Heating furnace capacity determination method, device, equipment and computer storage medium

Abstract

The embodiment of the present application provides a method, device, equipment and computer storage medium for determining the capacity of a heating furnace. The method includes: obtaining production data of a heating furnace, calculating a characteristic value and a characteristic value score of the production data of the heating furnace based on the production data, the obtained first value and the absolute deviation of the first value, and further calculating the capacity score of the heating furnace. According to the method for determining the capacity of a heating furnace in the embodiment of the present application, the capacity level of the heating furnace can be quantitatively evaluated to achieve an early warning function.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and in particular, relates to a method, device, equipment and computer storage medium for determining heating furnace capacity. Background Art

[0002] Hot rolling heating furnace is an extremely important equipment in the steel production process. It heats the ingot to the temperature required for hot rolling, meets the thermal deformation conditions of the ingot and improves the performance of the plate and strip. It is also the key to reducing fuel consumption and costs in the steel production process. Therefore, the stable operation of the heating furnace is very important, and measuring the process assurance capability of the heating furnace has also become an important task. Summary of the invention

[0003] The embodiments of the present application provide a method, device, equipment and computer storage medium for determining the capacity of a heating furnace, which can quantitatively evaluate the heating capacity of a heating furnace and facilitate monitoring and early warning of the heating furnace.

[0004] In a first aspect, an embodiment of the present application provides a method for determining heating furnace capacity, the method comprising:

[0005] Acquire the production data of the heating furnace, the production data including temperature data and production data of the target object;

[0006] Extracting characteristic values ​​of production data of target objects;

[0007] According to the temperature data, a first numerical value and a first numerical value absolute deviation corresponding to a characteristic value of production data of the target object are obtained, wherein the first numerical value is a standard value of the characteristic value, and the first numerical value deviation is an absolute deviation of the standard value of the characteristic value;

[0008] Determine a score of the characteristic value according to the characteristic value, the first value and the absolute deviation of the first value, wherein the score of the characteristic value is used to measure the numerical level of the characteristic value;

[0009] Calculate the score of the target object according to the score of the feature value and the preset feature value weight, and the score of the target object is used to characterize the performance of the target object;

[0010] A target score is determined based on the rating of the target object, and is used to describe the capability level of the heating furnace.

[0011] In a second aspect, an embodiment of the present application provides a device for determining heating furnace capacity, the device comprising:

[0012] An acquisition module is used to acquire the production data of the heating furnace, the production data including temperature data and production data of the target object;

[0013] An extraction module, used to extract characteristic values ​​of production data of a target object;

[0014] The acquisition module is further used to acquire, according to the temperature data, a first value and a first value absolute deviation corresponding to the characteristic value of the production data of the target object, wherein the first value is a standard value of the characteristic value, and the first value absolute deviation is an absolute deviation of the standard value of the characteristic value;

[0015] A determination module, used to determine a score of the characteristic value according to the characteristic value, the first numerical value and the absolute deviation of the first numerical value, wherein the score of the characteristic value is used to measure the numerical level of the characteristic value;

[0016] A calculation module, used to calculate a score of the target object according to the score of the feature value and a preset feature value weight, wherein the score of the target object is used to characterize the performance of the target object;

[0017] The determination module is further used to determine a target score based on the score of the target object, where the target score is used to describe the capability level of the heating furnace.

[0018] In a third aspect, an embodiment of the present application provides a device for determining the capacity of a heating furnace, the device comprising:

[0019] a processor, and a memory storing computer program instructions;

[0020] The processor reads and executes the computer program instructions to implement the heating furnace capacity determination method of the first aspect.

[0021] In a fourth aspect, an embodiment of the present application provides a computer storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the method for determining the capacity of a heating furnace of the first aspect is implemented.

[0022] In a fifth aspect, an embodiment of the present application provides a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the method for determining the heating furnace capacity of the first aspect.

[0023] The heating furnace capacity determination method, device, equipment and computer storage medium of the embodiments of the present application can calculate the characteristic value data of the production data based on the production data of the heating furnace, and further calculate the score of the target object based on the characteristic value data. The score of the heating furnace is determined by the score, thereby realizing the quantitative analysis of the heating furnace capacity and achieving the purpose of monitoring and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0025] Figure 1It is a flow chart of a method for determining heating furnace capacity provided in an embodiment of the present application;

[0026] Figure 2 It is a target score result diagram provided in an embodiment of the present application;

[0027] Figure 3 It is a heating furnace score diagram provided in an embodiment of the present application;

[0028] Figure 4 It is a structural schematic diagram of a device for determining heating furnace capacity provided in an embodiment of the present application;

[0029] Figure 5 It is a structural schematic diagram of a heating furnace capacity determination device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0031] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0032] In the field of steel production, the hot rolling heating furnace is an important processing equipment. By heating the ingot to the temperature required for hot rolling, the thermal deformation conditions of the ingot are met and the performance of the plate and strip is improved. In order to ensure the normal operation of the hot rolling heating furnace and realize monitoring and early warning, it is necessary to quantify the process assurance capability of the heating furnace.

[0033] In order to solve the problems in the prior art, the embodiments of the present application provide a method, device, equipment and computer storage medium for determining the capacity of a heating furnace. The method for determining the capacity of a heating furnace provided in the embodiments of the present application is first introduced below.

[0034] Figure 1 FIG. 1 is a flow chart of a method for determining heating furnace capacity according to an embodiment of the present application. Figure 1 As shown, the method may include the following steps:

[0035] S110, obtaining production data of the heating furnace, where the production data includes temperature data and production data of the target object.

[0036] The production data of the heating furnace when used to produce the target object is obtained, and the production data includes temperature data and production data of the target object, the temperature data is the temperature of the target object out of the furnace, and the production data of the target object is the condition data when the target object is produced. The target object is a production object, such as a steel coil, a steel plate, etc., which is not limited to this.

[0037] In some embodiments, the production data of the target object includes at least one of hot rolling temperature data, hot rolling force data, hot rolling current data, and hot rolling reduction data.

[0038] S120: Extract characteristic values ​​of production data of the target object.

[0039] The characteristic value of the production data of the target object is extracted. The characteristic value is a value that can describe the characteristics of the production data. For example, the characteristic value can be a standard deviation, a range, etc., which is not limited.

[0040] In one embodiment, the characteristic values ​​of the production data of the target object include at least one of the head abnormality evaluation, the middle abnormality evaluation, the tail abnormality evaluation, the standard deviation, the range, the slope, and the nominal convexity. Select the data before the production data, calculate the average value to obtain the head abnormality evaluation Head; select the data from ɑ to β in the production data, calculate the average value to obtain the middle abnormality evaluation Mid; select the data after 100%-β of the production data, calculate the average value to obtain the tail abnormality evaluation Tail. The calculation formula of the standard deviation σ is as follows:

[0041]

[0042] Among them, n is the number of data, y i is the ith data value, is the average value of n data.

[0043] Select the data from ɑ to β in the production data, determine the maximum value Max and the minimum value Min, and calculate the range R:

[0044] R=Max-Min

[0045] According to the production data, calculate the slope K:

[0046]

[0047] Among them, x i is the acquisition time data of the i-th data value, It is the average value of the acquisition time of n data.

[0048] Select the data before the production data and calculate the median, which is recorded as Me head ; Select the data from ɑ to β in the production data and calculate the median, which is recorded as Me mid ; Select the data after 100%-β of production data and calculate the median, recorded as Me tail . Calculate the nominal convexity CRW:

[0049]

[0050] Among them, ɑ and β can be set according to actual needs. The recommended value range of ɑ is 2% to 20%, and the recommended value range of β is 80% to 98%. The specific values ​​are not limited.

[0051] S130. Obtain, according to the temperature data, a first numerical value and a first numerical absolute deviation corresponding to the characteristic value of the production data of the target object, wherein the first numerical value is a standard value of the characteristic value, and the first numerical absolute deviation is an absolute deviation of the standard value of the characteristic value.

[0052] According to the furnace-out temperature of the target object, a first value corresponding to the characteristic value of the production data of the target object at the temperature is obtained. The absolute deviation from the first value MAD F The first value and the first value absolute deviation may be a preset standard value and a standard value deviation, or may be data obtained from a historical database.

[0053] S140. Determine a score of the characteristic value according to the characteristic value, the first numerical value, and the absolute deviation of the first numerical value, where the score of the characteristic value is used to measure the numerical level of the characteristic value.

[0054] According to the characteristic value F, the first value The absolute deviation from the first value MAD F , calculate the score S of the eigenvalue F The formula is as follows:

[0055]

[0056]

[0057] S150: Calculate a score of the target object according to the score of the feature value and the preset feature value weight, where the score of the target object is used to characterize the performance of the target object.

[0058] The product of the score of the feature value and the preset feature value weight is calculated as the score of the target object, and the obtained score is used to characterize the performance of the target object. The preset feature value weight can be manually set or data stored in a historical database, which is not limited.

[0059] In some embodiments, the preset feature value weights include a head abnormality evaluation weight ω Head , the middle abnormal evaluation weight ω Mid , tail anomaly evaluation weight ω Tail , standard deviation weight ω σ , range weight ω R , slope weight ω K , nominal convexity weight ω CRW , calculate the score S of the target object according to the preset weight value:

[0060] S=S Head ×ω Head +S Mid ×ω Mid +S Tail ×ω Tail +S K ×ω K +S σ ×ω σ +S R

[0061] ×ω R +S CRW ×ω CRW

[0062] In some embodiments, the production data of the target object includes hot rolling temperature data, hot rolling force data, hot rolling current data and hot rolling reduction data, and the score of the target object includes the hot rolling temperature data score S Temp , Hot rolling force data score S RF , Hot rolling current data score S Cur and hot rolling reduction data score S Shc .

[0063] In one example, the calculation results of the score S of the target object are shown in Table 1. For example, for object No. 1, that is, the head outlier score of the No. 1 steel coil is 21.89%, the middle outlier score is 72.99%, the tail outlier score is 50.94%, the standard deviation score is 57.70%, the extremely poor score is 45.42%, the slope score is 57.82%, the nominal convexity score is 21.62%. The score of this steel coil is calculated to be 48.60%, that is, the heating quality score of this steel coil is 48.60%.

[0064] Table 1

[0065]

[0066]

[0067] S160. Determine the target score based on the score of the target object, and the target score is used to describe the ability level of the heating furnace.

[0068] The score of the target object is the scores of multiple objects within a preset time period, and the target score is calculated based on the scores of multiple objects.

[0069] In some embodiments, the calculation formula for the comprehensive score SS of each object is as follows:

[0070] SS = S Temp × ω Temp + S RF × ω RF + S Cur × ω Cur + S Shc × ω Shc

[0071] Among them, ω Temp is the preset weight of the hot rolling temperature data, ω RF is the preset weight of the hot rolling rolling force data, ω Cur is the preset weight of the hot rolling current data, ω Shc is the preset weight of the hot rolling reduction data. The above weights can be the results of empirical evaluations, and there is no limitation on this. Calculate the average value Score of the comprehensive scores of all objects within the preset time period as the target score, which is used to describe the ability level of the heating furnace.

[0072] In some embodiments, the rating rules for the total score Score are: Score ≤ 5%, dangerous; 5% < Score ≤ 40%, poor; 40% < Score ≤ 70%, good; 70 < Score ≤ 100, excellent.

[0073] In one example, ω Temp 、ω RF 、ω Cur 、ωShc Set to 0.40, 0.40, 0.1, 0.1.

[0074] The method for determining the capacity of a heating furnace provided in the embodiment of the present application can calculate characteristic value data of the production data based on the production data of the heating furnace, and further calculate the score of the target object based on the characteristic value data. The score of the heating furnace is determined by the score, thereby realizing quantitative analysis of the capacity of the heating furnace and achieving the purpose of monitoring and early warning.

[0075] In some embodiments, before obtaining the production data of the heating furnace, the method includes: obtaining the historical production data of the heating furnace, the historical production data including historical temperature data and production data of historical target objects; extracting the eigenvalues ​​of the historical production data; and calculating the preset eigenvalue weights of the eigenvalues ​​according to preset rules. The historical production data of the heating furnace is obtained, the historical production data including historical temperature data and production data of historical target objects, and the historical temperature data is the furnace-out temperature data of the historical objects. The weights of each eigenvalue are determined by using the entropy weight method, the analytic hierarchy process, the genetic algorithm, and empirical evaluation, and the average of the eigenvalues ​​obtained by each method is taken as the preset eigenvalue weights of each eigenvalue. Specifically, the weights of each eigenvalue are calculated according to the entropy weight method. Among them, the sum of weights is 1; the weights of each eigenvalue are calculated according to the hierarchical analysis method Among them, the sum of weights is 1; the weights of each eigenvalue are calculated according to the genetic algorithm The weight of each eigenvalue is obtained based on empirical evaluation Calculate the preset feature weights Among them, F is each eigenvalue.

[0076] In one example, the eigenvalue weights calculated by different methods are shown in Table 2. For the eigenvalue head outliers, middle outliers, tail outliers, standard deviation, range, slope and nominal convexity, the eigenvalue weights calculated by the entropy weight method are 17.00%, 15.90%, 2.70%, 20.60%, 17.20%, 12.30%, 14.40%; the eigenvalue weights calculated by the analytic hierarchy process are 6.70%, 12.90%, 6.50%, 32.00%, 31.40%, 5.80%, 4.80%; the eigenvalue weights calculated by the genetic algorithm are 1 3.20%, 5.20%, 11.10%, 27.60%, 25.70%, 13.60%, 3.50%; the weights of each eigenvalue calculated by empirical evaluation are 5.00%, 5.00%, 5.00%, 30.00%, 25.00%, 15.00%, 15.00%; the final weights of each eigenvalue calculated, that is, the preset feature weights, are 10.48%, 9.75%, 6.33%, 27.55%, 24.83%, 11.68%, 9.43%.

[0077] Table 2

[0078]

[0079] In some embodiments, the method further includes: grouping the historical production data according to the historical temperature data; determining the median and median absolute deviation of each group of data to obtain a first result. The historical production data is grouped according to the historical temperature, and the grouping interval ranges from 20°C to 100°C. The median Me of the characteristic value in each group of data is calculated to obtain {Me Head 、Me Mid 、Me Tail 、Me σ 、Me R 、Me K 、Me CRW}, and further calculate the average median and the median absolute deviation (MAD) of the eigenvalues ​​in each group of data F , MAD F The calculation formula is as follows:

[0080] MAD F =median(|F-Me F |)

[0081] Among them, the first result is the average median and median absolute deviation MAD F .

[0082] In some embodiments, obtaining a first value and a first value absolute deviation corresponding to a characteristic value of production data of a target object according to temperature data includes: obtaining a first value and a first value absolute deviation in a historical database according to temperature. According to the furnace-out temperature of the target object, obtaining an average median of a corresponding temperature group in the historical database. and median absolute deviation MAD F .

[0083] In one example, the data in the historical database is shown in Table 3. For example, for the temperature group 1120°C, the number of samples is 11106, the average: head outlier value is -0.79, the standard deviation: head outlier value is 8.81, the median: head outlier value is -0.92, the median absolute deviation: head outlier value is 5.35, the average: middle outlier value is 1.01, the standard deviation: middle outlier value is 1.46, the median: middle outlier value is 0.81, the median absolute deviation: middle outlier value is 0.83, the average: tail outlier value is -2.28, the standard deviation: tail outlier value is 8.69, the median: tail outlier value is -1.39, the median absolute deviation: The tail outliers are 4.64, mean: standard deviation is 9.17, standard deviation: standard deviation is 7.41, median: standard deviation is 6.67, median absolute deviation: standard deviation is 2.43, mean: range is 24.73, standard deviation: range is 14.95, median: range is 21.56, median absolute deviation: range is 5.94, mean: slope is 0.00, standard deviation: slope is 0.02, median: slope is 0.00, median absolute deviation: slope is 0.01, mean: nominal convexity is 2.55, standard deviation: nominal convexity is 6.71, median: nominal convexity is 2.42, median absolute deviation: nominal convexity is 3.67.

[0084] Table 3

[0085]

[0086]

[0087] In one example, the target score result is as follows: Figure 2As shown, the current score of furnace No. 1 is 17.9, and the characteristic value data of the last steel burning are 55.54, 46.36, 45.16, 42.78, 52.42, 56.07 and 44.75. The current score of furnace No. 2 is 8.5, and the characteristic value data of the last steel burning are 55.83, 46.01, 47.21, 39.92, 50.77, 42.62 and 51.36. The current score of furnace No. 3 is 34.1, and the characteristic value data of the last steel burning are 47.41, 17.79, 14.69, 17.67, 25.14, 49.62 and 12.42. The current score of furnace No. 4 is 9.7, and the characteristic value data of the last steel burning are 49.33, 35.91, 35.63, 23.83, 28.18, 50.17 and 31.91.

[0088] In one example, the furnace scores are as follows: Figure 3 As shown, when the score of the heating furnace is lower than the threshold, an early warning is issued, that is, when the score of the heating furnace is at the position indicated by arrow 1 and arrow 2, an early warning is issued, which can avoid production cost losses. Among them, the threshold can be set and there is no limitation on this.

[0089] The method for determining the capacity of a heating furnace provided in the embodiment of the present application can calculate characteristic value data of the production data based on the production data of the heating furnace, and further calculate a score of the target object based on the characteristic value data, characteristic value weight data in a historical database, and a first numerical value. The score of the heating furnace is determined by the score, thereby realizing a quantitative analysis of the capacity of the heating furnace, obtaining the score data of the heating furnace when it is working, and achieving the purpose of monitoring and early warning.

[0090] Figure 4 Schematic diagram of a heating furnace capacity determination device provided in an embodiment of the present application. Figure 4 As shown, the apparatus may include an acquisition module 410 , an extraction module 420 , a determination module 430 and a calculation module 440 .

[0091] An acquisition module 410 is used to acquire production data of the heating furnace, where the production data includes temperature data and production data of the target object;

[0092] An extraction module 420, for extracting characteristic values ​​of production data of a target object;

[0093] The acquisition module 410 is further used to acquire, according to the temperature data, a first value and a first value absolute deviation corresponding to the characteristic value of the production data of the target object, wherein the first value is a standard value of the characteristic value, and the first value absolute deviation is an absolute deviation of the standard value of the characteristic value;

[0094] A determination module 430, configured to determine a score of the feature value according to the feature value, the first value and the absolute deviation of the first value, wherein the score of the feature value is used to measure the numerical level of the feature value;

[0095] A calculation module 440, configured to calculate a score of the target object according to the score of the feature value and a preset feature value weight, wherein the score of the target object is used to characterize the performance of the target object;

[0096] The determination module 430 is further configured to determine a target score based on the score of the target object, where the target score is used to describe the capability level of the heating furnace.

[0097] The heating furnace capacity determination device 400 provided in the embodiment of the present application can calculate the characteristic value data of the production data based on the production data of the heating furnace, and further calculate the score of the target object based on the characteristic value data, and determine the score of the heating furnace through the score, thereby realizing the quantitative analysis of the heating furnace capacity and achieving the purpose of monitoring and early warning.

[0098] In some embodiments, the device also includes: an acquisition module 410, used to acquire historical production data of the heating furnace, the historical production data including historical temperature data and historical production data of the target object; an extraction module 420, used to extract characteristic values ​​of the historical production data; and a calculation module 440, used to calculate preset characteristic value weights of the characteristic values ​​according to preset rules.

[0099] In some embodiments, the device further includes: a grouping module 450, which is used to group the historical production data according to the historical temperature data; and a determination module 430, which is further used to determine the median and median absolute deviation of each group of data to obtain a first result.

[0100] In some embodiments, the acquisition module 410 is used to obtain the first numerical value and the first numerical absolute deviation corresponding to the characteristic value of the production data of the target object according to the temperature data, including: the acquisition module 410 is used to obtain the first numerical value and the first numerical absolute deviation in the historical database according to the temperature data.

[0101] In some embodiments, the production data of the target object includes at least one of hot rolling temperature data, hot rolling rolling force data, hot rolling current data, and hot rolling reduction data.

[0102] In some embodiments, the characteristic values ​​of the production data of the target object include at least one of a head abnormality evaluation, a middle abnormality evaluation, a tail abnormality evaluation, a standard deviation, a range, a slope, and a nominal convexity.

[0103] The heating furnace capacity determination device provided in the embodiment of the present application can calculate the eigenvalue data of the production data based on the production data of the heating furnace, and further calculate the score of the target object based on the eigenvalue data, the eigenvalue weight data in the historical database and the first numerical value. The score of the heating furnace is determined by the score, and a quantitative analysis of the heating furnace capacity is achieved. The score data of the heating furnace when it is working is obtained, so as to achieve the purpose of monitoring and early warning.

[0104] Figure 4 Each module / unit in the device shown has the function of realizing Figure 1 The functions of each step in the process can achieve the corresponding technical effects, which will not be described in detail here for the sake of brevity.

[0105] Figure 5 A schematic diagram of the hardware structure of a heating furnace capacity determination device provided in an embodiment of the present application is shown.

[0106] The heating furnace capacity determination device may include a processor 501 and a memory 502 storing computer program instructions.

[0107] Specifically, the processor 501 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0108] The memory 502 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 502 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In one example, the memory 502 may include a removable or non-removable (or fixed) medium, or the memory 502 is a non-volatile solid-state memory. The memory 502 may be inside or outside the integrated gateway disaster recovery device.

[0109] In one example, the memory 502 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, typically, the memory 502 includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present application.

[0110] The processor 501 reads and executes the computer program instructions stored in the memory 502 to implement Figure 1 The method / steps S110 to S160 in the embodiment shown in the figure are achieved Figure 1 The corresponding technical effects achieved by executing the methods / steps in the example shown are not repeated here for the sake of brevity.

[0111] In one example, the heating furnace capacity determination device may further include a communication interface 503 and a bus 510. Figure 5 As shown, the processor 501, the memory 502, and the communication interface 503 are connected via a bus 510 and communicate with each other.

[0112] The communication interface 503 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0113] Bus 510 includes hardware, software or both, and couples the parts of the heating furnace capacity determination device to each other. For example, but not limitation, the bus may include Accelerated Graphics Port (AGP) or other graphics bus, Enhanced Industry Standard Architecture (EISA) bus, Front Side Bus (FSB), Hyper Transport (HT) interconnection, Industry Standard Architecture (ISA) bus, InfiniBand interconnection, Low Pin Count (LPC) bus, Memory bus, Micro Channel Architecture (MCA) bus, Peripheral Component Interconnect (PCI) bus, PCI-Express (PCI-X) bus, Serial Advanced Technology Attachment (SATA) bus, Video Electronics Standards Association Local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 510 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0114] The heating furnace capacity determination device can execute the heating furnace capacity determination method in the embodiment of the present application based on the acquired heating furnace production data, thereby realizing the combination of Figure 1 Described is a method for determining furnace capacity.

[0115] In addition, in combination with the heating furnace capacity determination method in the above embodiment, the present application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the heating furnace capacity determination methods in the above embodiment is implemented.

[0116] An embodiment of the present application provides a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes any one of the heating furnace capacity determination methods in the above embodiments.

[0117] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0118] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), appropriate firmware, plug-in, function card, etc. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or communication link by a data signal carried in a carrier. "Machine-readable medium" may include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (Radio Frequency, RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0119] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0120] The above reference is according to the method of the embodiment of the present application, the flow chart of the device (system) and the computer program product and / or the block diagram described various aspects of the present application.It should be understood that each square box in the flow chart and / or the block diagram and the combination of each square box in the flow chart and / or the block diagram can be realized by computer program instructions.These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the realization of the function / action specified in one or more square boxes of the flow chart and / or the block diagram.Such a processor can be but is not limited to a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit.It can also be understood that each square box in the block diagram and / or the flow chart and the combination of the square boxes in the block diagram and / or the flow chart can also be realized by the dedicated hardware that performs the specified function or action, or can be realized by the combination of dedicated hardware and computer instructions.

[0121] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for determining heating furnace capacity, characterized in that: include: Acquire production data of the heating furnace, wherein the production data includes temperature data and production data of the target object, wherein the temperature data is the furnace-out temperature of the target object, and the production data of the target object is condition data during the production of the target object, including at least one of hot rolling temperature data, hot rolling rolling force data, hot rolling current data, and hot rolling reduction data, wherein the target object is a production object, including a steel coil or a steel plate; Extracting characteristic values ​​of production data of the target object; Acquire, according to the temperature data, a first value and a first value absolute deviation corresponding to a characteristic value of the production data of the target object, wherein the first value is a standard value of the characteristic value, and the first value absolute deviation is an absolute deviation of the standard value of the characteristic value; Determine a score of the feature value according to the feature value, the first numerical value and the absolute deviation of the first numerical value, wherein the score of the feature value is used to measure the numerical level of the feature value; Calculating a score of the target object according to the score of the feature value and a preset feature value weight, wherein the score of the target object is used to characterize the performance of the target object; Determine a target score based on the score of the target object, wherein the target score is used to describe the capability level of the heating furnace; The score of the feature value is determined according to the feature value, the first numerical value and the absolute deviation of the first numerical value. The score of the feature value is used to measure the numerical level of the feature value. The formula for calculating the score of the feature value is: Where F is the eigenvalue, is the first value, MAD F is the first numerical absolute deviation, S F is the score of the feature value.

2. The method according to claim 1, characterized in that Before obtaining the production data of the heating furnace, the method includes: Acquire historical production data of the heating furnace, wherein the historical production data includes historical temperature data and production data of historical target objects; Extracting characteristic values ​​of the historical production data; The preset eigenvalue weight of the eigenvalue is calculated according to the preset rule.

3. The method according to claim 2, characterized in that The method further comprises: grouping the historical production data according to the historical temperature data; Determine the median and median absolute deviation for each set of data to obtain the first result.

4. The method according to claim 3, characterized in that The step of acquiring a first value and a first value absolute deviation corresponding to a characteristic value of the production data of the target object according to the temperature data includes: A first value and a first value absolute deviation in a history database are acquired according to the temperature data.

5. The method according to claim 1, characterized in that The characteristic values ​​of the production data of the target object include at least one of a head abnormality evaluation, a middle abnormality evaluation, a tail abnormality evaluation, a standard deviation, a range, a slope, and a nominal convexity.

6. A device for determining heating furnace capacity, characterized in that: The device comprises: an acquisition module, used to acquire production data of the heating furnace, wherein the production data includes temperature data and production data of a target object, wherein the temperature data is the furnace-out temperature of the target object, and the production data of the target object is condition data during the production of the target object, including at least one of hot rolling temperature data, hot rolling rolling force data, hot rolling current data, and hot rolling reduction data, wherein the target object is a production object, including a steel coil or a steel plate; An extraction module, used to extract characteristic values ​​of the production data of the target object; The acquisition module is further used to acquire, according to the temperature data, a first value and a first value absolute deviation corresponding to a characteristic value of the production data of the target object, wherein the first value is a standard value of the characteristic value, and the first value absolute deviation is an absolute deviation of the standard value of the characteristic value; a determination module, configured to determine a score of the feature value according to the feature value, the first numerical value and the absolute deviation of the first numerical value, wherein the score of the feature value is used to measure a numerical level of the feature value; A calculation module, used to calculate a score of the target object according to the score of the feature value and a preset feature value weight, wherein the score of the target object is used to characterize the performance of the target object; The determination module is further used to determine a target score based on the score of the target object, wherein the target score is used to describe the capability level of the heating furnace; The score of the feature value is determined according to the feature value, the first numerical value and the absolute deviation of the first numerical value. The score of the feature value is used to measure the numerical level of the feature value. The formula for calculating the score of the feature value is: Where F is the eigenvalue, is the first value, MAD F is the first numerical absolute deviation, S F is the score of the feature value.

7. A heating furnace capacity determination device, characterized in that: The heating furnace capacity determination device includes: a processor, and a memory storing computer program instructions; The processor reads and executes the computer program instructions to implement the method for determining the capacity of a heating furnace as described in any one of claims 1-5.

8. A computer storage medium, characterized in that: The computer storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method for determining the capacity of a heating furnace as described in any one of claims 1 to 5 is implemented.

9. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the method for determining the capacity of a heating furnace as described in any one of claims 1 to 5.

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