CN117367434A - Intelligent positioning method for mining area unmanned mine car - Google Patents
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Abstract
The invention relates to the technical field of vehicle positioning, and provides an intelligent positioning method for an unmanned mine car in a mining area. The invention aims to improve the accuracy of the positioning of the unmanned mine car and realize the accurate and intelligent positioning of the unmanned mine car in the mining area.
Description
Technical Field
The invention relates to the technical field of vehicle positioning, in particular to an intelligent positioning method for an unmanned mine car in a mining area.
Background
The mine car is mainly used in mining areas, various mineral resources such as coal mines, gold ores, iron ores, copper ores and the like are generally distributed in the mining areas, the mine car is used for conveying mined ores from mining sites to other places in the mining areas, besides the mining areas, the mine car can also be used in other industrial places such as construction sites, ports, storage facilities and the like and is used for conveying a large amount of raw materials, earthwork, coal and other articles, and therefore it is important to ensure safe running of unmanned mine cars.
Automation and digital mining of unmanned mining vehicles in mining areas are current development trends, mining companies seek to improve efficiency, reduce cost and improve safety by introducing automatic driving techniques and intelligent positioning systems, and also reduce exposure of personnel in hazardous environments, thereby improving labor conditions. However, the topography of mining areas is diversified, not only including steep mountains, tunnels and mines, but also having a large number of rocks, piles and equipment, etc. distributed therein, and these topography features may complicate the positioning of vehicles, possibly resulting in difficulty in accurate positioning of vehicles, and improvements are required.
Disclosure of Invention
In order to solve the technical problems, the invention provides an intelligent positioning method for an unmanned mine car in a mining area, which aims to solve the existing problems.
The intelligent positioning method of the mining area unmanned mine car adopts the following technical scheme:
the embodiment of the invention provides an intelligent positioning method for an unmanned mine car in a mining area, which comprises the following steps of:
collecting the topographic features, the vehicle-mounted environment sensor information and the vehicle motion information of each data acquisition time stamp in the mine unmanned mine car time period;
obtaining weight parameters representing the topographic features according to the topographic features of the data acquisition time stamps; obtaining weight parameters representing the vehicle-mounted environment sensor information according to the vehicle-mounted environment sensor information of each data acquisition time stamp; combining weight parameters representing the topographic features and weight parameters representing the information of the vehicle-mounted environmental sensor to obtain comprehensive topographic influence indexes of the unmanned mine car in each time period;
acquiring vehicle motion influence indexes of the unmanned mine car in each time period according to the vehicle motion information of each data acquisition time stamp; obtaining acceleration coefficients of PSO algorithm at each moment according to comprehensive topography influence indexes of the unmanned mine car in each time period;
obtaining social factors of PSO algorithm at each moment according to the vehicle motion influence indexes of the unmanned mine car in each time period; acquiring an objective function at each moment; and the intelligent positioning of the mining area unmanned mine car is completed by combining the acceleration coefficient of the PSO algorithm, the social factors of the PSO algorithm and the objective function at each moment.
Preferably, the collecting the topography characteristic, the vehicle-mounted environment sensor information and the vehicle motion information of each data collection time stamp in the mining area unmanned mine car time period comprises the following steps:
the vehicle-mounted environment sensor comprises a vehicle, a vehicle-mounted environment sensor and a mine mouth, wherein the vehicle-mounted environment sensor comprises obstacle shapes, obstacle sizes and obstacle distances, the vehicle motion information comprises vehicle speed, driving direction deflection and acceleration, influence parameters are set, the position of an unmanned mine car is taken as the center of a circle, the area with the influence parameters being the diameter is marked as a signal influence range of the unmanned mine car, the mountain information is the ratio of mountain terrain areas in the signal influence range of the unmanned mine car, the mine mouth information is the ratio of pit areas in the signal influence range of the unmanned mine car, and the mine mouth information is the ratio of mine mouth areas in the signal influence range of the unmanned mine car.
Preferably, the weight parameter representing the topographic feature is obtained according to the topographic feature of each data acquisition time stamp, and the expression is:
in the method, in the process of the invention,a weight parameter representing a topographical feature,represent the firstMountain information data values under the secondary data acquisition time stamp,represent the firstThe tunnel information data value under the secondary data acquisition time stamp,represent the firstMine port information data values under the secondary data acquisition time stamp,for a period of timeThe number of data acquisition time stamps within,represents the firstTime stamping of secondary data acquisitionA data value for a data type.
Preferably, the weight parameter representing the vehicle-mounted environment sensor information is obtained according to the vehicle-mounted environment sensor information of each data acquisition time stamp, and the expression is:
in the method, in the process of the invention,weight parameters representing in-vehicle environmental sensor information,represent the firstObstacle shape data values under the secondary data acquisition time stamp,represent the firstObstacle size data values at the secondary data acquisition time stamp,represent the firstObstacle distance data values under the secondary data acquisition time stamp,for a period of timeThe number of data acquisition time stamps within,is the firstTime stamping of secondary data acquisitionThe data value of the data type of the seed,representing an exponential term.
Preferably, the comprehensive terrain influence index of the unmanned mine car in each time period is obtained by combining the weight parameter representing the terrain characteristic and the weight parameter representing the information of the vehicle-mounted environment sensor, and the expression is as follows:
in the method, in the process of the invention,for unmanned mine cars during a time periodThe overall topography within the model influences the index,for a period of timeThe number of data acquisition time stamps within,for the number of different data types under one data acquisition time stamp,represents the firstTime stamping of secondary data acquisitionThe data value of the data type of the seed,a weight parameter representing a topographical feature,weight parameters representing in-vehicle environmental sensor information,representing an exponential term.
Preferably, the obtaining the vehicle motion influence index of the unmanned mine car in each time period according to the vehicle motion information of each data acquisition time stamp includes:
for each data acquisition time stamp, the product of the calculated vehicle speed and the vehicle acceleration is recorded as a first product, the ratio of the deflection of the driving direction to the first product is calculated, the inverse number of the ratio is used as an index of an exponential function based on a natural constant, the inverse number of the square of the obstacle distance is calculated, the product of the calculated result of the exponential function and the inverse number is calculated and recorded as a second product, and the sum of the second products of all the data acquisition time stamps in each time period is used as a vehicle movement influence index in each time period.
Preferably, the obtaining the acceleration coefficient of the PSO algorithm at each moment according to the comprehensive topography influence index of the unmanned mine car in each time period comprises the following steps:
setting a first smoothing parameter, taking the comprehensive terrain influence index in a time period before each moment as an independent variable of an arctangent trigonometric function, and taking the ratio of the calculation result of the arctangent trigonometric function to the first smoothing parameter as an acceleration coefficient of a PSO algorithm at each moment.
Preferably, the social factors of the PSO algorithm at each moment are obtained according to the vehicle motion influence indexes of the unmanned mine car in each time period, including:
setting a second smoothing parameter, taking the opposite data of the vehicle motion influence index in a period of time before each moment as an index of an exponential function based on a natural constant, calculating the sum of the opposite number and 1 of the calculation result of the exponential function, and taking the product of the sum and the second smoothing parameter as a social factor of the PSO algorithm at each moment.
Preferably, the acquiring the objective function at each moment includes:
setting a weight coefficient, calculating the sum of mountain information, tunnel information and mine mouth information aiming at each data acquisition time stamp, calculating the product of the sum and the obstacle distance, marking the product as a third product, acquiring the position of the unmanned mine car at each moment according to an AOA algorithm, calculating the product of the position and the weight coefficient, marking the product as a fourth product, calculating the sum of the third product and the fourth product, and taking the sum of all data acquisition time stamps in a time period before each moment as an objective function of each moment.
Preferably, the intelligent positioning of the mining unmanned mine car is completed by combining the acceleration coefficient of the PSO algorithm at each moment, the social factor of the PSO algorithm at each moment and the objective function at each moment, and the specific method comprises the following steps:
and taking the acceleration coefficient of the PSO algorithm at each moment and the social factor of the PSO algorithm at each moment as the acceleration coefficient and the social factor of the PSO algorithm at each moment, taking the objective function at each moment as the input of the PSO algorithm at each moment, and taking the output of the PSO algorithm at each moment as the accurate positioning of the unmanned mine car at each moment.
The invention has at least the following beneficial effects:
the invention provides an intelligent positioning method for an unmanned mine car in a mining area, which combines the data of a topographic feature and a vehicle-mounted environment sensor, and solves the problem that in complex topography, the topographic feature and the vehicle-mounted environment sensor data are mutually influenced, signals can be reflected, diffracted and scattered, so that multipath propagation is caused, and accurate positioning cannot be realized;
furthermore, the invention improves the learning factor of the Particle Swarm Optimization (PSO) by correlating the comprehensive terrain impact index and the vehicle motion impact index, enhances the performance of the unmanned mine car accurate positioning system, improves the adaptability of the PSO algorithm, ensures that the PSO algorithm can better process the interference caused by complex terrain and dynamic vehicle motion, has an intelligent search strategy, can find the global optimal solution under multiple interference, and improves the accuracy and the robustness of the unmanned mine car accurate positioning.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of an intelligent positioning method for an unmanned mining car in a mining area according to an embodiment of the present invention;
FIG. 2 is a flowchart of the unmanned mining vehicle positioning index acquisition.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of the mining area unmanned mine car intelligent positioning method according to the invention with reference to the attached drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a concrete scheme of an intelligent positioning method for an unmanned mine car in a mining area, which is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of steps of a mining area unmanned mine car intelligent positioning method according to an embodiment of the invention is shown, the method includes the following steps:
and S001, collecting the topographic features, the vehicle-mounted environment sensor information and the vehicle motion information in the running process of the unmanned mine car in the mining area, and preprocessing the collected data.
Most unmanned mine cars work in mining areas, the terrain environment in the mining areas is complex, the terrain surface contains a large number of ravines, rock and ore piles, mining tunnels and the like, and aiming at the complex terrain features, an intelligent positioning system of the unmanned mine cars needs to acquire relevant data by using proper sensors and technologies so as to ensure that the unmanned mine cars can be positioned and navigated in the mining areas safely and efficiently. Before the accurate positioning of the unmanned mine car, a GPS or other positioning systems are used for roughly measuring the positioning position of the unmanned mine car as coarse positioning, however, in environments with complex terrains and a large number of shields such as mining areas, GPS signals can be interfered or blocked, so that the positioning accuracy is reduced, and therefore, the coarse positioning of the mine car obtained by using the GPS can only be used as reference assistance.
The unmanned mine car is roughly positioned as a circle center,The range with meter being diameter is recorded as the signal influence range, and the high-precision topographic mapping laser scanning technology is used for obtaining the coarse positioning of the unmanned mine carThe topographic feature data in the meter range is mountain information, gallery information and mine mouth information of the mine area respectively, and the mountain information, gallery information and mine mouth information of the mine area, namely the area occupation ratio of the mountain, gallery and mine mouth of the mine area in the information influence range is calculated respectively in the signal influence range of the unmanned mine car, for example: in a circle with the rough positioning of the unmanned vehicle as the center of the circle, the mountain landform area is s, and the mountain information isIn the present embodimentThe implementation can be set by the user according to the actual situation, and the present embodiment is not limited thereto, wherein the laser scanning technology is the prior art, and the detailed description of the present embodiment is omitted here.
The method comprises the steps that an ultrasonic sensor and camera equipment are assembled on an unmanned mine car to collect vehicle-mounted environment data, the vehicle-mounted environment data comprise the distance, the size and the shape information of a topographic obstacle on a path of the unmanned mine car, the shape data of the obstacle are outline corner points of the obstacle and are represented by vector data, the camera data comprise focal length, principal points, positions and directions, the camera is used for collecting obstacle images, the image coordinates of the corner points are obtained by carrying out canny edge detection and harris corner point detection by taking the obstacle images as input, the corner points in the images are mapped from an image coordinate system to a camera coordinate system by taking the image coordinates of the camera calibration parameters and the corner points as input, three-dimensional coordinates of the corner points are obtained, and the three-dimensional coordinates of all the corner points are used as vector data, for example: the obstacle on the unmanned mine car path has five angular points, the position of the ultrasonic sensor assembled by the unmanned mine car is taken as an original point, the running direction is taken as an x axis, the direction vertical to the x axis is taken as a y axis, the positive direction is directed to the right side, the negative direction is directed to the left side, the z axis is constructed vertical to the x axis and the y axis, the z axis is vertical to the ground, the positive direction is upward,the negative direction is downward, a three-dimensional coordinate system is constructed, and then five corner points of the barrier areThen, arranging the five corner coordinates according to the ascending order of the values of the x-axis as vector data; the obstacle size data is the width of the obstacle, specifically, the Euclidean distance between three-dimensional coordinates of each angular point is calculated, the largest Euclidean distance is used as the width of the obstacle, the obstacle distance data is the distance data from the obstacle to the sensor acquired by the ultrasonic sensor in real time when the unmanned mine car is running, and the distance and the size information of the obstacle are expressed in the unit of meters.
Collecting motion data of the unmanned mine car in real time in the motion of the unmanned mine car, and recording motion information of a vehicle, wherein the motion information comprises vehicle speed, driving direction deflection and acceleration, and the vehicle speed is the real-time speed of the unmanned mine car in meters per second; the driving direction deflection is the driving direction change between the time of the current data collection and the time of the last data collection, and the unit is degree; the acceleration is real-time acceleration of the unmanned mine car, and the unit is meter per square second.
After the related information is collected, the collected shape vector data of the obstacle is reduced to one-dimensional data by using a PCA principal component analysis method, the main characteristics of the shape information of the obstacle are maintained, the simplification is carried out, the subsequent analysis and calculation are convenient, and the principal component data after the dimension reduction is obtained, wherein the PCA principal component analysis method is the prior known technology, and the embodiment is not described in detail herein. And then combining the collected other data and the main component data of the obstacle shape after dimension reduction into a data matrix, and generating a statistical result by using a Pandas library of Python to facilitate subsequent analysis.
And step S002, improving the acceleration coefficient and the social factors in the PSO algorithm according to the topographic features, the vehicle-mounted environment sensor information and the vehicle motion information in the running process of the unmanned mine car in the mining area.
Specifically, the embodiment obtains the terrain features, the vehicle-mounted environment sensor information and the vehicle motion information in each time period of the unmanned mine car, obtains the comprehensive terrain influence indexes in each time period of the unmanned mine car according to the terrain features, the vehicle-mounted environment sensor information and the vehicle motion information in each time period, further obtains the vehicle motion influence indexes in each time period, combines the comprehensive terrain influence indexes and the vehicle motion influence indexes to improve the learning factors of the PSO algorithm, realizes intelligent positioning of the unmanned mine car in a mining area, and a specific unmanned mine car positioning index obtaining flow chart is shown in fig. 2. The improvement process of the learning factor of the PSO algorithm specifically comprises the following steps:
according to the various event categories collected in the first step, structured event statistics are constructed as shown in table 1:
TABLE 1
In Table 1Is the firstThe first data acquisition time stampA data value for a data type.
The time period is set in the present embodimentOne time periodComprisesEach data acquisition time stamp is used for carrying out data acquisition of three information types, namely one-time topographic feature, a vehicle-mounted environment sensor and vehicle motion information, and the time period in the embodiments, the data acquisition time stamp is every others, the practitioner can set the data acquisition once according to the actual situation, and the embodiment is not limited to this.
According to the data results of table 1, the above data are classified into two types, natural data organization including topographic features and in-vehicle environment sensor information, which in this embodiment are natural environment obstacle or topographic feature data, which provide geographic features and potential obstacle information in the mine area, and vehicle motion information, i.e., including vehicle speed, traveling direction, and acceleration, which is dynamic data, which provides information about the actual movement of the unmanned mining vehicle, which typically changes rapidly in a short time.
When the accurate positioning is carried out on unmanned mine cars in a mining area, due to the complex diversity of the mining area topography, signals can undergo multiple reflections, diffractions and scattering in the complex topography, so that the diversity of signal paths is caused, the time and direction of the signals reaching a receiver can be changed due to multipath propagation, the signals reach the receiver from different directions, and the normal feedback of the signals can be difficult to determine by a positioning system because the same signal can have multiple source directions. And topographical features such as mountains, tunnels and mine heads may block or reflect signals, causing the signals to fade or distort in certain areas or directions. In order to consider the influence factors of a plurality of terrains and obstacles on the positioning signals, the performance of the positioning system is comprehensively estimated, the accuracy of the terrains on the positioning signals is summarized and calculated, and the unmanned mine car is constructed in a time periodThe specific expression of the comprehensive terrain influence index is as follows:
in the method, in the process of the invention,for unmanned mine cars during a time periodThe overall topography within the model influences the index,for a period of timeThe number of data acquisition time stamps within,for the number of different data types under one data acquisition time stamp, in this embodimentThe number of data types representing the topographical features and the in-vehicle environmental sensor information,represents the firstTime stamping of secondary data acquisitionThe data value of the data type of the seed,represents the firstTime stamping of secondary data acquisitionThe data value of the data type of the seed,is the firstTime stamping of secondary data acquisitionA data value of a seed data type;
a weight parameter representing a topographical feature,represent the firstMountain information data values under the secondary data acquisition time stamp,represent the firstThe tunnel information data value under the secondary data acquisition time stamp,represent the firstMine port information data values under the secondary data acquisition time stamp,the larger the terrain features are, the greater the degree of influence of the terrain features on the positioning accuracy is;
weight parameters representing in-vehicle environmental sensor information,represent the firstObstacle shape data values under the secondary data acquisition time stamp,represent the firstObstacle size data values at the secondary data acquisition time stamp,represent the firstObstacle distance data values under the secondary data acquisition time stamp,the greater the degree of influence of the in-vehicle environment sensor information on the positioning accuracy,the representative index term is used for representing the nonlinear influence of the vehicle-mounted environment sensor information, and can reflect the nonlinear influence of different vehicle-mounted environment sensor information on the signal propagation accuracyThe implementation can be set by the implementation personnel according to the actual situation, and the embodiment is not limited to this.
Combining the data items together by synthesizing the terrain influence indexes, taking different weight parameters and index items into consideration, thereby quantifying the influence of the terrain features and the vehicle-mounted environment sensor information on the signal transmission accuracy, and calculating the weight parametersAndthe importance and the influence degree of each factor in the actual scene can be better reflected, and the method is introducedEntry index termThe nonlinear influence of the vehicle-mounted environment sensor information can be judged, the characteristics of the vehicle-mounted environment sensor data can be better captured,represent the firstThe data acquisition is performed on the value of each in-vehicle environment sensor information data item,the size of the system reflects the intensity of the information of the specific vehicle-mounted environment sensor, and the comprehensive terrain influence index can followIs increased by an increase in (a).
Weighting parameters for topographical features，Representing mountain information, tunnel information and mine mouth information in the topographic feature data,calculate the product of these data and then matchMultiplication in whichTaking values of 1 to 3, and calculating and averaging to obtain the weight parameters of the topographic features through topographic feature data at each momentThe degree of influence of the topographic features on the positioning accuracy can be reflected,the multiplication of the three reflects the complexity of the topographic data,can be roughly positioned along with the unmanned mine carThe complexity of the topographic information in the rice becomes large, andthe larger the size, the rough positioning of the unmanned mine carThe more complex the topographic information in the meter is, the greater the influence degree of the topographic information on the positioning of the unmanned mine car is, and the greater the comprehensive topographic influence index is.
Weight parameters for in-vehicle environmental sensor information，Represents the shape, the size and the distance of the obstacle in the information data of the vehicle-mounted environment sensor,in relation toIs calculated as a non-exponential term, in relation toSince the influence of the shape of the obstacle on the driving path of the unmanned mine car on the signal propagation is small relative to the size and distance of the obstacle in the driving positioning of the unmanned mine car, the index term is introduced to amplify the influence of the size and distance of the obstacle on the driving path of the unmanned mine carMultiplying by each other to obtain the travel path of the unmanned mining vehicleThe weight parameters of the vehicle-mounted environment sensor information are calculated according to the obstacle condition,reflects the complex condition of the obstacle information, increases with the increase of the obstacle information, andthe larger the obstacle information acquired by the unmanned mine car on the driving path is, the more complex the obstacle information is, and the comprehensive topography influence index is increased.
The vehicle motion of unmanned mine car can produce multiple influence to signal propagation, firstly, the motion of vehicle causes Doppler effect, leads to the change of signal frequency, probably influences the accuracy of signal, and secondly, the orbit of vehicle can change signal propagation path, increases path loss, leads to signal intensity uneven distribution, and in addition, the vehicle can become the shelter from the thing of signal, has hindered the straight line propagation of signal. Vehicle vibration and motion may also cause signal phase distortion, affecting signal decoding, and vehicle motion may also cause multipath propagation, causing the signal to undergo multiple propagation paths, resulting in multipath interference of the signal. In order to eliminate the influence of vehicle motion on signal propagation, a vehicle motion influence index is constructed, wherein the vehicle motion influence index specifically comprises the following expression:
in the method, in the process of the invention,indicating that the unmanned mine car is in a time periodThe movement of the vehicle within the vehicle affects the index,for a period of timeData acquisition withinThe number of time stamps is set to be the number of time stamps,is the firstObstacle distance data values under the secondary data acquisition time stamp,is the firstTime stamping of secondary data acquisitionThe data value of the data type of the seed,is the firstTime stamping of secondary data acquisitionThe data value of the data type of the seed,is the firstTime stamping of secondary data acquisitionThe data value of the data type of the seed,is a natural constant which is used for the production of the high-temperature-resistant ceramic material,to control parameters for controlling the degree of influence of vehicle motion on signals, in this embodimentThe implementer can self-carry out according to the actual situationThe row setting is not limited in this regard, and willIs recorded as a first product, willAnd is noted as the second product.
Representing the inverse relationship of the inverse of the obstacle distance square to the vehicle motion effect, the farther the obstacle is from the vehicle motion effect index will be smaller,an exponential term is represented, including speed, direction deflection, and acceleration information of the vehicle, which is used to simulate the exponential decay effect of vehicle motion on the signal,the decay rate is controlled. As the speed and acceleration intensity of the vehicle are greater,the larger the vehicle movement influence index increases, conversely, when the direction of the vehicle deflectsThe larger the size of the product,the smaller the vehicle motion influence index is, the smaller the vehicle motion influence index is.
When the unmanned mine car is accurately positioned, a wireless sensor network based on an angle of arrival (AOA) is used for positioning, however, an AOA algorithm is often interfered by multipath propagation, signal attenuation, obstacles and the like in a complex environment, so that an accurate positioning result is difficult to obtain. The particle swarm algorithm (Particle Swarm Optimization, PSO) is used as a computational intelligence algorithm, can adaptively optimize the positions of sensor nodes or target positions, integrate multi-sensor data, and iterateAnd searching the global optimal solution, and enhancing the robustness, thereby improving the performance of the unmanned mine car positioning system. However, during the driving of the mine unmanned vehicle in the mining area, the topography features such as mountain, tunnel and mine mouth information, the vehicle-mounted environment sensor information including the shape, size and distance of the obstacle, and the vehicle movement information such as speed, driving direction and acceleration have a great influence on the particle swarm algorithm (PSO), because these information directly influence the input parameters and search space of the PSO algorithm, the topography features change the complexity of knowing the space and the constraint conditions, the vehicle-mounted environment sensor information provides the key measurement data, and the vehicle movement information determines the dynamic nature of the positioning, so two learning factors of the PSO algorithm are determined according to the comprehensive topography influence index and the vehicle movement influence indexAndthe improvement is carried out:
in the method, in the process of the invention,andtwo learning factors respectively representing PSO algorithm, whereinIn order for the acceleration factor to be a factor,as a social factor of the process, the process is,as a function of the inverse trigonometric tangent,for unmanned mine cars during a time periodThe overall topography within the model influences the index,indicating that the unmanned mine car is in a time periodThe movement of the vehicle within the vehicle affects the index,is a natural constant which is used for the production of the high-temperature-resistant ceramic material,as a first smoothing parameter,as the second smoothing parameter, in this embodimentThe implementation can be set by the implementation personnel according to the actual situation, and the embodiment is not limited to this.
The PSO algorithm includes two main learning factors: acceleration coefficientAnd social factors，The speed at which the particles are searched and adjusted according to their own experience (individual best position) is controlled,the speed of searching and adjusting the particles according to the experience (global optimal position) of the whole population is controlled;
comprehensive terrain influenceThe index indicates how much the topographical features affect the accuracy of the positioning. Improvements in or relating toCorrelating it with the integrated terrain impact index by addingThe PSO algorithm focuses more on the local optimal position of the individual, so that the local optimal position is more likely to be explored nearby, the adaptability is improved, the algorithm can be more suitable for complex terrain conditions, and the accurate positioning capability under the complex terrain conditions is enhanced;
the vehicle motion influence index represents the influence degree of the motion state of the vehicle on the positioning system and is improvedCorrelating it to a vehicle motion impact indicator, the PSO algorithm can more intelligently adjust the search strategy to accommodate vehicle dynamics by addingThe PSO algorithm focuses more on the global optimal position, so that the PSO algorithm is more likely to search near the global optimal position, the robustness of the algorithm is improved, and an accurate positioning result is ensured, particularly in a dynamic environment.
And S003, combining an AOA algorithm and an improved PSO algorithm to realize the accurate positioning of the unmanned mine car.
Deployment in mining areasEach base station, which ensures that enough antenna is used to measure the arrival angle of signals when AOA positioning is used, each base station receives signals on the unmanned mine car and records the arrival angle, and the positions of the unmanned mine car at all times are calculated by using the arrival angle information through a triangulation method, in the embodimentThe implementation can be set by the practitioner according to the actual situation, the embodiment is not limited to this, and the PSO algorithmBoth the AOA algorithm and the AOA algorithm are known in the art, and the embodiment is not described in detail here.
Defining the precise AOA positioning problem as an optimization problem, wherein the position of the unmanned mine car is the variable to be optimized, and the objective function is to minimize the error of the positioning of the unmanned mine car by the obstacles near the unmanned mine car and the movement of the unmanned mine car, initializing a group of particles, each of which represents a possible position of the unmanned mine car, the positions and speeds of the particles being randomly initialized, and then calculating the fitness of each particle, i.e. the value of the objective function evaluates their performance, the objective functionThe method comprises the following steps:
wherein,is the firstAn objective function of time of day, for the firstTime period preceding the momentIn which, data of various data types are acquired,for a period of timeThe number of data acquisition time stamps within,represent the firstMountain information data values under the secondary data acquisition time stamp,represent the firstThe tunnel information data value under the secondary data acquisition time stamp,represent the firstMine port information data values under the secondary data acquisition time stamp,represent the firstObstacle distance data values under the secondary data acquisition time stamp,is a weight coefficient, in this embodimentThe implementation can be set by the practitioner according to the actual situation, the embodiment is not limited to this,is the firstThe AOA algorithm of moment locates the position, willIs recorded as a third product, willAnd is noted as the fourth product.
The objective of the objective function is to minimizeAllows the PSO algorithm to consider historical data during the positioning process when the unmanned mining vehicle is in the peripheryThe duty cycle surrounding all special terrainsGreater thanRepresenting the characteristic of the terrain surrounding the unmanned mine car versus the objective functionIs greater, the topographical features dominate the positioning problem,less thanThe time represents that the topography characteristic contribution is smaller, and the PSO algorithm is more prone to selecting an AOA positioning position as an optimal position.
Two improved learning factors to be describedAndfor the data which continuously changes along with time, when the learning factors at the current moment are needed to be calculated, the comprehensive terrain influence index and the vehicle motion influence index in the previous time period of the current moment are taken for calculation, so that two improved learning factors at each moment are obtained. In the PSO algorithm, the speed and position of each particle are updated according to the fitness of the particle and the historical optimal position, the steps of evaluating and updating the position and speed of the particle are repeated until the stopping condition is met, namely the maximum iteration number is reached, in the embodiment, the maximum iteration number is 100, an implementer can set the position according to the actual situation, the PSO algorithm does not limit the situation, and the position of the finally found particle is used as the accurate position of the unmanned mine car. And then the positions of the unmanned mine car at all the moments are accurately positioned according to the two improved learning factors and the objective function at all the moments.
In summary, the embodiment of the invention solves the problem that in complex terrains, the terrains characteristics and the data of the vehicle-mounted environment sensor are mutually influenced, signals can reflect, diffract and scatter to cause multipath propagation, the determination of learning factors in the PSO algorithm is not combined with the actual environment, and thus the accurate positioning cannot be performed, and the PSO algorithm is improved by combining the comprehensive terrains influence index and the vehicle motion influence index, so that the accuracy of mining area unmanned mine car positioning is improved.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. An intelligent positioning method for an unmanned mine car in a mining area is characterized by comprising the following steps:
collecting the topographic features, the vehicle-mounted environment sensor information and the vehicle motion information of each data acquisition time stamp in the mine unmanned mine car time period;
obtaining weight parameters representing the topographic features according to the topographic features of the data acquisition time stamps; obtaining weight parameters representing the vehicle-mounted environment sensor information according to the vehicle-mounted environment sensor information of each data acquisition time stamp; combining weight parameters representing the topographic features and weight parameters representing the information of the vehicle-mounted environmental sensor to obtain comprehensive topographic influence indexes of the unmanned mine car in each time period;
acquiring vehicle motion influence indexes of the unmanned mine car in each time period according to the vehicle motion information of each data acquisition time stamp; obtaining acceleration coefficients of PSO algorithm at each moment according to comprehensive topography influence indexes of the unmanned mine car in each time period;
obtaining social factors of PSO algorithm at each moment according to the vehicle motion influence indexes of the unmanned mine car in each time period; acquiring an objective function at each moment; and the intelligent positioning of the mining area unmanned mine car is completed by combining the acceleration coefficient of the PSO algorithm, the social factors of the PSO algorithm and the objective function at each moment.
2. The intelligent positioning method for mining unmanned mine vehicles in mining areas according to claim 1, wherein the collecting of the topographic features, the vehicle-mounted environment sensor information and the vehicle motion information of each data collecting time stamp in the mining area unmanned mine vehicle time period comprises the following steps:
the vehicle-mounted environment sensor comprises a vehicle, a vehicle-mounted environment sensor and a mine mouth, wherein the vehicle-mounted environment sensor comprises obstacle shapes, obstacle sizes and obstacle distances, the vehicle motion information comprises vehicle speed, driving direction deflection and acceleration, influence parameters are set, the position of an unmanned mine car is taken as the center of a circle, the area with the influence parameters being the diameter is marked as a signal influence range of the unmanned mine car, the mountain information is the ratio of mountain terrain areas in the signal influence range of the unmanned mine car, the mine mouth information is the ratio of pit areas in the signal influence range of the unmanned mine car, and the mine mouth information is the ratio of mine mouth areas in the signal influence range of the unmanned mine car.
3. The intelligent positioning method for the mining unmanned mine car according to claim 1, wherein the weight parameters representing the topographic features are obtained according to the topographic features of each data acquisition time stamp, and the expression is as follows:
;
in the method, in the process of the invention,weight parameter representing topographical features, +.>Indicate->Mountain information data value under sub-data acquisition time stamp,/->Indicate->Tunnel information data value under secondary data acquisition time stamp, < >>Indicate->Mine port information data value under secondary data acquisition time stamp, < >>For a period of time +.>The number of data acquisition time stamps in +.>Represents->The>A data value for a data type.
4. The intelligent positioning method of the mining unmanned mine car according to claim 1, wherein the weight parameter representing the information of the vehicle-mounted environment sensor is obtained according to the information of the vehicle-mounted environment sensor of each data acquisition time stamp, and the expression is as follows:
;
in the method, in the process of the invention,weight parameter representing information of vehicle-mounted environment sensor, < ->Indicate->Obstacle shape data value under secondary data acquisition timestamp +.>Indicate->Obstacle size data value under secondary data acquisition timestamp, +.>Represent the firstObstacle distance data value under the secondary data acquisition time stamp +.>For a period of time +.>The number of data acquisition time stamps in +.>Is->The>Data value of seed data type +.>Representing an exponential term.
5. The intelligent positioning method for the unmanned mining car in the mining area according to claim 1, wherein the comprehensive topography influence index of the unmanned mining car in each time period is obtained by combining the weight parameter representing topography characteristics and the weight parameter representing vehicle-mounted environment sensor information, and the expression is as follows:
;
in the method, in the process of the invention,for unmanned mine car in a time period +.>An integrated topography influence index in>For a period of time +.>The number of data acquisition time stamps in +.>For the number of different data types under one data acquisition time stamp,/for the number of different data types under one data acquisition time stamp>Represents the firstThe>Data value of seed data type +.>Weight parameter representing topographical features, +.>Weight parameter representing information of vehicle-mounted environment sensor, < ->Representing an exponential term.
6. The intelligent positioning method for the unmanned mining car in the mining area according to claim 2, wherein the obtaining the vehicle movement influence index of the unmanned mining car in each time period according to the vehicle movement information of each data acquisition time stamp comprises the following steps:
for each data acquisition time stamp, the product of the calculated vehicle speed and the vehicle acceleration is recorded as a first product, the ratio of the deflection of the driving direction to the first product is calculated, the inverse number of the ratio is used as an index of an exponential function based on a natural constant, the inverse number of the square of the obstacle distance is calculated, the product of the calculated result of the exponential function and the inverse number is calculated and recorded as a second product, and the sum of the second products of all the data acquisition time stamps in each time period is used as a vehicle movement influence index in each time period.
7. The intelligent positioning method for the unmanned mining car in the mining area according to claim 1, wherein the step of obtaining the acceleration coefficient of the PSO algorithm at each moment according to the comprehensive topography influence index of the unmanned mining car in each time period comprises the following steps:
setting a first smoothing parameter, taking the comprehensive terrain influence index in a time period before each moment as an independent variable of an arctangent trigonometric function, and taking the ratio of the calculation result of the arctangent trigonometric function to the first smoothing parameter as an acceleration coefficient of a PSO algorithm at each moment.
8. The intelligent positioning method for the unmanned mine car in the mining area according to claim 1, wherein the social factors of the PSO algorithm at each moment are obtained according to the vehicle motion influence indexes of the unmanned mine car in each time period, and the method comprises the following steps:
setting a second smoothing parameter, taking the opposite data of the vehicle motion influence index in a period of time before each moment as an index of an exponential function based on a natural constant, calculating the sum of the opposite number and 1 of the calculation result of the exponential function, and taking the product of the sum and the second smoothing parameter as a social factor of the PSO algorithm at each moment.
9. The intelligent positioning method for the unmanned mining car in the mining area according to claim 2, wherein the step of obtaining the objective function at each moment comprises the following steps:
setting a weight coefficient, calculating the sum of mountain information, tunnel information and mine mouth information aiming at each data acquisition time stamp, calculating the product of the sum and the obstacle distance, marking the product as a third product, acquiring the position of the unmanned mine car at each moment according to an AOA algorithm, calculating the product of the position and the weight coefficient, marking the product as a fourth product, calculating the sum of the third product and the fourth product, and taking the sum of all data acquisition time stamps in a time period before each moment as an objective function of each moment.
10. The intelligent positioning method for the mining unmanned mine car according to claim 1, wherein the intelligent positioning for the mining unmanned mine car is completed by combining the acceleration coefficient of the PSO algorithm at each moment, the social factor of the PSO algorithm at each moment and the objective function at each moment, and comprises the following specific steps:
and taking the acceleration coefficient of the PSO algorithm at each moment and the social factor of the PSO algorithm at each moment as the acceleration coefficient and the social factor of the PSO algorithm at each moment, taking the objective function at each moment as the input of the PSO algorithm at each moment, and taking the output of the PSO algorithm at each moment as the accurate positioning of the unmanned mine car at each moment.
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