Blast furnace operating parameter adjustment method, device, electronic equipment and storage medium

Abstract

The present invention provides a method, device, electronic device and storage medium for adjusting the operating parameters of a blast furnace. The method comprises: performing similarity matching based on first historical operating data and current operating data to obtain multiple historical similar furnace conditions similar to the current operating data, determining an initial operating parameter adjustment rule based on similar operating changes of each similar operating parameter in each historical similar furnace condition and similar characterization changes of each similar furnace condition characterization parameter, wherein the initial operating parameter adjustment rule includes multiple initial operating adjustment intervals and multiple initial adjustment characterization rules, determining an adjustment reference sample based on second historical operating data and each initial operating adjustment interval, and determining a target operating parameter adjustment rule based on the adjustment reference sample and each initial adjustment characterization rule, so as to optimize and adjust the current operating parameters of the blast furnace through the target operating parameter adjustment rule; the subjectivity of furnace condition judgment is reduced, the accuracy of parameter adjustment is increased, and the degree of automation of adjustment is increased.

Description

Technical Field

[0001] The present application relates to the technical field of blast furnace ironmaking, and in particular to a method, device, electronic equipment and storage medium for adjusting blast furnace operating parameters. Background Art

[0002] The steel industry plays a vital role in my country's infrastructure and economic development, serving as a pillar of modern industry and the national economy. The steel industry is also an integral and crucial component of the national economy, and blast furnace ironmaking is the core of the entire steel industry and a key process for energy and mass conversion in the steelmaking process. Blast furnace ironmaking primarily involves the production of molten iron through a complex series of physical and chemical reactions within the blast furnace, combining iron ore, fuel, and flux.

[0003] Large-scale blast furnace systems feature exceptionally complex process mechanisms, severe multi-field and multi-phase coupling, and dynamic and volatile operating conditions. Currently, the adjustment and optimization of blast furnace operating parameters is highly dependent on human intervention. Operators adjust and optimize blast furnace operating parameters based on years of operating experience and their own judgment of current furnace conditions. However, accumulating knowledge and experience in blast furnace control takes a long time, and operators typically require several years of training before they can independently analyze on-site conditions. Because blast furnace operations typically run in three shifts, different operators have varying operating styles, leading to a high degree of subjectivity during the adjustment process. This makes it difficult to maintain systematic and consistent adjustments. Furthermore, the complex parameters characterizing furnace conditions make it difficult for operators to fully grasp the furnace's condition. Summary of the Invention

[0004] The present application provides a blast furnace operating parameter adjustment method, device, electronic equipment and storage medium to solve the technical problems of strong subjectivity, inaccurate furnace condition judgment and low degree of automation when optimizing and adjusting the above-mentioned blast furnace operating parameters.

[0005] In one embodiment of the present application, the present application provides a method for adjusting blast furnace operating parameters, comprising: obtaining historical operating target data and current operating data of the blast furnace, wherein the historical operating target data includes historical operating first data and historical operating second data; performing similarity matching based on the historical operating first data and the current operating data to obtain a plurality of historical similar furnace conditions similar to the current operating data, wherein the historical similar furnace conditions include a plurality of similar operating parameters and a plurality of similar furnace condition characterization parameters; determining an initial operating parameter based on similar operating changes of each of the similar operating parameters in each of the historical similar furnace conditions and similar characterization changes of each of the similar furnace condition characterization parameters in each of the historical similar furnace conditions. The target operation parameter adjustment rule is determined according to the second historical operation data and each of the initial operation adjustment intervals, and the target operation parameter adjustment rule is determined according to the adjustment reference sample and each of the initial adjustment characterization rules, so as to optimize and adjust the current operation parameters of the blast furnace through the target operation parameter adjustment rule; wherein, the first historical operation data is obtained by filtering the invalid data of the initial historical operation data and then increasing the data granularity, and the second historical operation data is obtained by filtering the invalid data of the initial historical operation data and then performing a differential operation.

[0006] In one embodiment of the present application, an initial operating parameter adjustment rule is determined based on similar operating changes of each of the similar operating parameters in each of the historical similar furnace conditions and similar characterization changes of each of the similar furnace condition characterization parameters in each of the historical similar furnace conditions. The initial operating parameter adjustment rule includes multiple initial operating adjustment intervals and multiple initial adjustment characterization rules, including: performing operation classification on each of the historical similar furnace conditions based on a preset operating parameter interval and each of the similar operating parameters to obtain multiple similar classified furnace conditions corresponding to each similar operating parameter; characterizing and classifying the classified furnace condition characterization parameters of each of the similar classified furnace conditions according to a preset quantile to obtain characterization lower-level parameters and characterization upper-level parameters of each classified furnace condition characterization parameter; comparing each of the characterization lower-level parameters and each of the characterization upper-level parameters in multiple adjacent similar classified furnace conditions based on the preset operating parameter interval, obtaining each initial adjustment characterization rule based on the comparison result, and determining each initial operating adjustment interval based on the preset operating parameter interval corresponding to multiple adjacent similar classified furnace conditions; determining the initial operating parameter adjustment rule based on multiple initial adjustment characterization rules and multiple initial operating adjustment intervals.

[0007] In one embodiment of the present application, based on the comparison of each characterizing lower parameter and each characterizing upper parameter in a plurality of adjacent similar classified furnace conditions within the preset operating parameter interval, each initial adjustment characterization rule is obtained according to the comparison result, and each initial operation adjustment interval is determined according to the preset operating parameter interval corresponding to the plurality of adjacent similar classified furnace conditions, including: if the first lower parameter of the first classified furnace condition is greater than or equal to the second upper parameter of the second classified furnace condition, then an initial operation adjustment interval is determined according to the first operating parameter interval corresponding to the first classified furnace condition and the second operating parameter interval corresponding to the second classified furnace condition, and an initial operation adjustment interval is determined according to the first lower parameter and the second upper parameter. An initial adjustment characterization rule is determined; if the first upper parameter of the first classification furnace condition is greater than or equal to the second lower parameter of the second classification furnace condition, an initial operation adjustment interval is determined according to the first operation parameter interval corresponding to the first classification furnace condition and the second operation parameter interval corresponding to the second classification furnace condition, and an initial adjustment characterization rule is determined according to the first upper parameter and the second lower parameter; wherein, the first classification furnace condition and the second classification furnace condition are obtained by two similar classification furnace conditions adjacent to the preset operation parameter interval, the first upper parameter and the second upper parameter are obtained by the characterization upper parameter, and the first lower parameter and the second lower parameter are obtained by the characterization lower parameter.

[0008] In one embodiment of the present application, similarity matching is performed based on the historical first operating data and the current operating data to obtain multiple historical similar furnace conditions similar to the current operating data, including: taking the historical first operating data within a preset first time period as a furnace condition reference vector, and segmenting the furnace condition reference vector according to the length of a preset second time period to obtain multiple historical furnace condition vectors; determining the current furnace condition vector based on the current operating data and the historical first operating data within a preset third time period; performing similarity matching between each of the historical furnace condition vectors and the current furnace condition vector to obtain multiple similar furnace condition vectors; determining each of the historical similar furnace conditions based on the historical operating data of a preset fourth time period in each of the similar furnace condition vectors; wherein the preset third time period is obtained by the time of the current operating data and the length of the preset second time period.

[0009] In one embodiment of the present application, an adjustment reference sample is determined based on the historical operation second data and each of the initial operation adjustment intervals, and a target operation parameter adjustment rule is determined based on the adjustment reference sample and each of the initial adjustment characterization rules, including: if a reference operation parameter in multiple historical reference furnace conditions meets the pre-adjustment adjustment interval, then each of the reference operation parameters and the differential result are superimposed to obtain a superimposed operation parameter; if the superimposed operation parameter meets the post-adjustment adjustment interval, then each of the historical reference furnace conditions corresponding to the reference operation parameter and each of the historical reference furnace conditions corresponding to the superimposed operation parameter are determined as adjustment reference samples, and the adjustment reference sample includes multiple adjustment reference furnace conditions; if If the reference characterization change of the reference furnace condition characterization parameter in each of the adjusted reference furnace conditions is consistent with the initial adjustment characterization law, then each of the adjusted reference furnace conditions is determined as a characterization reference sample; if the ratio of the characterization reference sample to the adjustment reference sample is greater than or equal to the preset first threshold, then the initial operation parameter adjustment law is determined as the target operation parameter adjustment law; wherein, the pre-adjustment adjustment interval is used to characterize the pre-adjustment operation parameter interval in an initial operation adjustment interval, and the post-adjustment adjustment interval is used to characterize the post-adjustment operation parameter interval in the initial operation adjustment interval, the historical operation second data includes historical reference samples and differential results, and the historical reference samples include multiple historical reference furnace conditions.

[0010] In one embodiment of the present application, before obtaining the historical operating target data and current operating data of the blast furnace, the blast furnace operating parameter adjustment method also includes: obtaining the historical operating initial data of the blast furnace; if the discrete degree of the historical operating initial data within the preset fifth time period is greater than the preset second threshold, the historical operating initial data within the preset fifth time period is filtered, and the filtered historical operating data is used as the first intermediate operating data; if the first intermediate operating data is greater than the preset third threshold, and the first intermediate operating data is less than the preset fourth threshold, the first intermediate operating data is determined as the second intermediate operating data; and the data granularity of the second intermediate operating data is increased according to the preset sixth time period to obtain the historical operation second data.

[0011] In one embodiment of the present application, after the first intermediate operating data is determined as the second intermediate operating data, the blast furnace operating parameter adjustment method also includes: performing median smoothing processing on the second intermediate operating data according to a preset seventh time period to obtain smoothed sample data; performing differential operation on the smoothing operating parameters of the smoothed sample data to obtain a differential result; if the differential result is greater than or equal to a preset fifth threshold, determining the smoothed sample data corresponding to the differential result as a historical reference sample; and obtaining historical operating second data based on the differential result and the historical reference sample.

[0012] In one embodiment of the present application, the present application provides a blast furnace operating parameter adjustment device, comprising: a data acquisition module, for acquiring historical operating target data and current operating data of the blast furnace, wherein the historical operating target data includes historical operating first data and historical operating second data; a similarity determination module, for performing similarity matching based on the historical operating first data and the current operating data, to obtain a plurality of historical similar furnace conditions similar to the current operating data, wherein the historical similar furnace conditions include a plurality of similar operating parameters and a plurality of similar furnace condition characterization parameters; a rule determination module, for determining a rule based on similar operating changes of each of the similar operating parameters in each of the similar historical furnace conditions and similarity of the similar furnace condition characterization parameters in each of the similar historical furnace conditions. The characterization changes are used to determine the initial operating parameter adjustment rules, and the initial operating parameter adjustment rules include multiple initial operating adjustment intervals and multiple initial adjustment characterization rules; a rule verification module is used to determine the adjustment reference sample according to the second historical operation data and each of the initial operation adjustment intervals, and to determine the target operating parameter adjustment rule according to the adjustment reference sample and each of the initial adjustment characterization rules, so as to optimize and adjust the current operating parameters of the blast furnace through the target operating parameter adjustment rule; wherein, the first historical operation data is obtained by filtering the invalid data of the initial historical operation data and then increasing the data granularity, and the second historical operation data is obtained by filtering the invalid data of the initial historical operation data and then performing a differential operation.

[0013] In one embodiment of the present application, the present application provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device implements the blast furnace operating parameter adjustment method as described in any one of the above embodiments.

[0014] In one embodiment of the present application, the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor of a computer, the computer executes the blast furnace operating parameter adjustment method as described in any one of the above embodiments.

[0015] Beneficial effects of the present invention: The present invention provides a method, device, equipment and medium for adjusting the operating parameters of a blast furnace. The present invention obtains multiple historical similar furnace conditions similar to the current operating data by performing similarity matching based on the first historical operating data and the current operating data, determines the initial operation adjustment interval by similar operation changes of similar operating parameters in the historical similar furnace conditions, determines the initial adjustment characterization law based on similar characterization changes of similar furnace condition characterization parameters, verifies the initial adjustment characterization law and the initial operation adjustment interval based on the second historical operating data, obtains the target operating parameter adjustment law, realizes the determination of the blast furnace operating parameter adjustment law, reduces the subjectivity of furnace condition judgment, increases the accuracy of adjustment, and increases the degree of automation of adjustment.

[0016] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are incorporated into and constitute a part of the specification, illustrating embodiments consistent with the present application and, together with the specification, serving to explain the principles of the present application. It is obvious that the drawings described below are merely some embodiments of the present application, and a person of ordinary skill in the art can derive other drawings based on these drawings without inventive effort. In the drawings:

[0018] Figure 1 A schematic flow chart of a method for adjusting blast furnace operating parameters according to one embodiment of the present application is shown;

[0019] Figure 2 A schematic diagram of an implementation process for adjusting blast furnace operating parameters according to an embodiment of the present application is shown;

[0020] Figure 3 A block diagram of a blast furnace operating parameter adjustment device according to an embodiment of the present application is shown;

[0021] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0022] The following describes the embodiments of the present application through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0023] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. Therefore, the illustrations only show components related to the present application and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0024] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present application difficult to understand.

[0025] See also Figure 1 , Figure 1 FIG. 1 is a flow chart showing a method for adjusting blast furnace operating parameters according to an embodiment of the present application. Figure 1 As shown, in an exemplary embodiment, the method for adjusting blast furnace operating parameters includes at least steps S110 to S140, which are described in detail as follows:

[0026] Step S110, obtaining historical operation target data and current operation data of the blast furnace.

[0027] Among them, the historical operation target data includes the historical operation first data and the historical operation second data. The historical operation first data is obtained by filtering the invalid data of the historical operation initial data and then increasing the data granularity. The historical operation second data is obtained by filtering the invalid data of the historical operation initial data and then performing a differential operation.

[0028] In one embodiment of the present application, before obtaining the historical operating target data and current operating data of the blast furnace, the blast furnace operating parameter adjustment method also includes: obtaining the historical operating initial data of the blast furnace; if the discrete degree of the historical operating initial data within the preset fifth time period is greater than the preset second threshold, the historical operating initial data within the preset fifth time period is filtered, and the filtered historical operating data is used as the first intermediate operating data; if the first intermediate operating data is greater than the preset third threshold, and the first intermediate operating data is less than the preset fourth threshold, the first intermediate operating data is determined as the second intermediate operating data; and the data granularity of the second intermediate operating data is increased according to the preset sixth time period to obtain the historical operation second data.

[0029] In one embodiment of the present application, the historical initial operation data includes blast furnace air supply data, material distribution data, furnace temperature data, air flow data, iron tapping data, and slag formation data. The operating parameters are blast furnace air supply data and material distribution data, and the characterization parameters are furnace temperature data, air flow data, iron tapping data, and slag formation data. Blast furnace air supply data include, but are not limited to, air volume and air temperature, and air flow data include, but are not limited to, pressure difference. The degree of dispersion can be determined by, for example, the mean and variance of the historical initial operation data. This step is used to filter the historical initial operation data in the hot blast furnace switching phase. A preset third threshold and a preset fourth threshold are used to filter the historical initial operation data recorded when a data acquisition device malfunctions. The preset third threshold and the preset fourth threshold can be obtained using sigma criteria, including but not limited to the three sigma criteria. By filtering the data twice, the accuracy of the target operating parameter adjustment rule for the blast furnace operating parameters can be improved.

[0030] In one embodiment of the present application, the preset sixth time period can be set to 1 hour. This is an example, and the present application does not limit the value. If the historical operation initial data is collected at intervals of 1 hour, the historical operation initial data is determined as the historical operation second data. If the historical operation initial data is not collected at intervals of 1 hour: if the historical operation initial data is fabric data, the historical operation initial data most recent before the current moment is determined as the historical operation second data; if the historical operation initial data is the other 5 categories, the historical operation initial data most recent within 1 hour before the current moment is determined as the historical operation second data. By increasing the data granularity of historical operation initial data of different data granularities, historical operation initial data of different data types can be associated and bound, thereby improving the effectiveness and comprehensiveness of the subsequent construction of the furnace condition vector.

[0031] In one embodiment of the present application, after the first intermediate operating data is determined as the second intermediate operating data, the blast furnace operating parameter adjustment method also includes: performing median smoothing on the second intermediate operating data according to a preset seventh time period to obtain smoothed sample data; performing differential operation on the smoothed operating parameters of the smoothed sample data to obtain a differential result; if the differential result is greater than or equal to the preset fifth threshold, determining the smoothed sample data corresponding to the differential result as a historical reference sample; and obtaining the historical operating second data based on the differential result and the historical reference sample.

[0032] In one embodiment of the present application, median smoothing can effectively remove glitches caused by noise interference in the operating parameters of the second intermediate operating data. For example, the seventh time period is preset to 20 minutes. This is just an example, and this application does not restrict the value. Median smoothing calculates the median of an operating parameter within 20 minutes before and after each moment and assigns the median to the current moment.

[0033] In one embodiment of the present application, a differential calculation is performed on the smoothed smoothing operation parameters to obtain a differential result. The preset fifth threshold is obtained by the distribution of the absolute value of the differential and the process mechanism operation parameters. For example, when adjusting the air volume, when the adjustment unit is cubic meters per minute, the adjustment change threshold of the air volume operation can be set to 100, that is, the preset fifth threshold is 100. This is an example, and the present application does not limit the value. By performing a differential calculation on the smoothed smoothing operation parameters, setting the adjustment change threshold and sample screening, the adjustment behavior of the operation parameters can be automatically obtained.

[0034] In one embodiment of the present application, the differential result and the historical reference sample are associated in the time dimension to obtain the second historical operation data, which is used to verify the adjustment rules of the initial operation parameters of subsequent operations.

[0035] In one embodiment of the present application, the initial operation target operation parameter adjustment rule

[0036] Step S220 , performing similarity matching based on the first historical operation data and the current operation data to obtain a plurality of historical similar furnace conditions that are similar to the current operation data.

[0037] Wherein, a historical similar furnace condition includes a plurality of similar operating parameters and a plurality of similar furnace condition characterization parameters.

[0038] In one embodiment of the present application, the historical first operating data within a preset first time period is used as a furnace condition reference vector, and the furnace condition reference vector is segmented according to the length of a preset second time period to obtain multiple historical furnace condition vectors; the current furnace condition vector is determined based on the current operating data and the historical first operating data within a preset third time period; similarity matching is performed between each historical furnace condition vector and the current furnace condition vector to obtain multiple similar furnace condition vectors; each historical similar furnace condition is determined based on the historical operating data of a preset fourth time period in each similar furnace condition vector; wherein the preset third time period is obtained by the time of the current operating data and the length of the preset second time period.

[0039] In one embodiment of the present application, if the data granularity of the historical operation first data is 1 hour, the preset first period can be set to 1 year, the preset second period can be set to 8 hours, and the preset third period is 7 hours. This is an example, and the present application does not limit the value. Also, the historical operation first data of 1 year is used as the furnace condition reference vector, and the furnace condition reference vector is divided into multiple historical furnace condition vectors at intervals of 8 hours, and the historical operation first data of the 7 hours before the current moment of the historical operation data is added to the current furnace condition vector. By constructing a vector, the current state and development trend of the furnace condition can be fully reflected, laying the foundation for subsequent similarity matching.

[0040] In one embodiment of the present application, similarity matching involves calculating multiple Manhattan distances between each historical furnace condition vector and the current furnace condition vector using a dynamic time warping algorithm. If the Manhattan distance is less than a preset distance threshold, the furnace condition vector corresponding to the Manhattan distance is determined to be a similar furnace condition vector. The preset distance threshold can be set based on a Manhattan distance distribution, for example, by setting the preset distance threshold to the 25th percentile distance value, or directly setting the preset distance threshold. This is merely an example, and this application does not impose any restrictions on the value.

[0041] In one embodiment of the present application, the preset fourth time period may be the 8th hour, that is, the historical operating data corresponding to the 8th hour in each similar furnace condition vector is determined as each historical similar furnace condition.

[0042] Step S230 , determining an initial operating parameter adjustment rule based on similar operating changes of similar operating parameters in each historical similar furnace condition and similar characterization changes of similar furnace condition characterization parameters in each historical similar furnace condition.

[0043] The initial operation parameter adjustment rule includes multiple initial operation adjustment intervals and multiple initial adjustment characterization rules.

[0044] In one embodiment of the present application, each historical similar furnace condition is operationally classified based on a preset operating parameter interval and each similar operating parameter to obtain multiple similar classified furnace conditions corresponding to each similar operating parameter; the classified furnace condition characterization parameters of each similar classified furnace condition are characterized and classified according to a preset quantile to obtain the characterization lower-level parameters and the characterization upper-level parameters of each classified furnace condition characterization parameter; each characterization lower-level parameter and each characterization upper-level parameter in multiple similar classified furnace conditions adjacent to the preset operating parameter interval are compared, and each initial adjustment characterization law is obtained according to the comparison result, and each initial operation adjustment interval is determined according to the preset operating parameter interval corresponding to the adjacent multiple similar classified furnace conditions; the initial operation parameter adjustment law is determined according to the multiple initial adjustment characterization laws and the multiple initial operation adjustment intervals.

[0045] In one embodiment of the present application, similar operating parameters are used to characterize each operating parameter in each historical similar furnace condition, that is, each operating parameter in the historical operation first data corresponding to each historical similar furnace condition, and there is no similar relationship between similar operating parameters of each category in a historical similar furnace condition; similar operating changes are used to characterize the changes in each similar operating parameter in each historical similar furnace condition, and can be obtained by classifying each historical similar furnace condition by operation through a preset operating parameter interval. Similar furnace condition characterization parameters are used to characterize each furnace condition characterization parameter in each historical similar furnace condition, that is, each furnace condition characterization parameter in the historical operation first data corresponding to each historical similar furnace condition, and there is no similar relationship between similar furnace condition characterization parameters of each category in a historical similar furnace condition; similar characterization changes are used to characterize the changes in each similar furnace condition characterization parameter in each historical similar furnace condition, and can be obtained by comparing each characterization lower-level parameter and each characterization upper-level parameter in multiple similar classified furnace conditions adjacent to each other based on a preset operating parameter interval. The initial operating parameter adjustment rule can be obtained by comparing the correlation between similar operating changes and similar characterization changes.

[0046] In one embodiment of the present application, the preset operating parameter interval can be set according to the distribution law of each similar operating parameter, combined with the process mechanism and operating procedures. For example, the adjustment range of the wind temperature is usually around 20 degrees. Assuming that the normal distribution range of the wind temperature of a blast furnace is 1160 to 1220, the preset operating interval of the wind temperature can be set to [1160, 1180], [1180, 1200] and [1200, 1220]. This is an example, and this application does not limit the value. According to the preset operating interval, each historical similar furnace condition can be classified to obtain multiple similar classified furnace conditions corresponding to each similar operating parameter.

[0047] In one embodiment of the present application, if the first lower-level parameter of the first classification furnace condition is greater than or equal to the second upper-level parameter of the second classification furnace condition, an initial operation adjustment interval is determined according to the first operating parameter interval corresponding to the first classification furnace condition and the second operating parameter interval corresponding to the second classification furnace condition, and an initial adjustment characterization law is determined according to the first lower-level parameter and the second upper-level parameter; if the first upper-level parameter of the first classification furnace condition is greater than or equal to the second lower-level parameter of the second classification furnace condition, an initial operation adjustment interval is determined according to the first operating parameter interval corresponding to the first classification furnace condition and the second operating parameter interval corresponding to the second classification furnace condition, and an initial adjustment characterization law is determined according to the first upper-level parameter and the second lower-level parameter; wherein, the first classification furnace condition and the second classification furnace condition are obtained from two similar classification furnace conditions adjacent to the preset operating parameter interval, the first upper-level parameter and the second upper-level parameter are obtained from the characterization upper-level parameter, and the first lower-level parameter and the second lower-level parameter are obtained from the characterization lower-level parameter.

[0048] In one embodiment of the present application, the preset quantiles may be quartiles, that is, the lower quartile and upper quartile of each classification furnace condition characterization parameter are calculated. This is merely an example and is not limiting. The lower quartile and upper quartile of each classification furnace condition characterization parameter are compared horizontally for similar classification furnace conditions within adjacent preset operating parameter intervals.

[0049] In one embodiment of the present application, the preset quantiles may be sextiles, and the lower sextile and upper sextile of each classification furnace condition characterization parameter need to be calculated. This is merely an example and is not limiting. A horizontal comparison is performed between the lower sextile and upper sextile of each classification furnace condition characterization parameter for similar classification furnace conditions within adjacent preset operating parameter intervals.

[0050] In one embodiment of the present application, if the upper quartile of the pressure difference of the wind temperature in the range of [1160, 1180] is 155 kPa, and the lower quartile of the pressure difference of the wind temperature in the range of [1180, 1200] is 165 kPa, then the wind temperature is increased from the range of [1160, 1180] to the range of [1180, 1200] as the initial operation adjustment range, and the pressure difference is increased by 10 kPa as the initial adjustment characterization rule.

[0051] Step S240, determining an adjustment reference sample based on the second historical operation data and each initial operation adjustment interval, and determining a target operating parameter adjustment rule based on the adjustment reference sample and each initial adjustment characterization rule, so as to optimize and adjust the current operating parameters of the blast furnace through the target operating parameter adjustment rule.

[0052] In one embodiment of the present application, if a reference operating parameter among multiple historical reference furnace conditions satisfies the pre-adjustment adjustment interval, the reference operating parameter is superimposed with the differential result to obtain the superimposed operating parameter; if the superimposed operating parameter satisfies the post-adjustment adjustment interval, the historical reference furnace conditions corresponding to each reference operating parameter and the historical reference furnace conditions corresponding to the superimposed operating parameter are determined as adjustment reference samples, and the adjustment reference samples include multiple adjustment reference furnace conditions; if the reference characterization change of the reference furnace condition characterization parameter in each adjusted reference furnace condition is consistent with an initial adjustment characterization law, each adjusted reference furnace condition is determined as a characterization reference sample; if the ratio of the characterization reference sample to the adjustment reference sample is greater than or equal to a preset first threshold, the initial operating parameter adjustment law is determined as the target operating parameter adjustment law; wherein, the pre-adjustment adjustment interval is used to characterize the pre-adjustment operating parameter interval in an initial operation adjustment interval, and the post-adjustment adjustment interval is used to characterize the post-adjustment operating parameter interval in the initial operation adjustment interval, the historical operation second data includes historical reference samples and differential results, and the historical reference samples include multiple historical reference furnace conditions.

[0053] In one embodiment of the present application, if an initial operating parameter adjustment rule states that the pressure difference will increase by 10 kPa after the air temperature is increased from [1160, 1180] to [1180, 1200], then the pre-adjustment adjustment interval is the air temperature range of [1160, 1180], and the post-adjustment adjustment interval is the air temperature range of [1180, 1200]. If a reference operating parameter among multiple historical reference furnace conditions meets the pre-adjustment adjustment interval, the reference operating parameter is superimposed with the corresponding differential result to obtain a superimposed operating parameter; if the superimposed operating parameter meets the post-adjustment adjustment interval, the historical reference furnace conditions corresponding to each reference operating parameter and the historical reference furnace conditions corresponding to the superimposed operating parameter are determined as adjustment reference samples, and the adjustment reference samples include multiple adjustment reference furnace conditions.

[0054] In one embodiment of the present application, the preset first threshold value may be 80%. This is an example, and the present application does not impose any limitation on this value. If the ratio of the characterization reference samples to the adjustment reference samples is 85%, which is greater than 80%, then the initial operating parameter adjustment rule is determined as the target operating parameter adjustment rule. The target operating parameter adjustment rule includes a target operating adjustment interval and a target adjustment characterization rule.

[0055] In one embodiment of the present application, the ratio of the characterization reference sample to the adjustment reference sample is determined as the optimization probability, and the blast furnace operating parameter adjustment suggestion is determined according to the target operating parameter adjustment rule and the optimization probability. For example, if the wind temperature is increased from the range of [1160, 1180] to the range of [1180, 1200], the gas utilization rate is increased by 0.5, and the probability of increasing the gas utilization rate is 85%. The blast furnace operating parameter adjustment suggestion can avoid the subjectivity and incomplete understanding of the furnace condition in the process of manual operation parameter adjustment. After the operation parameters are adjusted intelligently, the furnace condition characterization parameters are adjusted, thereby improving the safety and iron production of blast furnace ironmaking.

[0056] In one embodiment of the present application, see Figure 2 , Figure 2 FIG1 shows a schematic diagram of an implementation process of adjusting blast furnace operating parameters according to an embodiment of the present application. Figure 2, step S201 collects the historical operation initial data and current operation data of the blast furnace; step S202 preprocesses and performs a first association on the historical operation initial data to obtain the historical operation first data: the preprocessing is to filter out the historical operation initial data of the hot blast stove switching stage and the data acquisition equipment failure stage, and the first association is to associate and bind the historical operation initial data of different data categories after increasing the data granularity; step S203 performs a difference and a second association on the preprocessed historical operation initial data to obtain the historical operation second data: the preprocessed historical operation initial data is smoothed to obtain smoothed sample data, the smoothed sample data is differentially calculated, and the smoothed sample data whose differential result meets the preset fifth threshold is secondly associated with the corresponding differential result to obtain the historical operation second data; step S204 determines the historical similar furnace conditions according to the historical operation first data and the current operation data: constructs multiple historical furnace condition vectors based on the historical operation first data, constructs the current furnace condition vector according to the historical operation first data and the current operation data, and performs similarity calculation based on each historical furnace condition vector and the current furnace condition vector to obtain. to multiple similar furnace condition vectors and obtain multiple historical similar furnace conditions according to each similar furnace condition vector; step S205 determines the initial operating parameter adjustment rule according to the changes in similar operating parameters and similar furnace condition characterization parameters in the historical similar furnace conditions: classify the historical similar furnace conditions according to the similar operating parameters, and compare the quantile size relationship of the classified furnace condition characterization parameters adjacent to the classified operating parameters horizontally to obtain the initial operating parameter adjustment rule; step S206 verifies the initial operating parameter adjustment rule according to the second historical operation data to obtain the optimization probability: determine the adjustment reference sample based on the initial operation adjustment interval, the differential result and the second historical operation data, determine the characterization reference sample according to the adjustment reference sample and the initial adjustment characterization rule, and the ratio of the characterization reference sample to the adjustment reference sample is the optimization probability; step S207 determines the blast furnace operating parameter adjustment suggestion according to the optimization probability and the preset proportion threshold: if the optimization probability is greater than or equal to the preset proportion threshold, the initial operating parameter adjustment rule corresponding to the optimization probability can be determined as the target operating parameter adjustment rule, and the blast furnace operating parameter adjustment suggestion is obtained according to the target operating parameter adjustment rule and the optimization probability.

[0057] See also Figure 3 , Figure 3 A block diagram of a blast furnace operating parameter adjustment device according to one embodiment of the present application is shown. The device can be specifically configured in a computer device. The device can also be applied to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment in which the device is applicable.

[0058] like Figure 3As shown, a blast furnace operating parameter adjustment device 300 according to an embodiment of the present application includes: a data acquisition module 301, a similarity determination module 302, a rule determination module 303 and a rule verification module 304.

[0059] Among them, the data acquisition module 301 is used to obtain the historical operation target data and current operation data of the blast furnace, and the historical operation target data includes the historical operation first data and the historical operation second data; the similarity determination module 302 is used to perform similarity matching based on the historical operation first data and the current operation data to obtain multiple historical similar furnace conditions similar to the current operation data, and a historical similar furnace condition includes multiple similar operating parameters and multiple similar furnace condition characterization parameters; the rule determination module 303 is used to determine the initial operation parameters based on the similar operation changes of each similar operating parameter in each historical similar furnace condition and the similar characterization changes of each similar furnace condition characterization parameter in each historical similar furnace condition. The target operation parameter adjustment rule is a rule verification module 304 for determining an adjustment reference sample based on the second historical operation data and each initial operation adjustment interval, and determining a target operation parameter adjustment rule based on the adjustment reference sample and each initial adjustment characterization rule, so as to optimize and adjust the current operation parameters of the blast furnace through the target operation parameter adjustment rule; wherein, the first historical operation data is obtained by filtering the invalid data of the initial historical operation data and then increasing the data granularity, and the second historical operation data is obtained by filtering the invalid data of the initial historical operation data and then performing a differential operation.

[0060] It should be noted that the blast furnace operating parameter adjustment device provided in the above embodiment and the blast furnace operating parameter adjustment method provided in the above embodiment are based on the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment and will not be repeated here. In actual application, the blast furnace operating parameter adjustment device provided in the above embodiment can allocate the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0061] An embodiment of the present application also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by one or more processors, the electronic device implements the blast furnace operating parameter adjustment method provided in the above-mentioned embodiments.

[0062] See also Figure 4 , Figure 4 The following is a schematic diagram showing the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application. Figure 4The computer system 400 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0063] like Figure 4 As shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 into the random access memory (RAM) 403, such as executing the method described in the above embodiment. Various programs and data required for system operation are also stored in the RAM 403. The CPU 401, ROM 402 and RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0064] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. Removable media 411, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 410 as needed, so that computer programs read therefrom can be installed into the storage section 408 as needed.

[0065] According to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 409, and / or installed from a removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, the various functions defined in the system of the present application are executed.

[0066] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable computer program. This propagated data signal can take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0067] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0068] The units involved in the embodiments described in the present application can be implemented by software or by hardware, and the units described can also be set in a processor. The names of these units do not constitute a limitation on the units themselves under certain circumstances. Therefore, the technical solution according to the embodiment of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present application.

[0069] Another aspect of the present application provides a computer-readable storage medium having a computer program stored thereon. When executed by a computer processor, the computer program causes the computer to perform the blast furnace operating parameter adjustment methods provided in the above-described embodiments. The computer-readable storage medium may be included in the electronic device described in the above-described embodiments, or may exist independently and not be incorporated into the electronic device.

[0070] In the above embodiments, unless otherwise specified, the use of serial numbers such as "first" and "second" to describe common objects only indicates that they refer to different instances of the same object, rather than indicating that the objects being described must adopt a given order, whether in time, space, sorting or any other way.

[0071] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, any equivalent modifications or alterations accomplished by a person of ordinary skill in the art without departing from the spirit and technical concepts disclosed in this application shall be covered by the claims of this application.

Claims

1. A method for adjusting blast furnace operating parameters, characterized in that: The blast furnace operating parameter adjustment method comprises: Acquire historical operation target data and current operation data of the blast furnace, wherein the historical operation target data includes historical operation first data and historical operation second data; Performing similarity matching on the first historical operation data and the current operation data to obtain a plurality of historical similar furnace conditions similar to the current operation data, each of the historical similar furnace conditions including a plurality of similar operating parameters and a plurality of similar furnace condition characterization parameters; Determine an initial operating parameter adjustment rule based on similar operating changes of each of the similar operating parameters in each of the historical similar furnace conditions and similar characterization changes of each of the similar furnace condition characterization parameters in each of the historical similar furnace conditions, wherein the initial operating parameter adjustment rule includes multiple initial operating adjustment intervals and multiple initial adjustment characterization rules; specifically including: performing operation classification on each of the historical similar furnace conditions based on a preset operating parameter interval and each of the similar operating parameters to obtain multiple similar classified furnace conditions corresponding to each similar operating parameter; performing characterization classification on the classified furnace condition characterization parameters of each of the similar classified furnace conditions according to a preset quantile to obtain characterization lower-level parameters and characterization upper-level parameters of each classified furnace condition characterization parameter; comparing each of the characterization lower-level parameters and each of the characterization upper-level parameters in multiple similar classified furnace conditions adjacent to the preset operating parameter interval, obtaining each initial adjustment characterization rule according to the comparison result, and determining each initial operating adjustment interval according to the preset operating parameter interval corresponding to multiple adjacent similar classified furnace conditions; determining the initial operating parameter adjustment rule according to multiple initial adjustment characterization rules and multiple initial operating adjustment intervals; determining an adjustment reference sample based on the second historical operation data and each of the initial operation adjustment intervals, and determining a target operating parameter adjustment rule based on the adjustment reference sample and each of the initial adjustment characterization rules, so as to optimize and adjust the current operating parameters of the blast furnace according to the target operating parameter adjustment rule; The first historical operation data is obtained by filtering invalid data from the initial historical operation data and then increasing the data granularity, and the second historical operation data is obtained by filtering invalid data from the initial historical operation data and then performing a differential operation.

2. The method for adjusting blast furnace operating parameters according to claim 1, wherein: Comparing each of the characterizing lower-level parameters and each of the characterizing upper-level parameters in a plurality of similarly classified furnace conditions adjacent to the preset operating parameter interval, obtaining each initial adjustment characterization rule according to the comparison result, and determining each initial operation adjustment interval according to the preset operating parameter intervals corresponding to the plurality of adjacent similarly classified furnace conditions includes: If the first subordinate parameter of the first classification furnace condition is greater than or equal to the second superordinate parameter of the second classification furnace condition, an initial operation adjustment interval is determined according to the first operating parameter interval corresponding to the first classification furnace condition and the second operating parameter interval corresponding to the second classification furnace condition, and an initial adjustment characterization rule is determined according to the first subordinate parameter and the second superordinate parameter; If the first upper parameter of the first classification furnace condition is greater than or equal to the second lower parameter of the second classification furnace condition, an initial operation adjustment interval is determined according to the first operating parameter interval corresponding to the first classification furnace condition and the second operating parameter interval corresponding to the second classification furnace condition, and an initial adjustment characterization rule is determined according to the first upper parameter and the second lower parameter; Among them, the first classification furnace condition and the second classification furnace condition are obtained from two similar classification furnace conditions adjacent to the preset operation parameter interval, the first upper parameter and the second upper parameter are obtained from the characterization upper parameter, and the first lower parameter and the second lower parameter are obtained from the characterization lower parameter.

3. The method for adjusting blast furnace operating parameters according to claim 1, wherein: Performing similarity matching on the first historical operation data and the current operation data to obtain a plurality of historical similar furnace conditions similar to the current operation data includes: Using historical operation first data within a preset first time period as a furnace condition reference vector, and segmenting the furnace condition reference vector according to the length of a preset second time period to obtain a plurality of historical furnace condition vectors; Determine a current furnace condition vector based on the current operating data and historical first operating data within a preset third time period; Performing similarity matching between each of the historical furnace condition vectors and the current furnace condition vector to obtain a plurality of similar furnace condition vectors; Determining each of the historical similar furnace conditions based on historical operation data of a preset fourth period in each of the similar furnace condition vectors; The preset third time period is obtained by the time of the current operation data and the length of the preset second time period.

4. The method for adjusting blast furnace operating parameters according to claim 1, wherein: Determining an adjustment reference sample according to the second historical operation data and each of the initial operation adjustment intervals, and determining a target operation parameter adjustment rule according to the adjustment reference sample and each of the initial adjustment characterization rules, including: If a reference operating parameter among the multiple historical reference furnace conditions satisfies the pre-adjustment adjustment interval, superimposing the reference operating parameter and the difference result to obtain a superimposed operating parameter; If the superimposed operating parameter satisfies the post-adjustment adjustment interval, each of the historical reference furnace conditions corresponding to the reference operating parameter and each of the historical reference furnace conditions corresponding to the superimposed operating parameter are determined as adjustment reference samples, wherein the adjustment reference samples include a plurality of adjustment reference furnace conditions; If the reference characterization change of the reference furnace condition characterization parameter in each of the adjusted reference furnace conditions is consistent with an initial adjustment characterization rule, then each of the adjusted reference furnace conditions is determined as a characterization reference sample; If the ratio of the characterization reference samples to the adjustment reference samples is greater than or equal to a preset first threshold, determining the initial operating parameter adjustment rule as the target operating parameter adjustment rule; Among them, the pre-adjustment adjustment interval is used to characterize the pre-adjustment operating parameter interval in an initial operation adjustment interval, and the post-adjustment adjustment interval is used to characterize the post-adjustment operating parameter interval in the initial operation adjustment interval. The historical operation second data includes historical reference samples and differential results, and the historical reference samples include multiple historical reference furnace conditions.

5. The method for adjusting blast furnace operating parameters according to any one of claims 1 to 4, characterized in that: Before obtaining the historical operation target data and current operation data of the blast furnace, the blast furnace operation parameter adjustment method further includes: Obtain the historical initial operation data of the blast furnace; If the discrete degree of the historical operation initial data within the preset fifth time period is greater than the preset second threshold, filtering the historical operation initial data within the preset fifth time period, and using the filtered historical operation data as the first intermediate operation data; If the first intermediate operating data is greater than a preset third threshold value and the first intermediate operating data is less than a preset fourth threshold value, determining the first intermediate operating data as the second intermediate operating data; The data granularity of the second intermediate operation data is increased according to a preset sixth time period to obtain the second historical operation data.

6. The method for adjusting blast furnace operating parameters according to claim 5, characterized in that: After determining the first intermediate operating data as the second intermediate operating data, the blast furnace operating parameter adjustment method further includes: Performing median smoothing on the second intermediate running data according to a preset seventh time period to obtain smoothed sample data; performing a differential operation on the smoothing operation parameters of the smoothed sample data to obtain a differential result; If the difference result is greater than or equal to a preset fifth threshold, determining the smoothed sample data corresponding to the difference result as a historical reference sample; Historical running second data is obtained based on the differential result and the historical reference sample.

7. A blast furnace operating parameter adjustment device, characterized in that: The blast furnace operating parameter adjustment device includes: A data acquisition module is used to acquire historical operation target data and current operation data of the blast furnace, wherein the historical operation target data includes historical operation first data and historical operation second data; a similarity determination module, configured to perform similarity matching based on the first historical operation data and the current operation data to obtain a plurality of historical similar furnace conditions similar to the current operation data, each of the historical similar furnace conditions including a plurality of similar operating parameters and a plurality of similar furnace condition characterization parameters; A rule determination module is used to determine an initial operating parameter adjustment rule based on similar operating changes of each of the similar operating parameters in each of the historical similar furnace conditions and similar characterization changes of each of the similar furnace condition characterization parameters in each of the historical similar furnace conditions, wherein the initial operating parameter adjustment rule includes multiple initial operating adjustment intervals and multiple initial adjustment characterization rules; specifically comprising: performing operation classification on each of the historical similar furnace conditions based on a preset operating parameter interval and each of the similar operating parameters to obtain multiple similar classified furnace conditions corresponding to each similar operating parameter; performing characterization classification on the classified furnace condition characterization parameters of each of the similar classified furnace conditions according to a preset quantile to obtain characterization lower-level parameters and characterization upper-level parameters of each classified furnace condition characterization parameter; comparing each of the characterization lower-level parameters and each of the characterization upper-level parameters in multiple similar classified furnace conditions adjacent to the preset operating parameter interval, obtaining each initial adjustment characterization rule according to the comparison result, and determining each initial operating adjustment interval according to the preset operating parameter interval corresponding to multiple adjacent similar classified furnace conditions; determining the initial operating parameter adjustment rule according to multiple initial adjustment characterization rules and multiple initial operation adjustment intervals; a law verification module, configured to determine an adjustment reference sample based on the second historical operation data and each of the initial operation adjustment intervals, and determine a target operating parameter adjustment law based on the adjustment reference sample and each of the initial adjustment characterization laws, so as to optimize and adjust the current operating parameters of the blast furnace according to the target operating parameter adjustment law; The first historical operation data is obtained by filtering invalid data from the initial historical operation data and then increasing the data granularity, and the second historical operation data is obtained by filtering invalid data from the initial historical operation data and then performing a differential operation.

8. An electronic device, characterized in that: The electronic device comprises: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the electronic device to implement the blast furnace operating parameter adjustment method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor of a computer, the computer is caused to execute the blast furnace operating parameter adjustment method according to any one of claims 1 to 6.

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