阅读说明：本技术 基于煤量检测的皮带及采煤机的自动调速方法 (Automatic speed regulating method of belt and coal mining machine based on coal quantity detection ) 是由 张健 尚军科 宋彦东 方鹏华 季伟境 朱仁涛 缪亚辉 邢植信 安洋 李涵阳 于 2021-10-27 设计创作，主要内容包括：本发明公开了一种基于煤量检测的皮带运输机及采煤机的自动调速方法,包括：进行皮带运输机的煤流量检测；进行皮带运输机的状态数据信息收集；进行采煤机的状态数据信息收集；根据皮带运输机的煤流量检测、皮带运输机的状态数据信息收集以及采煤机的状态数据信息收集建立算法模型；以及根据算法模型,得到皮带运输机的煤流量控制阈值和采煤机的速度控制阈值,从而控制皮带运输机和采煤机的速度。本发明的基于煤量检测的皮带运输机及采煤机的自动调速方法,能够可根据煤量检测以及采煤机状态数据信息,实现皮带和采煤机的优化控制。(The invention discloses an automatic speed regulating method of a belt conveyor and a coal mining machine based on coal quantity detection, which comprises the following steps: detecting the coal flow of the belt conveyor; collecting state data information of the belt conveyor; collecting state data information of the coal mining machine; establishing an algorithm model according to coal flow detection of a belt conveyor, state data information collection of the belt conveyor and state data information collection of a coal mining machine; and obtaining a coal flow control threshold value of the belt conveyor and a speed control threshold value of the coal mining machine according to the algorithm model, so as to control the speeds of the belt conveyor and the coal mining machine. The automatic speed regulating method of the belt conveyor and the coal mining machine based on the coal quantity detection can realize the optimal control of the belt and the coal mining machine according to the coal quantity detection and the state data information of the coal mining machine.)

1. An automatic speed regulation method of a belt conveyor and a coal mining machine based on coal quantity detection is characterized by comprising the following steps:

detecting the coal flow of the belt conveyor;

collecting state data information of the belt conveyor;

collecting state data information of the coal mining machine;

establishing an algorithm model according to the coal flow detection of the belt conveyor, the state data information collection of the belt conveyor and the state data information collection of the coal mining machine; and

and obtaining a coal flow control threshold value of the belt conveyor and a speed control threshold value of the coal mining machine according to the algorithm model, so as to control the speeds of the belt conveyor and the coal mining machine.

2. The method for automatically adjusting the speed of a belt conveyor and a shearer loader based on coal amount detection as claimed in claim 1, wherein the coal flow detection of the belt conveyor is in the form of image analysis technology and a camera is mounted above the belt conveyor.

3. The method for automatically adjusting the speed of a belt conveyor and a shearer based on coal amount detection as claimed in claim 2, wherein the coal flow detection of the belt conveyor comprises mobile phone data through a data acquisition unit and outputting the collected data through a data detection unit in real time.

4. The method for automatically regulating the speed of a belt conveyor and a shearer based on coal amount detection as claimed in claim 1, wherein the collecting of the state data information of the belt conveyor includes collecting information of speed, width and temperature of a belt of the belt conveyor.

5. The method for automatically regulating the speed of a belt conveyor and a shearer based on coal quantity detection as claimed in claim 1, wherein the shearer state data information collection comprises collecting shearer speed, position and process step information.

6. The method for automatically regulating the speed of a belt conveyor and a shearer based on coal amount detection as claimed in claim 1, wherein the establishing an algorithm model comprises:

calculating the total transportation volume of the belt conveyor in real time by using the state data information of the belt conveyor and the coal flow information of the belt conveyor;

analyzing according to historical data of the state data information of the belt conveyor, and obtaining a historical data set of the state data information of the belt conveyor;

analyzing historical data of coal flow information correspondingly obtained according to the state data information of the coal mining machine, and obtaining a historical data set of the coal flow information; and

and carrying out model building and training on the historical data set of the state data information of the belt conveyor and the historical data set of the coal flow information to obtain a trained model, and controlling the automatic speed regulation of the belt conveyor and the coal mining machine by using the trained model.

7. The method for automatically governing a belt conveyor and a shearer based on coal quantity detection as recited in claim 6, wherein controlling the automatic governing of the conveyor and the shearer with the trained models comprises:

obtaining coal flow prediction of the belt conveyor within a few minutes in the future according to historical data of the state data information of the belt conveyor and a historical data set of the coal flow information;

according to the coal flow prediction, the state data information of the coal mining machine and the coal flow real-time data of the belt conveyor, obtaining a speed control threshold range of the belt conveyor and the coal mining machine, and according to the speed control threshold range, carrying out speed control on the belt conveyor and the coal mining machine;

when detecting that the real-time coal flow data of the belt conveyor exceeds a set threshold, controlling the belt conveyor to reduce the speed, and when detecting that the coal output of the coal mining machine is insufficient, controlling the coal mining machine to accelerate; and

when the belt conveyor breaks down, the coal mining machine is controlled to stop immediately, and coal piling is prevented.

8. The method for automatically regulating the speed of a belt conveyor and a shearer based on coal amount detection as claimed in claim 6, wherein the algorithm model comprises an LSSVM algorithm model, and the basic principle is as follows:

for training sample according to { x_i,y_i},i＝1,2,…,n,x_i∈R_nWhen nonlinear regression is carried out, a nonlinear mapping function (x) is introduced, and training samples are mapped to a high-dimensional characteristic space for linear regression; the LSSVM model in the feature space is represented as:

wherein, W is a weight vector, and k is an offset;

the objective function is:

wherein, delta is a prediction error variable of the training set, and theta >0 is a normalization parameter;

the constraint conditions to be met are as follows:

introducing Lagrange multiplier beta, and converting into Lagrange function:

according to the optimization conditions:

the following system of linear equations is obtained:

wherein the content of the first and second substances,is a kernel function, which is the inner product of a high-dimensional feature space;

the regression function formula of the LSSVM is obtained as follows:

different kernel functions are taken to form different LSSVM, and radial basis functions are selected As a kernel function of LSSVM.

Technical Field

The invention relates to the field of coal mining automation, in particular to a belt based on coal quantity detection and an automatic speed regulating method of a coal mining machine.

Background

At present, the belt conveyor driven by the variable frequency motor is more and more widely applied to coal mines, and the automatic mining degree of a coal mining machine is also improved. The variable frequency speed regulation of the existing belt conveyor is generally controlled manually, or sensors with low reliability, high interference and high cost are adopted for monitoring, but the variable frequency speed regulation is limited by practical conditions, and a plurality of belt variable frequency conveyor do not realize intelligent speed regulation, only play the role of a soft starter and do not reflect the differentiation and superiority of the drive of a frequency converter and other drive modes. Under the normal condition, the belt conveyor always keeps high-speed operation, the speed regulation is not carried out in a matching way according to the excavation condition of the coal mining machine, the belt is seriously abraded, the waste of electric energy is caused, and the loss speed of the belt is accelerated. In view of the above, it is desirable to design a belt and an automatic speed regulating method of a coal mining machine based on coal quantity detection to solve the above problems in the prior art.

The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

Disclosure of Invention

The invention aims to provide a belt conveyor and an automatic speed regulating method of a coal mining machine based on coal quantity detection, which can realize the optimal control of a belt and the coal mining machine according to the coal quantity detection and the state data information of the coal mining machine.

In order to achieve the aim, the invention provides an automatic speed regulating method of a belt conveyor and a coal mining machine based on coal quantity detection, which comprises the following steps: detecting the coal flow of the belt conveyor; collecting state data information of the belt conveyor; collecting state data information of the coal mining machine; establishing an algorithm model according to coal flow detection of a belt conveyor, state data information collection of the belt conveyor and state data information collection of a coal mining machine; and obtaining a coal flow control threshold value of the belt conveyor and a speed control threshold value of the coal mining machine according to the algorithm model, so as to control the speeds of the belt conveyor and the coal mining machine.

In a preferred embodiment, the coal flow detection of the belt conveyor takes the form of image analysis techniques and a camera mounted above the belt conveyor.

In a preferred embodiment, the coal flow detection of the belt conveyor comprises the steps of carrying out mobile phone data through the data acquisition unit and outputting the collected data in real time through the data detection unit.

In a preferred embodiment, the collecting of the status data information of the belt conveyor comprises collecting information of the speed, width and temperature of the belt conveyor.

In a preferred embodiment, the state data information collection of the shearer includes collecting the shearer's speed, position, and process step information.

In a preferred embodiment, establishing the algorithmic model comprises: calculating the total transportation volume of the belt conveyor in real time by using the state data information of the belt conveyor and the coal flow information of the belt conveyor; analyzing according to historical data of the state data information of the belt conveyor, and obtaining a historical data set of the state data information of the belt conveyor; analyzing historical data of coal flow information obtained correspondingly according to the state data information of the coal mining machine, and obtaining a historical data set of the coal flow information; and carrying out model building and training on the historical data set of the state data information of the belt conveyor and the historical data set of the coal flow information to obtain a trained model, and controlling the automatic speed regulation of the belt conveyor and the coal mining machine by using the trained model.

In a preferred embodiment, controlling the automatic timing of the conveyor and shearer with the trained model comprises: according to the historical data of the state data information of the belt conveyor and the historical data set of the coal flow information, the coal flow prediction of the belt conveyor in the future several minutes is obtained; according to the coal flow prediction, the state data information of the coal mining machine and the coal flow real-time data of the belt conveyor, obtaining the speed control threshold range of the belt conveyor and the coal mining machine, and according to the speed control threshold range, carrying out speed control on the belt conveyor and the coal mining machine; when the real-time coal flow data of the belt conveyor is detected to exceed a set threshold value, controlling the belt conveyor to reduce the speed, and when the coal output of the coal mining machine is detected to be insufficient, controlling the coal mining machine to accelerate; and when the belt conveyor breaks down, the coal mining machine is controlled to stop immediately, so that coal piling is prevented.

In a preferred embodiment, the algorithm model comprises an LSSVM algorithm model, the basic principle of which is as follows: for training sample according to { x_i,y_i},i＝1,2,…,n,x_i∈R_nWhen nonlinear regression is carried out, a nonlinear mapping function (x) is introduced, and training samples are mapped to a high-dimensional characteristic space for linear regression; the LSSVM model in the feature space is represented as:

wherein, W is a weight vector, and k is an offset;

the objective function is:

wherein, delta is a prediction error variable of the training set, and theta >0 is a normalization parameter;

the constraint conditions to be met are as follows:

introducing Lagrange multiplier beta, and converting into Lagrange function:

according to the optimization conditions:

the following system of linear equations is obtained:

wherein the content of the first and second substances,is a kernel function, which is the inner product of a high-dimensional feature space;

the regression function formula of the LSSVM is obtained as follows:

different kernel functions are taken to form different LSSVM, and radial basis functions are selected As a kernel function of LSSVM.

Compared with the prior art, the belt and the automatic speed regulation method of the coal mining machine based on the coal quantity detection have the following beneficial effects: the optimal control speed of the belt and the coal mining machine can be obtained according to coal quantity detection and prediction and comprehensive test of the model, and accordingly, the belt and the coal mining machine are automatically controlled in speed regulation, belt speed regulation of the belt conveyor is guaranteed to be consistent with the actual condition of mine production, electric energy consumption is reduced, mechanical abrasion is reduced, and therefore production cost is reduced, and the aims of saving energy and reducing consumption are achieved; meanwhile, the frequency conversion energy-saving function of the frequency converter can be fully exerted, the electric energy of the conveyor is saved, and the abrasion of the conveyor is reduced.

Drawings

FIG. 1 is a schematic diagram of an automatic governor method according to an embodiment of the present invention;

fig. 2 is a schematic diagram of the LSSVM model of the automatic speed regulation method according to an embodiment of the present invention with respect to input and output.

Detailed Description

The following detailed description of the present invention is provided in conjunction with the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the specific embodiments.

Throughout the specification and claims, unless explicitly stated otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or component but not the exclusion of any other element or component.

As shown in fig. 1, an automatic speed regulating method of a belt conveyor and a coal cutter based on coal amount detection according to a preferred embodiment of the present invention includes: detecting the coal flow of the belt conveyor; collecting state data information of the belt conveyor; collecting state data information of the coal mining machine; establishing an algorithm model according to coal flow detection of a belt conveyor, state data information collection of the belt conveyor and state data information collection of a coal mining machine; and obtaining a coal flow control threshold value of the belt conveyor and a speed control threshold value of the coal mining machine according to the algorithm model, so as to control the speeds of the belt conveyor and the coal mining machine.

In some embodiments, the coal flow detection of the belt conveyor takes the form of image analysis techniques and a camera mounted above the belt conveyor.

In some embodiments, the coal flow detection of the belt conveyor comprises mobile phone data through the data acquisition unit and outputting the collected data in real time through the data detection unit.

In some embodiments, the collection of belt conveyor status data information includes, but is not limited to, collecting information about the speed, width, and temperature of the belt conveyor.

In some embodiments, the state data information collection of the shearer includes, but is not limited to, collecting information of the shearer such as its speed, position, and process steps.

In some embodiments, establishing the algorithmic model comprises: calculating the total transportation volume of the belt conveyor in real time by using the state data information of the belt conveyor and the coal flow information of the belt conveyor; analyzing according to historical data of the state data information of the belt conveyor, and obtaining a historical data set of the state data information of the belt conveyor; analyzing historical data of coal flow information obtained correspondingly according to the state data information of the coal mining machine, and obtaining a historical data set of the coal flow information; and carrying out model building and training on the historical data set of the state data information of the belt conveyor and the historical data set of the coal flow information to obtain a trained model, and controlling the automatic speed regulation of the belt conveyor and the coal mining machine by using the trained model.

In some embodiments, controlling the automatic timing of the conveyor and shearer with the trained model comprises: according to the historical data of the state data information of the belt conveyor and the historical data set of the coal flow information, the coal flow prediction of the belt conveyor in the future several minutes is obtained; according to the coal flow prediction, the state data information of the coal mining machine and the coal flow real-time data of the belt conveyor, obtaining the speed control threshold range of the belt conveyor and the coal mining machine, and according to the speed control threshold range, carrying out speed control on the belt conveyor and the coal mining machine; when the real-time coal flow data of the belt conveyor is detected to exceed a set threshold value, controlling the belt conveyor to reduce the speed, and when the coal output of the coal mining machine is detected to be insufficient, controlling the coal mining machine to accelerate; and when the belt conveyor breaks down, the coal mining machine is controlled to stop immediately, so that coal piling is prevented.

In some embodiments, the algorithm model comprises an LSSVM algorithm model, the rationale of which is as follows: for training sample according to { x_i,y_i},i＝1,2,…,n,x_i∈R_NWhen nonlinear regression is carried out, a nonlinear mapping function (x) is introduced, and training samples are mapped to a high-dimensional characteristic space for linear regression; the LSSVM model in the feature space is represented as:

wherein, W is a weight vector, and k is an offset;

the objective function is:

wherein, delta is a prediction error variable of the training set, and theta >0 is a normalization parameter;

the constraint conditions to be met are as follows:

introducing Lagrange multiplier beta, and converting into Lagrange function:

according to the optimization conditions:

the following system of linear equations is obtained:

wherein the content of the first and second substances,is a kernel function, which is the inner product of a high-dimensional feature space;

the regression function formula of the LSSVM is obtained as follows:

different kernel functions are taken to form different LSSVM, and radial basis functions are selected As a kernel function of LSSVM.

In some embodiments, for example but not limited to 8 variables as LSSVM inputs that affect the amount of coal produced and the coal production efficiency as the output of the LSSVM, the LSSVM model is related to the inputs and outputs as shown in fig. 2, and the range of speed control thresholds for each belt and shearer is derived based on the range set by the coal production efficiency η.

In summary, the belt and coal mining machine automatic speed regulation method based on coal quantity detection has the following advantages: the optimal control speed of the belt and the coal mining machine can be obtained according to coal quantity detection and prediction and comprehensive test of the model, and accordingly, the belt and the coal mining machine are automatically controlled in speed regulation, belt speed regulation of the belt conveyor is guaranteed to be consistent with the actual condition of mine production, electric energy consumption is reduced, mechanical abrasion is reduced, and therefore production cost is reduced, and the aims of saving energy and reducing consumption are achieved; meanwhile, the frequency conversion energy-saving function of the frequency converter can be fully exerted, the electric energy of the conveyor is saved, and the abrasion of the conveyor is reduced.

The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. It is not intended to limit the invention to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain certain principles of the invention and its practical application to enable one skilled in the art to make and use various exemplary embodiments of the invention and various alternatives and modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the claims and their equivalents.