A deformation resistance self-learning coefficient diagnosis method, device, equipment and medium

Abstract

The application discloses a deformation resistance self-learning coefficient diagnosis method and device, equipment and medium, including: obtaining the deformation resistance self-learning coefficient table to be diagnosed; determining N deformation resistance self-learning coefficients from the deformation resistance self-learning coefficient table, taking the N deformation resistance self-learning coefficients as target deformation resistance self-learning coefficients respectively, and executing the following steps: determining M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficient; determining the difference value between the target deformation resistance self-learning coefficient and each reference deformation resistance self-learning coefficient, and determining whether the target deformation resistance self-learning coefficient is in an abnormal state according to the relationship between each difference value and a preset threshold. The embodiment can mark the state of the target deformation resistance self-learning coefficient, so that professionals can quickly determine the abnormal deformation resistance self-learning coefficient that needs to be corrected, and the diagnosis efficiency and accuracy of the deformation resistance self-learning coefficient table are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of rolling technology, and in particular to a deformation resistance self-learning coefficient diagnosis method, device, equipment and medium. BACKGROUND

[0002] The deformation resistance of metal is a basic quantity representing the pressure processing performance of steel, and correctly determining the deformation resistance of metal under different deformation conditions is a necessary condition for improving the control accuracy of continuous rolling mills. In the past, when establishing the relationship model of deformation resistance and deformation amount, deformation rate and temperature, a multivariate nonlinear regression method is usually used to process the measured data to obtain the corresponding deformation resistance model. The deformation resistance model is the most important model in the finishing rolling model, which directly determines the rolling force prediction accuracy, thickness accuracy and rolling stability of the finishing rolling.

[0003] The deformation resistance model mainly embodies the stratified deformation resistance self-learning coefficient in the form of a table. However, the tabular deformation resistance self-learning coefficient is prone to sudden changes between adjacent layers, thereby causing a large change in load distribution, affecting production stability, and even leading to scrap steel. In related technologies, the tabular deformation resistance self-learning coefficient is mainly diagnosed and identified by manpower, but this method has the problems of low efficiency and low accuracy. SUMMARY

[0004] The present application provides a deformation resistance self-learning coefficient diagnosis method, device, equipment and medium, which solves the technical problem that the tabular deformation resistance self-learning coefficient cannot be quickly and accurately diagnosed and identified in the prior art, and achieves the technical effect of quickly and accurately diagnosing and identifying the tabular deformation resistance self-learning coefficient.

[0005] In a first aspect, the present application provides a deformation resistance self-learning coefficient diagnosis method, which comprises:

[0006] Obtaining a deformation resistance self-learning coefficient table to be diagnosed, the deformation resistance self-learning coefficient table comprising deformation resistance self-learning coefficients corresponding to different entry temperatures and different deformation rates of a target steel grade on a target stand;

[0007] Determining N deformation resistance self-learning coefficients from the deformation resistance self-learning coefficient table, taking the N deformation resistance self-learning coefficients as target deformation resistance self-learning coefficients, and performing the following steps:

[0008] Determining M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficients, and taking the M deformation resistance self-learning coefficients as reference deformation resistance self-learning coefficients of the target deformation resistance self-learning coefficients; N and M are positive integers, and N>M;

[0009] determining a difference value between the target deformation resistance self-learning coefficient and each reference deformation resistance self-learning coefficient, and determining whether the target deformation resistance self-learning coefficient is in an abnormal state according to a relationship between each difference value and a preset threshold.

[0010] Further, the N deformation resistance self-learning coefficients are determined from the deformation resistance self-learning coefficient table, including:

[0011] All deformation resistance self-learning coefficients in the deformation resistance self-learning coefficient table except for the deformation resistance self-learning coefficient being the initial value are taken as the N deformation resistance self-learning coefficients.

[0012] Further, the M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficient are determined, including:

[0013] The M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficient are determined from the N deformation resistance self-learning coefficients.

[0014] Further, whether the target deformation resistance self-learning coefficient is in an abnormal state is determined according to a relationship between each difference value and a preset threshold, including:

[0015] If at least one of the difference values is greater than a first preset threshold, it is determined that the target deformation resistance self-learning coefficient is in a first abnormal state.

[0016] If at least one of the difference values is less than or equal to the first preset threshold and greater than a second preset threshold, it is determined that the target deformation resistance self-learning coefficient is in a second abnormal state.

[0017] If all of the difference values are less than or equal to the second preset threshold, it is determined that the target deformation resistance self-learning coefficient is in a normal state; wherein the preset threshold includes the first preset threshold and the second preset threshold.

[0018] Further, the method further includes:

[0019] If the target deformation resistance self-learning coefficient is in both the first abnormal state and the second abnormal state, the state of the target deformation resistance self-learning coefficient is recorded as the first abnormal state.

[0020] Further, after determining whether the target deformation resistance self-learning coefficient is in an abnormal state, the method further includes:

[0021] If the target deformation resistance self-learning coefficient is in the first abnormal state, the target deformation resistance self-learning coefficient or a corresponding cell is marked in a first color.

[0022] If the target deformation resistance self-learning coefficient is in the second abnormal state, the target deformation resistance self-learning coefficient or a corresponding cell is marked in a second color.

[0023] If the target deformation resistance self-learning coefficient is in a normal state, mark the target deformation resistance self-learning coefficient or the corresponding cell in a third color.

[0024] In a second aspect, the present application provides a deformation resistance self-learning coefficient diagnosis device, the device comprising:

[0025] An acquisition module is configured to acquire a deformation resistance self-learning coefficient table to be diagnosed, the deformation resistance self-learning coefficient table comprising deformation resistance self-learning coefficients corresponding to different entry temperatures and different deformation rates of a target steel grade on a target stand;

[0026] A first screening module is configured to determine N deformation resistance self-learning coefficients from the deformation resistance self-learning coefficient table, take the N deformation resistance self-learning coefficients as target deformation resistance self-learning coefficients respectively, and perform the following steps:

[0027] A second screening module is configured to determine M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficients, and take the M deformation resistance self-learning coefficients as reference deformation resistance self-learning coefficients of the target deformation resistance self-learning coefficients; N and M are positive integers, and N>M.

[0028] A diagnosis module is configured to determine difference values between the target deformation resistance self-learning coefficient and each reference deformation resistance self-learning coefficient, and determine whether the target deformation resistance self-learning coefficient is in an abnormal state according to a relationship between each difference value and a preset threshold.

[0029] Further, the first screening module comprises:

[0030] A first screening submodule is configured to take all deformation resistance self-learning coefficients in the deformation resistance self-learning coefficient table except the deformation resistance self-learning coefficients with initial values as the N deformation resistance self-learning coefficients.

[0031] In a third aspect, the present application provides an electronic device, comprising:

[0032] A processor;

[0033] A memory for storing processor-executable instructions;

[0034] The processor is configured to execute to implement the deformation resistance self-learning coefficient diagnosis method provided in the first aspect.

[0035] In a fourth aspect, the present application provides a non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by the processor of the electronic device, the electronic device can execute the deformation resistance self-learning coefficient diagnosis method provided in the first aspect.

[0036] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0037] The embodiments of the present application filter the coefficients in the self-learning coefficient table of the deformation resistance to be diagnosed, obtain N self-learning coefficients of the deformation resistance to be diagnosed, diagnose the N self-learning coefficients of the deformation resistance as target self-learning coefficients of the deformation resistance respectively, determine the difference values between the target self-learning coefficients of the deformation resistance and the adjacent reference self-learning coefficients of the deformation resistance, determine whether the target self-learning coefficients of the deformation resistance are in an abnormal state according to the relationship between each difference value and a preset threshold, and mark the state of the target self-learning coefficients of the deformation resistance (for example, mark with color), so that professionals can quickly determine the abnormal self-learning coefficients of the deformation resistance to be corrected, and the diagnosis efficiency and accuracy of the self-learning coefficient table of the deformation resistance are improved. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0039] Figure 1 A flowchart of a deformation resistance self-learning coefficient diagnosis method provided by the present application;

[0040] Figure 2 An example diagram of a deformation resistance self-learning coefficient table;

[0041] Figure 3 An example diagram of a deformation resistance self-learning coefficient table after diagnosis; Figure 2

[0042] Figure 4 A structural diagram of a deformation resistance self-learning coefficient diagnosis device provided by the present application;

[0043] Figure 5 A structural diagram of an electronic device provided by the present application. DETAILED DESCRIPTION

[0044] The embodiments of the present application provide a deformation resistance self-learning coefficient diagnosis method, which solves the technical problem that the tabular deformation resistance self-learning coefficient cannot be quickly and accurately diagnosed and recognized in the prior art.

[0045] The technical solutions of the embodiments of the present application are as follows to solve the above technical problems:

[0046] ​The application discloses a method for diagnosing a deformation resistance self-learning coefficient, and the method comprises the following steps: obtaining a deformation resistance self-learning coefficient table to be diagnosed, wherein the deformation resistance self-learning coefficient table comprises deformation resistance self-learning coefficients corresponding to different entry temperatures and different deformation rates of a target steel grade on a target rack; determining N deformation resistance self-learning coefficients from the deformation resistance self-learning coefficient table, taking the N deformation resistance self-learning coefficients as target deformation resistance self-learning coefficients respectively, and performing the following steps: determining M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficients and taking the M deformation resistance self-learning coefficients as reference deformation resistance self-learning coefficients of the target deformation resistance self-learning coefficients; N and M are positive integers, and N>M; determining difference values between the target deformation resistance self-learning coefficients and the reference deformation resistance self-learning coefficients respectively, and determining whether the target deformation resistance self-learning coefficients are in an abnormal state according to a relationship between each difference value and a preset threshold.

[0047] The embodiment filters coefficients in the deformation resistance self-learning coefficient table to be diagnosed, obtains N deformation resistance self-learning coefficients to be diagnosed, takes the N deformation resistance self-learning coefficients as target deformation resistance self-learning coefficients respectively, diagnoses the target deformation resistance self-learning coefficients, determines difference values between the target deformation resistance self-learning coefficients and adjacent reference deformation resistance self-learning coefficients, determines whether the target deformation resistance self-learning coefficients are in an abnormal state according to a relationship between each difference value and a preset threshold, and marks the state of the target deformation resistance self-learning coefficients (for example, marks the state by using color) so that professionals can quickly determine abnormal deformation resistance self-learning coefficients to be corrected, and the diagnosis efficiency and accuracy of the deformation resistance self-learning coefficient table are improved.

[0048] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in combination with the drawings of the specification and specific embodiments.

[0049] Firstly, the term "and / or" appearing in the present document only describes the association relationship of the associated objects, and indicates that there can be three relationships, for example, A and / or B can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in the present document generally represents an "or" relationship between the front and rear associated objects.

[0050] The embodiment provides a method for diagnosing a deformation resistance self-learning coefficient, as shown in Figure 1 The method comprises steps S11-S12.

[0051] In step S11, a deformation resistance self-learning coefficient table to be diagnosed is obtained, wherein the deformation resistance self-learning coefficient table comprises deformation resistance self-learning coefficients corresponding to different entry temperatures and different deformation rates of a target steel grade on a target rack.

[0052] Step S12: Determine N deformation resistance self-learning coefficients from the deformation resistance self-learning coefficient table, use the N deformation resistance self-learning coefficients as target deformation resistance self-learning coefficients, and execute the following steps S121-S122.

[0053] Step S121: Determine M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficient, and use the M deformation resistance self-learning coefficients as reference deformation resistance self-learning coefficients for the target deformation resistance self-learning coefficient; N and M are positive integers, N > M;

[0054] Step S122: Determine the difference between the target deformation resistance self-learning coefficient and each reference deformation resistance self-learning coefficient, and determine whether the target deformation resistance self-learning coefficient is in an abnormal state based on the relationship between each difference value and a preset threshold.

[0055] Specifically, regarding step S11, the deformation resistance self-learning coefficient table to be diagnosed is obtained. The deformation resistance self-learning coefficient table includes the deformation resistance self-learning coefficients corresponding to different inlet temperatures and different deformation rates of the target steel race on the target frame.

[0056] There are many types of steel. Generally, steels with similar hardness, chemical composition, and specifications are grouped into the same deformation resistance self-learning coefficient, meaning they are classified as a single steel class. Continuous rolling mills consist of multiple stands, each with different rolling requirements, thus necessitating different deformation resistance self-learning coefficients. Therefore, different deformation resistance self-learning coefficient tables correspond to different steel classes and different stands. Deformation resistance self-learning coefficients for the same steel class and the same stand are summarized in a single deformation resistance self-learning coefficient table.

[0057] like Figure 2 The table shown presents the self-learning coefficients of deformation resistance for a specific steel type on a specific target frame, corresponding to different inlet temperatures and deformation rates. In the second row, 0-19 refers to the numbers of different temperature ranges, and the corresponding column (the third row) indicates the specific temperature range. For example, number 4 corresponds to the temperature range [860-886°C]. Similarly, the second column, 0-19, refers to the numbers of different deformation rate ranges, and the corresponding column (the third column) indicates the specific deformation rate range. For example, number 6 corresponds to the deformation rate range [6-8 mm / s].

[0058] In the related art, when product specifications change or production processes fluctuate (such as RT2 temperature fluctuations, finishing temperature changes, load distribution adjustments), the tabularized deformation resistance self-learning coefficient table is prone to sudden changes in deformation resistance coefficients between adjacent layers, and the deformation resistance self-learning coefficients jump. If there is a sudden change in deformation resistance coefficients between adjacent layers, it will cause a sudden change in deformation resistance calculation values, thereby causing a large change in load distribution, affecting production stability, and in severe cases, causing scrap steel. Therefore, this tabularized deformation resistance self-learning coefficient table requires continuous maintenance by professionals. However, the number of deformation resistance self-learning coefficient layers is huge, and the maintenance work is extremely complex. For example, for a certain production line deformation resistance model, there are 400 layers for each rack for each steel grade, and there are 2800 layers for seven racks. There are a total of 99 steel grades, so there are a total of 277200 cells that need to be checked. Therefore, it is extremely difficult to manually check the deformation resistance self-learning coefficient table. To solve this problem, the present embodiment further provides a deformation resistance self-learning coefficient diagnosis method to quickly and accurately diagnose the deformation resistance self-learning coefficient table.

[0059] The above step S11 is to obtain the deformation resistance self-learning coefficient table corresponding to Figure 2 Similar deformation resistance self-learning coefficient tables that need to be diagnosed are provided for step S12 to perform specific diagnosis.

[0060] Specifically, regarding step S12, N deformation resistance self-learning coefficients are determined from the deformation resistance self-learning coefficient table, and the N deformation resistance self-learning coefficients are respectively taken as target deformation resistance self-learning coefficients, and the following steps S121-S122 are performed.

[0061] Determining N deformation resistance self-learning coefficients from the deformation resistance self-learning coefficient table includes: taking all deformation resistance self-learning coefficients in the deformation resistance self-learning coefficient table except the deformation resistance self-learning coefficient as an initial value as N deformation resistance self-learning coefficients.

[0062] Each deformation resistance self-learning coefficient in the deformation resistance self-learning coefficient table is obtained for a certain target steel grade in the actual production process on a certain specific production line involving the entry temperature and the deformation rate, and the deformation resistance self-learning coefficient corresponding to the entry temperature and the deformation rate that has not been involved is unknown. Therefore, an initial value is usually used to replace the unknown deformation resistance self-learning coefficient. For example, referring to Figure 2 “1” is taken as the initial value for annotation, where the cell corresponding to the 4th row and the 10th column is 1, meaning that the target steel grade has not yet appeared on the target rack with the rolling parameters of the entry temperature [880-896) (unit: °C) and the deformation rate [0-1) (unit: mm / s).

[0063] That is, when implementing step S12, the purpose is to take all deformation resistance self-learning coefficients except the deformation resistance self-learning coefficient with the initial value as N deformation resistance self-learning coefficients, so as to avoid diagnosing the data of the unit cell whose deformation resistance self-learning coefficient is the initial value, thereby improving the diagnosis efficiency and making the diagnosis more targeted, and in addition, reducing the probability of diagnosis errors due to the existence of the initial value. For example, taking Table 2 as an example, all deformation resistance self-learning coefficients except the unit cell 1 are taken as N deformation resistance self-learning coefficients.

[0064] After selecting N deformation resistance self-learning coefficients from the deformation resistance self-learning coefficient table, the N deformation resistance self-learning coefficients are sequentially traversed, and subsequent steps S121-step S122 are executed respectively. It should be noted that the traversal order of the N deformation resistance self-learning coefficients can be set according to actual conditions, for example, it can be the order combined from top to bottom and left to right, or it can be the order combined from bottom to top and right to left, or it can be a random order, etc.

[0065] For example, the traversal order can be to traverse the data in each row that can be a target deformation resistance self-learning coefficient from top to bottom, and referring to Table 2, the traversal order is the 6th row, the 7th row, the 8th row, etc., wherein the traversal order of the 6th row is the 6th row, the 11th column (cell data bit 0.8), the 6th row, the 12th column (cell data bit 1.02917), the 6th row, the 13th column (cell data bit 0.93848), the 6th row, the 14th column (cell data bit 0.93072), the 6th row, the 15th column (cell data bit 0.93928), and after the 6th row is traversed, the 7th row is traversed, and the traversal order is the 7th row, the 10th column (cell data bit 0.93288), the 7th row, the 11th column (cell data bit 0.8), and so on.

[0066] N deformation resistance self-learning coefficients are sequentially taken as target deformation resistance self-learning coefficients to implement steps S121-S122.

[0067] Step S121, determine M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficient, and take the M deformation resistance self-learning coefficients as reference deformation resistance self-learning coefficients of the target deformation resistance self-learning coefficient; N and M are positive integers, and N>M.

[0068] Determine M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficient, including:

[0069] Determine M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficient from the N deformation resistance self-learning coefficients.

[0070] Referring to Table 2, each cell of the deformation resistance self-learning coefficient includes 8 adjacent other cells (i.e., M is a positive integer less than or equal to 8), and for the cell of the target deformation resistance self-learning coefficient in the N deformation resistance self-learning coefficients, although the number of adjacent cells is 8, the cell whose deformation resistance self-learning coefficient is the initial value needs to be excluded. That is, the M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficient are the coefficients corresponding to the cells whose index values are not the initial value. For example, in Table 2, the cell data bit 0.93288 in the 7th row and the 10th column (cell) is taken as the target deformation resistance self-learning coefficient, and the corresponding adjacent M deformation resistance self-learning coefficients are 5 (i.e., M = 5), which specifically include: the cell data bit 0.8 in the 6th row and the 11th column, the cell data bit 0.8 in the 7th row and the 11th column, the cell data bit 0.95128 in the 8th row and the 11th column, the cell data bit 0.86098 in the 8th row and the 10th column, and the cell data bit 0.85291 in the 8th row and the 9th column.

[0071] That is, step S121 determines the M reference deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficient, and then step S122 is further executed.

[0072] In step S122, the difference values between the target deformation resistance self-learning coefficient and each reference deformation resistance self-learning coefficient are determined, and according to the relationship between each difference value and a preset threshold, it is determined whether the target deformation resistance self-learning coefficient is in an abnormal state.

[0073] The difference values between each reference deformation resistance self-learning coefficient and the target deformation resistance self-learning coefficient are calculated (the difference value is an absolute value), and the difference values are compared with the preset threshold. The difference value can be a difference value, or other deformation value related to the difference value. In this embodiment, the difference value is taken as an example for description. The preset threshold includes a first preset threshold and a second preset threshold. Of course, the preset threshold can also include a third preset threshold, a fourth preset threshold, etc. The specific number of preset thresholds can be determined according to actual needs. For example, when the accuracy requirement is high, a larger number of preset thresholds can be set to more finely divide the state of the deformation resistance self-learning coefficient; when the accuracy requirement is low, a smaller number of preset thresholds can be set to improve the diagnosis efficiency. In this embodiment, two preset thresholds (including a first preset threshold and a second preset threshold) are taken as an example for subsequent description. The preset threshold can be obtained by statistical analysis according to the historical production data of the corresponding production line, and can be determined according to the relationship between the deformation resistance and the finishing load distribution.

[0074] If at least one of the difference values is greater than the first preset threshold, it is determined that the target deformation resistance self-learning coefficient is in a first abnormal state;

[0075] If at least one of the difference values is less than or equal to the first preset threshold and greater than the second preset threshold, it is determined that the target deformation resistance self-learning coefficient is in a second abnormal state;

[0076] If the target deformation resistance self-learning coefficient is in both the first abnormal state and the second abnormal state, the state of the target deformation resistance self-learning coefficient is recorded as the first abnormal state.

[0077] If all the difference values are less than or equal to the second preset threshold, it is determined that the target deformation resistance self-learning coefficient is in a normal state.

[0078] After determining whether the target deformation resistance self-learning coefficient is in an abnormal state, the method further comprises:

[0079] If the target deformation resistance self-learning coefficient is in the first abnormal state, the target deformation resistance self-learning coefficient or the corresponding cell is marked with a first color;

[0080] If the target deformation resistance self-learning coefficient is in the second abnormal state, the target deformation resistance self-learning coefficient or the corresponding cell is marked with a second color;

[0081] If the target deformation resistance self-learning coefficient is in the normal state, the target deformation resistance self-learning coefficient or the corresponding cell is marked with a third color.

[0082] In summary, the embodiment filters the coefficients in the deformation resistance self-learning coefficient table to be diagnosed to obtain N deformation resistance self-learning coefficients that need to be diagnosed, and diagnoses the N deformation resistance self-learning coefficients as target deformation resistance self-learning coefficients respectively, determines the difference values between the target deformation resistance self-learning coefficients and the adjacent reference deformation resistance self-learning coefficients, determines whether the target deformation resistance self-learning coefficient is in an abnormal state according to the relationship between each difference value and a preset threshold, and marks the state of the target deformation resistance self-learning coefficient (such as marking with color) to enable professionals to quickly determine the abnormal deformation resistance self-learning coefficient that needs to be corrected, thereby improving the diagnosis efficiency and accuracy of the deformation resistance self-learning coefficient table.

[0083] In actual operation of the embodiment, the following steps can be performed:

[0084] A deformation resistance self-learning coefficient table to be diagnosed is obtained, and the deformation resistance self-learning coefficients in the table are denoted as F ijtkrepresents, i takes 1, 2, …, n1; j takes 1, 2, …, n2; t takes 1, 2, …, n3; k takes 1, 2, …, n4. i represents the steel grade number, and there are n1 steel grades; j is the rack number, and there are n2 racks; t is the rack inlet temperature layer number, and there are n3 rack inlet temperature layers; k is the deformation rate layer number, and there are n4 deformation rate layers. Reference Figure 2 is the deformation resistance self-learning coefficient table of the target steel grade on the target rack, i and j are determined, and t and k can be adjusted to determine Figure 2 The deformation resistance self-learning coefficient of any cell in the table.

[0085] Determine N deformation resistance self-learning coefficients from the deformation resistance self-learning coefficient table, that is, the deformation resistance self-learning coefficients in Table 2 except the coefficient with an initial value of 1 are taken as the N deformation resistance self-learning coefficients. According to Table 2, N takes 130 at this time.

[0086] Take the 130 deformation resistance self-learning coefficients as target deformation resistance self-learning coefficients respectively, determine the difference value between the target deformation resistance self-learning coefficient and the adjacent reference deformation resistance self-learning coefficient with a value that is not an initial value, and according to the difference value and a preset threshold, determine whether the target deformation resistance self-learning coefficient is in an abnormal state.

[0087] If at least one of the difference values is greater than a first preset threshold, it is determined that the target deformation resistance self-learning coefficient is in a first abnormal state, and the target deformation resistance self-learning coefficient or the corresponding cell is marked as a first color;

[0088] If at least one of the difference values is less than or equal to a first preset threshold and greater than a second preset threshold, it is determined that the target deformation resistance self-learning coefficient is in a second abnormal state, and the target deformation resistance self-learning coefficient or the corresponding cell is marked as a second color;

[0089] If each of the difference values is less than or equal to the second preset threshold, it is determined that the target deformation resistance self-learning coefficient is in a normal state, and the target deformation resistance self-learning coefficient or the corresponding cell is marked as a third color; wherein the preset threshold includes the first preset threshold and the second preset threshold.

[0090] As shown in Figure 3 is a table obtained after the deformation resistance self-learning coefficient table shown in Figure 2 is diagnosed by the above method provided in the embodiment.

[0091] Based on the same inventive concept, the embodiment provides a deformation resistance self-learning coefficient diagnosis device as shown in Figure 4 The device comprises:

[0092] The acquisition module 41 is configured to acquire a deformation resistance self-learning coefficient table to be diagnosed, the deformation resistance self-learning coefficient table including deformation resistance self-learning coefficients corresponding to different entry temperatures and different deformation rates of a target steel grade on a target stand.

[0093] The first screening module 42 is configured to determine N deformation resistance self-learning coefficients from the deformation resistance self-learning coefficient table, take the N deformation resistance self-learning coefficients as target deformation resistance self-learning coefficients respectively, and perform the following steps.

[0094] The second screening module 421 is configured to determine M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficients from the N deformation resistance self-learning coefficients, and take the M deformation resistance self-learning coefficients as reference deformation resistance self-learning coefficients of the target deformation resistance self-learning coefficients; N and M are positive integers, and N>M.

[0095] The diagnosis module 422 is configured to determine difference values between the target deformation resistance self-learning coefficients and the reference deformation resistance self-learning coefficients respectively, and determine whether the target deformation resistance self-learning coefficients are in an abnormal state according to relationships between the difference values and preset threshold values.

[0096] Further, the first screening module 42 includes:

[0097] The first screening sub-module is configured to take all deformation resistance self-learning coefficients in the deformation resistance self-learning coefficient table except for deformation resistance self-learning coefficients with initial values as the N deformation resistance self-learning coefficients.

[0098] Further, the second screening module 421 includes:

[0099] The second screening sub-module is configured to determine the M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficients from the N deformation resistance self-learning coefficients.

[0100] Further, the diagnosis module 422 includes:

[0101] The first diagnosis sub-module is configured to determine that the target deformation resistance self-learning coefficients are in a first abnormal state if at least one of the difference values is greater than a first preset threshold value.

[0102] The second diagnosis sub-module is configured to determine that the target deformation resistance self-learning coefficients are in a second abnormal state if at least one of the difference values is less than or equal to the first preset threshold value and greater than a second preset threshold value.

[0103] The third diagnosis sub-module is configured to determine that the target deformation resistance self-learning coefficients are in a normal state if all the difference values are less than or equal to the second preset threshold value; wherein the preset threshold values include the first preset threshold value and the second preset threshold value.

[0104] Further, the apparatus further comprises:

[0105] The state correction module is configured to record the state of the target deformation resistance self-learning coefficient as the first abnormal state if the target deformation resistance self-learning coefficient is both the first abnormal state and the second abnormal state.

[0106] Further, the apparatus further comprises a marking module configured to:

[0107] After determining whether the target deformation resistance self-learning coefficient is in an abnormal state, if the target deformation resistance self-learning coefficient is the first abnormal state, marking the target deformation resistance self-learning coefficient or the corresponding cell as a first color;

[0108] If the target deformation resistance self-learning coefficient is the second abnormal state, marking the target deformation resistance self-learning coefficient or the corresponding cell as a second color;

[0109] If the target deformation resistance self-learning coefficient is the normal state, marking the target deformation resistance self-learning coefficient or the corresponding cell as a third color.

[0110] Based on the same inventive concept, the embodiment provides an electronic device as shown in Figure 5 The electronic device comprises:

[0111] A processor 51;

[0112] A memory 52 for storing instructions executable by the processor 51;

[0113] The processor 51 is configured to execute to implement the deformation resistance self-learning coefficient diagnosis method as provided in the foregoing.

[0114] Based on the same inventive concept, the embodiment provides a non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by the processor 51 of the electronic device, the electronic device can execute to implement the deformation resistance self-learning coefficient diagnosis method as provided in the foregoing.

[0115] Since the electronic device introduced in the embodiment is the electronic device used to implement the information processing method in the embodiment, based on the information processing method introduced in the embodiment, those skilled in the art can understand the specific implementation of the electronic device in the embodiment and its various forms, so the electronic device how to implement the method in the embodiment will not be introduced in detail. As long as the electronic device used to implement the information processing method in the embodiment is implemented by those skilled in the art, it belongs to the scope of the present application.

[0116] Those skilled in the art will appreciate that embodiments of the present application can be devised for a variety of applications. It is therefore intended that the present application cover all such modifications and variations of the application disclosed herein provided they come within the scope of the appended claims and their equivalents. It is intended to

[0117] The present application is described in reference to the drawings using a flowchart and / or a block diagram of the method, apparatus (system) and computer program product according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagram, and combinations of blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing device or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 means for performing each of the functions specified in the flowchart and / or block diagram block or blocks.

[0118] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 means for performing each of the functions specified in the flowchart and / or block diagram block or blocks.

[0119] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 means for performing each of the functions specified in the flowchart and / or block diagram block or blocks.

[0120] While the preferred embodiments of the application have been described, additional variations and modifications can be employed by those skilled in the art. Therefore, the appended claims are intended to cover all such modifications and variations as fall within the scope of the present application.

[0121] Obviously, many modifications and variations of the present application are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A method of diagnosing a deformation resistance self-learning coefficient, characterized by, The method comprises: obtaining a deformation resistance self-learning coefficient table to be diagnosed, the deformation resistance self-learning coefficient table comprising: deformation resistance self-learning coefficients corresponding to different entry temperatures and different deformation rates of a target steel grade on a target stand; determining N deformation resistance self-learning coefficients from the deformation resistance self-learning coefficient table, taking the N deformation resistance self-learning coefficients as target deformation resistance self-learning coefficients respectively, and performing the following steps: determining M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficients and taking the M deformation resistance self-learning coefficients as reference deformation resistance self-learning coefficients of the target deformation resistance self-learning coefficients; N and M are positive integers, and N>M; the M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficients refer to the deformation resistance self-learning coefficients in the cells adjacent to the target deformation resistance self-learning coefficients in a two-dimensional table; determining difference values between the target deformation resistance self-learning coefficient and each reference deformation resistance self-learning coefficient, and determining whether the target deformation resistance self-learning coefficient is in an abnormal state according to a relationship between each difference value and a preset threshold value; the step of determining N deformation resistance self-learning coefficients from the deformation resistance self-learning coefficient table comprises: taking all deformation resistance self-learning coefficients in the deformation resistance self-learning coefficient table except deformation resistance self-learning coefficients with initial values as the N deformation resistance self-learning coefficients; the step of determining M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficients comprises: determining M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficients from the N deformation resistance self-learning coefficients; the step of determining whether the target deformation resistance self-learning coefficient is in an abnormal state according to a relationship between each difference value and a preset threshold value comprises: if at least one of the difference values is greater than a first preset threshold value, determining that the target deformation resistance self-learning coefficient is in a first abnormal state; if at least one of the difference values is less than or equal to the first preset threshold value and greater than a second preset threshold value, determining that the target deformation resistance self-learning coefficient is in a second abnormal state; if all the difference values are less than or equal to the second preset threshold value, determining that the target deformation resistance self-learning coefficient is in a normal state; wherein the preset threshold value comprises the first preset threshold value and the second preset threshold value.

2. The method of claim 1, wherein, The method further comprises: if the target deformation resistance self-learning coefficient is in both the first abnormal state and the second abnormal state, recording a state of the target deformation resistance self-learning coefficient as the first abnormal state.

3. The method of claim 1, wherein, After determining whether the target deformation resistance self-learning coefficient is in an abnormal state, the method further comprises: if the target deformation resistance self-learning coefficient is in the first abnormal state, marking the target deformation resistance self-learning coefficient or a corresponding cell as a first color; if the target deformation resistance self-learning coefficient is in the second abnormal state, marking the target deformation resistance self-learning coefficient or the corresponding cell as a second color; If the target deformation resistance self-learning coefficient is in the normal state, the target deformation resistance self-learning coefficient or a corresponding cell is marked as a third color.

4. A device for diagnosing a self-learning coefficient of a deformation resistance, characterized by, The device comprises: An acquisition module is configured to acquire a deformation resistance self-learning coefficient table to be diagnosed, wherein the deformation resistance self-learning coefficient table comprises deformation resistance self-learning coefficients corresponding to different entry temperatures and different deformation rates of a target steel grade in a target stand. A first screening module is configured to determine N deformation resistance self-learning coefficients from the deformation resistance self-learning coefficient table, take the N deformation resistance self-learning coefficients as target deformation resistance self-learning coefficients respectively, and perform the following steps: A second screening module is configured to determine M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficients and take the M deformation resistance self-learning coefficients as reference deformation resistance self-learning coefficients of the target deformation resistance self-learning coefficients; N and M are positive integers, and N>M. A diagnosis module is configured to determine difference values between the target deformation resistance self-learning coefficients and each reference deformation resistance self-learning coefficient, and determine whether the target deformation resistance self-learning coefficient is in an abnormal state according to a relationship between each difference value and a preset threshold. The first screening module comprises: A first screening submodule is configured to take all deformation resistance self-learning coefficients except deformation resistance self-learning coefficients with initial values in the deformation resistance self-learning coefficient table as the N deformation resistance self-learning coefficients. The second screening module comprises: A second screening submodule is configured to determine M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficients from the N deformation resistance self-learning coefficients; the M deformation resistance self-learning coefficients adjacent to the target deformation resistance self-learning coefficients refer to deformation resistance self-learning coefficients in cells adjacent to the target deformation resistance self-learning coefficients in a two-dimensional table. The diagnosis module comprises: A first diagnosis submodule is configured to determine that the target deformation resistance self-learning coefficient is in a first abnormal state if at least one of the difference values is greater than a first preset threshold. A second diagnosis submodule is configured to determine that the target deformation resistance self-learning coefficient is in a second abnormal state if at least one of the difference values is less than or equal to the first preset threshold and greater than a second preset threshold. A third diagnosis submodule is configured to determine that the target deformation resistance self-learning coefficient is in a normal state if all the difference values are less than or equal to the second preset threshold; wherein the preset threshold comprises the first preset threshold and the second preset threshold.

5. An electronic device, comprising: comprises: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to execute to implement a deformation resistance self-learning coefficient diagnosis method according to any one of claims 1 to 3.

6. A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a processor of an electronic device, enabling the electronic device to perform a deformation resistance self-learning coefficient diagnosis method according to any one of claims 1 to 3.

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