CN115977633A - Multi-information fusion feedback-based jet flow and cutting cooperative regulation and control method - Google Patents
Multi-information fusion feedback-based jet flow and cutting cooperative regulation and control method Download PDFInfo
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Abstract
The invention relates to a method for cooperatively regulating and controlling jet flow and cutting based on multi-information fusion feedback, which comprises the following steps: establishing a database which needs jet flow in different coal mining states, and acquiring signals of a coal mining machine in operation through the database to obtain the linear and nonlinear relation of each signal; obtaining the change slope of each signal in different time periods through the linear and nonlinear relation of each signal, and obtaining the weight of each signal based on the change slope; optimizing the weight through a CNN neural network, taking an optimization result as an input value of a D-S evidence theory, outputting a target weight coefficient, and judging the jet flow of the coal mining machine according to the target weight coefficient. According to the invention, the time when the coal mining machine should jet is effectively analyzed through the weighted values of the angle signals, so that the optimal effect is achieved.
Description
Technical Field
The invention relates to the technical field of intelligent control of coal mining machines, in particular to a method for cooperative regulation and control of jet flow and cutting based on multi-information fusion feedback.
Background
The controllable and efficient mining of the hard coal seam is one of major problems which cannot be avoided in the coal mining process, and the mining problem of the hard coal seam is increasingly prominent along with the reduction of the total reserve of coal. China, northern Shaanxi, has abundant coal resources which account for more than 30 percent of national coal resources, and the coal seam is buried shallowly and has high heat productivity, but the coal seam in the area has high hardness, compact structure and no crack growth, so that the existing mechanized mining equipment has serious abrasion, poor mining safety and low efficiency, and the main manifestations are as follows: (1) Cutting dust is much, cutting sparks are uncontrollable, and dynamic disasters such as dust explosion and the like are easy to happen; (2) The coal mining machine has the advantages of large cutting energy consumption, serious cutting tooth loss, low cutting speed and high failure rate, greatly reduces the production efficiency and seriously influences the coal mining process.
Therefore, the realization of controllable and efficient mining of the hard coal seam has great significance for improving the recovery rate of the coal mine. The water jet assisted cutting and rock breaking technology can reduce coal dust generation, eliminate explosion hidden danger, has good cracking effect on coal walls, can effectively reduce cutting energy consumption of a coal mining machine, and is an important technical means for realizing high-efficiency safe mining of hard coal beds.
Disclosure of Invention
The invention provides a method for the cooperative regulation and control of jet flow and cutting based on multi-information fusion feedback, which aims to achieve the purposes of preventing cutting dust, controlling cutting sparks, preventing dust explosion, reducing the cutting energy consumption and cutting tooth loss of a coal mining machine, accelerating the cutting speed, reducing the failure rate, improving the production efficiency and accelerating the coal mining process.
In order to achieve the purpose, the invention provides the following scheme:
a method for jet flow and cutting cooperative regulation and control based on multi-information fusion feedback comprises the following steps:
establishing a database which needs jet flow in different coal mining states, and acquiring signals of a coal mining machine in operation through the database to obtain the linear and nonlinear relations of the signals;
obtaining the change slope of each signal in different time periods through the linear and nonlinear relation of each signal, and obtaining the weight of each signal based on the change slope;
optimizing the weight through a CNN neural network, taking an optimization result as an input value of a D-S evidence theory, outputting a target weight coefficient, and judging the jet flow of the coal mining machine according to the target weight coefficient.
Preferably, the database comprises a rocker arm signal characteristic database, a traction motor and cutting motor signal characteristic database, a machine body signal characteristic database and a cutting pick signal characteristic database.
Preferably, the collecting the signals of the coal mining machine during operation comprises:
the method comprises the steps of respectively collecting a rocker arm inclination angle signal, a dynamic torque signal, a temperature signal, a current signal, a voltage signal, an acceleration signal, a machine body inclination angle signal and a pressure signal through a rocker arm inclination angle sensor, a dynamic torque sensor, a temperature sensor, a current sensor, a voltage sensor, an acceleration sensor, a machine body inclination angle sensor and a pressure sensor of the coal mining machine.
Preferably, obtaining the change slope of each signal in different time periods comprises:
and utilizing Origin software to perform nonlinear fitting on a rocker arm signal characteristic database, a traction motor and cutting motor signal characteristic database, a machine body signal characteristic database and a pick signal characteristic database, analyzing linear data by utilizing an nlinfit function in matlab, and combining two software analysis results to obtain the change slope of each signal in different time periods.
Preferably, according to the change slope, the weight distribution of the signals in different states is obtained:
wherein, K i ,K j For the slope of the change in the sensor signal, j represents eight different signals.
Preferably, obtaining the weight of each signal comprises:
and respectively calculating the weights of different signals under the same working condition based on the weight distribution formulas of the signals under different states, and determining the preference characteristics of the signals under different cutting ratios according to the weight values.
Preferably, the obtained weight is added to the CNN neural network for primary data weight optimization, the output result of the CNN neural network is used as the input value of the D-S evidence theory, the obtained weight is used for optimizing the weight in the D-S evidence theory again, a target weight coefficient is output, and coal cutter jet flow judgment is performed according to the target weight coefficient.
The beneficial effects of the invention are as follows:
according to the method, the signals of multiple sensors are compared, the linear relation of each signal is determined, analysis is carried out through Origin and matlab software to obtain the change slopes of different signals, the weight of each signal is determined, the problems that the result is not timely, is not smooth and is not accurate and the like are effectively solved through a single sensor signal or according to single data such as motor power and the traction force of a coal mining machine in the jet flow decision process of the coal mining machine, and the weight values of multiple angle signals are used for effectively analyzing when the coal mining machine is required to jet flow, so that the optimal effect is achieved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is an overall flowchart of a method for cooperative regulation and control of jet flow and cutting based on multi-information fusion feedback in an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a flow of a specific signal processing process of a method for cooperative regulation and control of jet flow and cutting based on multi-information fusion feedback in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a fusion method of a jet flow and cutting cooperative regulation method based on multi-information fusion feedback in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Referring to fig. 1-3, the invention provides a method for cooperative regulation of jet flow and cutting based on multi-information fusion feedback, which analyzes the characterization conditions of each signal, analyzes whether the same signal is in a linear relation in different cutting states, calculates the weight distribution of each signal in different states by using a data linear or nonlinear relation, determines the preference characteristics of the jet flow according to the current signal weight distribution value, optimizes a multi-sensing information fusion method by using a weight value, then fuses a rocker arm inclination angle signal, a dynamic torque signal, a temperature signal, a current signal, a voltage signal, an acceleration signal, a machine body inclination angle signal and a pressure signal which are collected when a coal mining machine works, improves the reliability and accuracy of the rocker arm inclination angle signal, the dynamic torque signal, the temperature signal, the current signal, the voltage signal, the acceleration signal, the machine body inclination angle signal and the pressure signal under different coal rock hardness conditions, and further improves the jet flow judgment accuracy.
Specifically, the method for the jet flow and cutting cooperative regulation based on multi-information fusion feedback is composed of four parts, namely a jet flow condition classification part, a data test part, a data acquisition and processing part, a data fitting and weight distribution part and a multi-sensing information fusion part, and jet flow decision is realized by utilizing the relevance and the certainty existing between the parts.
And data testing, namely acquiring and processing a rocker arm inclination angle sensor, a dynamic torque sensor, a temperature sensor, a current sensor, a voltage sensor, an acceleration sensor, a machine body inclination angle sensor and a pressure sensor which are installed when the coal mining machine works, acquiring a rocker arm inclination angle signal, a dynamic torque signal, a temperature signal, a current signal, a voltage signal, an acceleration signal, a machine body inclination angle signal and a pressure signal, performing local feature extraction and analysis on the signals by combining a wavelet packet decomposition and reconstruction method, optimizing a data sample, and constructing a corresponding rocker arm signal feature database, a traction motor and cutting motor signal feature database, a machine body signal feature database and a cutting tooth signal feature database.
The rocker arm signals are monitoring signals of a rocker arm inclination angle sensor and a dynamic torque sensor.
The signals of the traction motor and the cutting motor are the monitoring signals of the corresponding temperature sensor, current sensor and voltage sensor.
The fuselage signals are acceleration sensor and fuselage inclination sensor monitoring signals.
Pick signal is the pressure sensor signal.
Data fitting and weight distribution, namely, firstly, eight signals under different cutting states need to be analyzed and analogized, and Origin software is utilized to carry out nonlinear fitting on a rocker arm signal characteristic database, a traction motor and cutting motor signal characteristic database, a machine body signal characteristic database and a cutting pick signal characteristic database; for linear data, nlifit function analysis in matlab can be utilized, two software analyses are combined to obtain linear or nonlinear relations among signals, change slopes of the signals in different time periods are obtained, and weight distribution of the signals in different states is obtained according to the change slopes, and the following formula is shown:
in the formula K i ,K j The formula has normalization, weights of different signals under the same working condition can be respectively obtained, preference characteristics of the signals under different cutting ratios are determined according to the weight values, then the reliability of the signals under different coal rock hardness is improved, and the multi-sensing information fusion method for judging when the coal mining machine jets is used is optimized by using the weight preference values obtained by the signals.
The obtained signal weight values are added into a CNN neural network for primary data weight optimization, the output result of the CNN neural network is used as the input value of a D-S evidence theory, and the obtained signal weight values are optimized for the weights in the D-S evidence theory again, so that the traditional equal weight proportion distribution method can be abandoned, and the preference characteristics of signals in different states are more prominent.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (7)
1. A method for cooperative regulation and control of jet flow and cutting based on multi-information fusion feedback is characterized by comprising the following steps:
establishing a database needing jet flow in different coal mining states, and acquiring signals of a coal mining machine during operation through the database to obtain the linear and nonlinear relation of each signal;
obtaining the change slope of each signal in different time periods through the linear and nonlinear relation of each signal, and obtaining the weight of each signal based on the change slope;
optimizing the weight through a CNN neural network, taking an optimization result as an input value of a D-S evidence theory, outputting a target weight coefficient, and judging the jet flow of the coal mining machine according to the target weight coefficient.
2. The method for the cooperative regulation and control of jet flow and cutting based on the multi-information fusion feedback as claimed in claim 1, wherein the database comprises a rocker arm signal characteristic database, a traction motor and cutting motor signal characteristic database, a machine body signal characteristic database and a cutting pick signal characteristic database.
3. The method for the cooperative regulation and control of jet flow and cutting based on multi-information fusion feedback as claimed in claim 1, wherein the collecting the signals of the coal mining machine during operation comprises:
the method comprises the steps of respectively collecting a rocker arm inclination angle signal, a dynamic torque signal, a temperature signal, a current signal, a voltage signal, an acceleration signal, a machine body inclination angle signal and a pressure signal through a rocker arm inclination angle sensor, a dynamic torque sensor, a temperature sensor, a current sensor, a voltage sensor, an acceleration sensor, a machine body inclination angle sensor and a pressure sensor of the coal mining machine.
4. The method for the cooperative regulation and control of jet flow and cutting based on multi-information fusion feedback as claimed in claim 1, wherein obtaining the change slope of each signal in different time periods comprises:
and carrying out nonlinear fitting on a rocker arm signal characteristic database, a traction motor and cutting motor signal characteristic database, a machine body signal characteristic database and a pick signal characteristic database by using Origin software, analyzing linear data by using an nlifit function in matlab, and combining the analysis results of the two softwares to obtain the change slope of each signal in different time periods.
5. The method for the cooperative regulation and control of jet flow and cutting based on multi-information fusion feedback as claimed in claim 4, wherein the weight distribution of the signals under different states is obtained according to the change slope:
wherein, K i ,K j For the slope of the change in the sensor signal, j represents eight different signals.
6. The method for the cooperative regulation of jetting and cutting based on multi-information fusion feedback as claimed in claim 5, wherein obtaining the weight of each signal comprises:
and respectively calculating the weights of different signals under the same working condition based on the weight distribution formulas of the signals under different states, and determining the preference characteristics of the signals under different cutting ratios according to the weight values.
7. The method for the cooperative regulation of jet flow and cutting based on multi-information fusion feedback as claimed in claim 1, wherein the obtained weight is added to the CNN neural network for primary data weight optimization, the output result of the CNN neural network is used as the input value of the D-S evidence theory, the obtained weight is used for optimizing the weight in the D-S evidence theory again, a target weight coefficient is output, and the coal mining machine jet flow judgment is performed according to the target weight coefficient.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117335689A (en) * | 2023-11-24 | 2024-01-02 | 太原理工大学 | Moment optimal control method for cutting part of multi-servo driving coal mining machine |
| CN117432414A (en) * | 2023-12-20 | 2024-01-23 | 中煤科工开采研究院有限公司 | Method and system for regulating and controlling top plate frosted jet flow seam formation |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117335689A (en) * | 2023-11-24 | 2024-01-02 | 太原理工大学 | Moment optimal control method for cutting part of multi-servo driving coal mining machine |
| CN117335689B (en) * | 2023-11-24 | 2024-02-20 | 太原理工大学 | Moment optimal control method for cutting part of multi-servo driving coal mining machine |
| CN117432414A (en) * | 2023-12-20 | 2024-01-23 | 中煤科工开采研究院有限公司 | Method and system for regulating and controlling top plate frosted jet flow seam formation |
| CN117432414B (en) * | 2023-12-20 | 2024-03-19 | 中煤科工开采研究院有限公司 | Method and system for regulating and controlling top plate frosted jet flow seam formation |
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