CN114509765A - Method for automatically adjusting counter wave mechanical rotating shaft by utilizing deep reinforcement learning - Google Patents
Method for automatically adjusting counter wave mechanical rotating shaft by utilizing deep reinforcement learning Download PDFInfo
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Abstract
The invention discloses a method for automatically adjusting a wave-alignment mechanical rotating shaft by utilizing deep learning, which comprises the following steps: s1, calculating a linear distance according to the longitude and latitude and the height of the transmitting end and the receiving end; s2, calculating a link attenuation value according to a free attenuation theory; s3, combining the horizontal angle and the vertical angle of the link transmitting end and the receiving end to obtain a combined characteristic value; s4, establishing a training set and a test set for the link level value and the horizontal and vertical angle model; s5, establishing a link level value and horizontal vertical angle model based on a deep learning algorithm and training; and S6, based on the generated model, the mechanical axis is self-adjusted to the effect meeting the requirement, thereby completing the link wave pairing. The invention can provide a microwave link self-adjusting method under the condition of reducing a large amount of manpower and material resources, and realizes automatic wave alignment.
Description
Technical Field
The invention relates to the technical field of installation and debugging of microwave link equipment, in particular to a method for automatically adjusting a counter wave mechanical rotating shaft by utilizing deep reinforcement learning.
Background
Rainfall activities are closely related to human life, hydrological weather, water resources and other factors. The high-frequency microwave link rain measurement is a new technology for monitoring rainfall data by using attenuation effect in recent years. The high-frequency microwave link can obtain real gridding rainfall information near the surface of the earth. The novel microwave rain measurement technology has the characteristics of rapid deployment, real-time transmission, accurate monitoring, high resolution and the like.
At present, the microwave link rain measuring technology is utilized to start just, equipment is installed on various tower building foundations, most of the equipment belongs to high-altitude installation, the equipment is installed usually by depending on experience of constructors, and no installation process for different scenes exists, so that a large amount of manpower and time are needed during installation.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides the method for automatically adjusting the wave-aligning mechanical rotating shaft by utilizing deep reinforcement learning, which saves manpower and material resources and has good wave-aligning effect.
The technical scheme is as follows: in order to achieve the above object, the present invention provides a method for automatically adjusting a mechanical rotating shaft of a wave pair by deep reinforcement learning, comprising the following steps:
s1, calculating a linear distance according to the longitude and latitude and the height of the transmitting end and the receiving end;
s2, calculating a link attenuation value according to a free attenuation theory;
s3, combining the horizontal angle and the vertical angle of the transmitting end and the receiving end of the link to obtain a combined characteristic value;
s4, establishing a training set and a test set for the link level value and the horizontal and vertical angle model;
s5, establishing a reward and punishment model between a link level value and a horizontal vertical angle by a depth reinforcement learning algorithm;
and S6, based on the generated reward and punishment model, the mechanical axis is self-adjusted until the effect meets the requirement, so that the wave pairing of the link is completed.
Further, the step S1 specifically includes:
s1.1, calculating the horizontal distance between links according to the geographical positions of a transmitting end and a receiving end, wherein the distance formula is as follows:
in the formula, lat1, lat2, lon1 and lon2 respectively represent the longitude and latitude of the transmitting end and the receiving end.
S1.2, calculating to obtain the linear distance between the transmitting end and the receiving end according to the horizontal distance obtained by the calculation of S1 and the height relation between the transmitting end and the receiving end, wherein the linear distance formula is as follows:
in the formula, h1、h2The altitude of the transmitting end and the receiving end respectively.
Further, the step S2 specifically includes:
s2.1, calculating a link attenuation value according to a free attenuation theory; and calculating a link attenuation value by the linear distance calculated in the S1.2 and the frequency band selected by the microwave link through a free space attenuation theory, wherein the formula is as follows:
L＝32.4+20log10s+20log10f
wherein f is the frequency band selected by the microwave link.
Further, the step S3 specifically includes:
s4.1, collecting related angle information of the transmitting end and the receiving end, wherein the horizontal angle between the transmitting end and the receiving end is 0 degree in the north-south direction, 180 degrees in the south-south direction, and the vertical angle is 0 degree below the club face;
s4.2, recording a microwave link level value according to the horizontal angle and the vertical angle of the transmitting end and the receiving end in the S1;
and S4.3, normalizing the acquired data to form corresponding characteristic vectors.
Further, the step S5 specifically includes:
s5.1, constructing a self-adjustable deep reinforcement learning network with an input and output structure;
s5.2, establishing a relation training set of each angle and the corresponding attenuation value through the collected angle data and the corresponding attenuation value data;
s5.3, training by using a Deep Q-Learning method and combining the actually measured attenuation value of the training set and the Deep reinforcement Learning network, and establishing a mapping relation of the link attenuation values of the angle adjustment of the transmitting terminal and the receiving terminal at the next moment; the mapping relationship may be expressed as:
NewQ(t,a)＝Q(t,a)+θ[R(t,a)+γmaxa′Q′(t′,a′)-Q(t,a)]
in the formula, a represents the operation at the current time, t represents the state at the current time, Q (t, a) is a table in which the operation and the state are recorded, R (t, a) represents that feedback is obtained in the operation state at the current time, a 'represents the operation at the next time, and at' represents the state at the next time.
And S5.4, adjusting the angles of the transmitting end and the receiving end according to the error between the actually measured attenuation value and the theoretical attenuation value of the angle adjustment of the transmitting end and the receiving end at the next moment until the error reaches the allowable range or the maximum iteration number.
And S5.5, according to the deep reinforcement learning model, the optimal thinnest purpose is achieved.
In step S6, the depth-enhanced learning reward and punishment model algorithm includes, but is not limited to, depth learning and enhanced learning.
Has the advantages that: the invention discloses a method for automatically adjusting a mechanical wave-alignment rotating shaft by utilizing deep reinforcement learning, which comprises the following steps of: s1, calculating a linear distance according to the longitude and latitude and the height of the transmitting end and the receiving end; s2, calculating a link attenuation value according to a free attenuation theory; s3, combining the horizontal angle and the vertical angle of the transmitting end and the receiving end of the link to obtain a combined characteristic value; s4, establishing a training set and a test set for the link level value and the horizontal and vertical angle model; s5, establishing a reward and punishment model between a link level value and a horizontal vertical angle by a depth reinforcement learning algorithm; s6, based on the generated reward and punishment model, the mechanical axis is self-adjusted until the effect meets the requirement, so that the wave pairing of the link is completed; by utilizing a deep reinforcement learning method, through a reward and punishment model in the deep reinforcement learning, the model self-learns, so that the mechanical axis is automatically adjusted, the optimal wave-pairing effect is realized, and a foundation is laid for the installation of a high-frequency microwave link in the early stage of rain measurement.
Drawings
FIG. 1 is a flow chart of a method for automatically adjusting a counter wave mechanical spindle using deep reinforcement learning;
FIG. 2 is a graph of action state adjustment based on an algorithm;
FIG. 3 is a diagram of a deep reinforcement learning-based network model.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
The invention utilizes the horizontal angle and the disposal angle of the transmitting end and the receiving end of the microwave link and the corresponding link attenuation value thereof to realize the link self-wave-pairing through deep reinforcement learning, as shown in figure 1. The following method for automatically adjusting the mechanical counter-wave rotating shaft by utilizing deep reinforcement learning by taking a microwave link as an embodiment mainly comprises the following steps:
the method comprises the following steps: installing a transmitting end and a receiving end on a tower, calculating the linear length of a link through the longitude and latitude and the altitude of the transmitting end and the receiving end, wherein the link distance s can be expressed as:
in the formula, lat1, lat2, lon1 and lon2 respectively represent the longitude and latitude of the transmitting end and the receiving end, d is the horizontal distance between the transmitting end and the receiving end, and h is the horizontal distance between the transmitting end and the receiving end1、h2The altitude of the transmitting end and the receiving end respectively.
Step two: theoretical attenuation values are obtained by utilizing free space attenuation theory calculation, and the theoretical attenuation value L can be expressed as:
L＝32.4+20log10s+20log10f
step three: the mechanical shaft passes through the built-in module, collects the horizontal angle and the vertical angle of the transmitting end and the receiving end, and respectively records as (alpha)r,βr),(αt,βt) And corresponding to this fact, the actual attenuation value LsAnd combining into a feature vector.
Step four: and preprocessing the combined characteristic vectors to generate a training set of the model.
Step five: according to a well-processed training set, based on deep reinforcement learning, a reward and punishment model between a link level value and a horizontal vertical angle is designed, so that a mechanical axis is automatically finely adjusted, the purpose of automatically aligning waves is realized, and the method specifically comprises the following processes:
(1) fig. 2 is a schematic diagram of the deep reinforcement learning network for realizing automatic wave alignment according to the present invention, and the deep reinforcement learning network includes a sequence input layer, 2 convolution layers, 2 full-connection layers, and an output layer.
(2) The deep reinforcement learning network extracts the rotating position information of the mechanical shaft through 2 convolution layers, obtains a corresponding feedback of the adjustment direction of the mechanical shaft, and establishes a mapping relation between the next adjustment direction and a link time attenuation value through a full connection layer through the feedback information.
(3) Through a plurality of iterations until the relative error RE is less than 0.3, the maximum number of iterations is 500.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (5)
1. A method for automatically adjusting a wave-aligning mechanical rotating shaft by utilizing deep reinforcement learning is characterized by comprising the following steps:
s1, calculating a linear distance according to the longitude and latitude and the height of the transmitting end and the receiving end;
s2, calculating a link attenuation value according to a free attenuation theory;
s3, combining the horizontal angle and the vertical angle of the transmitting end and the receiving end of the link to obtain a combined characteristic value;
s4, establishing a training set and a test set for the link level value and the horizontal and vertical angle model;
s5, establishing a reward and punishment model between a link level value and a horizontal vertical angle by a depth reinforcement learning algorithm;
and S6, based on the generated reward and punishment model, the mechanical axis is self-adjusted until the effect meets the requirement, so that the wave pairing of the link is completed.
2. The method for automatically adjusting a mechanical rotating shaft for wave alignment by using deep reinforcement learning as claimed in claim 1, wherein the step S1 specifically comprises:
s1.1, calculating the horizontal distance between links according to the geographical positions of a transmitting end and a receiving end, wherein the distance formula is as follows:
in the formula, lat1, lat2, lon1 and lon2 respectively represent the longitude and latitude of the transmitting end and the receiving end;
s1.2, calculating to obtain the linear distance between the transmitting end and the receiving end according to the horizontal distance obtained by the calculation of S1 and the height relation between the transmitting end and the receiving end, wherein the linear distance formula is as follows:
in the formula, h1、h2The altitude of the transmitting end and the receiving end respectively.
3. The method for automatically adjusting a mechanical wave-aligning rotating shaft by means of deep reinforcement learning according to claim 2, wherein the step S2 specifically includes:
s2.1, calculating a link attenuation value according to a free attenuation theory; and calculating a link attenuation value by the linear distance calculated in the S1.2 and the frequency band selected by the microwave link through a free space attenuation theory, wherein the formula is as follows:
L＝32.4+20log10s+20log10f
wherein f is the frequency band selected by the microwave link.
4. The method for automatically adjusting the mechanical rotating shaft of the wave pair according to claim 1,
characterized in that step S3 specifically includes:
s4.1, collecting related angle information of the transmitting end and the receiving end, wherein the horizontal angle between the transmitting end and the receiving end is 0 degree in the north-south direction, 180 degrees in the south-south direction, and the vertical angle is 0 degree below the club face;
s4.2, recording a microwave link level value according to the horizontal angle and the vertical angle of the transmitting end and the receiving end in the S1;
and S4.3, normalizing the acquired data to form corresponding characteristic vectors.
5. The method for automatically adjusting a mechanical wave-aligning rotating shaft by means of deep reinforcement learning according to claim 1, wherein the step S5 specifically includes:
s5.1, constructing a self-adjustable deep reinforcement learning network with an input and output structure;
s5.2, establishing a relation training set of each angle and the corresponding attenuation value through the collected angle data and the corresponding attenuation value data;
s5.3, training by using a Deep Q-Learning method and combining the actually measured attenuation value of the training set and the Deep reinforcement Learning network, and establishing a mapping relation of the link attenuation values of the angle adjustment of the transmitting terminal and the receiving terminal at the next moment; the mapping relationship may be expressed as:
NewQ(t,a)＝Q(t,a)+θ[R(t,a)+γmaxa′Q′(t′,a′)-Q(t,a)]
wherein a represents the action at the current time, t represents the state at the current time, Q (t, a) is a table recording the action and the state, R (t, a) represents that feedback is obtained in the action state at the current time, a 'represents the action at the next time, and at' represents the state at the next time;
s5.4, adjusting the angles of the transmitting end and the receiving end according to the error between the actually measured attenuation value and the theoretical attenuation value adjusted by the angle of the transmitting end and the receiving end at the next moment until the error reaches the allowable range or the maximum iteration number;
and S5.5, according to the deep reinforcement learning model, the optimal thinnest purpose is achieved.
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