CN115496143A - Method and device for detecting abnormity of blast furnace thermocouple temperature measurement data and storage medium - Google Patents
Method and device for detecting abnormity of blast furnace thermocouple temperature measurement data and storage medium Download PDFInfo
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Abstract
The invention provides a method, a device and a storage medium for detecting the abnormity of blast furnace thermocouple temperature measurement data, wherein the method comprises the following steps: s1, periodically collecting temperature measurement time sequence data of preset duration for any selected thermocouple embedded in a blast furnace body; s2, processing the acquired temperature measurement time sequence data in a sliding window mode to obtain a stacked two-dimensional array X of temperature measurement data; and S3, training the temperature measurement data in the two-dimensional array X by using an isolated forest algorithm, wherein each row in the two-dimensional array X is used as a different sample for training the isolated forest, each column is used as a different feature for training the isolated forest, calculating abnormal scores of the different samples, comparing the abnormal scores with a preset abnormal score threshold value, and determining abnormal samples according to the comparison result. By utilizing the technical scheme, the abnormal points in the temperature measurement data can be quickly found on line.
Description
Technical Field
The invention relates to the technical field of blast furnace ironmaking detection, in particular to a method and a device for detecting the abnormity of blast furnace thermocouple temperature measurement data and a storage medium.
Background
Blast furnaces, as important equipment in steel production, have significant construction and maintenance costs, so the long-life problem of blast furnaces has been highly appreciated by ironmakers. The general long-life target of the blast furnace mainly faces two problems, one is that the hearth and the bottom of the furnace hearth are easy to burn through, and the other is that the service life of the furnace belly and the lower part of the furnace body is limited. In order to know the working state and the erosion condition of the parts, thermocouples are generally arranged in a hearth bottom brick lining and a furnace body cooling device to detect the temperature. Particularly in the construction of large blast furnaces, hundreds of thermocouple temperature measuring points are buried in the hearth and bottom area, and the online temperature measuring mode becomes a main means for monitoring and calculating the corrosion state of the hearth and bottom.
However, in the production practice, as the operation time of the blast furnace goes on, the temperature variation continues, and in addition, the external environmental influence factors are more, and due to the conditions such as abnormal furnace conditions inside the blast furnace, damaged instruments or unstable circuits of the instruments, the measured values of the thermocouples may fluctuate abnormally. The blast furnace operator needs to grasp the occurrence of these abnormal situations and determine whether it is a change in the furnace conditions or other situations in order to make a next operation plan. Due to too many temperature measuring points, it is difficult for blast furnace operators and maintenance personnel to observe such abnormal conditions in a timely and complete manner by means of only manual confirmation. Therefore, a detection means is required to quickly and automatically observe abnormal fluctuations in thermocouple temperature measurement data.
Disclosure of Invention
The embodiment of the invention provides a method and a device for detecting the abnormality of blast furnace thermocouple temperature measurement data and a storage medium, so as to realize the online and rapid discovery of abnormal points in the thermocouple temperature measurement data.
In order to achieve the above object, in one aspect, a method for detecting an abnormality of temperature measurement data of a blast furnace thermocouple is provided, which includes the steps of:
s1, for any selected thermocouple buried in a blast furnace body, periodically collecting temperature measurement time sequence data with preset duration, wherein the preset duration is divided into T moments according to a collection period, each moment corresponds to one temperature measurement data, and T is a positive integer;
s2, processing the acquired temperature measurement time sequence data in a sliding window mode to obtain a stacked two-dimensional array X of temperature measurement data, wherein the length of the sliding window is w, w is more than 1, the two-dimensional array X is provided with T-w +1 rows and w columns, each row of the two-dimensional array X comprises the temperature measurement data with the length of one window, and different rows comprise the temperature measurement data of different windows obtained after the window slides; the columns of the two-dimensional array X contain temperature measurement data on the same positions of different windows;
and S3, training the temperature measurement data in the two-dimensional array X by using an isolated forest algorithm, wherein each row in the two-dimensional array X is used as a different sample for training an isolated forest, each column is used as a different feature for training the isolated forest, calculating abnormal scores of different samples, comparing the abnormal scores with a preset abnormal score threshold value, and determining abnormal samples according to the comparison result.
Preferably, the abnormality detection method is not limited to the one in which the thermocouple is embedded in the throat, shaft, waist, belly, hearth, or hearth region of the blast furnace.
Preferably, in the abnormality detection method, the arbitrarily selected thermocouple is an arbitrary thermocouple buried in a blast furnace body.
Preferably, the abnormality detection method, wherein the determining the abnormal sample according to the comparison result includes:
and determining the samples with the abnormal scores smaller than the preset abnormal score threshold value as abnormal samples.
The abnormality detection method according to claim 1, wherein the temperature measurement data collected at T times of a predetermined duration is set to y 1 ,y 2 ，…y T And the step S2 comprises the following steps:
s201, presetting a temporary storage array Z as an array with the length of w;
s202, taking any time T as a left boundary point of the sliding window, wherein the time range of T is 1, \8230;. T-w +1, and carrying out cyclic assignment operation according to the following steps:
let Z = [ y = t ,y t+1 ,…,y t+w-1 ]Stacking and filling Z as the t-th row into the two-dimensional array X, sliding a window to the right for one time, namely t = t +1, wherein the time range of the sliding window is r = (t),. ·, (t + w-1);
s203, obtaining a two-dimensional array X filled with all temperature measurement data, wherein any row of data of the two-dimensional array X represents a section of temperature measurement data corresponding to any moment T, and the time range of T is 1, \ 8230;, T-w +1.
In another aspect, an apparatus for anomaly detection of blast furnace thermocouple thermometry data is provided, comprising a memory and a processor, the memory storing at least one program, the at least one program being executed by the processor to implement the method as described in any one of the above.
In yet another aspect, a computer readable storage medium is provided, having stored therein at least one program, the at least one program being executable by a processor to implement a method as described in any one of the above.
The technical scheme has the following technical effects:
according to the technical scheme of the embodiment of the invention, the temperature measurement data of each thermocouple for a certain time length is collected, the stacked data stream of the two-dimensional array is obtained in a sliding window mode, the obtained stacked data stream is trained by using an isolated forest algorithm, each row of the two-dimensional array, namely a section of temperature measurement data corresponding to any time t, is taken as different samples for training the isolated forest algorithm, each column is taken as different characteristics for training the isolated forest, and the abnormal analysis of the data stream can be realized, so that the abnormal points in the temperature measurement data can be quickly found on line; the abnormality detection method provided by the embodiment of the invention can detect the abnormality of the time sequence data stream of a certain thermocouple such as a temperature measuring point, is suitable for the abnormality detection scene of thermocouple temperature measuring data, is beneficial to the judgment of furnace conditions in the operation of a blast furnace, and is also beneficial to the maintenance work of field equipment if the abnormal conditions such as interception, material wiping, pipelines and the like related to the structure of the blast furnace caused by abnormal furnace conditions occur.
Drawings
Fig. 1 is a schematic flow chart of a method for detecting an abnormality in blast furnace thermocouple temperature measurement data according to an embodiment of the present invention;
fig. 2 is a schematic structural view of an abnormality detection device for blast furnace thermocouple temperature measurement data according to an embodiment of the present invention.
Detailed Description
To further illustrate the various embodiments, the present invention provides the accompanying figures. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures. Elements in the figures are not drawn to scale and like reference numerals are generally used to indicate like elements.
The invention will now be further described with reference to the drawings and the detailed description.
The first embodiment is as follows:
fig. 1 is a schematic flow chart of a method for detecting an abnormality in blast furnace thermocouple temperature measurement data according to an embodiment of the present invention. As shown in fig. 1, the method for detecting an abnormality in blast furnace thermocouple temperature measurement data according to the embodiment includes the following steps:
s1, for any selected thermocouple buried in a blast furnace body, periodically acquiring temperature measurement time sequence data with preset time length, wherein the preset time length is divided into T moments according to an acquisition period, namely from 1 st moment to T th moment, wherein each moment corresponds to one temperature measurement data, and T is a preset positive integer;
the main role of this step is to collect data. The thermocouple embedded in the blast furnace body is not limited to those embedded in the areas such as the throat, shaft, waist, belly, hearth, and bottom of the blast furnace. The selected thermocouples can be any thermocouples, namely temperature measurement data of all the thermocouples can be collected, and whether all the thermocouples are abnormal or not is detected by using the technical scheme of the embodiment of the invention.
Illustratively, taking a sampling period of 20 seconds, that is, a time interval between every two data is 20s, a temperature measurement time within a predetermined time period of 30 minutes is acquiredThe sequence data, i.e. the predetermined time period, was 30 minutes. In this example, the number of 30-minute sampling times T =90. The values of the adopted period and the predetermined time period are only exemplary, and may be specifically adjusted according to actual needs. The thermometric data collected from time 1 to T is denoted as y 1…T I.e. y 1 ,y 2 ，…y T Wherein each time t corresponds to a temperature measurement value y t The time series data length is T.
S2, processing the acquired temperature measurement time sequence data in a sliding window mode to obtain a stacked two-dimensional array X of temperature measurement data, wherein the length of the sliding window is set to be w, w is greater than 1, the two-dimensional array X is T-w +1 rows and w columns, each row of the two-dimensional array X comprises temperature measurement data of w moments of one window, and different rows comprise temperature measurement data of different windows obtained after the window slides; the column of the two-dimensional array X comprises temperature measurement data on the same position of different windows;
specifically, the temperature measurement data collected from the time point 1 to the time point T is y 1…T For example, the window length w =10 may be taken, and step S2 specifically includes:
s201, presetting a temporary storage array Z as an array with the length of w;
s202, taking any time T as a left boundary point of the sliding window, wherein the time range of T is 1, \8230;. T-w +1, and carrying out cyclic assignment operation according to the following steps:
let Z = [ y = t ,y t+1 ,…,y t+w-1 ]Stacking and filling Z as the t-th row into the two-dimensional array X, sliding a window to the right for one time, namely t = t +1, wherein the time range of the sliding window is r = (t),. ·, (t + w-1);
s203, obtaining a two-dimensional array X filled with all temperature measurement data, wherein any row of data represents a section of temperature measurement data corresponding to any moment T, and the time range of T is 1, \ 8230;, T-w +1.
And S3, training the temperature measurement data in the two-dimensional array X by using an isolated forest algorithm, wherein each row in the two-dimensional array X is used as a different sample for training the isolated forest, each column is used as a different feature for training the isolated forest, calculating to obtain abnormal scores corresponding to the different samples, comparing the abnormal scores with a preset abnormal score threshold, and determining abnormal samples according to the comparison result.
Wherein, the adopted isolated forest algorithm is as follows:
a) Given an input two-dimensional array X, the number of trees n t Sub-sample size r s Initializing isolated forest
b) Set tree height limit to l = ceiling (log) 2 r s ) Here ceiling () points to round up;
c) For i =1 \ 8230t, the following operations are performed cyclically:
randomly sampling X according to r to obtain sub-sampling data X';
constructing an isolated tree iTree (X ',0, 1), and performing iForest → iForest { (X', 0, l) }.
d) And after circulation is finished, obtaining the result of the isolated forest iForest, and calculating the abnormal score of each sample.
In the steps of the isolated forest algorithm, the steps of constructing an isolated tree iTree are as follows:
a) Giving an input two-dimensional array X, the current tree height e and the tree height limit 1;
b) Judging whether the condition is satisfied: e is more than or equal to l or | X | is less than or equal to 1, wherein | X | represents the number of samples of X; if the condition is satisfied, executing step c), otherwise executing step d);
c) Let X be the tail node, output the node, end;
d) Let Q be the tabulation to express all characteristics of X, choose a characteristic Q in Q at random, and regard maximum value and minimum value of all sample characteristics Q in X as the boundary, choose a value p as the demarcation point at random;
e) Collecting samples with characteristic q values less than p in X as left set array X l Collecting samples with the characteristic q value larger than p in the X as a right set array Xr;
f) Establishing a node inode with a left subtree of iTree (X) l E +1, l), the right child thereofThe tree is iTree (X) r E +1, l), the boundary attribute is q, the boundary value is p, the node is output, and the process is finished; wherein the establishment of the two subtrees is realized by recursively invoking the algorithm.
Wherein, the abnormal score of each sample is calculated by the following formula:
wherein x is any sample; n represents the number of samples;
here, H () is a harmonic series, H (n-1) = ln (n-1) + ξ, ξ is an euler constant, c (n) represents the average path length of n samples to construct a binary tree; h (x) = e (x) + c (t.size), where e (x) denotes the number of bounds that sample x undergoes during its passage from the root node to the leaf nodes of the tree, and t.size denotes the number of samples that are at the same leaf node as sample x; e (h (x)) represents the mean of the samples h (x) in all trees of the isolated forest.
The anomaly score obtained by the above formula has a numerical range of-0.5 to 0.5.
It should be noted that, a section of temperature measurement data corresponding to any time t in each row in the two-dimensional array is regarded as different samples in the training soliton forest, and each column is regarded as different features in the training soliton forest.
A certain abnormal scoring threshold value is preset according to the area where each thermocouple temperature measuring point is located, the temperature change characteristics and the like, and then an abnormal sample is determined according to the set threshold value so as to obtain abnormal points in temperature measuring data. For example, different thermocouples may set different anomaly score thresholds.
Preferably, the samples with the abnormal scores smaller than a preset score threshold value in the samples in the isolated forest algorithm are determined as abnormal samples. In this example, the abnormal score threshold is set to-0.3, that is, when the abnormal score of the input sample is less than-0.3, the sample is determined as the abnormal sample, that is, the abnormal thermometry data.
According to the steps, the abnormity detection of any blast furnace thermocouple temperature measurement data can be completed.
Example two:
the present invention further provides a device for detecting an abnormality of temperature measurement data of a blast furnace thermocouple, as shown in fig. 2, the device includes a processor 201, a memory 202, a bus 203, and a computer program stored in the memory 202 and operable on the processor 201, the processor 201 includes one or more processing cores, the memory 202 is connected to the processor 201 through the bus 203, the memory 202 is used for storing program instructions, and the steps in the above-mentioned method embodiments of the first embodiment of the present invention are implemented when the processor executes the computer program.
Further, as an executable scheme, the device for detecting the abnormality of the blast furnace thermocouple temperature measurement data may be a computer unit, and the computer unit may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer unit may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the above-described constituent structures of the computer unit are merely examples of the computer unit, and do not constitute a limitation on the computer unit, and may include more or less components than those described above, or some of the components may be combined, or different components may be included. For example, the computer unit may further include an input/output device, a network access device, a bus, and the like, which is not limited in this embodiment of the present invention.
Further, as an executable solution, the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like which is the control center for the computer unit and which is connected to various parts of the overall computer unit by various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may implement the various functions of the computer unit by running or executing the computer programs and/or modules stored in the memory, as well as by invoking data stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system and an application program required by at least one function; the storage data area may store data created according to the use of the mobile phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Example three:
the invention also provides a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned method of an embodiment of the invention.
The computer unit integrated module/unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments described above may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is appropriately increased or decreased as required by legislation and patent practice in the jurisdiction.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A method for detecting the abnormity of temperature measurement data of a blast furnace thermocouple is characterized by comprising the following steps:
s1, for any selected thermocouple buried in a blast furnace body, periodically collecting temperature measurement time sequence data of a preset time length, wherein the preset time length is divided into T moments according to a collection period, each moment corresponds to one temperature measurement data, and T is a positive integer;
s2, processing the acquired temperature measurement time sequence data in a sliding window mode to obtain a stacked two-dimensional array X of the temperature measurement data, wherein the length of the sliding window is w, w is more than 1, the two-dimensional array X is provided with T-w +1 rows and w columns, each row of the two-dimensional array X comprises the temperature measurement data with the length of one window, and different rows comprise the temperature measurement data of different windows obtained after the window slides; the columns of the two-dimensional array X contain temperature measurement data on the same positions of different windows;
and S3, training the temperature measurement data in the two-dimensional array X by using an isolated forest algorithm, wherein each row in the two-dimensional array X is used as a different sample for training an isolated forest, each column is used as a different feature for training the isolated forest, calculating abnormal scores of the different samples, comparing the abnormal scores with a preset abnormal score threshold value, and determining abnormal samples according to the comparison result.
2. The abnormality detection method according to claim 1, wherein the thermocouple embedded in the blast furnace body is not limited to a thermocouple embedded in a region of a throat, a shaft, a waist, a belly, a hearth, or a bottom of the blast furnace.
3. The abnormality detection method according to claim 1, characterized in that said arbitrarily selected thermocouple is an arbitrary thermocouple buried in a blast furnace body.
4. The abnormality detection method according to claim 1, wherein determining the abnormal sample based on the comparison result includes:
and determining the samples with the abnormal scores smaller than the preset abnormal score threshold value as abnormal samples.
5. The abnormality detection method according to claim 1, wherein the temperature measurement data collected at T times of a predetermined duration is set to y 1 ,y 2 ，…y T And the step S2 comprises the following steps:
s201, presetting a temporary storage array Z as an array with the length of w;
s202, taking any time T as a left boundary point of the sliding window, wherein the time range of T is 1, \8230;. T-w +1, and carrying out cyclic assignment operation according to the following steps:
let Z = [ y = t ,y t+1 ,…,y t+w-1 ]Stacking and filling Z as the t-th row into the two-dimensional array X, sliding a window to the right for one time, namely t = t +1, wherein the time range of the sliding window is r = (t),. ·, (t + w-1);
s203, obtaining a two-dimensional array X filled with all temperature measurement data, wherein any row of data of the two-dimensional array X represents a section of temperature measurement data corresponding to any moment T, and the time range of T is 1, \ 8230;, T-w +1.
6. An apparatus for anomaly detection of blast furnace thermocouple thermometry data comprising a memory and a processor, said memory storing at least one program, said at least one program being executable by the processor to implement the method of any one of claims 1 to 5.
7. A computer-readable storage medium, in which at least one program is stored, which at least one program is executed by a processor to perform the method according to any one of claims 1 to 5.
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