CN112115602B - Coal mill pipeline blocking state determining method and device, storage medium and computing equipment - Google Patents
Coal mill pipeline blocking state determining method and device, storage medium and computing equipment Download PDFInfo
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Abstract
The application discloses a coal pulverizer pipeline jam state determination method, device, storage medium and computing equipment, and the method includes: acquiring historical operation data of a coal mill, and extracting air outlet dust pressure data and a plurality of influence factor data related to the air outlet dust pressure data; taking the dust pressure data of the air outlet as a dependent variable, and taking a plurality of influence factor data which have a correlation with the dust pressure data of the air outlet as independent variables, and establishing a multiple regression model; training the model to delete the influence factor data with the correlation coefficient smaller than the threshold value with the air outlet dust pressure data, and matching weights with the influence factor data with the correlation coefficient larger than or equal to the threshold value with the air outlet dust pressure data to obtain a regression equation so as to evaluate the air outlet dust pressure data of the coal mill according to the current operation data of the coal mill and determine the pipeline blockage state of the coal mill. The technical problem that prediction coal pulverizer pipeline blocking state rate of accuracy is low among the prior art is solved to this application.
Description
Technical Field
The application relates to the technical field of coal mills, in particular to a method and a device for determining a pipeline blockage state of a coal mill, a storage medium and computing equipment.
Background
The coal mill pipeline is blocked, so that the safe operation of the boiler is influenced, the spontaneous combustion of coal dust is possibly caused by serious blockage, a coal-fired pipeline is damaged, the normal operation and grid connection of a unit are seriously influenced, and the energy conservation, consumption reduction and environmental protection emission of power generation enterprises are not facilitated. In order to improve the economic benefit of power generation enterprises, the power generation enterprises increasingly pay attention to and strengthen the purging management of the coal mill pipelines, however, the blockage of the coal mill pipelines is a very complex problem, because the factors influencing the blockage are many, and the regular cleaning is not economical and timely enough.
At present, a plurality of coal mill pipeline blockage judging methods exist, however, the methods are difficult to comprehensively and accurately measure the influence of various factors in the operation of the coal mill on the blockage state, so that the prediction of the pipeline blockage of the coal mill is deviated. Aiming at the technical problem that the accuracy rate for predicting the blocking state of the coal mill pipeline in the prior art is low, no effective solution is proposed at present.
Disclosure of Invention
The embodiment of the application provides a method, a device, a storage medium and computing equipment for determining the blocking state of a coal mill pipeline, which are used for at least solving the technical problem of low accuracy in predicting the blocking state of the coal mill pipeline in the prior art.
According to one aspect of the embodiments of the present application, there is provided a method for determining a clogging state of a coal pulverizer pipe, including: acquiring historical operation data of a coal mill, and extracting air outlet dust pressure data and a plurality of influence factor data related to the air outlet dust pressure data; taking the dust pressure data of the air outlet as a dependent variable, and taking a plurality of influence factor data which have a correlation with the dust pressure data of the air outlet as independent variables, and establishing a multiple regression model; training the multiple regression model to delete the influence factor data with the correlation coefficient smaller than the threshold value with the dust pressure data of the air outlet, and matching weights with the influence factor data with the correlation coefficient larger than or equal to the threshold value with the dust pressure data of the air outlet to obtain a regression equation; the regression equation is used for evaluating the current air outlet dust pressure data of the coal mill according to the current operation data of the coal mill so as to determine the blocking state of the pipeline of the coal mill.
According to another aspect of the embodiments of the present application, there is also provided a coal pulverizer pipe blockage status determining device, including: the acquisition module is used for acquiring historical operation data of the coal mill and extracting air outlet dust pressure data and a plurality of influence factor data related to the air outlet dust pressure data; the building module is used for building a multiple regression model by taking the air outlet dust pressure data as a dependent variable and taking a plurality of influence factor data which have a correlation with the air outlet dust pressure data as independent variables; the training module is used for training the multiple regression model so as to delete the influence factor data with the correlation coefficient smaller than the threshold value of the dust pressure data of the air outlet, and match weights for the influence factor data with the correlation coefficient larger than or equal to the threshold value of the dust pressure data of the air outlet, so as to obtain a regression equation; the regression equation is used for evaluating the current air outlet dust pressure data of the coal mill according to the current operation data of the coal mill so as to determine the blocking state of the pipeline of the coal mill.
According to another aspect of the embodiment of the application, a storage medium is provided, the storage medium comprises a stored program, and equipment where the storage medium is located is controlled to execute the coal mill pipeline blockage state determining method when the program runs.
According to another aspect of the embodiment of the application, there is also provided a computing device, including a processor, where the processor is configured to run a program, and the program executes the method for determining a blocking state of a coal pulverizer pipeline.
In the embodiment of the application, the historical operation data of the coal mill are obtained, and the air outlet dust pressure data and a plurality of influence factor data related to the air outlet dust pressure data are extracted; adopting air outlet dust pressure data as dependent variables and a plurality of influence factor data which are related to the air outlet dust pressure data as independent variables, and establishing a multiple regression model; training the multiple regression model to delete the influence factor data with the correlation coefficient smaller than the threshold value with the dust pressure data of the air outlet, and matching weights with the influence factor data with the correlation coefficient larger than or equal to the threshold value with the dust pressure data of the air outlet to obtain a regression equation; the regression equation is used for evaluating the current air outlet dust pressure data of the coal mill according to the current operation data of the coal mill so as to determine the pipeline blocking state of the coal mill, and the air outlet dust pressure data is predicted according to the current influence factor data through the trained multiple regression model, so that the technical effect of predicting the pipeline blocking state of the coal mill is achieved.
In the process, the operation data of the coal mill are extracted, the influence factors of the blockage of the pipeline of the coal mill are analyzed through the multiple regression model, the abnormal early warning and visual analysis of the pipeline of the coal mill are realized, and then the analysis method of the blockage factors of the pipeline of the coal mill based on the multiple regression model is realized, so that a new and accurate analysis reference method is provided for the power generation personnel to perform the cleaning analysis of the blockage of the pipeline of the coal mill. And further solves the technical problem of low accuracy in predicting the blocking state of the coal mill pipeline in the prior art.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a block diagram of the hardware architecture of a computer terminal (or mobile device) for implementing a coal pulverizer line blockage status determination method, in accordance with an embodiment of the present application;
FIG. 2 is a flow chart of a method of determining a coal pulverizer line blockage status in accordance with an embodiment of the present application;
FIG. 3 is a flow chart of an alternative coal pulverizer line blockage status determination method, according to an embodiment of the present application; and
Fig. 4 is a schematic structural view of a coal pulverizer line blockage status determination device, according to an embodiment of the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
In accordance with embodiments of the present application, there is also provided a coal pulverizer line blockage status determination method embodiment, it being noted that the steps shown in the flowchart of the figures may be performed in a computer system, such as a set of computer executable instructions, and, although a logical sequence is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than what is shown or described herein.
The method embodiment provided in the first embodiment of the present application may be executed in a mobile terminal, a computer terminal or a similar computing device. Fig. 1 shows a block diagram of the hardware architecture of a computer terminal (or mobile device) for implementing a method for determining the state of a coal pulverizer pipe blockage. As shown in fig. 1, the computer terminal 10 (or mobile device 10) may include one or more (shown as 102a, 102b, … …,102 n) processors 102 (the processors 102 may include, but are not limited to, a microprocessor MCU, a programmable logic device FPGA, etc. processing means), a memory 104 for storing data, and a transmission means 106 for communication functions. In addition, the method may further include: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power supply, and/or a camera. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
It should be noted that the one or more processors 102 and/or other data processing circuits described above may be referred to generally herein as "data processing circuits. The data processing circuit may be embodied in whole or in part in software, hardware, firmware, or any other combination. Furthermore, the data processing circuitry may be a single stand-alone processing module, or incorporated, in whole or in part, into any of the other elements in the computer terminal 10 (or mobile device). As referred to in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of the path of the variable resistor termination to interface).
The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the coal pulverizer pipeline blockage status determination method in the embodiments of the present application, and the processor 102 executes the software programs and modules stored in the memory 104, thereby performing various functional applications and data processing, that is, implementing the coal pulverizer pipeline blockage status determination method described above. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission means 106 is arranged to receive or transmit data via a network. The specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.
The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computer terminal 10 (or mobile device).
Here, it should be noted that, in some alternative embodiments, the computer device (or mobile device) shown in fig. 1 may include hardware elements (including circuits), software elements (including computer code stored on a computer readable medium), or a combination of both hardware elements and software elements. It should be noted that fig. 1 is only one example of a specific example, and is intended to illustrate the types of components that may be present in the computer device (or mobile device) described above.
The method for determining the pipeline blockage state of the coal mill operates in the operating environment as shown in fig. 2. FIG. 2 is a flow chart of a method of determining a coal pulverizer line blockage status, according to an embodiment of the disclosure, as can be appreciated from FIG. 2, the method of determining a coal pulverizer line blockage status may include:
step S202, historical operation data of the coal mill is obtained, and air outlet dust pressure data and a plurality of influence factor data related to the air outlet dust pressure data are extracted.
In an alternative scheme, the coal mill basic information data in one period in the SIS system is collected through an API interface and used as the historical operation data, and the historical operation data is stored in a csv format. And extracting and converting big data by using an API provided by the SIS system in the factory, wherein the data extraction comprises a data normal area and a data abnormal area, and the data normal area and the data abnormal area are stored as csv files. The plurality of influence factor data related to the dust pressure data of the air outlet comprise information data such as generator load of a generator set, coal feeding quantity feedback signals, coal feeding current, hot primary air duct pressure and the like in an SIS system.
In step S204, a multiple regression model is established by taking the air outlet dust pressure data as a dependent variable and taking a plurality of influence factor data related to the air outlet dust pressure data as independent variables.
In an alternative scheme, a multiple linear regression method is used for analysis and identification, and multiple regression analysis is a common method for researching influence factors of a certain variable, and can accurately grasp influence degree of the certain research variable by one or more other variables, so that a more scientific basis is provided for a prediction result.
In an alternative scheme, the influence factors are taken as independent variables, the outlet wind dust pressure is taken as the dependent variables, and regression parameter estimation is carried out by adopting a least square method, so that a multiple regression model of the outlet wind dust pressure and the outlet wind dust pressure influence factors is obtained.
The multiple regression equation is:
for outlet wind dust pressure, x k Is the influencing factor of the dust pressure of the outlet wind, +.>Is a regression parameter.
And S206, training the multiple regression model to delete the influence factor data with the correlation coefficient smaller than the threshold value with the dust pressure data of the air outlet, and matching weights with the influence factor data with the correlation coefficient larger than or equal to the threshold value with the dust pressure data of the air outlet to obtain a regression equation. The regression equation is used for evaluating the current air outlet dust pressure data of the coal mill according to the current operation data of the coal mill so as to determine the blocking state of the pipeline of the coal mill.
In an alternative scheme, the built model is subjected to goodness-of-fit test, F test and t test to verify whether the built model is an optimal model. The goodness-of-fit test is used to measure the significance of the linear relationship of all independent variables to the random variable y, the F test is used to measure whether there is a significant effect between the independent variable as a whole and the dependent variable, and the t test is used to test whether each independent variable has a significant effect on the dependent variable.
In an alternative scheme, aiming at the influence factors, analyzing the influence factors of the outlet wind dust pressure under the conditions of coal feeding quantity, coal feeding current, hot primary wind main pipe pressure and the like, and obtaining weights of the influence factors of the outlet pressure data under different conditions by a regression equation finally obtained by a stepwise regression method; the visualization of the dust pressure data of the wind is realized, and a new analysis reference method is provided for the operation personnel to analyze the pipeline blockage. When the air dust pressure of the outlet of the coal mill is required to be predicted, the current air dust pressure data of the outlet of the coal mill can be obtained only by acquiring the current influence factor data of the coal mill and inputting the current influence factor data into a trained multiple regression model, so that the air dust pressure data of the outlet of the coal mill can be used for predicting the blocking state of a pipeline of the coal mill.
In the embodiment of the application, the historical operation data of the coal mill are obtained, and the air outlet dust pressure data and a plurality of influence factor data related to the air outlet dust pressure data are extracted; adopting air outlet dust pressure data as dependent variables and a plurality of influence factor data which are related to the air outlet dust pressure data as independent variables, and establishing a multiple regression model; training the multiple regression model to delete the influence factor data with the correlation coefficient smaller than the threshold value with the dust pressure data of the air outlet, and matching weights with the influence factor data with the correlation coefficient larger than or equal to the threshold value with the dust pressure data of the air outlet to obtain a regression equation; the regression equation is used for evaluating the current air outlet dust pressure data of the coal mill according to the current operation data of the coal mill so as to determine the pipeline blocking state of the coal mill, and the air outlet dust pressure data is predicted according to the current influence factor data through the trained multiple regression model, so that the technical effect of predicting the pipeline blocking state of the coal mill is achieved.
In the process, the operation data of the coal mill are extracted, the influence factors of the blockage of the pipeline of the coal mill are analyzed through the multiple regression model, the abnormal early warning and visual analysis of the pipeline of the coal mill are realized, and then the analysis method of the blockage factors of the pipeline of the coal mill based on the multiple regression model is realized, so that a new and accurate analysis reference method is provided for the power generation personnel to perform the cleaning analysis of the blockage of the pipeline of the coal mill. According to the method, the influence factors which have significant influence on the dust pressure of the air outlet can be comprehensively and accurately selected from the operation data of the coal mill, and the multiple regression equation is constructed based on the influence factors, so that power generation personnel can intuitively and accurately see the influence factors which have significant influence on the dust pressure of the air outlet, key monitoring and analysis can be conveniently carried out on the factors which have significant influence, meanwhile, the dust pressure of the air outlet can be accurately estimated according to the current operation data of the coal mill based on the multiple regression equation, and the pipeline blocking state of the coal mill can be conveniently predicted in advance. In conclusion, the technical problem of low accuracy in predicting the blocking state of the coal mill pipeline in the prior art is solved by the aid of the scheme.
Further, in step S204, before establishing the multiple regression model by using the air outlet dust pressure data as the dependent variable and using the plurality of influence factor data related to the air outlet dust pressure data as the independent variable, the method further includes:
step S203: screening data such as outlet wind dust pressure according to index units, available areas (started units and started coal mills), normal time periods/abnormal time periods, and the like, and primarily judging normal event sets and abnormal event sets through empirical analysis and graphic comparison. The basic judgment rule of the normal operation of the coal mill is that the wind dust pressure of the outlet of the coal mill and other independent variable influence factor data can be subjected to curve fitting. An anomaly is determined if the outlet wind dust pressure cannot be fitted or demonstrated in a straight-line like manner. Before the follow-up analysis is carried out depending on the data, abnormal data can be removed, or warning information can be pushed to a manager, so that the correctness of the data can be further confirmed, the model is prevented from being trained by adopting error data, and the model training precision is improved.
Further, in step S204, with the air outlet dust pressure data as a dependent variable and a plurality of influence factor data related to the air outlet dust pressure data as independent variables, establishing the multiple regression model further includes:
The following multiple regression equation is constructed,
wherein,for outlet windDust pressure, x k Is the influencing factor of the dust pressure of the outlet wind, +.>Is a regression parameter.
In an alternative, the regression parameters are estimated by a least squares method, and then the model is checked by a plurality of preset check criteria to correct the model.
Further, step S206 trains the multiple regression model to delete the influence factor data with the correlation coefficient smaller than the threshold value with the air outlet dust pressure data, and matches the weight with the influence factor data with the correlation coefficient greater than or equal to the threshold value with the air outlet dust pressure data, and the obtaining the regression equation includes:
step S2062: acquiring one or more check standards of the correlation of preset influence factor data and air outlet dust pressure data;
step S2064: calculating a correlation coefficient of each influence factor data and the air outlet dust pressure data based on the inspection standard;
step S2066: deleting influence factor data of which the correlation coefficient with the dust pressure data of the air outlet is smaller than a threshold value;
step S2068: training the multiple regression model, and matching weights for the influence factor data with the correlation coefficient of the air outlet dust pressure data being greater than or equal to the threshold value to obtain a regression equation.
In an alternative, the test criteria comprises a goodness-of-fit test, wherein the goodness-of-fit test comprises: calculating the determinable coefficient R 2 ' and a complex correlation coefficient R, deleting the influence factor data of which the complex correlation coefficient R is smaller than a threshold value with the air outlet dust pressure data, wherein
Wherein SSR is the sum of squares of regression, SST is the sum of squares of total dispersion, SSE is the sum of squares of residual error, y i As a function of the actual value of the variable,fitting values for dependent variables +.>The coefficient R can be determined as the mean value of dependent variables 2 The value of (2) is between 0 and 1.
In an alternative, the test criteria comprises an F test, wherein the F test comprises: calculating a test statistic F to determine if the independent variable as a whole has a significant effect with the dependent variable, wherein:
wherein SSR is the sum of squares of regression, n is the number of samples, k is the number of limiting conditions, a is the confidence coefficient, F a/2 Representing the quantile of the density function, when the check value F is greater than the critical value F α/2 (k, n-1-k) prove x 1 ,x 2 ,…，x k The linear relationship with y is significant.
Further, when the test statistic F is smaller than the predetermined threshold, it indicates that the independent variable as a whole cannot generate a significant effect with the dependent variable, so that it is necessary to re-extract a plurality of influence factor data for determining a correlation with the air outlet dust pressure data, so as to ensure that the extracted influence factor data is an accurate and comprehensive factor capable of influencing the birth opening dust pressure, and ensure the accuracy of the model.
In an alternative, the test criteria comprises a t-test, wherein the t-test comprises: calculating a significance variable ti, and deleting the influence factor data of which the significance variable ti is smaller than a threshold value with the air outlet dust pressure data, wherein:
wherein c ii Is a matrix (X) T X) -1 [X＝(x 1 ,x 2 ,···,x k )]The ith element, x, on the diagonal i From the selected samples, bi is the regression parameter, k is the number of constraints, y i As a function of the actual value of the variable,fitting the values for the dependent variables,is the significance threshold, when->When bi is not considered to be significant 0, i.e., the linear effect of the independent variable xi on the dependent variable y is significant.
In an alternative solution, after the steps of performing the goodness-of-fit test, the F test, and the t test, or deleting the influence factor data having the correlation coefficient with the air outlet dust pressure data smaller than the threshold value, the coal mill pipeline blockage status determining method further includes the steps of:
acquiring the minimum number of preset influence factor data related to the air outlet dust pressure data;
and when the number of the influence factor data with the correlation coefficient larger than or equal to the threshold value with the air outlet dust pressure data is not the minimum number, re-executing the step of acquiring the historical operation data of the coal mill and extracting the air outlet dust pressure data and a plurality of influence factor data related to the air outlet dust pressure data. Through the steps, enough influence factor data can be reserved in the multiple regression model, and the problem of model accuracy reduction caused by too few influence factor data is avoided.
Wherein, re-executing the acquisition of the historical operation data of the coal mill may include:
acquiring first historical operation data of the coal mill in a first historical period and second historical operation data of the coal mill in a second historical period;
calculating the similarity of the first historical operating data and the second historical operating data;
and when the number of the influence factor data which is determined according to the first historical operation data and has the correlation coefficient with the dust pressure data of the air outlet more than or equal to the threshold value does not reach the minimum number, acquiring second historical operation data with the similarity with the first historical operation data less than the threshold value. Through the steps, the problem that an accurate multiple regression model cannot be effectively built when the corresponding operation data of the first historical period is abnormal can be avoided. By selecting the operation data of the second historical period with smaller similarity with the operation data of the first historical period, the data singleness can be automatically avoided, and the effectiveness of multiple regression model training is improved.
FIG. 3 is a flow chart of an alternative coal pulverizer line blockage status determination method, as can be appreciated from FIG. 3, the coal pulverizer line blockage status determination method can include:
(1) The basic data information of the coal mill is based on an SIS real-time database, and basic data of the coal mill is acquired through an API programming program, and mainly comprises unit load, hot primary air duct wind pressure, primary air pressure of the coal mill, outlet wind powder pressure of the coal mill, current of the coal mill, coal quantity of the coal mill and the like.
(2) Screening and mainly analyzing a dust pressure event set of the mouth wind according to conditions such as index units, available areas, normal/abnormal time periods and the like;
(3) And taking the influence factors as independent variables, taking the outlet wind dust pressure as the dependent variables, and carrying out regression parameter estimation by adopting a least square method to obtain a multiple regression model of the outlet wind dust pressure and the outlet wind dust pressure influence factors.
The multiple regression equation is:
for outlet wind dust pressure, x k Is the influencing factor of the dust pressure of the outlet wind, +.>Is a regression parameter.
(4) And (3) checking the model, wherein the built model is subjected to fitting goodness test, F test and t test so as to verify whether the built model is an optimal model.
1) Fitting goodness test
The determinable coefficients may be defined:
SSR is the sum of squares of the regression, SST is the sum of squares of the total dispersion, SSE is the sum of squares of the residual error. The calculation formula is as follows:
y i as the dependent variable actual value，Fitting values for dependent variables +.>Is the mean value of the dependent variable.
Determinable coefficient R 2 The value of R is between 0 and 1 2 The closer to 1, the better the regression fit is shown.
Complex correlation coefficient:
to measure the significance of the linear relationship of all independent variables to the random variable y.
2) F test, t test
Principal check argument x 1 ,x 2 ,…，x k And whether a significant linear relation exists between the equation and the dependent variable y, namely, equation significance test.
The F test statistic is:
where n is the number of samples. Given a level of significance, when the test value F is greater than the threshold value F α/2 (k, n-1-k) prove x 1 ,x 2 ,…，x k The linear relationship with y is significant. Equation significance testing may also be performed using the P-value test method.
To reject the minor, insignificant independent variables, a saliency check of the regression equation coefficients, i.e., a parametric saliency check, is also performed:
c ii is a matrix (X) T X) -1 [X＝(x 1 ,x 2 ,···,x k )]The i-th element on the diagonal.
When |t i |≥t α/2 When think of b i Not significantly 0, i.e. independent variable x i The linear effect on the dependent variable y is significant. Parameter significance testing can also be performed using a 2-fold test and a P-value test.
The F test is to test whether there is a significant effect between the independent variable as a whole and the dependent variable, and when the F test passes, it does not represent that each independent variable has a significant effect on the dependent variable, but the t test is to test whether each independent variable has a significant effect on the dependent variable, so the t test is performed based on the F test. If the parameter estimates do not pass the t-test, the model may exhibit multiple collinearity.
(5) Multiple co-linearity analysis, i.e., a linear model, there is a strong correlation of one or more variables, resulting in model instability. In order to avoid inaccurate model estimation caused by the existence of multiple collinearity when estimating regression model parameters, a stepwise regression method is adopted to select independent variables, find out the explanatory variable causing multiple collinearity, and exclude the explanatory variable.
(6) And (3) a regression equation finally obtained through a stepwise regression method, namely a coal mill air outlet dust pressure influence factor analysis model.
(7) According to the analysis model of the influence factors of the dust pressure of the air outlet of the coal mill, the unit load, the wind pressure of the hot primary wind main pipe, the primary wind pressure of the coal mill, the current of the coal mill, the coal quantity of the coal mill and other influence factors are obtained when the coal mill runs, and the state of the dust pressure of the air outlet of the coal mill can be evaluated, so that the blocking state of the coal mill is predicted.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.
From the description of the above embodiments, it will be clear to those skilled in the art that the method for determining the clogging state of a coal pulverizer pipe according to the above embodiments may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method of the embodiments of the present application.
Example 2
There is also provided, in accordance with an embodiment of the present application, a coal pulverizer pipe blockage status determining apparatus for implementing the above-described coal pulverizer pipe blockage status determining method, as shown in fig. 4, the apparatus 400 including: the acquisition module 4002, the creation module 4004 and the training module 4006. Wherein:
the acquisition module 4002 is used for acquiring historical operation data of the coal mill and extracting air outlet dust pressure data and a plurality of influence factor data related to the air outlet dust pressure data;
The establishing module 4004 is configured to establish a multiple regression model by using the air outlet dust pressure data as a dependent variable and using a plurality of influence factor data related to the air outlet dust pressure data as independent variables;
the training module 4006 is configured to train the multiple regression model to delete the influence factor data whose correlation coefficient with the air outlet dust pressure data is smaller than the threshold value, and match the weight with the influence factor data whose correlation coefficient with the air outlet dust pressure data is greater than or equal to the threshold value, so as to obtain a regression equation;
the regression equation is used for evaluating the current air outlet dust pressure data of the coal mill according to the current operation data of the coal mill so as to determine the blocking state of the pipeline of the coal mill.
Here, it should be noted that the acquisition module 4002, the establishment module 4004 and the training module 4006 correspond to steps S202 to S206 in embodiment 1, and the three modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to those disclosed in embodiment 1. It should be noted that the above-described module may be operated as a part of the apparatus in the computer terminal 10 provided in embodiment 1.
In an alternative, the plurality of influencing factor data related to the air outlet dust pressure data includes at least one of: unit load, hot primary air duct wind pressure, primary air pressure of the coal mill, outlet wind powder pressure of the coal mill, current of the coal mill and coal quantity of the coal mill.
In an alternative, the apparatus 400 further comprises a screening module for screening the set of outlet wind dust pressure events according to index units, available area, normal time periods, and/or abnormal time periods.
In an alternative, the establishment module 4004 is configured to construct a multiple regression equation,
wherein,for outlet wind dust pressure, x k Is the influencing factor of the dust pressure of the outlet wind, +.>Is a regression parameter.
In an alternative, the training module 4006 further includes: the device comprises an acquisition unit, a calculation unit, a deletion unit and a training unit, wherein:
the acquisition unit is used for acquiring one or more inspection standards of the correlation between the preset influence factor data and the air outlet dust pressure data;
the calculating unit is used for calculating the correlation coefficient of each influence factor data and the air outlet dust pressure data based on the inspection standard;
the deleting unit is used for deleting the influence factor data of which the correlation coefficient with the dust pressure data of the air outlet is smaller than a threshold value;
the training unit is used for training the multiple regression model, and matching weights with influence factor data of which the correlation coefficient with the dust pressure data of the air outlet is greater than or equal to a threshold value to obtain a regression equation.
Here, the acquisition unit, the calculation unit, the deletion unit, and the training unit correspond to steps S2062 to S2068 in embodiment 1, and the four units are the same as the examples and application scenarios achieved by the corresponding steps, but are not limited to those disclosed in embodiment 1. It should be noted that the above-described module may be operated as a part of the apparatus in the computer terminal 10 provided in embodiment 1.
Example 3
Embodiments of the present application may provide a computing device, which may be any one of a group of computer terminals. Alternatively, in this embodiment, the above-mentioned computing device may be replaced by a terminal device such as a mobile terminal.
Alternatively, in this embodiment, the computing device may be located in at least one network device of a plurality of network devices of the computer network.
Optionally, in this embodiment, the computing device includes one or more processors, a memory, and a transmission means. The memory may be used to store software programs and modules, such as program instructions/modules corresponding to the method and apparatus for determining a coal pulverizer pipeline blockage status in the embodiments of the present application. The processor executes various functional applications and data processing by running software programs and modules stored in the memory, namely, the method for determining the pipeline blockage state of the coal mill is realized.
Alternatively, the memory may comprise high-speed random access memory, and may also comprise non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory remotely located with respect to the processor, which may be connected to the computing device 120 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
In this embodiment, the processor in the computing device may execute the following method steps when running the stored program code: acquiring historical operation data of a coal mill, and extracting air outlet dust pressure data and a plurality of influence factor data related to the air outlet dust pressure data; taking the dust pressure data of the air outlet as a dependent variable, and taking a plurality of influence factor data which have a correlation with the dust pressure data of the air outlet as independent variables, and establishing a multiple regression model; training the multiple regression model to delete the influence factor data with the correlation coefficient smaller than the threshold value with the dust pressure data of the air outlet, and matching weights with the influence factor data with the correlation coefficient larger than or equal to the threshold value with the dust pressure data of the air outlet to obtain a regression equation; the regression equation is used for evaluating the current air outlet dust pressure data of the coal mill according to the current operation data of the coal mill so as to determine the blocking state of the pipeline of the coal mill.
Further, in this embodiment, any method steps listed in embodiment 1 may be executed when the processor in the computing device executes the stored program code, which is not described in detail herein.
Example 4
Embodiments of the present application also provide a storage medium. Alternatively, in this embodiment, the storage medium may be used to store program code for execution by the coal pulverizer line blockage status determination method.
Alternatively, in this embodiment, the storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: acquiring historical operation data of a coal mill, and extracting air outlet dust pressure data and a plurality of influence factor data related to the air outlet dust pressure data; taking the dust pressure data of the air outlet as a dependent variable, and taking a plurality of influence factor data which have a correlation with the dust pressure data of the air outlet as independent variables, and establishing a multiple regression model; training the multiple regression model to delete the influence factor data with the correlation coefficient smaller than the threshold value with the dust pressure data of the air outlet, and matching weights with the influence factor data with the correlation coefficient larger than or equal to the threshold value with the dust pressure data of the air outlet to obtain a regression equation; the regression equation is used for evaluating the current air outlet dust pressure data of the coal mill according to the current operation data of the coal mill so as to determine the blocking state of the pipeline of the coal mill.
Further, in the present embodiment, the storage medium is configured to store the program code for performing any of the method steps listed in embodiment 1, which will not be repeated for the sake of brevity.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and are merely a logical functional division, and there may be other manners of dividing the apparatus in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application and are intended to be comprehended within the scope of the present application.
Claims (8)
1. A method for determining a blockage condition of a coal pulverizer pipeline, comprising:
acquiring historical operation data of a coal mill, and extracting air outlet dust pressure data and a plurality of influence factor data related to the air outlet dust pressure data;
taking the dust pressure data of the air outlet as a dependent variable, and taking a plurality of influence factor data which have a correlation with the dust pressure data of the air outlet as independent variables, and establishing a multiple regression model;
acquiring one or more check standards of the correlation of preset influence factor data and air outlet dust pressure data;
calculating the correlation coefficient of each influence factor data and the air outlet dust pressure data based on the inspection standard;
deleting influence factor data of which the correlation coefficient with the dust pressure data of the air outlet is smaller than a threshold value; training the multiple regression model, constructing the following multiple regression equation for matching weights with influence factor data of which the correlation coefficient with the dust pressure data of the air outlet is more than or equal to a threshold value,
Wherein,for outlet wind dust pressure, x k Is the influencing factor of the dust pressure of the outlet wind, +.>Is a regression parameter;
the regression equation is used for evaluating the current air outlet dust pressure data of the coal mill according to the current operation data of the coal mill so as to determine the blocking state of the pipeline of the coal mill.
2. The method of claim 1, wherein the plurality of influencing factor data related to the outlet dust pressure data comprises at least one of: unit load, hot primary air duct wind pressure, primary air pressure of the coal mill, outlet wind powder pressure of the coal mill, current of the coal mill and coal quantity of the coal mill.
3. The method of claim 1, wherein prior to establishing the multiple regression model using the outlet dust pressure data as a dependent variable and using a plurality of influencing factor data associated with the outlet dust pressure data as independent variables, the method further comprises:
and screening the outlet wind dust pressure event set according to index units, available areas, normal time periods and/or abnormal time periods.
4. The method of claim 1, wherein the test criteria include at least one of a goodness-of-fit test for measuring the degree of significance of the linear relationship of all independent variables to the random variable y, an F test for measuring whether there is a significant effect between the independent variable as a whole and the dependent variable, and a t test for testing whether each independent variable has a significant effect on the dependent variable.
5. The method of claim 1, wherein when the test criteria comprises a goodness-of-fit test, the goodness-of-fit test method comprises: calculating a determinable coefficient R2 and a complex correlation coefficient R, deleting the influence factor data of which the complex correlation coefficient R with the air outlet dust pressure data is smaller than a threshold value, wherein
Wherein: SSR is the sum of squares of regression, SST is the sum of squares of total dispersion, SSE is the sum of squares of residual error, y i As a function of the actual value of the variable,fitting values for dependent variables +.>The value of the determinable coefficient R2 is between 0 and 1 as the average value of dependent variables;
when the test criteria include F-test, the F-test method includes: calculating a test statistic F to determine whether the independent variable as a whole has a significant effect with the dependent variable, and re-extracting a plurality of influence factor data related to the dust pressure data of the air outlet when the test statistic F is smaller than a preset threshold value;
wherein:
wherein SSR is the sum of squares of regression, n7 is the number of samples, k is the number of limiting conditions, a is the confidence,the quantile representing the density function when the test value F is greater than the threshold value +.>When it indicates x 1 ，x 2 ，...，x k The linear relation between the three-dimensional coordinate system and y is obvious;
when the test criteria include a t-test, the t-test method includes: calculating a significance variable ti, and deleting the influence factor data of which the significance variable ti is smaller than a threshold value with the air outlet dust pressure data, wherein:
Wherein c ii Is a matrix (X) T X) -1 [X＝(x 1 ，x 2 ，…，x k )]The ith element, x, on the diagonal i From the selected samples, bi is the regression parameter, k is the number of constraints, y i As a function of the actual value of the variable,fitting values for dependent variables +.>Is the significance threshold, when->When the independent variable x i has a significant linear influence on the dependent variable y.
6. A coal pulverizer line blockage status determination device, comprising:
the acquisition module is used for acquiring historical operation data of the coal mill and extracting air outlet dust pressure data and a plurality of influence factor data related to the air outlet dust pressure data;
the building module is used for building a multiple regression model by taking the air outlet dust pressure data as a dependent variable and taking a plurality of influence factor data which have a correlation with the air outlet dust pressure data as independent variables;
the training module is used for acquiring one or more detection standards of the correlation between the preset influence factor data and the air outlet dust pressure data; calculating the correlation coefficient of each influence factor data and the air outlet dust pressure data based on the inspection standard; deleting influence factor data of which the correlation coefficient with the dust pressure data of the air outlet is smaller than a threshold value; training the multiple regression model, constructing the following multiple regression equation for matching weights with influence factor data of which the correlation coefficient with the dust pressure data of the air outlet is more than or equal to a threshold value,
Wherein,for outlet wind dust pressure, x k Is the influencing factor of the dust pressure of the outlet wind, +.>Is a regression parameter;
the regression equation is used for evaluating the current air outlet dust pressure data of the coal mill according to the current operation data of the coal mill so as to determine the blocking state of the pipeline of the coal mill.
7. A storage medium comprising a stored program, wherein the program, when run, controls a device in which the storage medium is located to perform the method of any one of claims 1-5.
8. A computing device comprising a processor, wherein the processor is configured to run a program, wherein the program, when run, performs the method of any of claims 1-5.
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| CN113578508B (en) * | 2021-07-14 | 2022-11-01 | 河北西柏坡发电有限责任公司 | Logic optimization method, device and system for protecting air volume at inlet of coal mill |
| CN114229485B (en) * | 2021-12-23 | 2024-06-07 | 沈阳昱铖科技有限公司 | PVC (polyvinyl chloride) dry bed powder conveying device and control method |
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