CN111428998B - Cloud cleaning robot layout method based on self-similar characteristics of railway transportation network - Google Patents
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Abstract
The invention relates to a cloud cleaning robot layout method based on self-similar characteristics of a railway transportation network, which comprises the following specific steps: setting each city along the railway as a node, and obtaining a clustering node of the network based on the self-similar characteristic index sum; calculating the number of maintenance indexes of the cluster nodes according to the interval time and the driving distance of all trains parked by the cluster nodes; comparing the obtained maintenance index number with the node type to determine the classification of the city; the number of robots is arranged according to the hierarchy of cities. The problem of realizing robot quantity arrangement in a complex railway network is solved. Is beneficial to improving the efficiency and saving the cost.
Description
Technical Field
The invention belongs to the technical field of railway cleaning system design and layout, and particularly relates to a cloud cleaning robot layout method based on self-similar characteristics of a railway transportation network.
Background
The disclosure of this background section is only intended to increase the understanding of the general background of the invention and is not necessarily to be construed as an admission or any form of suggestion that this information forms the prior art already known to those of ordinary skill in the art.
With the increasing perfection of railway construction planning in China and the rapid development of high-speed rail manufacturing industry in China, the research and manufacturing of the matched service equipment of high-speed rail are also attracting attention in recent years. Along with the improvement of the high-speed rail manufacturing process and the technology level, the requirements on a high-speed rail cleaning system are also improved, and correspondingly, the traditional manual cleaning mode is replaced by high-efficiency automatic cleaning.
The shape of the surface of the high-speed rail is extremely irregular, the traditional automatic cleaning equipment is difficult to cope with the cleaning task of the complex special-shaped surface, the high-speed rail is of a three-dimensional structure, the planar cleaning equipment similar to a sweeping robot is not available on the surface of the high-speed rail, and the difficult points greatly restrict the automatic process of the high-speed rail cleaning system.
The Chinese operators are wide, the population is numerous, and correspondingly, the railway network of China is large in scale, and thousands of sites are distributed in all provinces and cities. The railway network in China is expanded in scale, the quality is improved, the transportation capacity is rapidly expanded, the equipment level is improved, and meanwhile, if the cleaning equipment cannot be reasonably arranged, the operation efficiency of the railway network is reduced.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide a cloud cleaning robot layout method based on the self-similar characteristics of a railway transportation network.
In order to solve the technical problems, the technical scheme of the invention is as follows:
in a first aspect, a cloud cleaning robot layout method based on self-similar characteristics of a railway transportation network comprises the following specific steps:
setting each city along the railway as a node, and dividing the railway network into a plurality of sub-railway networks based on self-similar characteristic indexes;
calculating the degree of a plurality of nodes in a plurality of sub-railway networks, wherein the node with the highest degree value in one sub-railway network is a clustering node of the sub-railway network;
calculating the number of maintenance indexes of the nodes according to the interval time and the running distance of all trains parked by the nodes;
comparing the maintenance index number with a threshold value, and determining the classification of the city according to the types of the nodes;
and carrying out layout of the cloud cleaning robot according to the classification of the cities.
The robot layout method can solve the problem of the number arrangement of the cloud cleaning robots in the complicated railway network. Is beneficial to improving the efficiency and saving the cost.
The nodes in the invention represent cities along the railway, and whether the trains stop is not a mode for judging whether the trains stop or not, namely the stations are necessarily nodes, the nodes comprise the stations and the cities where the trains do not stop, and the invention takes the cities along the railway as the nodes. The invention selects the city which does not stop as the node, and can divide the city which the railway network passes into a plurality of sub-railway networks according to the self-similar characteristics.
The layout method of the invention has the advantages that: the whole railway network can be more reasonably distributed, the whole railway network is divided into a plurality of sub-railway networks according to the nodes and the clustering nodes, and one type of city, two types of cities and three types of cities in the railway network can be found. Unlike available human views, the present invention can find city, two kinds of city and three kinds of city in railway network. The cloud cleaning robot can be better distributed.
The layout method improves the accuracy of the distribution of the number of the cloud cleaning robots, the railway network is a huge network, the number of vehicles transported on the railway network is very large, and the transportation and the dispatching of the trains determine the accuracy and the safety of the transportation of the whole railway network. The distribution of the cloud cleaning robot determines that all trains on the railway network can be cleaned, and determines that the cleaning is effective and quick. The layout method of the invention can improve the train cleaning speed and the effectiveness in the railway network.
According to the invention, the robot layout is carried out by city classification, the direction of the demand in the railway network is classified, the utilization rate of the robot can be improved to the greatest extent, the manpower demand is reduced most effectively, the sudden task and the occasional faults are classified and laid out according to the demand in the railway network, and the robustness and the high tolerance are improved.
The invention has the beneficial effects that:
according to the invention, through defining the passed cities as nodes, according to the characteristics of the self-similar network, the self-similar characteristics are utilized to find out the clustering nodes, the clustering points of the railway network are found out, the cities represented by the clustering nodes are calculated, the maintenance indexes are compared according to the maintenance indexes and the threshold value, the classification of each city on the railway network is obtained, and the reasonable robot distribution is realized.
The different workloads of the railway network to be processed in each city can be comprehensively considered;
according to the invention, the AGVs are matched with the mechanical arms to perform the growing layout distribution of the railway network, the automatic guiding device of the AGVs is utilized to realize the running of the guiding path, the intelligent sharing of the whole railway network can be realized, and the utilization efficiency of the cleaning equipment is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a schematic view of a cleaning cart incorporating an AGV and robotic arm used in the present invention;
FIG. 2 is a flow chart of an algorithm for obtaining the self-similarity characteristic index of the railway network by box method calculation in the invention;
FIG. 3 is a flowchart of an algorithm for a multi-robot growth layout strategy according to the self-similar characteristics as proposed in the present invention.
Detailed Description
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. 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.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
In a first aspect, a layout method of a cloud cleaning robot based on self-similar characteristics of a railway transportation network includes the specific steps of:
setting each city along the railway as a node, and dividing the railway network into a plurality of sub-railway networks based on self-similar characteristic indexes;
in a plurality of sub-railway networks, determining cluster nodes in each sub-railway network by calculating the degrees of the plurality of nodes;
calculating the number of maintenance indexes of the nodes according to the interval time and the running distance of all trains parked by the nodes;
and comparing the maintenance index number with a threshold value, and determining the classification of the city according to the types of the nodes.
In some embodiments of the present invention, the whole network is divided into a plurality of sub-networks according to the fractal dimension characteristics after calculating the fractal dimension of the whole network using the box method according to the self-similarity characteristics of the railway network.
The self-similarity characteristic index of the railway network is calculated by utilizing a box method, and the specific process is as follows:
numbering each node in a railway network as i, wherein the range of i is 1-N, N is the total number of nodes, and the nodes represent each city along the railway;
when the node i takes 1, the color is assigned to 0;
computing node<Distance L between other node j of i and node i ij ；
Setting the size L of the box b ＝1；
Select L ij >L b Color value that is not used at the current box size for node j of (i) as node i at L b Color values of (2);
compute array C ij The total number of color values used in each column, the slope of log (Nb (Lb)) versus log (Lb) is fitted;
obtaining a self-similarity characteristic index;
where Nb is the number of boxes.
According to the self-similarity characteristic index (fractal dimension), the index with the same characteristic in the railway network can be selected, and the method is a typical self-similarity network based on the railway network. Trains of a railway network run bi-directionally, and the bi-directional network has typical self-similar characteristics, such as geometric self-acquaintance of national railway network and provincial railway network. Therefore, the self-similar characteristic indexes are obtained by box operation, and a plurality of sub-railway networks are obtained by comparison.
In some embodiments of the invention, the calculation method of the degree of the node comprises the following steps:
setting a clustering node i;
calculating the side weights of the nodes i which are adjacent to each other, and summing the side weights of the nodes i to obtain the degree of the node i;
side weight W ij The calculation formula of (2) is as follows:
W ij distance between i and j×number of passes;
the city j is a city adjacent to the city i;
degree S of node i i The calculation formula of (2) is as follows:
the degree of a node is the sum of the edge weights of one node and all adjacent cities. The invention selects the distance between two adjacent cities and the number of train numbers as the index for measuring the clustering nodes, and aims at finding out the key node with the heaviest cleaning task and the most attention in a plurality of nodes.
The method for calculating the selected degree can better obtain the most focused node, takes the railway network as a line graph with a plurality of nodes connected, obtains the key nodes through the sum of the edge weights, and provides a better calculation mode for finding the clustering nodes.
In some embodiments of the present invention, the maintenance index is calculated as:
wherein T is ij For the interval time between two adjacent maintenance of the j-th train parked in i city, L ij And maintaining the driving distance between two adjacent times of the j-th train parked in the city for i.
The calculation modes of the maintenance indexes and the calculation modes of the degrees are different, and the interval time variable is added in the maintenance modes, so that the workload of one node can be better obtained, and the classification of the measured cities is facilitated.
In some embodiments of the invention, the method of city classification is:
judging the maintenance index, the threshold value and the type of the node as the basis;
if the maintenance index of the corresponding city of the node is higher than a preset threshold value and the node is a cluster node, defining the cluster node as a city (such as a regional big station);
if the maintenance index of the node is higher than the threshold value, and the node is a non-clustered node, the node is defined as a class-II city (for example, a plurality of large stations are in adjacent cities);
if the maintenance index of the node is lower than the threshold value and the node is a non-clustered node, three types of cities (arranged from high to low according to the maintenance index) are defined.
In some embodiments of the present invention, the method for performing the layout of the cloud cleaning robot according to the classification of the city includes: according to the classification of cities, the total number of robots, the cleaning quality requirement and the working capacity of the robots, weight distribution is carried out, and the number of the cloud cleaning robots is distributed; or, according to city classification, one city is allocated according to 50-60% of the total number of the cloud cleaning robots, the second city is allocated according to 25-35% of the cloud cleaning robots, and the third city is allocated according to 10-18% of the cloud cleaning robots.
In some embodiments of the invention, a cloud cleaning robot comprises a traveling trolley (AGV), a mechanical arm and a vision module, wherein the mechanical arm is arranged on the traveling trolley, and the vision module is arranged at the front end of one side of the traveling trolley.
The invention will be further illustrated by the following examples
Railway networks are typically self-similar networks, each city along a railway represents a respective "node", the "edge weights" between two nodes are defined as the distance between two cities multiplied by the number of times, the "degree" of each node is the sum of the edge weights connecting all edges of the node, and the two-way network has typically self-similar characteristics (e.g., geometric self-acquaintance of national railway network and intra-provincial railway network). However, whether the train stops is not a mode for judging the node, namely the station is necessarily a node, but the node is not necessarily a station, and the city where the railway passes is taken as the node in the patent; the number of passes is the number of passes between two cities.
Example 1
Railway networks are self-similar networks, and each city along a railway is represented as a "node" that includes a site, i.e., the site must be a node, but the node includes cities along the railway other than the site.
Firstly, calculating self-similarity characteristic indexes of a railway network by using a box method:
numbering each node in a railway network as i, wherein the range of i is 1-N, N is the total number of nodes, and the nodes represent each city along the railway;
when the node i takes 1, the color is assigned to 0;
computing node<Distance L between other node j of i and node i ij ；
Setting the size L of the box b ＝1；
Select L ij >L b Color value that is not used at the current box size for node j of (i) as node i at L b Color values of (2);
compute array C ij The total number of color values used in each column, the slope of log (Nb (Lb)) versus log (Lb) is fitted;
the fractal dimension (self-similarity characteristic) of the whole network is obtained through the slope obtained through fitting, the whole network is divided into a plurality of sub-networks according to the fractal dimension characteristic, and a plurality of nodes 1-N in the railway network are classified. Based on self-similarity, similar nodes are classified into a class, i.e., into a sub-rail network.
Second, the degree of progress is calculated:
side weight W between two adjacent cities i and j along railway line ij The method comprises the following steps:
W ij distance between i and j, number of passes.
For example, the distance between Jinan and Taian is 88km, and the number of high-speed rail passes between Jinan and Taian is 40 times, so W ij ＝3530。
Degree S of Jinan node i The method comprises the following steps:
wherein k represents the kth city along the railway adjacent to i.
Side weight W ij The number of passes in (a) refers to the number of passes to and from two cities. It is understood that the connection between i and j is W ij For the side weights of two cities, degree S i Is the sum of the side weights of all cities adjacent to the city i and the periphery.
The degree value of the node with more parking times can be obtained through the calculation of the slave degree, and the degree value of the node with less parking times is smaller.
For example, take Jinan to Taian as an example, jinan is i city, taian is j city, jinan and Taian are two adjacent cities.
W ij Distance between i and j, number of passes.
The distance between Jinan and Taian is 88km, and the number of high-speed rail passes between Jinan and Taian is 40 times, so W ij ＝3530。
Degree S of Jinan i For the sum of the total edge weights of several cities adjacent to Jinan city, such as other cities adjacent to Jinan, also have Texas, etc. Meanwhile, the large stations are larger in degree, the small stations are smaller in degree, but the number of the small stations is not quite large compared with the number of the large stations, and the small stations are still quite large in maintenance work because the number of stations adjacent to the small stations is also more and has a certain number of stops; the workload of large stations increases in power law.
In a sub-railway network, the degrees of all nodes are compared, and the node with the largest degree is the clustering node.
Thirdly, calculating the number of maintenance indexes of the nodes;
furthermore, a maintenance index M of i city is defined i The method comprises the following steps:
wherein T is ij For the interval time between two adjacent maintenance of the j-th train parked in i city, L ij And maintaining the driving distance between two adjacent times of the j-th train parked in the city for i.
Examples: t (T) ij For the interval time between two adjacent maintenance of the jth train parked in Jinan city, the jth vehicle is set as the 2 nd vehicle, L ij The distance travelled between two adjacent maintenance runs of the 2 nd vehicle is set to be 196km, L ij The distance travelled between two adjacent maintenance runs of the 1 st vehicle was set to 400km.
Then to T ij And M i And respectively carrying out normalization and adopting dimensionless calculation.
The requirement of the work load of the station is proportional to the maintenance index, so that the maintenance index of the city i, namely the Jinan city, can be obtained according to the maintenance index.
S can be obtained i And M i The heavy tail distribution characteristic is satisfied, and the maintenance index of the train is kept at a certain value even if the number of trains parked in a certain city is small.
The nodes are divided into three types, and a threshold value is set according to the cleaning task requirement.
Judging the maintenance index, the threshold value and the type of the node as the basis;
if the maintenance index of the corresponding city of the node is higher than a preset threshold value and the node is a cluster node, defining the cluster node as a city (such as a regional big station);
if the maintenance index of the node is higher than the threshold value, and the node is a non-clustered node, the node is defined as a class-II city (for example, a plurality of large stations are in adjacent cities);
if the maintenance index of the node is lower than the threshold value and the node is a non-clustered node, three types of cities (arranged from high to low according to the maintenance index) are defined.
The threshold value is a value that is manually specified,
the robot layout is carried out according to the first-class city, the second-class city and the third-class city, so that the utilization rate of the robot can be improved to the greatest extent, the manpower demand is reduced most effectively, and the robot has strong robustness and high tolerance to sudden tasks and accidental faults.
A method of performing a robot layout is illustrated,
according to the first method, weight distribution is carried out according to the classification of cities, the total number of robots, the requirement of cleaning quality and the working capacity of the robots, and the number distribution of the cloud cleaning robots is carried out. The distribution method is the existing method, urban classification of the railway network can be optimally obtained through the method, and then the distribution of the number of the final cloud cleaning robots is obtained according to weight distribution in the railway network.
The second method, such as 35 robots, is 20 cleaning robots arranged in one city (57% of the total number), 10 cleaning robots arranged in two cities (28% of the total number), and 5 cleaning robots arranged in three types (15% of the total number).
The robot adopts a cloud cleaning robot with a combination of a guiding trolley AGV (Automated Guided Vehicle) and a mechanical arm, wherein the guiding trolley AGV refers to a transport vehicle which is provided with an optical automatic guiding device and can run along a specified guiding path and has the functions of safety protection and various transfer; the mechanical arm is a cooperative mechanical arm with multiple degrees of freedom. As shown in FIG. 1, a mechanical arm is arranged on an AGV of a guiding trolley, a vision module guides the guiding trolley, a navigation code belt is arranged on the outer side of the guiding trolley, and the guiding trolley can walk along the navigation code belt.
Self-guided vehicles (Automated Guided Vehicle, AGVs) are intelligent mobile devices capable of autonomous planning tasks, performing real-time environmental identification to achieve obstacle avoidance operations. The intelligent logistics system is an important foundation of modern intelligent logistics and is widely applied to the fields of machining, material handling and the like. Generally, an AGV car is equipped with an electromagnetic or optical self-guiding device, and can travel along a predetermined guide path, and has a safe and reliable feature.
The robot provided by the invention can be conveniently used in a high-speed railway station by utilizing an integrated mobile robot, and the mechanical arm can be used for multi-angle adjustment, so that the problem that a good cleaning effect cannot be achieved due to irregular surface shape of the high-speed railway is solved.
Meanwhile, the robot utilizing the AGV and the mechanical arm has the characteristic of low cost, the robots among all nodes nationwide can realize intelligent sharing, and the average cost is reduced after the number of the robots is increased along with the popularization of the number of the robots.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A cloud cleaning robot layout method based on self-similar characteristics of a railway transportation network is characterized by comprising the following steps of: the method comprises the following specific steps:
setting each city along the railway as a node, and dividing the railway network into a plurality of sub-railway networks based on self-similar characteristics;
calculating the degree of a plurality of nodes in a plurality of sub-railway networks, wherein the node with the highest degree value in one sub-railway network is a clustering node of the sub-railway network;
calculating the number of maintenance indexes of the nodes according to the interval time and the running distance of all trains parked by the nodes;
comparing the maintenance index number with a threshold value, and determining the classification of the city according to the types of the nodes;
and carrying out layout of the cloud cleaning robot according to the classification of the cities.
2. The cloud cleaning robot layout method based on the self-similar characteristics of the railway transportation network according to claim 1, wherein: according to the self-similarity characteristic of the railway network, after the fractal dimension of the whole network is calculated by using a box method, the whole network is divided into a plurality of sub-networks according to the fractal dimension characteristic.
3. The cloud cleaning robot layout method based on the self-similar characteristics of the railway transportation network according to claim 2, wherein: the self-similarity characteristic index of the railway network is calculated by utilizing a box method, and the specific process is as follows:
numbering each node in a railway network as i, wherein the range of i is 1-N, N is the total number of nodes, and the nodes represent each city along the railway;
when the node i takes 1, the color is assigned to 0;
computing node<Distance L between other node j of i and node i ij ；
Setting the size L of the box b ＝1；
Select L ij >L b Color value that is not used at the current box size for node j of (i) as node i at L b Color values of (2);
compute array C ij The total number of color values used in each column, the slope of log (Nb (Lb)) versus log (Lb) is fitted;
obtaining a self-similarity characteristic index;
where Nb is the number of boxes.
4. The cloud cleaning robot layout method based on the self-similar characteristics of the railway transportation network according to claim 1, wherein: the calculation method of the node degree comprises the following steps:
setting a clustering node i;
calculating the side weights of other adjacent nodes with the node i, and summing the side weights of the node i to obtain the degree of the node i;
side weight W ij The calculation formula of (2) is as follows:
W ij distance between i and j×number of passes;
the city j is a city adjacent to the city i;
degree S of node i i The calculation formula of (2) is as follows:
5. the cloud cleaning robot layout method based on the self-similar characteristics of the railway transportation network according to claim 1, wherein: the maximum value of each sub-railway network is a clustering node.
6. The cloud cleaning robot layout method based on the self-similar characteristics of the railway transportation network according to claim 1, wherein: the calculation formula of the maintenance index is as follows:
wherein T is ij For the interval time between two adjacent maintenance of the j-th train parked in i city, L ij And maintaining the driving distance between two adjacent times of the j-th train parked in the city for i.
7. The cloud cleaning robot layout method based on the self-similar characteristics of the railway transportation network according to claim 1, wherein: the city classification method comprises the following steps:
judging the maintenance index, the threshold value and the type of the node as the basis;
if the maintenance index of the node corresponding to the city is higher than a preset threshold value and the node is a cluster node, defining the node as a city;
if the maintenance index of the node is higher than the threshold value and the node is a non-clustered node, defining the node as a class-II city;
if the maintenance index of the node is lower than the threshold value and the node is a non-clustered node, three types of cities are defined.
8. The cloud cleaning robot layout method based on the self-similar characteristics of the railway transportation network according to claim 1, wherein: the method for carrying out the layout of the cloud cleaning robot according to the classification of the city comprises the following steps: and carrying out weight distribution according to the classification of cities, the total number of robots, the cleaning quality requirement and the working capacity of the robots, and carrying out the quantity distribution of the cloud cleaning robots.
9. The cloud cleaning robot layout method based on the self-similar characteristics of the railway transportation network according to claim 1, wherein: the method for carrying out the layout of the cloud cleaning robot according to the classification of the city comprises the following steps: according to city classification, one city is allocated according to 50-60% of total number of the cloud cleaning robots, two cities are allocated according to 25-35% of the cloud cleaning robots, and three cities are allocated according to 10-18% of the cloud cleaning robots.
10. The cloud cleaning robot layout method based on the self-similar characteristics of the railway transportation network according to claim 1, wherein: the robot comprises a walking trolley, a mechanical arm and a vision module, wherein the mechanical arm is arranged on the walking trolley, and the vision module is arranged on one side of the walking trolley.
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