CN116393217A - Intelligent monitoring method for material level of steel ball coal mill - Google Patents
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Abstract
The application relates to the technical field of material level detection of a steel ball coal mill, in particular to an intelligent material level monitoring method of the steel ball coal mill. Comprising the following steps: acquiring historical operation data of a coal mill, and establishing a coal mill material level model; acquiring real-time running data of the coal mill to generate pretreatment data, and generating real-time material level values of the coal mill according to the pretreatment data and a material level model of the coal mill; and setting the running parameters of the coal mill according to the real-time material level value of the coal mill, judging whether an early warning instruction is generated according to the real-time material level value of the coal mill, fusing indexes such as inlet and outlet pressure differences of the coal mill, bearing vibration, current of the coal mill and the like, establishing a material level model of the coal mill, learning through a neural network, and adjusting weight matrixes of all layers of the network to optimize the material level model, so that accurate measurement of the material level of the steel ball coal mill is realized. And adjusting the real-time rotating speed of the coal mill and the powder feeding amount in unit time according to the real-time material level of the coal mill, so that the coal grinding material level reaches an optimal value, the output of the coal mill is optimized, and the power consumption for pulverizing is reduced.
Description
Technical Field
The application relates to the technical field of material level detection of a steel ball coal mill, in particular to an intelligent material level monitoring method of the steel ball coal mill.
Background
The steel ball mill belongs to a high-energy-consumption and low-efficiency device. The electricity consumption of the ball mill accounts for 15% -25% of the electricity consumption of the power plant, and is one of the power consuming households of the power plant. The coal grinding unit consumption of the medium speed coal mill pulverizing system is generally about 10 kW.h/t, the coal grinding unit consumption of the medium storage pulverizing system steel ball coal mill is about 20 kW.h/t, and the coal grinding unit consumption of the double-inlet double-outlet steel ball coal mill is generally 25-30 kW.h/t.
The steel ball coal mill is multivariable, nonlinear, strong-coupling and large-delay objects, so that the characteristics are complex and the variables become slow. The ball mill level is thus a variable that exhibits complex relationships, subject to a number of factors. So far, soft measurement of the material level by using a single signal equivalent simulation material level by a plurality of scholars at home and abroad has a differential pressure method, a noise method, a power method, an oil pressure method, an air pressure differential method, a strain method, a bearing vibration method and the like, but the traditional soft measurement methods adopted at present are all used for measuring the material level of the ball mill by using the single signal equivalent simulation material level, and have the defects of poor unrealism, low precision and low reliability.
Disclosure of Invention
The purpose of the present application is: in order to solve the technical problems, the application provides an intelligent monitoring method for the material level of the steel ball coal mill, which aims to accurately measure the material level of the steel ball coal mill, optimize the output of the coal mill and reduce the power consumption for pulverizing.
In some embodiments of the present application, indexes such as inlet and outlet pressure differences of a coal mill, bearing vibration, coal mill current, etc. are fused through an intelligent fusion technology, a coal mill material level model is established, and weight matrixes of all layers of a network are adjusted to optimize through neural network learning, so that accurate measurement of the steel ball coal mill material level is realized.
In some embodiments of the present application, the real-time rotational speed of the coal mill and the powder feeding amount per unit time are adjusted according to the real-time material level of the coal mill, and the periodic correction is performed according to the preset time node, so that the coal grinding material level reaches the optimal value, the output of the coal mill is optimized, and the power consumption for pulverizing is reduced.
In some embodiments of the present application, an intelligent monitoring method for a material level of a steel ball coal mill is provided, including:
acquiring historical operation data of a coal mill, and establishing a coal mill material level model;
acquiring real-time running data of a coal mill to generate pretreatment data, and generating a real-time material level value of the coal mill according to the pretreatment data and a material level model of the coal mill;
and setting the rotating speed of the coal mill according to the real-time material level value of the coal mill, and judging whether to generate an early warning instruction according to the real-time material level value of the coal mill.
In some embodiments of the present application, when acquiring historical operating data of a coal pulverizer, the method includes:
presetting direct evaluation type data and indirect evaluation type data according to the operation data types of the coal mill;
acquiring a direct evaluation type data historical operation value, and generating a direct evaluation historical data packet through normalization processing;
obtaining an indirect evaluation type data history operation value, and generating an indirect evaluation history data packet through numerical conversion and normalization processing;
and the data in the direct evaluation historical data packet and the indirect evaluation historical data packet are in the same section.
In some embodiments of the present application, when establishing a coal mill level model, the method includes:
establishing an LSTM model according to the direct evaluation type historical data packet and the indirect evaluation type historical data;
setting optimizing parameters, neuron number, learning rate and training iteration times;
initializing PSO parameters and setting a fitness function of particles;
comparing the fitness value of the particles to generate an individual optimal position and a global optimal position, and correcting the optimal fitness value;
judging whether the current iteration number value is the maximum iteration number value, if so, generating an optimal parameter, training the LSTM model according to the optimal parameter, and establishing a coal mill material level model;
and if the current iteration times are not the maximum iteration times, continuing to compare the particle fitness values.
In some embodiments of the present application, acquiring coal mill real-time operational data generates pre-processing data, including:
obtaining direct evaluation data, and generating a direct evaluation real-time data packet through normalization processing;
obtaining indirect evaluation data, and generating an indirect evaluation real-time data packet through numerical conversion and normalization processing;
the indirect evaluation real-time data packet and the data in the indirect evaluation real-time data packet are in the same interval.
In some embodiments of the present application, generating an indirect evaluation real-time data packet through a numerical transformation and normalization process includes:
acquiring vertical vibration acceleration a1 of a coal mill bearing, and generating vibration speed a2 of the coal mill bearing according to a preset processing function;
the vibration acceleration a1 in the vertical direction of the coal mill bearing and the vibration speed a2 of the coal mill bearing generate a vibration signal effective value of the coal mill bearing through phase transformation;
and the effective value of the vibration signal of the coal mill bearing is normalized to generate a real-time evaluation value of the vibration of the coal mill bearing.
In some embodiments of the present application, when setting the rotational speed of the coal pulverizer according to the real-time level value, the method includes:
presetting a coal mill material level matrix B, and setting B (B1, B2, B3 and B4), wherein B1 is a preset first coal mill material level, B2 is a preset second coal mill material level, B3 is a preset third coal mill material level, B4 is a preset fourth coal mill material level, and B1 is more than 2 and less than B3 and less than B4;
presetting a coal mill rotation speed matrix C, and setting C (C1, C2, C3 and C4), wherein C1 is a preset first coal mill rotation speed, C2 is a preset second coal mill rotation speed, C1 is a preset first coal mill rotation speed, and C1 is more than C2 and less than C3 and less than C4;
acquiring a real-time material level value b of a coal mill, and setting a real-time rotating speed value c of the coal mill according to the real-time material level value b of the coal mill;
if B1 is smaller than B2, setting the real-time rotating speed value C of the coal mill as a preset first coal mill rotating speed C1, namely c=C1;
if B2 is less than B3, setting the real-time rotating speed value C of the coal mill as a preset second coal mill rotating speed C2, namely c=C2;
if B3 is smaller than B4, setting the real-time rotating speed value C of the coal mill as a preset rotating speed C3 of the third coal mill, namely c=C3;
if B > B4, the real-time rotational speed value C of the coal mill is set to be the preset fourth rotational speed C4 of the coal mill, namely c=c4.
In some embodiments of the present application, when setting the rotational speed of the coal pulverizer according to the real-time level value, the method further includes:
presetting a correction time node, and acquiring a real-time material level value of a coal mill of the current correction time node;
generating a predicted coal mill material level value b2 of the next time node according to the real-time material level value of the coal mill of the current correction time node;
and acquiring a real-time material level value b3 of the next time node, and correcting the real-time rotating speed c of the coal mill according to the absolute value d of the difference between the predicted material level value of the next time node and the real-time material level value b 3.
In some embodiments of the present application, when correcting the real-time rotation speed c of the coal mill according to the absolute value d of the difference between the predicted level value b2 and the real-time level value b3 of the next time node, the method includes:
presetting a difference matrix D, and setting D (D1, D2, D3 and D4), wherein D1 is a preset first difference, D2 is a preset second difference, and D1 is less than D2;
presetting a correction coefficient matrix N, and setting N (N1, N2, N3, N4), wherein N1 is a preset first correction coefficient, N2 is a preset second correction coefficient, N3 is a preset third correction coefficient, N4 is a preset fourth correction coefficient, and N1 is more than N2 and less than 1 and less than N3 and less than N4;
when b2 is more than b3, setting a real-time correction coefficient n according to the absolute value d of the difference value;
if D is less than D1, not correcting the real-time rotating speed c of the coal mill;
if D1 is less than D and less than D2, setting n=n3, and correcting the real-time rotating speed c1=n3+ Ci of the coal mill;
if D is more than D2, setting n=n4, and correcting the real-time rotating speed c1=n4. Ci of the coal mill;
when b2 is less than b3, setting a real-time correction coefficient n according to the absolute value d of the difference value;
if D is less than D1, not correcting the real-time rotating speed c of the coal mill;
if D1 is less than D and less than D2, setting n=n2, and correcting the real-time rotating speed c1=n2+ Ci of the coal mill;
if D > D2, n=n1 is set, and the real-time rotation speed c1=n1_Ci of the coal mill is corrected.
In some embodiments of the present application, when judging whether to generate the early warning instruction according to the real-time material level value of the coal mill, the method includes:
if the difference value D between the predicted material level value and the real-time material level value of the current time node is more than D1, setting the current time node as an abnormal node;
and acquiring the number M of the abnormal nodes, and generating an early warning instruction if the number M of the abnormal nodes is larger than a preset threshold M1 of the number of the abnormal nodes.
In some embodiments of the present application, when judging whether to generate the early warning instruction according to the real-time material level value of the coal mill, the method includes:
presetting a material level safety interval (D5, D6), wherein D5 is less than D1, and D6 is more than D4;
and acquiring a real-time material level value D of the coal mill, and generating an early warning instruction if D is less than D5 or D is more than D6.
Compared with the prior art, the intelligent monitoring method for the material level of the steel ball coal mill has the beneficial effects that:
through intelligent fusion technology, indexes such as inlet and outlet pressure difference of a coal mill, bearing vibration, current of the coal mill and the like are fused, a coal mill material level model is established, and weight matrixes of all layers of a network are adjusted to be optimized through neural network learning, so that accurate measurement of the material level of the steel ball coal mill is realized.
And adjusting the real-time rotating speed of the coal mill and the powder feeding amount in unit time according to the real-time material level of the coal mill, and periodically correcting according to a preset time node to ensure that the coal grinding material level reaches an optimal value, optimize the output of the coal mill and reduce the power consumption for pulverizing.
Drawings
FIG. 1 is a schematic flow chart of an intelligent monitoring method for the material level of a steel ball coal mill in a preferred embodiment of the application;
FIG. 2 is a schematic flow chart of a coal pulverizer level model in a preferred embodiment of the present application.
Detailed Description
The detailed description of the present application is further described in detail below with reference to the drawings and examples. The following examples are illustrative of the present application, but are not intended to limit the scope of the present application.
In the description of the present application, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate description of the present application and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present application.
The terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art in a specific context.
As shown in fig. 1-2, an intelligent monitoring method for a material level of a steel ball coal mill according to a preferred embodiment of the present application includes:
s101: acquiring historical operation data of a coal mill, and establishing a coal mill material level model;
s102: acquiring real-time running data of the coal mill to generate pretreatment data, and generating real-time material level values of the coal mill according to the pretreatment data and a material level model of the coal mill;
s103: and setting the running parameters of the coal mill according to the real-time material level value of the coal mill, and judging whether to generate an early warning instruction according to the real-time material level value of the coal mill.
Specifically, when obtaining the historical operation data of the coal mill, the method comprises the following steps:
presetting direct evaluation type data and indirect evaluation type data according to the operation data types of the coal mill;
acquiring a direct evaluation type data historical operation value, and generating a direct evaluation historical data packet through normalization processing;
obtaining an indirect evaluation type data history operation value, and generating an indirect evaluation history data packet through numerical conversion and normalization processing;
the data in the direct evaluation historical data packet and the indirect evaluation historical data packet are in the same section.
Specifically, the direct evaluation class data includes: differential pressure of inlet and outlet, current of coal mill and coal feeding amount of coal mill;
the indirect evaluation class data includes: vibration index of coal mill
Specifically, when establishing the coal mill material level model, the method comprises the following steps:
establishing an LSTM model according to the direct evaluation type historical data packet and the indirect evaluation type historical data;
setting optimizing parameters, neuron number, learning rate and training iteration times;
initializing PSO parameters and setting a fitness function of particles;
comparing the fitness value of the particles to generate an individual optimal position and a global optimal position, and correcting the optimal fitness value;
judging whether the current iteration number value is the maximum iteration number value, if so, generating an optimal parameter, training an LSTM model according to the optimal parameter, and establishing a coal mill material level model;
if the current iteration number is not the maximum iteration number, continuing to compare the particle fitness value.
It can be understood that in the above embodiment, indexes such as inlet and outlet pressure differences of the coal mill, bearing vibration, current of the coal mill and the like are fused through an intelligent fusion technology, a material level model of the coal mill is established, and weight matrixes of all layers of a network are adjusted to be optimized through neural network learning, so that accurate measurement of the material level of the steel ball coal mill is realized.
In a preferred embodiment of the present application, acquiring real-time operation data of a coal mill to generate pretreatment data includes:
obtaining direct evaluation data, and generating a direct evaluation real-time data packet through normalization processing;
obtaining indirect evaluation data, and generating an indirect evaluation real-time data packet through numerical conversion and normalization processing;
the indirect evaluation real-time data packet and the data in the indirect evaluation real-time data packet are in the same interval.
Specifically, coal mill data normalization: the normalization (Min-Max Normalization) method, also called dispersion normalization, is a linear transformation of historical data such that the resulting Min-max value is mapped between [ 0-1 ]. Let Xmin and Xmax be the minimum and maximum values of the index X, respectively, and the maximum and minimum normalization is to transform each original value X of X into a value Xmorm of the interval [0,1] by linearization, and the conversion function is as follows:
where Xmax is the maximum value of the sample data and Xmin is the minimum value of the sample data.
Specifically, when generating an indirect evaluation real-time data packet through numerical conversion and normalization processing, the method includes:
acquiring vertical vibration acceleration a1 of a coal mill bearing, and generating vibration speed a2 of the coal mill bearing according to a preset processing function;
the vibration acceleration a1 in the vertical direction of the coal mill bearing and the vibration speed a2 of the coal mill bearing generate a vibration signal effective value of the coal mill bearing through phase transformation;
and the effective value of the vibration signal of the coal mill bearing is normalized to generate a real-time evaluation value of the vibration of the coal mill bearing.
Specifically, the vibration index of the coal mill is an indirect index, the indirect index is subjected to numerical conversion, an acceleration vibration sensor is used for detecting an acceleration signal of vibration of the ball mill, and the detected signal is multiplied by a coefficient to obtain an acceleration value. An important index of the measuring method is a vibration signal of the coal mill, the working site condition of the ball mill is bad, and the sampling signal is inevitably affected by the pollution of noise with different degrees to the measuring accuracy, so the signal must be standardized. The intelligent variable amplitude adaptive filtering method is adopted to collect vibration signals, so that amplitude signals of sampling points tend to be more expected values in time sequence, and meanwhile, excessive peak values in measurement signals are filtered.
Specifically, the vibration of the bearing of the coal mill is decomposed into vibrations in both the vertical and horizontal directions, and only the amount of vibration in the vertical direction can be picked up as a reaction of the vibration of the bearing. The vibration mode of coal mill belongs to random vibration, its change rule can not be described by using defined time function, but its motion characteristics are obeyed a certain statistical rule, and its most basic characteristics are mean value, mean square value and variance, so that the shaft vibration energy can be represented by effective value of power, i.e
Where A is a constant, a is acceleration, and v is velocity.
I.e. the shaft vibration power can be calculated by measuring the vibration acceleration and speed. The method comprises the steps of picking up acceleration signals in the vertical direction by a piezoelectric acceleration sensor on a front bearing bush of the coal mill, obtaining speed signals by an integrating circuit, obtaining basic shaft vibration power effective values after phase transformation and corresponding synthesis operation, and obtaining the bearing vibration signal effective values of the coal mill through normalization and standardization processing.
In a preferred embodiment of the present application, when setting the operation parameters of the coal mill according to the real-time material level value of the coal mill, the method includes:
presetting a coal mill material level matrix B, and setting B (B1, B2, B3 and B4), wherein B1 is a preset first coal mill material level, B2 is a preset second coal mill material level, B3 is a preset third coal mill material level, B4 is a preset fourth coal mill material level, and B1 is more than 2 and less than B3 and less than B4;
presetting a coal mill rotation speed matrix C, and setting C (C1, C2, C3 and C4), wherein C1 is a preset first coal mill rotation speed, C2 is a preset second coal mill rotation speed, C1 is a preset first coal mill rotation speed, and C1 is more than C2 and less than C3 and less than C4;
acquiring a real-time material level value b of the coal mill, and setting a real-time rotating speed value c of the coal mill according to the real-time material level value b of the coal mill;
if B1 is smaller than B2, setting the real-time rotating speed value C of the coal mill as a preset first coal mill rotating speed C1, namely c=C1;
if B2 is less than B3, setting the real-time rotating speed value C of the coal mill as a preset second coal mill rotating speed C2, namely c=C2;
if B3 is smaller than B4, setting the real-time rotating speed value C of the coal mill as a preset rotating speed C3 of the third coal mill, namely c=C3;
if B > B4, the real-time rotational speed value C of the coal mill is set to be the preset fourth rotational speed C4 of the coal mill, namely c=c4.
Specifically, when setting the coal mill operation parameters according to the real-time material level value of the coal mill, the method further comprises the following steps:
presetting a coal feed amount matrix F in unit time of a coal mill, and setting F (F1, F2, F3 and F4), wherein F1 is the preset powder feed amount in unit time of a first coal mill, F2 is the preset powder feed amount in unit time of a second coal mill, F3 is the preset powder feed amount in unit time of a third coal mill, F4 is the preset powder feed amount in unit time of a fourth coal mill, and F1 is more than F2 and less than F3 and less than F4;
setting the real-time unit time powder feeding quantity f of the coal mill according to the real-time material level value b of the coal mill;
if B1 is less than B2, setting the real-time unit time powder feeding amount F of the real-time coal mill as the preset unit time powder feeding amount F1 of the first coal mill, namely f=F1;
if B2 is less than B3, setting the real-time unit time powder feeding amount F of the real-time coal mill as the preset unit time powder feeding amount F2 of the second coal mill, namely f=F2;
if B3 is less than B4, setting the real-time unit time powder feeding amount F of the real-time coal mill as the preset unit time powder feeding amount F3 of the third coal mill, namely f=F3;
if B is larger than B4, setting the real-time unit time powder feeding amount F of the real-time coal mill as the preset unit time powder feeding amount F4 of the fourth coal mill, namely f=F4.
It can be understood that in the above embodiment, the real-time rotational speed and the powder feeding amount per unit time of the coal mill are adjusted according to the real-time material level of the coal mill, and the periodic correction is performed according to the preset time node, so that the coal grinding material level reaches the optimal value, the output of the coal mill is optimized, and the power consumption for pulverizing is reduced.
In a preferred embodiment of the present application, when setting the rotational speed of the coal mill according to the real-time material level value, the method further includes:
presetting a correction time node, and acquiring a real-time material level value of a coal mill of the current correction time node;
generating a predicted coal mill material level value b2 of the next time node according to the real-time material level value of the coal mill of the current correction time node;
and acquiring a real-time material level value b3 of the next time node, and correcting the real-time rotating speed c of the coal mill according to the absolute value d of the difference between the predicted material level value of the next time node and the real-time material level value b 3.
Specifically, when correcting the real-time rotational speed c of the coal mill according to the absolute value d of the difference between the predicted level value b2 and the real-time level value b3 of the next time node, the method comprises:
presetting a difference matrix D, and setting D (D1, D2, D3 and D4), wherein D1 is a preset first difference, D2 is a preset second difference, and D1 is less than D2;
presetting a correction coefficient matrix N, and setting N (N1, N2, N3, N4), wherein N1 is a preset first correction coefficient, N2 is a preset second correction coefficient, N3 is a preset third correction coefficient, N4 is a preset fourth correction coefficient, and N1 is more than N2 and less than 1 and less than N3 and less than N4;
when b2 is more than b3, setting a real-time correction coefficient n according to the absolute value d of the difference value;
if D is less than D1, not correcting the real-time rotating speed c of the coal mill;
if D1 is less than D and less than D2, setting n=n3, and correcting the real-time rotating speed c1=n3+ Ci of the coal mill;
if D is more than D2, setting n=n4, and correcting the real-time rotating speed c1=n4. Ci of the coal mill;
when b2 is less than b3, setting a real-time correction coefficient n according to the absolute value d of the difference value;
if D is less than D1, not correcting the real-time rotating speed c of the coal mill;
if D1 is less than D and less than D2, setting n=n2, and correcting the real-time rotating speed c1=n2+ Ci of the coal mill;
if D > D2, n=n1 is set, and the real-time rotation speed c1=n1_Ci of the coal mill is corrected.
Specifically, the workload in the next time interval is estimated according to the current coal feeding amount, the rotating speed of the coal mill, the current material level and the historical operation data, when a large difference exists between the actual material level value and the predicted value, the current coal mill operation is described as problematic, and the operation parameters of the coal mill should be corrected in time to ensure that the coal grinding material level reaches the optimal value, optimize the output of the coal mill and reduce the power consumption for pulverizing.
In a preferred embodiment of the present application, when judging whether to generate an early warning instruction according to a real-time material level value of a coal mill, the method includes:
if the difference value D between the predicted material level value and the real-time material level value of the current time node is more than D1, setting the current time node as an abnormal node;
and acquiring the number M of the abnormal nodes, and generating an early warning instruction if the number M of the abnormal nodes is larger than a preset threshold value M1 of the number of the abnormal nodes.
Specifically, when the abnormal time nodes are more, the problem that the coal mill exists in a plurality of operation periods is described, when the number of the abnormal nodes exceeds a threshold value, an early warning instruction is timely sent, and an maintainer is reminded to timely carry out maintenance so as to avoid the coal mill from malfunctioning.
According to the first conception, indexes such as inlet and outlet pressure differences of a coal mill, bearing vibration, current of the coal mill and the like are fused through an intelligent fusion technology, a coal mill material level model is established, and weight matrixes of all layers of a network are adjusted to be optimized through neural network learning, so that accurate measurement of the steel ball coal mill material level is achieved.
According to the second conception, the real-time coal mill rotating speed and the unit time powder feeding amount are adjusted according to the real-time coal mill material level, and the coal mill material level is periodically corrected according to a preset time node to reach an optimal value, so that the output of the coal mill is optimized, and the power consumption for pulverizing is reduced.
The foregoing is merely a preferred embodiment of the present application, and it should be noted that modifications and substitutions can be made by those skilled in the art without departing from the technical principles of the present application, and these modifications and substitutions should also be considered as being within the scope of the present application.
Claims (10)
1. An intelligent monitoring method for the material level of a steel ball coal mill is characterized by comprising the following steps:
acquiring historical operation data of a coal mill, and establishing a coal mill material level model;
acquiring real-time running data of a coal mill to generate pretreatment data, and generating a real-time material level value of the coal mill according to the pretreatment data and a material level model of the coal mill;
and setting the running parameters of the coal mill according to the real-time material level value of the coal mill, and judging whether to generate an early warning instruction according to the real-time material level value of the coal mill.
2. The intelligent monitoring method for the material level of the steel ball coal mill according to claim 1, wherein the step of acquiring historical operation data of the coal mill comprises the following steps:
presetting direct evaluation type data and indirect evaluation type data according to the operation data types of the coal mill;
acquiring a direct evaluation type data historical operation value, and generating a direct evaluation historical data packet through normalization processing;
obtaining an indirect evaluation type data history operation value, and generating an indirect evaluation history data packet through numerical conversion and normalization processing;
and the data in the direct evaluation historical data packet and the indirect evaluation historical data packet are in the same section.
3. The intelligent monitoring method for the material level of the steel ball coal mill according to claim 2, wherein the building of the material level model of the coal mill comprises the following steps:
establishing an LSTM model according to the direct evaluation type historical data packet and the indirect evaluation type historical data;
setting optimizing parameters, neuron number, learning rate and training iteration times;
initializing PSO parameters and setting a fitness function of particles;
comparing the fitness value of the particles to generate an individual optimal position and a global optimal position, and correcting the optimal fitness value;
judging whether the current iteration number value is the maximum iteration number value, if so, generating an optimal parameter, training the LSTM model according to the optimal parameter, and establishing a coal mill material level model;
and if the current iteration times are not the maximum iteration times, continuing to compare the particle fitness values.
4. The intelligent monitoring method for the material level of the steel ball coal mill according to claim 1, wherein the step of acquiring real-time operation data of the coal mill to generate pretreatment data comprises the following steps:
obtaining direct evaluation data, and generating a direct evaluation real-time data packet through normalization processing;
obtaining indirect evaluation data, and generating an indirect evaluation real-time data packet through numerical conversion and normalization processing;
the indirect evaluation real-time data packet and the data in the indirect evaluation real-time data packet are in the same interval.
5. The intelligent monitoring method for the material level of the steel ball coal mill according to claim 4, wherein when the indirect evaluation real-time data packet is generated through numerical conversion and normalization processing, the method comprises the following steps:
acquiring vertical vibration acceleration a1 of a coal mill bearing, and generating vibration speed a2 of the coal mill bearing according to a preset processing function;
the vibration acceleration a1 in the vertical direction of the coal mill bearing and the vibration speed a2 of the coal mill bearing generate a vibration signal effective value of the coal mill bearing through phase transformation;
and the effective value of the vibration signal of the coal mill bearing is normalized to generate a real-time evaluation value of the vibration of the coal mill bearing.
6. The intelligent monitoring method for the material level of the steel ball coal mill according to claim 1, wherein when the operation parameters of the coal mill are set according to the real-time material level value of the coal mill, the intelligent monitoring method comprises the following steps:
presetting a coal mill material level matrix B, and setting B (B1, B2, B3 and B4), wherein B1 is a preset first coal mill material level, B2 is a preset second coal mill material level, B3 is a preset third coal mill material level, B4 is a preset fourth coal mill material level, and B1 is more than 2 and less than B3 and less than B4;
presetting a coal mill rotation speed matrix C, and setting C (C1, C2, C3 and C4), wherein C1 is a preset first coal mill rotation speed, C2 is a preset second coal mill rotation speed, C1 is a preset first coal mill rotation speed, and C1 is more than C2 and less than C3 and less than C4;
acquiring a real-time material level value b of a coal mill, and setting a real-time rotating speed value c of the coal mill according to the real-time material level value b of the coal mill;
if B1 is smaller than B2, setting the real-time rotating speed value C of the coal mill as a preset first coal mill rotating speed C1, namely c=C1;
if B2 is less than B3, setting the real-time rotating speed value C of the coal mill as a preset second coal mill rotating speed C2, namely c=C2;
if B3 is smaller than B4, setting the real-time rotating speed value C of the coal mill as a preset rotating speed C3 of the third coal mill, namely c=C3;
if B > B4, the real-time rotational speed value C of the coal mill is set to be the preset fourth rotational speed C4 of the coal mill, namely c=c4.
7. The intelligent monitoring method for the material level of the steel ball coal mill according to claim 6, wherein when the operation parameters of the coal mill are set according to the real-time material level value of the coal mill, the intelligent monitoring method further comprises the following steps:
presetting a coal feed amount matrix F in unit time of a coal mill, and setting F (F1, F2, F3 and F4), wherein F1 is the preset powder feed amount in unit time of a first coal mill, F2 is the preset powder feed amount in unit time of a second coal mill, F3 is the preset powder feed amount in unit time of a third coal mill, F4 is the preset powder feed amount in unit time of a fourth coal mill, and F1 is more than F2 and less than F3 and less than F4;
setting the real-time unit time powder feeding quantity f of the coal mill according to the real-time material level value b of the coal mill;
if B1 is less than B2, setting the real-time unit time powder feeding amount F of the real-time coal mill as the preset unit time powder feeding amount F1 of the first coal mill, namely f=F1;
if B2 is less than B3, setting the real-time unit time powder feeding amount F of the real-time coal mill as the preset unit time powder feeding amount F2 of the second coal mill, namely f=F2;
if B3 is less than B4, setting the real-time unit time powder feeding amount F of the real-time coal mill as the preset unit time powder feeding amount F3 of the third coal mill, namely f=F3;
if B is larger than B4, setting the real-time unit time powder feeding amount F of the real-time coal mill as the preset unit time powder feeding amount F4 of the fourth coal mill, namely f=F4.
8. The intelligent monitoring method for the material level of the steel ball coal mill according to claim 7, wherein when the rotational speed of the coal mill is set according to the real-time material level value, the intelligent monitoring method further comprises the following steps:
presetting a correction time node, and acquiring a real-time material level value of a coal mill of the current correction time node;
generating a predicted coal mill material level value b2 of the next time node according to the real-time material level value of the coal mill of the current correction time node;
and acquiring a real-time material level value b3 of the next time node, and correcting the real-time rotating speed c of the coal mill according to the absolute value d of the difference between the predicted material level value of the next time node and the real-time material level value b 3.
9. The intelligent monitoring method for the material level of the steel ball coal mill according to claim 8, wherein when the real-time rotation speed c of the coal mill is corrected according to the absolute value d of the difference between the predicted material level value b2 and the real-time material level value b3 of the next time node, the method comprises the following steps:
presetting a difference matrix D, and setting D (D1, D2, D3 and D4), wherein D1 is a preset first difference, D2 is a preset second difference, and D1 is less than D2;
presetting a correction coefficient matrix N, and setting N (N1, N2, N3, N4), wherein N1 is a preset first correction coefficient, N2 is a preset second correction coefficient, N3 is a preset third correction coefficient, N4 is a preset fourth correction coefficient, and N1 is more than N2 and less than 1 and less than N3 and less than N4;
when b2 is more than b3, setting a real-time correction coefficient n according to the absolute value d of the difference value;
if D is less than D1, not correcting the real-time rotating speed c of the coal mill;
if D1 is less than D and less than D2, setting n=n3, and correcting the real-time rotating speed c1=n3+ Ci of the coal mill;
if D is more than D2, setting n=n4, and correcting the real-time rotating speed c1=n4. Ci of the coal mill;
when b2 is less than b3, setting a real-time correction coefficient n according to the absolute value d of the difference value;
if D is less than D1, not correcting the real-time rotating speed c of the coal mill;
if D1 is less than D and less than D2, setting n=n2, and correcting the real-time rotating speed c1=n2+ Ci of the coal mill;
if D > D2, n=n1 is set, and the real-time rotation speed c1=n1_Ci of the coal mill is corrected.
10. The intelligent monitoring method for the material level of the steel ball coal mill according to claim 9, wherein when judging whether to generate the early warning command according to the real-time material level value of the coal mill, the intelligent monitoring method comprises the following steps:
if the difference value D between the predicted material level value and the real-time material level value of the current time node is more than D1, setting the current time node as an abnormal node;
and acquiring the number M of the abnormal nodes, and generating an early warning instruction if the number M of the abnormal nodes is larger than a preset threshold M1 of the number of the abnormal nodes.
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