulation model include strip secondary crown, equivalent flatness, edge drop, local high and low points, optimal bending roll setting, and optimal roll shifting setting; The historical production results obtained by retrieving past records include strip head convexity, head flatness, head wedge shape, bending roll setting, bending roll setting lower limit, bending roll setting upper limit, shifting roll setting, shifting roll setting lower limit, shifting roll setting upper limit, operator leveling amount, leveling amount lower limit, leveling amount upper limit, operator bending roll amount, operation bending roll lower limit, operation bending roll upper limit, loop height fluctuation, loop quantity fluctuation lower limit and loop quantity fluctuation upper limit.

7. The method according to claim 1, characterized in that When calculating the simulated production risk coefficient in step S4, several first-category evaluation indicators are preset, and the parameters in the simulated production results obtained by the simulation model are used as the basis for judging different first-category evaluation indicators. The corresponding parameters in the plate shape control target are used as the judgment standard. Each first-category evaluation indicator is evaluated and calculated separately to obtain the risk value of each first-category evaluation indicator, and the risk values of all first-category evaluation indicators are weighted and summed, and the result is used as the simulated production risk coefficient.

8. The method according to claim 7, characterized in that The number of the first type of evaluation indicators is preset to four, and the four evaluation indicator codes are represented by category M plus a serial number, M1-M4 respectively indicating whether the convexity exceeds the limit, whether the equivalent flatness exceeds the limit, whether the roll gap shape is abnormal, and whether the bending roll setting reaches the limit; under each first type of evaluation indicator, the corresponding parameters in the corresponding simulation production results are used as the basis for judgment, and the corresponding parameters in the corresponding plate shape control target are used as the judgment criteria, and the risk value under the evaluation indicator is calculated according to the corresponding evaluation criteria.

9. The method according to claim 1, characterized in that The consideration weight of each first-category evaluation indicator is calculated based on the specification transition information. The risk values of all first-category evaluation indicators are weighted and summed according to the consideration weight. The calculation formula is as follows: In formula (2), Score simu Represents the simulated production risk factor; i represents the i-th indicator; m represents the total number of evaluation indicators; M i Represents the risk value corresponding to the i-th indicator; Represents the simulated production consideration weight corresponding to the indicator.

10. A rationality evaluation system for hot strip rolling plan, characterized in that: The system includes: a rolling plan acquisition module, a simulation module, a historical production data acquisition module, a simulation production risk coefficient calculation module, a plate shape weight assignment module, a historical production risk coefficient calculation module, a plate shape risk coefficient calculation module, a traversal judgment module and a rationality evaluation module; wherein, The rolling plan acquisition module is used to obtain rolling plan information to be evaluated and corresponding product information; the rolling plan information includes the order of rolled steel coils, and the corresponding product information includes product steel grade specifications, flatness control targets and specification transition information; The simulation module is used to establish a rolling simulation model based on rolling plan information and corresponding product information, and obtain simulated production results through simulation model calculation as a basis for judging simulated production; The historical production data acquisition module is used to obtain historical production results recorded in the past as a basis for judging historical production; The simulated production risk coefficient calculation module is used to calculate the simulated production risk coefficient for the current steel coil based on the judgment basis of simulated production, flatness control target and specification transition information; The flatness consideration weight assignment module is used to assign a simulated production weight in the flatness consideration weight of the current steel coil that is first rolled to 1, and assign a simulated production weight in the flatness consideration weight of the current steel coil that is not first rolled to a preset value other than 1; The historical production risk coefficient calculation module is used to calculate the historical production risk coefficient of the current steel coil based on the historical production judgment basis, flatness control target, actual production situation and specification transition information; The flatness risk coefficient calculation module is used to allocate flatness consideration weights to the simulated production risk coefficient and the historical production risk coefficient, and perform weighted summation of the simulated production risk coefficient and the historical risk coefficient according to the flatness consideration weights to calculate the flatness problem risk coefficient of the current steel coil; The traversal judgment module is used to judge whether all the steel coils in the rolling plan have been traversed; if so, the rationality evaluation module is started; if not, the steel coil serial number is increased by 1, and the simulation production risk coefficient calculation module is started; The rationality evaluation module is used to preset evaluation criteria, arrange evaluation results and labels according to risk factors and evaluation criteria, and output evaluation results using labels as risk assessment results.

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