CN113094557A - Rolling mill data association method and system - Google Patents
Rolling mill data association method and system Download PDFInfo
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Abstract
The invention provides a rolling mill data association method and a rolling mill data association system. Because the invention directly collects the steel rolling data generated when the rolling mill rolls the billet, namely directly collects the rolling mill data, and does not utilize an external component to collect the steel rolling data, the invention can automatically correlate the rolling mill data when performing data correlation, thereby enhancing the accuracy, stability and effectiveness of the data correlation of the rolling mill frame on the basis of ensuring low cost and light weight.
Description
Technical Field
The invention relates to the technical field of steel smelting, in particular to a rolling mill data association method and system.
Background
In the actual production process of steel, steel billets are rolled by a series of rolling mills in sequence. However, since the rolling mills are not completely continuous and there is a certain time gap, the production data of each rolling mill needs to be accurately correlated before a certain billet bar is subjected to digital analysis and production research.
The existing scheme is mainly to add external devices (such as sensors) on each rolling mill to monitor the in and out of each billet, and then to correlate the monitored rolling mill production data. When the external components are used for monitoring the steel billet, the economic cost and the equipment maintenance cost of an enterprise are improved by the monitoring correlation mode due to the addition of the external components. Meanwhile, due to the sensitivity difference when the external component captures data, the external component is easy to float when capturing the data, and the data correlation quality is affected.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present invention provides a method and a system for associating mill data, which are used to solve the problem of data floating when monitoring and associating mill data by using external components in the prior art.
To achieve the above and other related objects, the present invention provides a rolling mill data association method, comprising the steps of:
acquiring steel rolling data when a plurality of rolling mills roll a plurality of steel billets;
slicing the rolled steel data according to the data generation time and a data source, and grouping the sliced data according to the number of rolling mills to obtain a plurality of data sets;
mapping the rolling mill and the data sets, and after mapping is completed, sequencing and numbering the data sets according to rolling mill numbers;
selecting the slice data with the earliest time from the first data set as the slice data of the target billet on the first rolling mill, and sequentially taking the slice data selected from the previous data set as the reference slice data of the next data set according to the sequence of the data sets;
selecting slicing data which is the closest to the reference slicing data time from the next data set as the slicing data of the target steel billet on the next rolling mill; and after the selection of the slice data is completed, removing the selected slice data from the data set;
iteratively selecting the slice data until the corresponding slice data is selected from the last data set;
and correlating the slice data selected from each data set as the rolling data of the target steel billet on all rolling mills.
Optionally, the method further comprises performing a value assessment on the plurality of data sets;
judging whether all the slice data in the data set are complete, recording a data set with complete all the slice data as 1, and recording a data set with incomplete slice data as 0;
judging whether the start time of the slice data in two adjacent data sets is in an increasing order, if so, recording as 1, otherwise, recording as 0;
judging whether the time duration of the slicing data in the two adjacent data sets is in an increasing order, if so, recording as 1, and if not, recording as 0;
and acquiring a judgment result of each data set under each judgment rule, and evaluating the value of each data set according to the preset weight value of each judgment rule.
Optionally, in performing a value assessment on each data set, further comprising calculating a value assessment value for each data set, including:
wherein m is the number of rolling mills;
y is the number of the judgment rules, and y is more than or equal to 1 and less than or equal to 3;
Qyjudging results of the y judgment rule;
Pyjudging the weight value of the rule for the y-th item;
Vmand evaluating the value of the data set corresponding to the mth rolling mill.
Optionally, the step of determining whether the time duration of the slice data in two adjacent data sets is in an increasing order includes:
respectively calculate Eij-TijAnd Ei(j+1)-Ti(j+1)If E isij-Tij＜Ei(j+1)-Ti(j+1)If the time duration of the slicing data in the two adjacent data sets is in an increasing order;
wherein, TijThe start time of the ith billet processed by the jth rolling mill is the start time of the ith billet processed by the jth rolling mill;
Ti(j+1)the start time of the processing of the ith billet by the (j +1) th rolling mill;
Eijthe end time of the ith billet processed by the jth rolling mill is the time;
Ei(j+1)is the end time of the ith billet processed by the (j +1) th rolling mill.
Optionally, when slice data closest to the reference slice data time is selected from the data set, calculating a time difference between the slice data time in the data set and the reference slice data, where:
Dij＝|Ti(j+1)-Tij|，1≤i≤k，1≤j＜m，1≤j+1≤m；
in the formula, k is the number of steel billets, and m is the number of rolling mills;
Tijthe start time of the ith billet processed by the jth rolling mill is the start time of the ith billet processed by the jth rolling mill;
Ti(j+1)the start time of the processing of the ith billet by the (j +1) th rolling mill;
Dijis the time difference between the slice data time in the data set and the reference slice data.
Optionally, the manner of removing the selected slice data from the data set comprises at least one of: physical removal, logical removal;
wherein the physical removal comprises at least one of: deleting, covering and filling preset data;
the logical removal includes at least one of: and disconnecting the interconnection among the data and changing the interconnection among the data.
Optionally, the manner of sorting the plurality of data sets includes: ascending or descending;
and after finishing the sorting, further comprising:
storing the plurality of data sets;
the means for storing includes at least one of: computer random access memory storage, computer read only memory storage.
The invention also provides a rolling mill data correlation system, which comprises:
the acquisition module is used for acquiring steel rolling data when a plurality of rolling mills roll a plurality of steel billets;
the slicing grouping module is used for slicing the rolled steel data according to the data generation time and the data source and grouping the sliced data according to the number of rolling mills to obtain a plurality of data sets;
the sequencing numbering module is used for mapping the rolling mill and the data sets, and sequencing and numbering the data sets according to the rolling mill numbers after mapping is finished;
the data reference module is used for selecting the slice data with the earliest time from the first data set as the slice data of the target billet on the first rolling mill, and sequentially taking the slice data selected from the previous data set as the reference slice data of the next data set according to the sequence of the data sets;
the data selection module is used for selecting the slicing data which is the closest to the reference slicing data time from the next data set as the slicing data of the target steel billet on the next rolling mill; and after the selection of the slice data is completed, removing the selected slice data from the data set; iteratively selecting the slice data until the corresponding slice data is selected from the last data set;
and the data association module is used for associating the slice data selected from each data set as the steel rolling data of the target steel billet on all rolling mills.
Optionally, the system further comprises a value assessment module for assessing the value of the plurality of data sets;
judging whether all the slice data in the data set are complete, recording a data set with complete all the slice data as 1, and recording a data set with incomplete slice data as 0;
judging whether the start time of the slice data in two adjacent data sets is in an increasing order, if so, recording as 1, otherwise, recording as 0;
judging whether the time duration of the slicing data in the two adjacent data sets is in an increasing order, if so, recording as 1, and if not, recording as 0;
and acquiring a judgment result of each data set under each judgment rule, and evaluating the value of each data set according to the preset weight value of each judgment rule.
Optionally, the value evaluation module, when evaluating the value of each data set, further calculates a value evaluation value of each data set, including:
wherein m is the number of rolling mills;
y is the number of the judgment rules, and y is more than or equal to 1 and less than or equal to 3;
Qyjudging results of the y judgment rule;
Pyjudging the weight value of the rule for the y-th item;
Vmand evaluating the value of the data set corresponding to the mth rolling mill.
As described above, the present invention provides a rolling mill data association method and system, which have the following beneficial effects: the method comprises the steps that steel rolling data when a plurality of steel billets are rolled by a plurality of rolling mills are obtained, the steel rolling data are sliced according to data generation time and a data source, and the sliced data are grouped according to the number of the rolling mills to obtain a plurality of data sets; mapping the rolling mill and the data sets, and after mapping is finished, sequencing and numbering the data sets according to rolling mill numbers; selecting the slice data with the earliest time from the first data set as the slice data of the target billet on the first rolling mill, and sequentially taking the slice data selected from the previous data set as the reference slice data of the next data set according to the sequence of the data sets; selecting slicing data which is the closest to the reference slicing data time from the next data set as the slicing data of the target steel billet on the next rolling mill; and after the selection of the slice data is completed, removing the selected slice data from the data set; iteratively selecting the slice data until the corresponding slice data is selected from the last data set; and correlating the slice data selected from each data set as the steel rolling data of the target steel billet on all rolling mills. The method comprises the steps of firstly collecting steel rolling data generated when a plurality of steel billets are rolled by a plurality of rolling mills, then slicing the collected steel rolling data, grouping the sliced data to form a plurality of data sets, selecting sliced data corresponding to target steel billets from each data set, and then associating the selected sliced data to complete association of the sliced data of the target steel billets. Because the invention directly collects the steel rolling data generated when the rolling mill rolls the billet, namely directly collects the rolling mill data, and does not utilize an external component to collect the steel rolling data, the invention can automatically correlate the rolling mill data when performing data correlation, thereby enhancing the accuracy, stability and effectiveness of the data correlation of the rolling mill frame on the basis of ensuring low cost and light weight.
Drawings
FIG. 1 is a schematic flow chart of a rolling mill data association method according to an embodiment;
FIG. 2 is a schematic flow chart of a rolling mill data association method according to another embodiment;
FIG. 3 is a schematic flow chart of an association algorithm according to an embodiment;
fig. 4 is a schematic flow chart illustrating an association result determination rule according to an embodiment;
FIG. 5 is a diagram illustrating data association results provided in accordance with an embodiment;
fig. 6 is a schematic diagram of a hardware structure of the rolling mill data association system according to an embodiment.
Description of the element reference numerals
M10 acquisition module
M20 slice grouping module
M30 sequencing numbering module
M40 data reference module
M50 data selection module
M60 data association module
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the present embodiment are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated. The structures, proportions, sizes, and other dimensions shown in the drawings and described in the specification are for understanding and reading the present disclosure, and are not intended to limit the scope of the present disclosure, which is defined in the claims, and are not essential to the art, and any structural modifications, changes in proportions, or adjustments in size, which do not affect the efficacy and attainment of the same are intended to fall within the scope of the present disclosure. In addition, the terms "upper", "lower", "left", "right", "middle" and "one" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not to be construed as a scope of the present invention.
Referring to fig. 1 and fig. 2, the present embodiment provides a rolling mill data association method, including the following steps:
s10, acquiring steel rolling data when a plurality of steel billets are rolled by a plurality of rolling mills; as an example, the number of rolling mills may be 18, and the number of billets may be 100.
S20, slicing the rolled steel data according to the data generation time and the data source, and grouping the sliced data according to the number of rolling mills to obtain a plurality of data sets;
s30, mapping the rolling mill and the data sets, and after mapping is completed, sequencing and numbering the data sets according to rolling mill numbers; after the mapping between the rolling mill and the data sets is completed, each rolling mill corresponds to one data set.
S40, selecting the slice data with the earliest time from the first data set as the slice data of the target billet on the first rolling mill, and sequentially using the slice data selected from the previous data set as the reference slice data of the next data set according to the sequence of the data sets;
s50, selecting the slicing data with the time closest to the reference slicing data from the next data set as the slicing data of the target steel billet on the next rolling mill; and after the selection of the slice data is completed, removing the selected slice data from the data set;
s60, selecting the slice data in an iterative manner until the corresponding slice data is selected from the last data set;
and S70, correlating the slice data selected from each data set as the steel rolling data of the target steel billet on all rolling mills.
In the embodiment, firstly, steel rolling data generated when a plurality of steel billets are rolled by a plurality of rolling mills is collected, then the collected steel rolling data is sliced, the sliced data is grouped to form a plurality of data sets, sliced data corresponding to target steel billets are selected from each data set, and then the selected sliced data are associated to complete the association of the sliced data of the target steel billets. Because the embodiment directly collects the steel rolling data generated when the rolling mill rolls the steel billet, namely directly collects the rolling mill data, rather than collecting the steel rolling data by using an external component, the embodiment can automatically correlate the rolling mill data when performing data correlation, thereby further enhancing the accuracy, stability and effectiveness of the data correlation of the rolling mill frame on the basis of ensuring low cost and light weight.
In accordance with the above, in an exemplary embodiment, the method further comprises performing a value assessment on the plurality of data sets; specifically, the method comprises the following steps:
judging whether all the slice data in the data set are complete, recording a data set with complete all the slice data as 1, and recording a data set with incomplete slice data as 0;
judging whether the start time of the slice data in two adjacent data sets is in increasing order, if so, recording as 1, otherwise, recording as 0,
judging whether the time duration of the slicing data in two adjacent data sets is in an increasing orderIf yes, the value is marked as 1, and if not, the value is marked as 0; the process of judging whether the time duration of the slice data in two adjacent data sets is in an increasing order comprises the following steps: respectively calculate Eij-TijAnd Ei(j+1)-Ti(j+1)If E isij-Tij＜Ei(j+1)-Ti(j+1)If the time duration of the slicing data in the two adjacent data sets is in an increasing order; wherein, TijThe start time of the ith billet processed by the jth rolling mill is the start time of the ith billet processed by the jth rolling mill; t isi(j+1)The start time of the processing of the ith billet by the (j +1) th rolling mill; eijThe end time of the ith billet processed by the jth rolling mill is the time; ei(j+1)Is the end time of the ith billet processed by the (j +1) th rolling mill.
And acquiring a judgment result of each data set under each judgment rule, and evaluating the value of each data set according to the preset weight value of each judgment rule. Wherein, when evaluating the value of each data set, the method further comprises calculating the value evaluation value of each data set, including:
wherein m is the number of rolling mills; y is the number of the judgment rules, and y is more than or equal to 1 and less than or equal to 3; qyJudging results of the y judgment rule; pyJudging the weight value of the rule for the y-th item; vmAnd evaluating the value of the data set corresponding to the mth rolling mill.
According to the above description, in an exemplary embodiment, when the slice data closest to the reference slice data time is selected from the data set, the method further includes calculating a time difference between the slice data time in the data set and the reference slice data, and the following steps are performed:
Dij＝|Ti(j+1)-Tij|，1≤i≤k，1≤j＜m，1≤j+1≤m；
in the formula, k is the number of steel billets, and m is the number of rolling mills; t isijThe start time of the ith billet processed by the jth rolling mill is the start time of the ith billet processed by the jth rolling mill; t isi(j+1)The start time of the processing of the ith billet by the (j +1) th rolling mill;Dijis the time difference between the slice data time in the data set and the reference slice data.
In accordance with the above, in an exemplary embodiment, the manner of removing the selected slice data from the data set includes at least one of: physical removal, logical removal; wherein the physical removal comprises at least one of: deleting, covering and filling preset data; the logical removal includes at least one of: and disconnecting the interconnection among the data and changing the interconnection among the data.
According to the above description, when sorting a plurality of data sets, the sorting manner includes ascending or descending; and after finishing the sorting, further comprising: storing the plurality of data sets; the means for storing includes at least one of: computer random access memory storage, computer read only memory storage.
As an example, as shown in fig. 2 to 5, the method further provides a specific process for realizing the association of the rolled steel data, and the specific process comprises the following steps:
s101, numbering a rolling mill;
s102, acquiring production data when a rolling mill rolls a billet;
s103, slicing the acquired production data to obtain a slice data set;
s104, grouping the slice data in the slice data set to obtain a grouped data set;
s105, performing data association on the grouped data set;
and S106, evaluating the data association result and generating an associated data set.
In step S101, the numbering rule for the roll stands is: n1. Wherein A is the serial number of the rolling mill, and N is a natural integer which is not zero. In the embodiment of the present application, the number of rolling mills is 18, that is, N is 18.
In step S103, the slicing process is performed on the production data of the billet rolled by the rolling mill, and the specific steps include:
slicing the production data according to the identification information of the rolling mill equipment and/or the time information when the data is generated, and numbering the sliced data according to the number of the rolling mills;
and generating t slice data sets according to the numbered slice data, wherein t is A.
In step S104, the specific method for grouping the data in the slice data set is to group the slice data in the slice data set according to the rolling mill number, and sort the grouped data sets according to the time sequence, so as to generate a grouped data sets. Wherein, the sorting method comprises but is not less than: ascending and descending.
In step S105, the process of performing data association on the packet data set includes:
s105-1, setting the initial value of a variable i to be 1, wherein i is more than or equal to 1 and less than or equal to k, and k is the total number of the billets;
s105-2, selecting the earliest slicing data in the slicing data as the slicing data of the first billet on the first rolling mill in the data set with the number of 1, and removing the selected slicing data in the data set with the number of 1;
s105-3, setting the initial value of the variable t to be 1;
s105-4, recording the found slice data as reference slice data of the next slice data search;
s105-5, in the data set with the number (t +1), selecting the slice data with the time closest to that in the reference slice data from the slice data as the slice data of the first billet in the (t +1) th data set, and removing the selected slice data from the data set with the number (t + 1);
s105-6, setting the value of the variable t as (t +1), if t is smaller than N, repeating the step S105-4 and the step S105-5, otherwise, entering the step S105-7;
and S105-7, recording all the data found in the steps S105-1 to S105-6 in sequence to form a data set which is recorded as a slicing data set on all rolling mills of the ith billet.
Wherein, the method for removing the data set in step S105-2 and step S105-5 includes but is not limited to physical removal and logical removal; among these, methods of physical removal include, but are not limited to: delete, override, and specific data fill; methods of logic removal include, but are not limited to: and disconnecting the interconnection among the data and changing the interconnection among the data.
When slice data with the time closest to the reference slice data is selected from the data set, the method also comprises the step of calculating the time difference between the slice data time in the data set and the reference slice data, and the method comprises the following steps:
Dij＝|Ti(j+1)-Tij|，1≤i≤k，1≤j＜m，1≤j+1≤m；
in the formula, k is the number of steel billets, and m is the number of rolling mills; t isijThe start time of the ith billet processed by the jth rolling mill is the start time of the ith billet processed by the jth rolling mill; t isi(j+1)The start time of the processing of the ith billet by the (j +1) th rolling mill; dijIs the time difference between the slice data time in the data set and the reference slice data.
In step S106, the process of evaluating the data association result includes:
s106-1, judging whether all slice data in the slice data set are complete, if so, recording the corresponding slice data set as 1, and if not, recording the corresponding slice data set as 0;
s106-2, judging whether the start time of the slice data in two adjacent slice data sets is in increasing order, if so, recording the corresponding slice data set as 1, otherwise, recording the corresponding slice data set as 0,
s106-3, judging whether the time duration of the slice data in two adjacent slice data sets is in an increasing order, if so, recording the corresponding slice data set as 1, and if not, recording the corresponding slice data set as 0; the process of judging whether the time duration of the slice data in two adjacent slice data sets is in order increasing order comprises the following steps: respectively calculate Eij-TijAnd Ei(j+1)-Ti(j+1)If E isij-Tij＜Ei(j+1)-Ti(j+1)If the time duration of the slicing data in the two adjacent slicing data sets is in an increasing order; wherein, TijThe start time of the ith billet processed by the jth rolling mill is the start time of the ith billet processed by the jth rolling mill; t isi(j+1)The start time of the processing of the ith billet by the (j +1) th rolling mill; eijThe end time of the ith billet processed by the jth rolling mill is the time; ei(j+1)For the ith billet to be processed(j +1) end time of mill treatment.
S106-4, setting a data set effective factor, recording results of the judgment rules from the step S106-1 to the step S106-3, and setting a weight for each judgment rule;
s106-5, carrying out associated data set value evaluation on the data association result by using the effective factor, namely obtaining the judgment result of each data set under each judgment rule, and carrying out value evaluation on each data set according to the weight value of each judgment rule. Wherein, when evaluating the value of each data set, the method further comprises calculating the value evaluation value of each data set, including:
wherein m is the number of rolling mills; y is the number of the judgment rules, and y is more than or equal to 1 and less than or equal to 3; qyJudging results of the y judgment rule; pyJudging the weight value of the rule for the y-th item; vmAnd evaluating the value of the data set corresponding to the mth rolling mill.
The method comprises the steps of firstly collecting steel rolling data generated when a plurality of steel billets are rolled by a plurality of rolling mills, then slicing the collected steel rolling data, grouping the sliced data to form a plurality of data sets, selecting sliced data corresponding to target steel billets from each data set, and then associating the selected sliced data to complete association of the sliced data of the target steel billets. According to the method, the rolled steel data generated when the rolling mill rolls the steel billet is directly collected, namely the rolled steel data is directly collected, and external components are not used for collecting the rolled steel data, so that the method can automatically correlate the rolled steel data when data correlation is carried out, and the accuracy, stability and effectiveness of the data correlation of the rolling mill frame are further enhanced on the basis of ensuring low cost and light weight.
As shown in fig. 6, a rolling mill data association system is characterized by comprising:
the acquisition module M10 is used for acquiring steel rolling data when a plurality of steel billets are rolled by a plurality of rolling mills;
a slicing and grouping module M20, configured to slice the rolled steel data according to the data generation time and the data source, and group the sliced data according to the number of rolling mills to obtain a plurality of data sets;
a sorting numbering module M30, configured to map the rolling mill with the data sets, and after the mapping is completed, sort and number the multiple data sets according to the rolling mill numbers;
the data reference module M40 is used for selecting the slice data with the earliest time from the first data set as the slice data of the target billet on the first rolling mill, and sequentially taking the slice data selected from the previous data set as the reference slice data of the next data set according to the sequence of the data sets;
a data selecting module M50, configured to select slice data that is the most recent time of the reference slice data from the next data set, as slice data of the target billet on the next rolling mill; and after the selection of the slice data is completed, removing the selected slice data from the data set; iteratively selecting the slice data until the corresponding slice data is selected from the last data set;
and the data correlation module M60 is used for correlating the slice data selected from each data set as the rolling data of the target steel billet on all rolling mills.
In the embodiment, firstly, steel rolling data generated when a plurality of steel billets are rolled by a plurality of rolling mills is collected, then the collected steel rolling data is sliced, the sliced data is grouped to form a plurality of data sets, sliced data corresponding to target steel billets are selected from each data set, and then the selected sliced data are associated to complete the association of the sliced data of the target steel billets. Because the embodiment directly collects the steel rolling data generated when the rolling mill rolls the steel billet, namely directly collects the rolling mill data, rather than collecting the steel rolling data by using an external component, the embodiment can automatically correlate the rolling mill data when performing data correlation, thereby further enhancing the accuracy, stability and effectiveness of the data correlation of the rolling mill frame on the basis of ensuring low cost and light weight.
In accordance with the above, in an exemplary embodiment, the system further comprises a value evaluation module for evaluating the value of the plurality of data sets; specifically, the method comprises the following steps:
judging whether all the slice data in the data set are complete, recording a data set with complete all the slice data as 1, and recording a data set with incomplete slice data as 0;
judging whether the start time of the slice data in two adjacent data sets is in increasing order, if so, recording as 1, otherwise, recording as 0,
judging whether the time duration of the slicing data in the two adjacent data sets is in an increasing order, if so, recording as 1, and if not, recording as 0; the process of judging whether the time duration of the slice data in two adjacent data sets is in an increasing order comprises the following steps: respectively calculate Eij-TijAnd Ei(j+1)-Ti(j+1)If E isij-Tij＜Ei(j+1)-Ti(j+1)If the time duration of the slicing data in the two adjacent data sets is in an increasing order; wherein, TijThe start time of the ith billet processed by the jth rolling mill is the start time of the ith billet processed by the jth rolling mill; t isi(j+1)The start time of the processing of the ith billet by the (j +1) th rolling mill; eijThe end time of the ith billet processed by the jth rolling mill is the time; ei(j+1)Is the end time of the ith billet processed by the (j +1) th rolling mill.
And acquiring a judgment result of each data set under each judgment rule, and evaluating the value of each data set according to the preset weight value of each judgment rule. Wherein, when evaluating the value of each data set, the method further comprises calculating the value evaluation value of each data set, including:
wherein m is the number of rolling mills; y is the number of the judgment rules, and y is more than or equal to 1 and less than or equal to 3; qyJudging results of the y judgment rule; pyIs judged for the yWeight value of broken rule; vmAnd evaluating the value of the data set corresponding to the mth rolling mill.
According to the above description, in an exemplary embodiment, when the slice data closest to the reference slice data time is selected from the data set, the method further includes calculating a time difference between the slice data time in the data set and the reference slice data, and the following steps are performed:
Dij＝|Ti(j+1)-Tij|，1≤i≤k，1≤j＜m，1≤j+1≤m；
in the formula, k is the number of steel billets, and m is the number of rolling mills; t isijThe start time of the ith billet processed by the jth rolling mill is the start time of the ith billet processed by the jth rolling mill; t isi(j+1)The start time of the processing of the ith billet by the (j +1) th rolling mill; dijIs the time difference between the slice data time in the data set and the reference slice data.
In accordance with the above, in an exemplary embodiment, the manner of removing the selected slice data from the data set includes at least one of: physical removal, logical removal; wherein the physical removal comprises at least one of: deleting, covering and filling preset data; the logical removal includes at least one of: and disconnecting the interconnection among the data and changing the interconnection among the data.
According to the above description, when sorting a plurality of data sets, the sorting manner includes ascending or descending; and after finishing the sorting, further comprising: storing the plurality of data sets; the means for storing includes at least one of: computer random access memory storage, computer read only memory storage.
As an example, as shown in fig. 2 to 5, the present system further provides a specific process for implementing the association of rolled steel data, including the following steps:
s101, numbering a rolling mill;
s102, acquiring production data when a rolling mill rolls a billet;
s103, slicing the acquired production data to obtain a slice data set;
s104, grouping the slice data in the slice data set to obtain a grouped data set;
s105, performing data association on the grouped data set;
and S106, evaluating the data association result and generating an associated data set.
In step S101, the numbering rule for the roll stands is: n1. Wherein A is the serial number of the rolling mill, and N is a natural integer which is not zero. In the embodiment of the present application, the number of rolling mills is 18, that is, N is 18.
In step S103, the slicing process is performed on the production data of the billet rolled by the rolling mill, and the specific steps include:
slicing the production data according to the identification information of the rolling mill equipment and/or the time information when the data is generated, and numbering the sliced data according to the number of the rolling mills;
and generating t slice data sets according to the numbered slice data, wherein t is A.
In step S104, the specific method for grouping the data in the slice data set is to group the slice data in the slice data set according to the rolling mill number, and sort the grouped data sets according to the time sequence, so as to generate a grouped data sets. Wherein, the sorting method comprises but is not less than: ascending and descending.
In step S105, the process of performing data association on the packet data set includes:
s105-1, setting the initial value of a variable i to be 1, wherein i is more than or equal to 1 and less than or equal to k, and k is the total number of the billets;
s105-2, selecting the earliest slicing data in the slicing data as the slicing data of the first billet on the first rolling mill in the data set with the number of 1, and removing the selected slicing data in the data set with the number of 1;
s105-3, setting the initial value of the variable t to be 1;
s105-4, recording the found slice data as reference slice data of the next slice data search;
s105-5, in the data set with the number (t +1), selecting the slice data with the time closest to that in the reference slice data from the slice data as the slice data of the first billet in the (t +1) th data set, and removing the selected slice data from the data set with the number (t + 1);
s105-6, setting the value of the variable t as (t +1), if t is smaller than N, repeating the step S105-4 and the step S105-5, otherwise, entering the step S105-7;
and S105-7, recording all the data found in the steps S105-1 to S105-6 in sequence to form a data set which is recorded as a slicing data set on all rolling mills of the ith billet.
Wherein, the method for removing the data set in step S105-2 and step S105-5 includes but is not limited to physical removal and logical removal; among these, methods of physical removal include, but are not limited to: delete, override, and specific data fill; methods of logic removal include, but are not limited to: and disconnecting the interconnection among the data and changing the interconnection among the data.
When slice data with the time closest to the reference slice data is selected from the data set, the method also comprises the step of calculating the time difference between the slice data time in the data set and the reference slice data, and the method comprises the following steps:
Dij＝|Ti(j+1)-Tij|，1≤i≤k，1≤j＜m，1≤j+1≤m；
in the formula, k is the number of steel billets, and m is the number of rolling mills; t isijThe start time of the ith billet processed by the jth rolling mill is the start time of the ith billet processed by the jth rolling mill; t isi(j+1)The start time of the processing of the ith billet by the (j +1) th rolling mill; dijIs the time difference between the slice data time in the data set and the reference slice data.
In step S106, the process of evaluating the data association result includes:
s106-1, judging whether all slice data in the slice data set are complete, if so, recording the corresponding slice data set as 1, and if not, recording the corresponding slice data set as 0;
s106-2, judging whether the start time of the slice data in two adjacent slice data sets is in increasing order, if so, recording the corresponding slice data set as 1, otherwise, recording the corresponding slice data set as 0,
s106-3, judging the concentrated cutting of two adjacent section dataIf the time duration of the slice data is in an increasing order, recording the corresponding slice data set as 1 if the time duration of the slice data is in the increasing order, and recording the corresponding slice data set as 0 if the time duration of the slice data is not in the increasing order; the process of judging whether the time duration of the slice data in two adjacent slice data sets is in order increasing order comprises the following steps: respectively calculate Eij-TijAnd Ei(j+1)-Ti(j+1)If E isij-Tij＜Ei(j+1)-Ti(j+1)If the time duration of the slicing data in the two adjacent slicing data sets is in an increasing order; wherein, TijThe start time of the ith billet processed by the jth rolling mill is the start time of the ith billet processed by the jth rolling mill; t isi(j+1)The start time of the processing of the ith billet by the (j +1) th rolling mill; eijThe end time of the ith billet processed by the jth rolling mill is the time; ei(j+1)Is the end time of the ith billet processed by the (j +1) th rolling mill.
S106-4, setting a data set effective factor, recording results of the judgment rules from the step S106-1 to the step S106-3, and setting a weight for each judgment rule;
s106-5, carrying out associated data set value evaluation on the data association result by using the effective factor, namely obtaining the judgment result of each data set under each judgment rule, and carrying out value evaluation on each data set according to the weight value of each judgment rule. Wherein, when evaluating the value of each data set, the method further comprises calculating the value evaluation value of each data set, including:
wherein m is the number of rolling mills; y is the number of the judgment rules, and y is more than or equal to 1 and less than or equal to 3; qyJudging results of the y judgment rule; pyJudging the weight value of the rule for the y-th item; vmAnd evaluating the value of the data set corresponding to the mth rolling mill.
The system firstly collects steel rolling data generated when a plurality of rolling mills roll a plurality of steel billets, then slices the collected steel rolling data, groups the sliced data to form a plurality of data sets, selects sliced data corresponding to target steel billets from each data set, and then associates the selected sliced data to complete the association of the sliced data of the target steel billets. Because the system directly collects the steel rolling data generated when the rolling mill rolls the steel billet, namely directly collects the rolling mill data, rather than collecting the steel rolling data by using an external component, the system can automatically correlate the rolling mill data when performing data correlation, thereby enhancing the accuracy, stability and effectiveness of the data correlation of the rolling mill frame on the basis of ensuring low cost and light weight.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A rolling mill data correlation method is characterized by comprising the following steps:
acquiring steel rolling data when a plurality of rolling mills roll a plurality of steel billets;
slicing the rolled steel data according to the data generation time and a data source, and grouping the sliced data according to the number of rolling mills to obtain a plurality of data sets;
mapping the rolling mill and the data sets, and after mapping is completed, sequencing and numbering the data sets according to rolling mill numbers;
selecting the slice data with the earliest time from the first data set as the slice data of the target billet on the first rolling mill, and sequentially taking the slice data selected from the previous data set as the reference slice data of the next data set according to the sequence of the data sets;
selecting slicing data which is the closest to the reference slicing data time from the next data set as the slicing data of the target steel billet on the next rolling mill; and after the selection of the slice data is completed, removing the selected slice data from the data set;
iteratively selecting the slice data until the corresponding slice data is selected from the last data set;
and correlating the slice data selected from each data set as the rolling data of the target steel billet on all rolling mills.
2. The mill data correlation method of claim 1, further comprising performing a value assessment on the plurality of data sets;
judging whether all the slice data in the data set are complete, recording a data set with complete all the slice data as 1, and recording a data set with incomplete slice data as 0;
judging whether the start time of the slice data in two adjacent data sets is in an increasing order, if so, recording as 1, otherwise, recording as 0;
judging whether the time duration of the slicing data in the two adjacent data sets is in an increasing order, if so, recording as 1, and if not, recording as 0;
and acquiring a judgment result of each data set under each judgment rule, and evaluating the value of each data set according to the preset weight value of each judgment rule.
3. The rolling mill data correlation method according to claim 2, wherein in the evaluation of the value of each data set, further comprising calculating the value evaluation value of each data set, there are:
wherein m is the number of rolling mills;
y is the number of the judgment rules, and y is more than or equal to 1 and less than or equal to 3;
Qyjudging results of the y judgment rule;
Pyjudging the weight value of the rule for the y-th item;
Vmand evaluating the value of the data set corresponding to the mth rolling mill.
4. The rolling mill data association method according to claim 2, wherein the process of determining whether the time duration of the slicing data in two adjacent data sets is in an increasing order comprises:
respectively calculate Eij-TijAnd Ei(j+1)-Ti(j+1)If E isij-Tij<Ei(j+1)-Ti(j+1)If the time duration of the slicing data in the two adjacent data sets is in an increasing order;
wherein, TijThe start time of the ith billet processed by the jth rolling mill is the start time of the ith billet processed by the jth rolling mill;
Ti(j+1)the start time of the processing of the ith billet by the (j +1) th rolling mill;
Eijthe end time of the ith billet processed by the jth rolling mill is the time;
Ei(j+1)is the end time of the ith billet processed by the (j +1) th rolling mill.
5. The rolling mill data correlation method according to claim 1, wherein when the slice data closest in time to the reference slice data is selected from the data set, further comprising calculating a time difference between the time of the slice data in the data set and the reference slice data, there is:
Dij＝|Ti(j+1)-Tij|，1≤i≤k，1≤j<m，1≤j+1≤m；
in the formula, k is the number of steel billets, and m is the number of rolling mills;
Tijthe start time of the ith billet processed by the jth rolling mill is the start time of the ith billet processed by the jth rolling mill;
Ti(j+1)the start time of the processing of the ith billet by the (j +1) th rolling mill;
Dijis the time difference between the slice data time in the data set and the reference slice data.
6. The mill data correlation method of claim 1, wherein removing selected slice data from the data set comprises at least one of: physical removal, logical removal;
wherein the physical removal comprises at least one of: deleting, covering and filling preset data;
the logical removal includes at least one of: and disconnecting the interconnection among the data and changing the interconnection among the data.
7. The mill data association method of claim 1, wherein sorting the plurality of data sets comprises: ascending or descending;
and after finishing the sorting, further comprising:
storing the plurality of data sets;
the means for storing includes at least one of: computer random access memory storage, computer read only memory storage.
8. A rolling mill data correlation system is characterized by comprising:
the acquisition module is used for acquiring steel rolling data when a plurality of rolling mills roll a plurality of steel billets;
the slicing grouping module is used for slicing the rolled steel data according to the data generation time and the data source and grouping the sliced data according to the number of rolling mills to obtain a plurality of data sets;
the sequencing numbering module is used for mapping the rolling mill and the data sets, and sequencing and numbering the data sets according to the rolling mill numbers after mapping is finished;
the data reference module is used for selecting the slice data with the earliest time from the first data set as the slice data of the target billet on the first rolling mill, and sequentially taking the slice data selected from the previous data set as the reference slice data of the next data set according to the sequence of the data sets;
the data selection module is used for selecting the slicing data which is the closest to the reference slicing data time from the next data set as the slicing data of the target steel billet on the next rolling mill; and after the selection of the slice data is completed, removing the selected slice data from the data set; iteratively selecting the slice data until the corresponding slice data is selected from the last data set;
and the data association module is used for associating the slice data selected from each data set as the steel rolling data of the target steel billet on all rolling mills.
9. The mill data correlation system of claim 8, further comprising a value assessment module for assessing the value of the plurality of data sets;
judging whether all the slice data in the data set are complete, recording a data set with complete all the slice data as 1, and recording a data set with incomplete slice data as 0;
judging whether the start time of the slice data in two adjacent data sets is in an increasing order, if so, recording as 1, otherwise, recording as 0;
judging whether the time duration of the slicing data in the two adjacent data sets is in an increasing order, if so, recording as 1, and if not, recording as 0;
and acquiring a judgment result of each data set under each judgment rule, and evaluating the value of each data set according to the preset weight value of each judgment rule.
10. The mill data correlation system of claim 9, wherein the value assessment module, in assessing the value of each data set, further comprises calculating a value assessment value for each data set, comprising:
wherein m is the number of rolling mills;
y is the number of the judgment rules, and y is more than or equal to 1 and less than or equal to 3;
Qyjudging results of the y judgment rule;
Pyjudging the weight value of the rule for the y-th item;
Vmand evaluating the value of the data set corresponding to the mth rolling mill.
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