CN118092362A - Method, device and equipment for analyzing abnormal reasons in sintering process - Google Patents
Method, device and equipment for analyzing abnormal reasons in sintering process Download PDFInfo
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Abstract
The invention discloses a method, a device and equipment for analyzing abnormal reasons in a sintering process, relates to the technical field of abnormal analysis in the sintering process, and can solve the technical problems that the reasons for abnormal reasons in the sintering process cannot be positioned timely, quickly and accurately according to manual experience, so that the energy consumption in the sintering process is increased and the production quality is reduced. The method comprises the following steps: determining sintering state quality abnormal parameters and abnormal moments; acquiring sintering operation parameters related to the first preset of sintering state quality abnormal parameters, determining a sintering operation period to be analyzed according to abnormal time, and acquiring sintering state quality abnormal parameter data and sintering operation parameter data corresponding to the sintering operation period; determining a target sintering operation parameter interval corresponding to the sintering state quality abnormal parameter interval according to the sintering state quality abnormal parameter data and the sintering operation parameter data; and analyzing abnormal reasons of the sintering process according to the sintering state quality abnormal parameter interval and the target sintering operation parameter interval.
Description
Technical Field
The invention relates to the technical field of abnormal analysis of sintering process, in particular to a method, a device and equipment for analyzing abnormal reasons of the sintering process.
Background
Sintering is an important link in the iron-making process, and the sintering process has the characteristics of long time delay, multiple influencing factors and strong parameter coupling. In the actual production process, sintering site operators position sintering operation parameters through own experience and adjust the numerical values of the sintering operation parameters, but the manual reaction speed is low, the labor intensity is high, errors are easy to occur, the abnormal recovery period of the sintering process is prolonged once the errors occur, and further the energy consumption and the production quality of the sintering process are increased.
Disclosure of Invention
In view of the above, the invention provides a method, a device and equipment for analyzing abnormal reasons in a sintering process, which can solve the technical problems that the reasons for abnormal reasons in the sintering process cannot be positioned timely, quickly and accurately according to manual experience, so that the energy consumption in the sintering process is increased and the production quality is reduced.
According to a first aspect of the present invention, there is provided a method of analyzing causes of abnormalities in a sintering process, the method comprising:
Determining sintering state quality abnormal parameters and abnormal moments;
Acquiring sintering operation parameters related to the first preset of the sintering state quality abnormal parameters, determining a sintering operation period to be analyzed according to the abnormal time, and acquiring sintering state quality abnormal parameter data and sintering operation parameter data corresponding to the sintering operation period;
determining a target sintering operation parameter interval corresponding to the sintering state quality abnormal parameter interval according to the sintering state quality abnormal parameter data and the sintering operation parameter data;
Analyzing abnormal reasons of the sintering process according to the sintering state quality abnormal parameter interval and the target sintering operation parameter interval.
Preferably, the determining the sintering state quality anomaly parameter includes:
Acquiring sintering process state quality parameters corresponding to the sintering mineral quality;
Acquiring the state quality parameter data of the sintering process in real time and the evaluation score corresponding to the state quality parameter data of the sintering process;
and determining the abnormal quality parameters of the sintering state according to the current moment evaluation score and the last moment evaluation score.
Preferably, the acquiring the sintering process state quality parameter corresponding to the sintering mineral quality includes:
And acquiring sintering process state quality parameters related to second preset sintering mineral quality in the whole sintering production line process, wherein the sintering process state quality parameters comprise: at least one of raw material parameters, bin parameters, mixing parameters and sintering machine parameters.
Preferably, the determining the target sintering operation parameter interval corresponding to the sintering state quality abnormality parameter interval according to the sintering state quality abnormality parameter data and the sintering operation parameter data includes:
dividing the sintering state quality abnormal parameter data and the space distance between the sintering operation parameter data to obtain a sintering state quality abnormal parameter interval and a sintering operation parameter interval corresponding to each sintering operation parameter;
And calculating the correlation sequence between the sintering operation parameter interval and the sintering state quality abnormal parameter interval, and determining a target sintering operation parameter interval according to the correlation sequence.
Preferably, the determining the target sintering operation parameter interval according to the related sequence includes:
And if the quantity of the sintering state quality abnormal parameters is one, determining a third preset related sintering operation parameter interval corresponding to each sintering operation parameter as a target sintering operation parameter interval.
Preferably, the determining the target sintering operation parameter interval according to the related sequence includes:
if the number of the sintering state quality abnormal parameters is larger than one, determining a fourth preset related sintering operation parameter interval corresponding to each sintering operation parameter of each sintering state quality abnormal parameter, and taking intersection sets of the fourth preset related sintering operation parameter intervals of the same sintering operation parameter of all the sintering state quality abnormal parameters according to preset rules to obtain a target sintering operation parameter interval.
Preferably, the analyzing the abnormal reason of the sintering process according to the abnormal parameter interval of the sintering state quality and the target sintering operation parameter interval includes:
And correspondingly transmitting the sintering state quality abnormal parameter interval and the target sintering operation parameter interval to an analysis terminal so that the analysis terminal obtains the abnormal reason of the sintering process.
According to a second aspect of the present invention, there is provided an apparatus for analyzing causes of abnormal sintering process, the apparatus comprising:
the first determining module is used for determining abnormal parameters and abnormal moments of the quality of the sintering state;
The acquisition module is used for acquiring sintering operation parameters related to the first preset sintering state quality abnormal parameters, determining a sintering operation period to be analyzed according to the abnormal time, and acquiring sintering state quality abnormal parameter data and sintering operation parameter data corresponding to the sintering operation period;
The second determining module is used for determining a target sintering operation parameter interval corresponding to the sintering state quality abnormal parameter interval according to the sintering state quality abnormal parameter data and the sintering operation parameter data;
And the analysis module is used for analyzing abnormal reasons of the sintering process according to the sintering state quality abnormal parameter interval and the target sintering operation parameter interval.
According to a third aspect of the present application, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described sintering process abnormality cause analysis method.
According to a fourth aspect of the present application, there is provided a computer device comprising a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, the processor implementing the above method for analyzing causes of sintering process anomalies when executing the program.
By means of the technical scheme, the sintering process abnormality cause analysis method, device and equipment provided by the application have the beneficial effects that firstly, compared with the prior art that manual monitoring is needed and sintering state quality abnormality parameters are needed to be judged manually, the sintering process abnormality cause analysis method, device and equipment provided by the application have the advantages that whether the sintering process state quality parameters are abnormal or not is automatically analyzed, so that manual monitoring is not needed, the sintering state quality abnormality parameters are not needed to be judged manually, the real-time performance, the efficiency and the accuracy of sintering process abnormality cause analysis are improved, the labor cost are reduced, secondly, the sintering operation parameters related to the first preset of the sintering state quality abnormality parameters are acquired, the sintering operation time period to be analyzed is determined according to the abnormal time of the sintering state quality abnormality parameters, so that real information is acquired from a large number of rich production line data in the time period, and after the large number of production line data are acquired, the information can be mined from the large number of production line data, and the mined information comprises: compared with the prior art that a large amount of production line data can only be analyzed through manual experience, the method has subjectivity, and the analysis efficiency of the large amount of data is low, and is difficult to accurately and quickly position the reasons for causing the abnormal parameters of the quality of the sintering state.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
fig. 1 shows a flow diagram of a method for analyzing reasons for abnormal sintering process according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of another method for analyzing reasons for abnormal sintering process according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of a device for analyzing reasons for abnormal sintering process according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of another analysis device for reasons of abnormal sintering process according to an embodiment of the present invention.
Detailed Description
The invention will be described in detail hereinafter with reference to the drawings in conjunction with embodiments. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The embodiment provides a method for analyzing reasons for abnormal sintering process, as shown in fig. 1, the method comprises the following steps:
101. And determining abnormal parameters and abnormal moments of the quality of the sintering state.
The sintering process state quality parameters corresponding to the sintering mineral quality are obtained, the sintering process state quality parameter data are obtained in real time, and whether the sintering process state quality parameters have abnormal sintering state quality parameters or not is monitored in real time according to the sintering process state quality parameter data, so that the analysis and adjustment can be quickly made, and the increase of energy consumption and the reduction of the production quality in the sintering process are avoided.
The abnormal time refers to the time when the abnormal parameters of the sintering state quality exist in the parameters of the sintering process state quality.
102. And acquiring sintering operation parameters related to the first preset of the sintering state quality abnormal parameters, determining a sintering operation period to be analyzed according to the abnormal time, and acquiring sintering state quality abnormal parameter data and sintering operation parameter data corresponding to the sintering operation period.
For example step 101, the abnormal time is determined because example step 102 determines the sintering operation time period to be analyzed according to the abnormal time, and since the sintering process has the characteristics of long time delay, multiple influencing factors and strong parameter coupling, i.e. the operation error may not appear abnormal in the state quality parameter of the sintering process, but only appears abnormal after a time period, the data of the time period of the sintering operation time period, rather than the data of the time point of the abnormal time, needs to be analyzed, so that the abundant information in the big data is mined, and the accuracy of analysis of the abnormal reason of the sintering process is improved.
Wherein, the first preset correlation refers to selecting the first preset number from the correlation ranks, or selecting the sintering operation parameters with the correlation values larger than the first preset value. Since the determination of the sintering state quality abnormality parameter is performed in real time, the acquisition of the sintering operation parameter related to the first preset of the sintering state quality abnormality parameter is also performed in real time based on the sintering state quality abnormality parameter.
Wherein the determination of the sintering operation period may be preset, for example, the sintering operation period is the first two same days from the start of the abnormal time. For obtaining the sintering state quality anomaly parameter data and the sintering operation parameter data corresponding to the sintering operation period, it should be noted that the sintering operation period is formed by a plurality of sintering operation moments, and therefore obtaining the sintering state quality anomaly parameter data and the sintering operation parameter data corresponding to the sintering operation period includes: and acquiring sintering state quality abnormal parameter data and sintering operation parameter data corresponding to each sintering operation time, wherein the sintering operation time, the sintering operation parameter data of at least one sintering operation parameter and the sintering state quality abnormal parameter data of at least one sintering state quality abnormal parameter can be listed in a table form as an implementation mode.
103. And determining a target sintering operation parameter interval corresponding to the sintering state quality abnormal parameter interval according to the sintering state quality abnormal parameter data and the sintering operation parameter data.
For this embodiment, as an implementation manner, using unsupervised learning, inputting sintering state quality anomaly parameter data and sintering operation parameter data, calculating a spatial distance between the sintering state quality anomaly parameter data and the sintering operation parameter data, obtaining all sections of the sintering state quality anomaly parameter and an initial sintering operation parameter section of each sintering operation parameter corresponding to each section, obtaining a preset anomaly range corresponding to each sintering state quality anomaly parameter, selecting a section with the most sintering state quality anomaly parameter data in the preset anomaly range from all sections as the sintering state quality anomaly parameter section, selecting an initial sintering operation parameter section of each sintering operation parameter corresponding to the sintering state quality anomaly parameter section as the sintering operation parameter section, and selecting a target sintering operation parameter section from all sintering operation parameter sections of each sintering operation parameter. As an embodiment, the target sintering operation parameter section may include a sintering operation parameter section having a forefront order with respect to the sintering state quality abnormality parameter section, which is not limited herein.
For example, one abnormal sintering state quality parameter is included, the abnormal sintering state quality parameter interval is [ a, B ], three sintering operation parameters are a, B and c respectively, the sintering operation parameter interval corresponding to the sintering operation parameter a is a1, a2 and a3, the sintering operation parameter interval corresponding to the sintering operation parameter B is B1, B2 and B3, the sintering operation parameter interval corresponding to the sintering operation parameter c is c1 and c2, the related sequence is a1 greater than a2 greater than a3, B1 greater than B2 greater than B3, and c1 greater than c2, and the target sintering operation parameter interval may be the sintering operation parameter interval a1, the sintering operation parameter interval B1 and the sintering operation parameter interval c1 which are the forefront in the related sequence corresponding to each sintering operation parameter.
Because the sintering state quality abnormal parameter data and the sintering operation parameter data come from the actual data information of the on-site production line, the information sources are real, and the target sintering operation parameter interval corresponding to the sintering state quality abnormal parameter interval is determined based on the sintering state quality abnormal parameter data and the sintering operation parameter data, the embodiment can accurately trace the abnormal target sintering operation parameter and the abnormal target sintering operation parameter interval, thereby improving the analysis accuracy of abnormal reasons in the sintering process.
104. Analyzing abnormal reasons of the sintering process according to the sintering state quality abnormal parameter interval and the target sintering operation parameter interval.
For the steps of this embodiment, since the reasons for the abnormality are the target sintering operation parameter and the target sintering operation parameter interval, the further reasons for the abnormality in the sintering process, that is, the reasons for the abnormality in the sintering process, can be analyzed based on the target sintering operation parameter and the target sintering operation parameter interval, and the positioning is not needed manually according to experience, thereby improving the efficiency and accuracy.
Compared with the prior art that manual monitoring is needed and manual judgment is needed for sintering state quality abnormal parameters, the sintering process abnormal reason analysis method, device and equipment provided by the application have the beneficial effects that whether the sintering process state quality parameters are abnormal or not is automatically analyzed, so that manual monitoring is not needed, the sintering state quality abnormal parameters are not needed to be judged manually, the real-time performance, the efficiency and the accuracy of sintering process abnormal reason analysis are improved, the labor cost is reduced, the sintering operation parameters related to the first preset of the sintering state quality abnormal parameters are acquired, the sintering operation time period to be analyzed is determined according to the abnormal time of the sintering state quality abnormal parameters, so that real information is acquired from a large amount of rich production line data in the time period, after a large amount of production line data are acquired, the information can be mined from the large amount of production line data, and the mined information comprises: compared with the prior art that a large amount of production line data can only be analyzed through manual experience, the method has subjectivity, and the analysis efficiency of the large amount of data is low, and is difficult to accurately and quickly position the reasons for causing the abnormal parameters of the quality of the sintering state.
Further, as a refinement and extension of the specific implementation of the foregoing embodiment, for a complete description of the specific implementation process in this embodiment, another method for analyzing the cause of abnormal sintering process is provided, as shown in fig. 2, which includes:
201. And determining abnormal parameters and abnormal moments of the quality of the sintering state.
For this embodiment, the determining the sintering state quality anomaly parameter includes: acquiring sintering process state quality parameters corresponding to the sintering mineral quality; acquiring the state quality parameter data of the sintering process in real time and the evaluation score corresponding to the state quality parameter data of the sintering process; and determining the abnormal quality parameters of the sintering state according to the current moment evaluation score and the last moment evaluation score.
Wherein, obtain the sintering process state quality parameter corresponding to sintering mineral quality, include: and acquiring sintering process state quality parameters related to the second preset sintering mineral quality in the whole sintering production line process. The sintering process state quality parameters include: at least one of raw material parameters, bin parameters, mixing parameters and sintering machine parameters. Wherein the second preset correlation refers to selecting a first second preset number from a ranking of correlations between the sintered mineral quality and the sintering process state quality parameter, or selecting a sintering process state quality parameter having a correlation value greater than the second preset value. Specifically, the whole sintering production line process may be divided into stages having a process sequence, each stage obtaining a sintering process state quality parameter related to a second preset of the sinter quality, for example, the whole sintering production line process may be divided into stages 1, 2, and 3 having a process sequence.
For the embodiment, because the whole process of the sintering production line is obtained instead of only monitoring a certain key process parameter, the analysis of the abnormal reasons of the sintering process is more comprehensive and can not be omitted, thereby ensuring the analysis accuracy of the abnormal reasons of the sintering process.
For acquiring the sintering process state quality parameter data in real time, specifically, acquiring sintering process state quality parameter historical data such as raw material historical data for stock yard sintering, sintering bin historical data, sintering mixing historical data, sintering machine parameter historical data, ring cooling parameter historical data, sintering ore inspection and test historical data, sintering ore metallurgy performance historical data, sintering economic technical index historical data and the like in real time; and identifying and replacing abnormal values in the sintering process state quality parameter historical data by a box graph method, filling the missing values in the sintering process state quality parameter historical data by an interpolation method, and cleaning the sintering process state quality parameter historical data to obtain the sintering process state quality parameter data.
The corresponding evaluation score of the sintering process state quality parameter data can be a corresponding relation between preset sintering process state quality parameter data and evaluation score according to expert experience or data analysis, so that when the sintering process state quality parameter data is acquired, the evaluation score can be automatically determined according to the sintering process state quality parameter data, the higher the evaluation score is, the more the sintering process state quality parameter data accords with the expectations of field operators, as an implementation mode, the sintering state quality abnormal parameter is determined according to the current time evaluation score and the last time evaluation score, specifically, the current time evaluation score and the last time evaluation score of the same sintering process state quality parameter are compared, the sintering process state quality parameter is ranked according to the degree of failure, the failure percentage in the sintering process state quality parameter is identified to be greater than a preset threshold value, the sintering state quality abnormal parameter is identified, the identification of the sintering state quality abnormal parameter is the dynamic identification by fusion of field experience and data analysis, the identification range is wide, and the adaptability and the interpretability of the production line are strong.
202. And acquiring sintering operation parameters related to the first preset of the sintering state quality abnormal parameters, determining a sintering operation period to be analyzed according to the abnormal time, and acquiring sintering state quality abnormal parameter data and sintering operation parameter data corresponding to the sintering operation period.
For the embodiment steps 201 and 202, the calculation of the first preset correlation and the second preset correlation may be performed based on the correlation analysis, specifically, a common correlation analysis method: (1) correlation coefficient analysis: to measure the strength and direction of the relationship between the two variables. Common correlation coefficients include pearson correlation coefficients, spearman correlation coefficients, and kendel correlation coefficients. (2) regression analysis: model relationships between one or more independent variables and dependent variables are established and described, and correlations between the independent variables and the dependent variables are estimated by fitting regression equations. (3) factor analysis: to explore hidden structures or patterns in a dataset, correlations between variables are determined by identifying common factors. (4) Borata analysis: by constructing shadow features, all variables associated with the object are selected.
For the present embodiment, the specific implementation is the same as that of embodiment step 102, and will not be described here again.
203. And dividing the sintering state quality abnormal parameter data and the space distance between the sintering operation parameter data to obtain a sintering state quality abnormal parameter interval and a sintering operation parameter interval corresponding to each sintering operation parameter.
204. And calculating the correlation sequence between the sintering operation parameter interval and the sintering state quality abnormal parameter interval, and determining a target sintering operation parameter interval according to the correlation sequence.
For this embodiment, as an implementation manner, using unsupervised learning, inputting sintering state quality anomaly parameter data and sintering operation parameter data, calculating a spatial distance between the sintering state quality anomaly parameter data and the sintering operation parameter data, obtaining all sections of the sintering state quality anomaly parameter and an initial sintering operation parameter section of each sintering operation parameter corresponding to each section, obtaining a preset anomaly range corresponding to each sintering state quality anomaly parameter, selecting a section with the most sintering state quality anomaly parameter data in the preset anomaly range from all sections as the sintering state quality anomaly parameter section, selecting an initial sintering operation parameter section of each sintering operation parameter corresponding to the sintering state quality anomaly parameter section as the sintering operation parameter section, and selecting a target sintering operation parameter section from all sintering operation parameter sections of each sintering operation parameter. As an embodiment, the target sintering operation parameter section may include a sintering operation parameter section that is in the first few bits in the order related to the sintering state quality abnormality parameter section, which is not limited herein.
Specifically, the unsupervised learning has the following method: (1) K-Means clustering (K-Means): the dataset is divided into K clusters, each cluster centered on its centroid, with samples assigned to the nearest cluster according to distance from the centroid. (2) hierarchical clustering (HIERARCHICAL CLUSTERING): by continuously merging or splitting samples to construct a hierarchical cluster structure, the cluster structure can be of a condensation type (from bottom to top) or a split type (from top to bottom). (3) DBSCAN (Density-Based Spatial Clustering of Applications with Noise): the high-density region is divided into clusters based on the density of the samples, and noise points can be identified.
Specifically, the screening of the related sequence includes a support degree and/or a confidence degree, that is, screening with a support degree or a confidence degree, screening with a support degree and then screening with a confidence degree, screening with a confidence degree and then screening with a support degree, or calculating a sum of a support degree and a confidence degree, which is not limited herein.
If the number of abnormal parameters of the sintering state quality is one, as an implementation manner, the determining the target sintering operation parameter interval according to the related sequence includes: and determining a third preset related sintering operation parameter interval corresponding to each sintering operation parameter as a target sintering operation parameter interval. The third preset correlations refer to a third preset number before selection in the correlation sequence, or sintering operation parameter intervals with correlation values greater than the third preset values are selected.
For example, one abnormal sintering state quality parameter is included, the abnormal sintering state quality parameter interval is [ a, B ], three sintering operation parameters are a, B and c respectively, the sintering operation parameter interval corresponding to the sintering operation parameter a is a1, a2 and a3, the correlation sequence is a1 greater than a2 greater than a3, the sintering operation parameter interval of the sintering operation parameter B is B1, B2 and B3, the correlation sequence is B1 greater than B2 greater than B3, the sintering operation parameter interval of the sintering operation parameter c is c1 and c2, the correlation sequence is c1 greater than c2, the target sintering operation parameter interval can be the first two sintering operation parameter intervals a1 (first order) and a2 (second order) of the correlation sequence corresponding to each sintering operation parameter; sintering operation parameter intervals b1 (first order), b2 (second order); sintering operation parameter intervals c1 (first order), c2 (second order).
If the number of abnormal parameters of the sintering state quality is greater than one, as another embodiment, the determining the target sintering operation parameter interval according to the related sequence includes: determining a fourth preset related sintering operation parameter interval corresponding to each sintering operation parameter of each sintering state quality abnormal parameter, and taking intersection of the fourth preset related sintering operation parameter intervals of the same sintering operation parameter of all the sintering state quality abnormal parameters according to a preset rule to obtain a target sintering operation parameter interval. The fourth preset correlations refer to a fourth preset number before selection in the correlation sequence, or sintering operation parameter intervals with correlation values greater than the fourth preset value are selected. Specifically, the following is a first and second examples.
First: taking the example that the previous fourth preset number of each sintering operation parameter is 1, that is, the sintering operation parameter interval related to the fourth preset is the most related sintering operation parameter interval, for example, two sintering state quality abnormal parameters are main negative pressures (the sintering state quality abnormal parameter interval is the main negative pressure a, the main negative pressure B), the sintering operation parameter interval corresponding to the main negative pressure is the end temperature (the sintering state quality abnormal parameter interval is the end temperature a, the end temperature B), three sintering operation parameters corresponding to the main negative pressure are respectively a, B and c, at least one sintering operation parameter interval corresponding to each sintering operation parameter is obtained, specifically, the sintering operation parameter interval of the sintering operation parameter a is a1, a2 and a3, the related sequence is that a1 is larger than a2 and is larger than a3, the sintering operation parameter interval of the sintering operation parameter B is B1, B2 and B3, the related sequence is that B1 is larger than B2 and is larger than B3, the sintering operation parameter interval of the sintering operation parameter c is c1 and c2, and the related sequence is c1 is larger than c2, and the sintering operation parameter interval corresponding to each sintering operation parameter a is obtained, that is the most related sintering operation parameter interval of the sintering operation parameter a is larger than B1.
Similarly, two sintering operation parameters corresponding to the end temperature are a and d respectively, at least one sintering operation parameter interval corresponding to each sintering operation parameter is obtained, specifically, the sintering operation parameter interval of the sintering operation parameter a is a4, a5 and a6, the related sequence is that a4 is larger than a5 and larger than a6, the sintering operation parameter interval of the sintering operation parameter d is d1 and d2, the related sequence is that d1 is larger than d2, and then the most relevant sintering operation parameter interval corresponding to each sintering operation parameter is obtained, namely, the sintering operation parameter interval a4 and the sintering operation parameter interval d1 are obtained for the end temperature.
Then, for the fourth preset number of 1, the preset rule is to make the most relevant sintering operation parameter intervals of the same sintering operation parameter of all the abnormal parameters of the sintering state quality correspond to each other, and the sintering operation parameter intervals of different types do not need to be intersected, that is, the sintering operation parameter of the same type of the main negative pressure and the end point temperature is a, the most relevant operation parameter interval a1 of the sintering operation parameter a of the main negative pressure and the most relevant operation parameter interval a4 of the end point temperature are intersected, for example, a1 is [ 780, 800 ], a4 is [ 770, 790 ], then the intersection is a [ 780, 790 ] and the sintering operation parameter intervals of different types do not need to be intersected, so that the target sintering operation parameter interval of the main negative pressure is a [ 780 ], the sintering operation parameter interval b1, the sintering operation parameter interval c1, and the target sintering operation parameter interval a1 of the end point temperature are obtained.
Second,: taking the example that the first fourth preset number of each sintering operation parameter is more than 1 (such as 2), obtaining sintering operation parameter sections a1 and a2 (the related sequence a1 is more than a 2), sintering operation parameter sections b1 and b2 (the related sequence b1 is more than b 2), and sintering operation parameter sections c1 and c2 (the related sequence c1 is more than c 2) for the main pipe negative pressure; similarly, if the end temperature obtains the sintering operation parameter intervals a4 and a5 (the relevant order a4 is greater than a 5), the sintering operation parameter intervals d1 and d2 (the relevant order d1 is greater than d 2), then the preset rule may be that the same sintering operation parameter corresponds to a secondary intersection, for example, for the sintering operation parameter a, the secondary intersection is a common sintering operation parameter of two abnormal quality parameters of the sintering state, the secondary intersection refers to the intersection of the first secondary with the first secondary, and the secondary intersection refers to the intersection of the second secondary with the second secondary, so that the intersection of a1 and a4 obtains the target sintering operation parameter interval of the first secondary of the sintering operation parameter a, and the intersection of a2 and a5 obtains the target sintering operation parameter interval of the second secondary of the sintering operation parameter a, and on the overall, whether the main pipe negative pressure or the end temperature is the target sintering operation parameter a: the intersection of a1 and a4 (first order), the intersection of a2 and a5 (second order), the target sintering operation parameter section for the main negative pressure further includes b1 (first order), b2 (second order) of the sintering operation parameter b, c1 (first order), c2 (second order) of the sintering operation parameter c, and the target sintering operation parameter section for the end temperature further includes d1 (first order), d2 (second order) of the sintering operation parameter d.
In the embodiment, steps 201 to 204, by acquiring, processing, analyzing and evaluating the sintering process state quality parameter data related to the second preset sintering mineral quality in real time in the whole sintering production line process, the sintering process state quality abnormal parameter can be determined in real time, a large amount of real production line data (the information source is accurate and real, the use cost is low, the generalization capability and the instantaneity of the analysis method are strong) in a period of time are acquired, information is mined from the real production line data, and the quantized mapping relation between the target sintering operation parameter and the sintering process state quality abnormal parameter is finally obtained, namely, the quantized mapping relation is positioned to the target sintering operation parameter and the sintering operation parameter interval, so that the abnormal reason of the sintering process is determined according to the timely and accurate quantized mapping relation. And on the basis of real-time data, the method carries out quick analysis by a big data technology, has strong instantaneity, strong generalization capability of the production line and accurate analysis result, is convenient for assisting on-site operators to quickly make decisions, further improves the overall stability of the sintering production line, and reduces the sintering production cost.
205. And correspondingly transmitting the sintering state quality abnormal parameter interval and the target sintering operation parameter interval to an analysis terminal so that the analysis terminal obtains the abnormal reason of the sintering process.
In this embodiment, the specific implementation manner is the same as that of embodiment step 105, and it should be noted that, as an implementation manner, the analysis terminal may also obtain an adjustment plan, and feed back the cause of the abnormal sintering process and the adjustment plan to the operation terminal, so that the operation terminal adjusts the target sintering operation parameters according to the cause of the abnormal sintering process and the adjustment plan.
The method, the device and the equipment for analyzing the abnormal reasons of the sintering process provided by the application have the beneficial effects that the method, the device and the equipment are firstly used for monitoring the state quality parameters of the sintering process in the whole process of the sintering production line (if the sintering quality is related to the whole process of the sintering production line, if the other parameters are abnormal, the reasons cannot be analyzed in time and the reasons cannot be solved, so that the sintering yield and the quality are reduced), and automatically analyzing whether the abnormal conditions are abnormal or not, compared with the prior art which needs to be manually monitored and manually judged the abnormal parameters of the sintering state quality, the method, the device and the equipment are unnecessary to manually judge the abnormal parameters of the sintering state quality, so that the instantaneity, the efficiency and the accuracy of the analysis of the abnormal reasons of the sintering process are improved, the labor cost is reduced, secondly used for acquiring the sintering operation time period to be analyzed according to the abnormal moment of the abnormal parameters of the sintering state quality, so that real information can be acquired from a large amount of abundant production line data in the time period, and the excavated information can be acquired from the large amount of production line data after the large amount of production line data is acquired, and the excavated information comprises: compared with the prior art that a large amount of production line data can only be analyzed through manual experience, but the manual experience can not fully mine information in the large amount of production line data, and the large amount of data is subjectivity, so that analysis of abnormal reasons of the sintering process is not objective and accurate enough, and the reason for the abnormal parameters of the sintering process is difficult to accurately and quickly locate.
Further, as a specific implementation of the method shown in fig. 1 and fig. 2, an embodiment of the present invention provides an apparatus for analyzing an abnormality cause in a sintering process, as shown in fig. 3, where the apparatus includes: a first determining module 31, an acquiring module 32, a second determining module 33, an analyzing module 34;
a first determining module 31, configured to determine a sintering state quality anomaly parameter and an anomaly time;
An obtaining module 32, configured to obtain sintering operation parameters related to the first preset sintering state quality abnormal parameter, determine a sintering operation period to be analyzed according to the abnormal time, and obtain sintering state quality abnormal parameter data and sintering operation parameter data corresponding to the sintering operation period;
A second determining module 33, configured to determine a target sintering operation parameter interval corresponding to the sintering state quality abnormality parameter interval according to the sintering state quality abnormality parameter data and the sintering operation parameter data;
the analysis module 34 is configured to analyze a cause of the sintering process abnormality according to the sintering state quality abnormality parameter interval and the target sintering operation parameter interval.
Accordingly, in order to determine the abnormal parameters of the sintering state quality, the first determining module 31 may be specifically configured to obtain the state quality parameters of the sintering process corresponding to the quality of the sintered ore; acquiring the state quality parameter data of the sintering process in real time and the evaluation score corresponding to the state quality parameter data of the sintering process; and determining the abnormal quality parameters of the sintering state according to the current moment evaluation score and the last moment evaluation score.
Accordingly, in order to obtain a sintering process state quality parameter corresponding to the sintering mineral quality, the first determining module 31 may be specifically further configured to obtain a sintering process state quality parameter related to the second preset sintering mineral quality in the whole sintering production line, where the sintering process state quality parameter includes: at least one of raw material parameters, bin parameters, mixing parameters and sintering machine parameters.
Accordingly, in order to determine a target sintering operation parameter interval corresponding to the sintering state quality abnormality parameter interval according to the sintering state quality abnormality parameter data and the sintering operation parameter data, the second determining module 33 may specifically include: a dividing unit 331, a calculating unit 332;
The dividing unit 331 is specifically configured to divide the sintering state quality abnormal parameter interval and the sintering operation parameter interval corresponding to each sintering operation parameter according to the spatial distance between the sintering state quality abnormal parameter data and the sintering operation parameter data;
The calculating unit 332 is specifically configured to calculate a correlation sequence between the sintering operation parameter sections and the sintering state quality abnormality parameter sections, and determine a target sintering operation parameter section according to the correlation sequence.
Accordingly, in order to determine a target sintering operation parameter interval according to the correlation sequence, the calculating unit 332 may specifically be further configured to determine, if the number of abnormal sintering state quality parameters is one, a third preset correlated sintering operation parameter interval corresponding to each of the sintering operation parameters as a target sintering operation parameter interval.
Correspondingly, in order to determine the target sintering operation parameter interval according to the correlation sequence, the calculating unit 332 may be specifically further configured to determine a fourth preset-related sintering operation parameter interval corresponding to each sintering operation parameter of each sintering state quality anomaly parameter if the number of sintering state quality anomaly parameters is greater than one, and take intersections of the fourth preset-related sintering operation parameter intervals of the same sintering operation parameter of all sintering state quality anomaly parameters according to a preset rule to obtain the target sintering operation parameter interval.
Correspondingly, in order to analyze the abnormal reason of the sintering process according to the abnormal parameter interval of the sintering state quality and the target sintering operation parameter interval, the analysis module 34 may be specifically configured to send the abnormal parameter interval of the sintering state quality and the target sintering operation parameter interval to an analysis terminal, so that the analysis terminal obtains the abnormal reason of the sintering process.
It should be noted that, in the other corresponding descriptions of the functional units related to the analysis device for abnormal reasons in the sintering process provided in this embodiment, reference may be made to the corresponding descriptions of fig. 1 to 2, and no further description is given here.
Based on the above-mentioned method shown in fig. 1 to 2, correspondingly, the present embodiment further provides a storage medium, which may be specifically volatile or nonvolatile, and has a computer program stored thereon, where the program when executed by a processor implements the above-mentioned method for analyzing the cause of the abnormal sintering process shown in fig. 1 to 2.
Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which may be stored in a storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.), and includes several instructions for causing a computer device (may be a personal computer, a server, or a network device, etc.) to execute the method of each implementation scenario of the present invention.
Based on the method shown in fig. 1 to 2 and the virtual device embodiments shown in fig. 3 and 4, in order to achieve the above object, the present embodiment further provides a computer device, where the computer device includes a storage medium and a processor; a storage medium storing a computer program; a processor for executing a computer program to implement the above-described method for analyzing the cause of the abnormal sintering process shown in fig. 1 to 2.
Optionally, the computer device may also include a user interface, a network interface, a camera, radio Frequency (RF) circuitry, sensors, audio circuitry, WI-FI modules, and the like. The user interface may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), etc.
It will be appreciated by those skilled in the art that the architecture of a computer device provided in this embodiment is not limited to this physical device, but may include more or fewer components, or may be combined with certain components, or may be arranged in a different arrangement of components.
An operational network communication module may also be included in the storage medium. Operations are programs that manage the computer device hardware and software resources described above, supporting the execution of information handling programs, as well as other software and/or programs. The network communication module is used for communication among components in the actual storage medium and communication with other hardware and software in the information processing entity device.
From the above description of the embodiments, it will be apparent to those skilled in the art that the present invention may be implemented by means of software plus necessary general hardware platforms, or may be implemented by hardware.
Compared with the prior art that manual monitoring is needed and manual judgment is needed for sintering state quality abnormal parameters, the sintering process abnormal reason analysis method, device and equipment provided by the application have the beneficial effects that whether the sintering process state quality parameters are abnormal or not is automatically analyzed, so that manual monitoring is not needed, the sintering state quality abnormal parameters are not needed to be judged manually, the real-time performance, the efficiency and the accuracy of sintering process abnormal reason analysis are improved, the labor cost is reduced, the sintering operation parameters related to the first preset of the sintering state quality abnormal parameters are acquired, the sintering operation time period to be analyzed is determined according to the abnormal time of the sintering state quality abnormal parameters, so that real information is acquired from a large amount of rich production line data in the time period, after a large amount of production line data are acquired, the information can be mined from the large amount of production line data, and the mined information comprises: compared with the prior art that a large amount of production line data can only be analyzed through manual experience, the method has subjectivity, and the analysis efficiency of the large amount of data is low, and is difficult to accurately and quickly position the reasons for causing the abnormal parameters of the quality of the sintering state.
Those skilled in the art will appreciate that the drawing is merely a schematic illustration of a preferred implementation scenario and that the modules or flows in the drawing are not necessarily required to practice the invention. Those skilled in the art will appreciate that modules in an apparatus in an implementation scenario may be distributed in an apparatus in an implementation scenario according to an implementation scenario description, or that corresponding changes may be located in one or more apparatuses different from the implementation scenario. The modules of the implementation scenario may be combined into one module, or may be further split into a plurality of sub-modules.
The above-mentioned inventive sequence numbers are merely for description and do not represent advantages or disadvantages of the implementation scenario. The foregoing disclosure is merely illustrative of some embodiments of the invention, and the invention is not limited thereto, as modifications may be made by those skilled in the art without departing from the scope of the invention.
Claims (8)
1. A method for analyzing causes of abnormal sintering processes, the method comprising:
determining a sintering state quality anomaly parameter and an anomaly time, wherein the determining the sintering state quality anomaly parameter comprises the following steps: acquiring sintering process state quality parameters corresponding to sintering mineral quality, acquiring the sintering process state quality parameter data and evaluation scores corresponding to the sintering process state quality parameter data in real time, and determining sintering state quality abnormal parameters according to the current time evaluation scores and the last time evaluation scores, wherein the acquiring of the sintering process state quality parameters corresponding to the sintering mineral quality comprises the following steps: and acquiring sintering process state quality parameters related to second preset sintering mineral quality in the whole sintering production line process, wherein the sintering process state quality parameters comprise: at least one of raw material parameters, bin parameters, mixing parameters and sintering machine parameters;
Acquiring sintering operation parameters related to the first preset of the sintering state quality abnormal parameters, determining a sintering operation period to be analyzed according to the abnormal time, and acquiring sintering state quality abnormal parameter data and sintering operation parameter data corresponding to the sintering operation period;
determining a target sintering operation parameter interval corresponding to the sintering state quality abnormal parameter interval according to the sintering state quality abnormal parameter data and the sintering operation parameter data;
Analyzing abnormal reasons of the sintering process according to the sintering state quality abnormal parameter interval and the target sintering operation parameter interval.
2. The method of claim 1, wherein determining a target sintering operation parameter interval corresponding to a sintering state quality anomaly parameter interval from the sintering state quality anomaly parameter data and the sintering operation parameter data comprises:
dividing the sintering state quality abnormal parameter data and the space distance between the sintering operation parameter data to obtain a sintering state quality abnormal parameter interval and a sintering operation parameter interval corresponding to each sintering operation parameter;
And calculating the correlation sequence between the sintering operation parameter interval and the sintering state quality abnormal parameter interval, and determining a target sintering operation parameter interval according to the correlation sequence.
3. The method of claim 2, wherein said determining a target sintering operation parameter interval from said correlation sequence comprises:
And if the quantity of the sintering state quality abnormal parameters is one, determining a third preset related sintering operation parameter interval corresponding to each sintering operation parameter as a target sintering operation parameter interval.
4. The method of claim 2, wherein said determining a target sintering operation parameter interval from said correlation sequence comprises:
if the number of the sintering state quality abnormal parameters is larger than one, determining a fourth preset related sintering operation parameter interval corresponding to each sintering operation parameter of each sintering state quality abnormal parameter, and taking intersection sets of the fourth preset related sintering operation parameter intervals of the same sintering operation parameter of all the sintering state quality abnormal parameters according to preset rules to obtain a target sintering operation parameter interval.
5. The method of claim 1, wherein analyzing the cause of the sintering process anomaly based on the sintering state quality anomaly parameter interval and the target sintering operation parameter interval comprises:
And correspondingly transmitting the sintering state quality abnormal parameter interval and the target sintering operation parameter interval to an analysis terminal so that the analysis terminal obtains the abnormal reason of the sintering process.
6. An apparatus for analyzing causes of abnormal sintering processes, the apparatus comprising:
The first determining module is configured to determine a sintering state quality anomaly parameter and an anomaly time, where determining the sintering state quality anomaly parameter includes: acquiring sintering process state quality parameters corresponding to sintering mineral quality, acquiring the sintering process state quality parameter data and evaluation scores corresponding to the sintering process state quality parameter data in real time, and determining sintering state quality abnormal parameters according to the current time evaluation scores and the last time evaluation scores, wherein the acquiring of the sintering process state quality parameters corresponding to the sintering mineral quality comprises the following steps: and acquiring sintering process state quality parameters related to second preset sintering mineral quality in the whole sintering production line process, wherein the sintering process state quality parameters comprise: at least one of raw material parameters, bin parameters, mixing parameters and sintering machine parameters;
The acquisition module is used for acquiring sintering operation parameters related to the first preset sintering state quality abnormal parameters, determining a sintering operation period to be analyzed according to the abnormal time, and acquiring sintering state quality abnormal parameter data and sintering operation parameter data corresponding to the sintering operation period;
The second determining module is used for determining a target sintering operation parameter interval corresponding to the sintering state quality abnormal parameter interval according to the sintering state quality abnormal parameter data and the sintering operation parameter data;
And the analysis module is used for analyzing abnormal reasons of the sintering process according to the sintering state quality abnormal parameter interval and the target sintering operation parameter interval.
7. A storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for analyzing causes of abnormal sintering process according to any one of claims 1 to 5.
8. A computer device comprising a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, characterized in that the processor implements the method for analyzing causes of sintering process anomalies according to any one of claims 1 to 5 when the computer program is executed.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH042732A (en) * | 1990-04-20 | 1992-01-07 | Nippon Steel Corp | Controller for sintering process |
| JP2003129141A (en) * | 2001-08-15 | 2003-05-08 | Nkk Corp | Sintered ore for blast furnace and manufacturing method therefor |
| CN106802977A (en) * | 2016-12-14 | 2017-06-06 | 同济大学 | One kind is used for sintering performance index prediction and Quality evaluation method |
| CN111308894A (en) * | 2020-03-06 | 2020-06-19 | 马鞍山钢铁股份有限公司 | Parameter adjusting method for coupling ore blending and control process in sintering process |
| CN111607673A (en) * | 2020-05-28 | 2020-09-01 | 江苏省沙钢钢铁研究院有限公司 | Pre-iron diagnostic system and method |
| CN113626501A (en) * | 2021-08-13 | 2021-11-09 | 石横特钢集团有限公司 | Method for applying big data in iron-making system |
| CN117196364A (en) * | 2023-07-21 | 2023-12-08 | 东北大学 | Real-time evaluation method and system for sintering state quality |
-
2024
- 2024-04-18 CN CN202410467719.XA patent/CN118092362B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH042732A (en) * | 1990-04-20 | 1992-01-07 | Nippon Steel Corp | Controller for sintering process |
| JP2003129141A (en) * | 2001-08-15 | 2003-05-08 | Nkk Corp | Sintered ore for blast furnace and manufacturing method therefor |
| CN106802977A (en) * | 2016-12-14 | 2017-06-06 | 同济大学 | One kind is used for sintering performance index prediction and Quality evaluation method |
| CN111308894A (en) * | 2020-03-06 | 2020-06-19 | 马鞍山钢铁股份有限公司 | Parameter adjusting method for coupling ore blending and control process in sintering process |
| CN111607673A (en) * | 2020-05-28 | 2020-09-01 | 江苏省沙钢钢铁研究院有限公司 | Pre-iron diagnostic system and method |
| CN113626501A (en) * | 2021-08-13 | 2021-11-09 | 石横特钢集团有限公司 | Method for applying big data in iron-making system |
| CN117196364A (en) * | 2023-07-21 | 2023-12-08 | 东北大学 | Real-time evaluation method and system for sintering state quality |
Non-Patent Citations (3)
| Title |
|---|
| LI, X等: "Research on cascade intelligent sinter quality prediction system based on big data technology", IRONMAKING & STEELMAKING, vol. 51, no. 1, 29 February 2024 (2024-02-29), pages 3 - 14 * |
| 储满生等: "基于大数据的智能化烧结技术研究进展", 钢铁, vol. 58, no. 9, 30 September 2023 (2023-09-30), pages 26 - 38 * |
| 刘然等: "大数据与智能计算在烧结系统中的应用研究", 烧结球团, vol. 48, no. 6, 31 December 2023 (2023-12-31), pages 31 - 43 * |
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