CN110428161B - Unmanned mine car cloud intelligent scheduling method based on end edge cloud architecture - Google Patents
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Abstract
The invention discloses an intelligent unmanned mine car cloud scheduling method based on an end edge cloud framework, wherein an intelligent unmanned mine car cloud scheduling system mainly comprises four subsystems, namely a monitoring emergency system, an intelligent parking lot system, an unmanned mine car and a cloud intelligent scheduling platform; according to the intelligent unmanned mine car cloud scheduling method based on the end edge cloud framework, the new application scene and implementation method of the unmanned car are set, a new thought and method is provided for unmanned mine car scheduling, and the operation cost of an enterprise can be effectively reduced; the unmanned mine car can be effectively prevented from occupying road resources in a mining area under the task-free condition, and the overall transportation efficiency is improved.
Description
Technical Field
The invention relates to the field of intelligent transportation, in particular to an unmanned mine car cloud intelligent scheduling method based on an end edge cloud framework.
Background
The automatic driving is a product of deep integration of the automobile industry and new generation information technologies such as artificial intelligence, internet of things and high-performance computing, and is a main direction of intelligent and networking development of the current global automobile and traffic travel field. In the driving process, sensor assemblies such as a video camera, a radar sensor and a laser range finder are used for detecting the information of obstacles around the vehicle, and the vehicle is controlled to avoid the obstacles around the vehicle. Meanwhile, the working environment of the mining area is severe, the danger coefficient is high, and a large number of large-scale mechanical devices are provided, so that the problems of personnel recruitment and difficult management exist on one hand, and the road boundary of the mining area is not obvious, so that safety accidents are easily sent in a high-dust environment and other severe weather, and serious personal casualties and property loss are caused.
The use of the unmanned mine car improves the health performance index of enterprises to a certain extent, reduces the labor cost of a mining area, improves the production efficiency of the mining area, and is beneficial to building green mining areas and intelligent mining areas. Based on the above background, the application of unmanned mine cars to open pit mines would yield tremendous benefits. The system not only can thoroughly solve the problems of difficult recruitment and management of drivers in the mining area, but also can reasonably plan the unmanned mine car dispatching scheme through the intelligent platform, thereby improving the comprehensive operation benefit of the mining area.
In the existing domestic patent, the research on the unmanned mine car is less, the research on the design of the unmanned mine car and the cooperative control of the car is more in content, and how to intelligently schedule the unmanned mine car under different tasks and multiple loading points is not involved, so that reasonable task allocation is carried out, and the comprehensive operation benefit maximization of a mining area is realized. Therefore, it is very necessary to provide an intelligent unmanned mine car cloud scheduling system based on an end edge cloud architecture.
Disclosure of Invention
In order to solve the problems, the invention provides an unmanned mine car cloud intelligent scheduling method based on an end edge cloud architecture, which is realized by adopting an unmanned mine car cloud intelligent scheduling system, wherein the system mainly comprises four subsystems, namely a monitoring emergency system, an intelligent parking lot system, an unmanned mine car and a cloud intelligent scheduling platform.
The monitoring emergency system is a special function module loaded on the unmanned mine car and comprises a monitoring module and an emergency management module. The monitoring module is used for monitoring the mine car task completion condition, the monitoring module comprises a field task condition sent back by a vehicle-mounted video sensor of the unmanned mine car and a video sent back by monitoring installed in a mine area, and managers can switch the video at will to check and master the operation condition of the whole mine area; the emergency management module is used for controlling the mine car through the emergency management module when the automatic driving mine car has an emergency problem, and forcing the mine car to stop a task so as to prevent safety accidents.
The unmanned mine car comprises a communication module, an environment sensing module, a planning and decision-making module, a positioning module, a self-service parking module and a control module. The communication module is used for receiving a control instruction sent by the cloud intelligent scheduling platform and simultaneously transmitting decision information and real-time running conditions of the unmanned mine car back to the cloud intelligent scheduling platform, wherein the decision information and the real-time running conditions comprise the geographical position, the speed, a planned running route and the like of the mine car; the environment sensing module acquires the mine area environment where the unmanned mine car is located through equipment such as a sensor and a video, acquires the travelable road condition information, is used for passable road surface identification, barrier identification and the like, and provides a basis for the operation control of the unmanned mine car; the positioning module is used for acquiring the geographical position information of the unmanned mine car; the self-service parking module finishes two key tasks of parking space searching and automatic backing and warehousing when the loading task of the unmanned mine car is finished and no new task is accessed, searches a parking area close to the current position of the mine car by taking the current geographic position of the mine car and surrounding environment parameters as base points, obtains the information of the number of the remaining parking spaces, and obtains a correct parking strategy and a parking path according to a specific parking algorithm. Detecting parking space information through an environment sensing system of a vehicle body, and automatically parking the vehicle into a position through an automatic driving system; and the control device controls the unmanned mine car according to the road information, the position information and the control instruction to meet the operation requirement of the unmanned mine car.
The intelligent parking lot system is a set of auxiliary systems arranged for realizing self-service parking of unmanned mine cars, and can realize the functions of real-time monitoring and management of parking places in a parking area of a mining area, vehicle identification and the like. The intelligent parking lot system can release the information of the remaining parking spaces in real time on line and perform information interaction with the unmanned mine car, so that the rapid warehousing of the vehicles is assisted. The setting in intelligence parking area can conveniently manage and overhaul the mine car, and on the other hand can avoid unmanned mine car when not having new task to access, and the parking original place occupies road surface space in the mining area, influences other vehicles current. The intelligent parking lot system mainly comprises a geomagnetic sensor, a wireless communication module, a real-time information management and release module and the like.
The cloud intelligent scheduling platform receives the excavator loading task information and the real-time position and speed information of the unmanned mine car, analyzes the task demand and the task position point information, comprehensively considers the factors such as the transportation efficiency, the task level importance and the vehicle loading capacity, generates a vehicle scheduling instruction and issues the vehicle scheduling instruction to the unmanned mine car.
The method comprises the steps of uploading a mining area work task to a cloud intelligent scheduling platform. The work task information includes: number of loading area, size of loading amount, time for starting loading, etc.; the cloud intelligent scheduling platform collects task orders and updates the current running/waiting state of the unmanned mine car;
and step two, a computing module of the cloud intelligent scheduling platform contains map files of the mining area, and the mine cars matched with the computing module are searched by analyzing the geographic position of the loading area and the required loading capacity in the task requirement. The mine car searched by the cloud intelligent scheduling platform is not only a static waiting vehicle in a parking lot of a mining area, but also comprises an unmanned mine car with a loading allowance in a working state. If the unmanned mine car in the working state with the loading allowance can be matched, the vehicle is preferentially selected, so that the using quantity of the unmanned mine car is reduced, and the operation cost is reduced; if the in-transit vehicle meeting the conditions is not searched within the specified time, the static waiting vehicle is searched. In the process, the cloud intelligent scheduling platform needs to complete the establishment of a scheduling model and the algorithm solution of the multi-target multi-unmanned mine car, and the specific description is as follows:
s201, establishing a multi-target multi-unmanned mine car scheduling model. And matching the loading task requirements to a mining area map, marking the loading task requirements as nodes, and setting the running path of the unmanned mine car as a connecting line of each node on the path. The number of unmanned mine cars in the dispatching system is determined, and the system is driven at a constant speed during operation, the system is refreshed once when a new task is accessed, matching is performed once on the basis of an initial allocation scheme, if an unmanned mine car in transit with a loading margin can be found, the mine car is preferentially arranged, otherwise, a new mine car is arranged.
Description of the symbols:
set of loading task points as T0＝{1，2，...，NT}，NTThe number of task points is loaded;
the loading amount is integrated into
For loading task point NTThe loading requirements of (a);
set of unloading areas as T1＝{1，2，...，NX}，NXThe number of unloading areas;
mining area parking lot set B ═ {1, 2., NB}，NBThe number of parking lots in the mining area;
total set T ═ T of loading and unloading task points in mining area0∪T1The total set N ═ T of all nodes in the mining area0∪B∪T1
The route between the loading and unloading task points is ATThe method comprises the steps of (i, j) i, j ∈ T ^ i ≠ j), and line sets A between different loading and unloading task points and different parking areas0{ (i, j) | i ∈ B ^ j ∈ T, or i ∈ T ^ j ∈ B };
each path (i, j) is obtained by planning the path of the unmanned mine car, dij(ii) represents the distance the unmanned tramcar travels along (i, j);
tijrepresenting the travel time from i to j,
v represents the running speed of the mine car;
An objective function:
a minimum transportation time cost
Wherein N is a set of all nodes;
a p-th unmanned mine car which is a decision variable and takes the value of 0 or 1 and is positioned in the parking area b
When the loading and unloading task point is from i to j, the value is 1, otherwise, the value is 0, tijRepresenting the travel time from i to j,
show unmanned mine car
The time for carrying out loading and unloading task stay at the point i;
secondly, the total number of the unmanned mine cars for completing the loading and unloading tasks is minimum
Show unmanned mine car
Starting from the parking area b to the loading area j;
constraint conditions are as follows:
the number of the unmanned mine cars for executing the loading and unloading tasks in each parking area does not exceed the maximum number of the unmanned mine cars owned by the parking area
Secondly, because each unmanned mine car is limited by oil consumption, if the vehicles do not consume oil in the loading process, the total working time of any unmanned mine car does not exceed Tpmaxmin
And returning the unmanned mine car to the parking area nearest to the last task point if no new task is accessed after the unmanned mine car executes the loading and unloading task.
Show unmanned mine car
Starting from the parking area b, the vehicle arrives at the loading area j, and the value is 0 or 1.
Show unmanned mine car
Returning from the loading area i to the parking area d and taking the value of 0 or 1
And (4) constraint of the loading capacity of the unmanned mine car: the number of people in the loading capacity of the mine car can not exceed the maximum capacity in the process of carrying out the loading task, some
In the formula
Show mine car
Tonnage of ore loaded at i load point, Q represents maximum allowable load of unmanned mine car
And (3) constraint of the demand of five mining areas: any loading area has loading task requirements, and the unmanned mine car needs to complete the task amount Q of each loading areaiIndicates the required load of the i load zone,
show unmanned mine car
Tonnage of ore loaded at i load point
Six time sequence constraints are adopted, and meanwhile, the unmanned mine car is guaranteed to be loaded and unloaded firstly.
Show unmanned mine car
Time to reach load/unload point i, tijRepresenting the travel time from i to j,
show unmanned mine car
Time of arrival at loading/unloading point j
S202, solving the proposed multi-target multi-unmanned mine car scheduling model, and finding out a better solution by using a particle swarm algorithm. In the d-dimensional space, there are n particles. Wherein the position Xi of the particle i is (x)i1，xi2，...，xid) At a velocity of Vi＝(vi1，vi2，...，vid) ，xi1A first dimension parameter, x, representing the position of a particle i in d-dimensional spacei2Second dimension parameter, x, representing the position of particle i in d-dimensional spaceidD-dimensional parameter, v, representing the position of particle i in d-dimensional spacei1A first dimension parameter, v, representing the velocity of a particle i in d-dimensional spacei2A second dimension parameter, v, representing the velocity of the particle i in d-dimensional spaceidA d-th dimension parameter representing the velocity of the particle i in the d-dimension space. Best position Pbest searched by particle iid＝(pbesti1，pbesti2，...，pbestid)，pbesti1The first dimension parameter, pbest, representing the best position found by particle i in d-dimensional spacei2A second dimension parameter, pbest, representing the best position found by particle i in d-dimensional spaceidD-dimension parameter representing the best position searched by particle i in d-dimension space, the best position Gbest experienced by the populationid＝(gbesti1，gbesti2，...，gbestid) ，gbesti1The first dimension parameter, gbest, representing the best position experienced by the population in d-dimensional spacei2A second dimension parameter, gbest, representing the best position experienced by the population in d-dimensional spacei3A d-th dimension parameter representing the best position experienced by the population in the d-dimensional space. D-dimension velocity of ith particle in k-th iteration
And position
The update formula of (2) is:
in the formula:
showing the d-dimension position of the ith particle in the (k-1) th iteration,
indicating the best position found by the ith particle at the kth iteration,
representing the best position the population experiences at the kth iteration, w is the inertial weight; c. C1And c2Is a learning factor; r is1And r2Are random numbers uniformly distributed between (0, 1). The d-th dimension has a position variation range of [ -XMaxd，XMaxd]The speed variation range is [ -VMaxXd，VMaxXd]。
For each particle, its current fixness value is compared to the fixness value corresponding to the historically best location (Pbest). If the current fixness value is higher, the current position is updated to Pbest. For each particle, its current fixness value is compared to the fixnessvalue corresponding to the location (Gbest) for which the population history is best. If the current fixness value is higher, the current location is updated to Gbest. The unmanned mine car cloud intelligent scheduling model based on the end edge cloud framework has 2 objective functions, due to different dimensions, normalization is converted into single-objective solution, and the obtained fitness function expression is as follows:
in the formula N1、N2In order to eliminate the artificially selected weight coefficients of different dimensions, i and j represent task points, N represents the total set of all nodes in a mining area,
is the car number of the parking area b, b is the number of the parking area, p is the car number of the parking area,
indicating parking areasb the set of unmanned mine cars,
is a decision variable with the value of 0 or 1, and is positioned in the p-th unmanned mine car in the parking area b
When the loading and unloading task point is from i to j, the value is 1, otherwise, the value is 0, tijRepresenting the travel time from i to j, representing the unmanned tramcar
The time of the loading and unloading task staying at the point j, B is the set of the parking lots in the mining area, T0Is to load a set of task points,
show unmanned mine car
Starting from the parking area b, the vehicle arrives at the loading area j, and the value is 0 or 1. The particle swarm algorithm parameters mainly comprise inertia weight, learning factors, population size, iteration times and penalty coefficients. The value of the inertia weight is generally 0.2 ≦ w ≦ 1, and is usually [0.4, 0.9 ]]The effect is better, and through many experiments, the inertia weight value w is set to be 0.8 and the population size is set to be 50.
The constraints of the model are satisfied during the particle encoding process. The distribution problem of the unmanned mine car is represented by a 2 xM (M is the total number of subtasks dividing each loading area according to the loading capacity) dimensional vector, the first dimension of the particles represents the task distribution and the transportation sequence of the loading areas, and the second dimension represents the unloading area corresponding to each subtask. The first dimension of task allocation and shipping order is generated using a roulette strategy. The second dimension is a random arrangement of the unloading zone. Taking 3 loading zones as an example, the loading capacity can be divided into 9 subtasks. For example, let 4 random numbers be generated from the interval [0, 1 ]:
r1＝0.14，r2＝0.18，r3＝0.57，r4＝0.96
the selection probability table generated by the 4 unmanned mine cars according to the roulette strategy is as follows:
step three, after the cloud intelligent scheduling platform matches the task point of the loading and unloading area of the mining area with the unmanned mine car, a scheduling instruction is sent to the vehicle, the vehicle is started and starts to execute the task, in the process, the monitoring emergency system of the unmanned mine car is started, and managers can check the real-time working condition of the vehicle;
and step four, in the working process of the unmanned mine car, the driving route and the driving state of the unmanned mine car are mastered by the cloud intelligent scheduling platform, and when a new task is required to be accessed, the cloud intelligent scheduling platform judges whether vehicles meeting the requirements exist. If so, sending the new task to the unmanned mine car, repeating the third step, and completing the tasks in sequence; if not, after the vehicle completes the original task, the self-service parking module of the unmanned mine car is started. And interacting with an intelligent parking lot system in a mining area based on the current vehicle geographic position to obtain the parking area site and the number of parking spaces, and determining a parking route according to an optimal parking path algorithm so as to control the unmanned mine car to go to the parking point and back to enter the garage.
And fifthly, carrying out a waiting mode after the unmanned mine car backs up and enters the garage, sending final position information to the cloud intelligent scheduling platform and updating the vehicle state.
The invention has the advantages and positive effects that:
(1) the invention provides an intelligent unmanned mine car cloud scheduling method based on an end edge cloud framework, which sets a new application scene and an implementation method of an unmanned car and provides a new method for an unmanned mine car scheduling;
(2) in the matching process of the unmanned mine car and the loading task area, on the basis of ensuring that the initial allocation scheme is not changed, the unmanned mine car with the surplus is selected to receive a new task, so that excessive heavy machinery is prevented from being started, and the operation cost of an enterprise can be effectively reduced;
(3) the invention provides the requirement of autonomous parking for the unmanned mine car under the condition of no task, and sets the concept of the intelligent parking lot, thereby effectively avoiding the unmanned mine car from occupying road resources in the mining area under the condition of no task and improving the overall transportation efficiency.
Drawings
FIG. 1 is a structural diagram of an unmanned mine car cloud intelligent dispatching system based on an end edge cloud architecture;
FIG. 2 is a specific operation flow of the unmanned mine car cloud intelligent scheduling system based on the end edge cloud architecture;
FIG. 3 is a schematic diagram of the information communication between the intelligent parking lot system and the unmanned tramcar according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Referring to fig. 1, the unmanned mine car cloud intelligent scheduling system based on the end edge cloud architecture of the embodiment includes a monitoring emergency system, an intelligent parking lot system, an unmanned mine car and a cloud intelligent scheduling platform. The monitoring emergency system is used for monitoring the real-time working condition of the unmanned mine car and the condition of the whole mining area, and in an emergency situation, a manager brakes the mine car forcibly and takes over the unmanned mine car; the intelligent parking lot system is a basic system for self-service parking of the unmanned mine car, and acquires parking space occupation information through the geomagnetic sensor and performs information communication with the unmanned mine car; the unmanned mine car comprises a communication module, an environment sensing module, a planning and decision-making module, a control module and a self-service parking module, wherein the communication module is used for receiving a control instruction sent by a cloud intelligent scheduling platform and simultaneously transmitting decision-making information and real-time running conditions of the unmanned mine car back to the cloud intelligent scheduling platform, the environment sensing module is used for collecting the environment of a mining area where the unmanned mine car is located and acquiring road information for obstacle identification, the positioning module is used for acquiring geographical position information of the unmanned mine car, the self-service parking module is in information communication with an intelligent parking lot system to help the vehicle to quickly lock the parking lot and automatically park in the parking lot, and the control device is used for controlling the unmanned mine car according to the road information, the position information and the control instruction to meet running requirements of the unmanned mine car; and the cloud intelligent scheduling platform performs task allocation on the unmanned mine car through a scheduling algorithm.
The embodiment provides a set of unmanned mine car cloud intelligent scheduling system based on end edge cloud framework, and the transportation service function of loading and unloading in a mining area is efficiently realized.
As shown in fig. 2, an embodiment of the present invention provides a specific operation flow of an unmanned mine car cloud intelligent scheduling system based on an end edge cloud architecture, including:
step one, a manager uploads a working task of a mining area to a cloud intelligent scheduling platform. The work task information includes: number of loading area, size of loading amount, time for starting loading, etc.; the cloud intelligent scheduling platform collects task orders and updates the current running/waiting state of the unmanned mine car;
and step two, a computing module of the cloud intelligent scheduling platform contains map files of the mining area, and the mine cars matched with the computing module are searched by analyzing the geographic position of the loading area and the required loading capacity in the task requirement. The mine car searched by the cloud intelligent scheduling platform is not only a static waiting vehicle in a parking lot of a mining area, but also comprises an unmanned mine car with a loading allowance in a working state. If the unmanned mine car in the working state with the loading allowance can be matched, the vehicle is preferentially selected, so that the using quantity of the unmanned mine car is reduced, and the operation cost is reduced; if the in-transit vehicle meeting the conditions is not searched within the specified time, the static waiting vehicle is searched. Taking 5min as an example, if no qualified mine car is found within 5min, a waiting mine car in a parking area near the loading area is found, and a new vehicle is dispatched to perform the loading task. In the process, the cloud intelligent scheduling platform needs to complete the establishment of the scheduling model and the algorithm solution of the multi-target multi-unmanned mine car, and is specifically described as follows:
s201, establishing a multi-target multi-unmanned mine car scheduling model. And matching the loading task requirements to a mining area map, marking the loading task requirements as nodes, and setting the running path of the unmanned mine car as a connecting line of each node on the path. The number of unmanned mine cars in the dispatching system is determined, and the system is driven at a constant speed during operation, the system is refreshed once when a new task is accessed, matching is performed once on the basis of an initial allocation scheme, if an unmanned mine car in transit with a loading margin can be found, the mine car is preferentially arranged, otherwise, a new mine car is arranged.
An objective function:
a minimum transportation time cost
Wherein N is a set of all nodes;
a p-th unmanned mine car which is a decision variable and takes the value of 0 or 1 and is positioned in the parking area b
When the loading and unloading task point is from i to j, the value is 1, otherwise, the value is 0, tijRepresenting the travel time from i to j,
show unmanned mine car
The time for carrying out loading and unloading task stay at the point i;
secondly, the total number of the unmanned mine cars for completing the loading and unloading tasks is minimum
Indicating that the unmanned tramcar departs from the parking area b to reach the loading area j;
constraint conditions are as follows:
the number of the unmanned mine cars for executing the loading and unloading tasks in each parking area does not exceed the maximum number of the unmanned mine cars owned by the parking area
Secondly, because each unmanned mine car is limited by oil consumption, if the vehicles do not consume oil in the loading process, the total working time of any unmanned mine car does not exceed Tpmaxmin
And returning the unmanned mine car to the parking area nearest to the last task point if no new task is accessed after the unmanned mine car executes the loading and unloading task.
Show unmanned mine car
Starting from the parking area b to the loading area j, the value is 0 or 1,
show unmanned mine car
Returning from the loading area i to the parking area d and taking the value of 0 or 1
And (4) constraint of the loading capacity of the unmanned mine car: the number of people in the loading capacity of the mine car can not exceed the maximum capacity in the process of carrying out the loading task, some
In the formula
Show mine car
Tonnage of ore loaded at i load point, Q represents maximum allowable load of unmanned mine car
And (3) constraint of the demand of five mining areas: any loading area has loading task requirements, and the unmanned mine car needs to complete the task amount Q of each loading areaiIndicates the required load of the i load zone,
show unmanned mine car
Tonnage of ore loaded at i load point
Six time sequence constraints are adopted, and meanwhile, the unmanned mine car is guaranteed to be loaded and unloaded firstly.
Show unmanned mine car
Time to reach load/unload point i, tijRepresenting the travel time from i to j,
show unmanned mine car
Time of arrival at loading/unloading point j
S202, solving the proposed multi-target multi-unmanned mine car scheduling model, and finding out a better solution by using a particle swarm algorithm. In the d-dimensional space, there are n particles. Wherein the position X of the particle ii＝(xi1，xi2，...，xid) At a velocity of Vi＝(vi1，vi2，...，vid) ，xi1A first dimension parameter, x, representing the position of a particle i in d-dimensional spacei2Second dimension parameter, x, representing the position of particle i in d-dimensional spaceidD-dimensional parameter, v, representing the position of particle i in d-dimensional spacei1Representing the velocity of a particle i in d-dimensional spaceV of a first dimension ofi2A second dimension parameter, v, representing the velocity of the particle i in d-dimensional spaceidA d-th dimension parameter representing the velocity of the particle i in the d-dimension space. Best position Pbest searched by particle iid＝(pbesti1，pbesti2，...，pbestid) ，pbesti1The first dimension parameter, pbest, representing the best position found by particle i in d-dimensional spacei2A second dimension parameter, pbest, representing the best position found by particle i in d-dimensional spaceidD-dimension parameter representing the best position searched by particle i in d-dimension space, the best position Gbest experienced by the populationid＝(gbesti1，gbesti2，...，gbestid) ，gbesti1The first dimension parameter, gbest, representing the best position experienced by the population in d-dimensional spacei2A second dimension parameter, gbest, representing the best position experienced by the population in d-dimensional spacei3A d-th dimension parameter representing the best position experienced by the population in the d-dimensional space. D-dimension velocity of ith particle in k-th iteration
And position
The update formula of (2) is:
in the formula:
showing the d-dimension position of the ith particle in the (k-1) th iteration,
indicating the best position found by the ith particle at the kth iteration,
representing the best position the population experiences at the kth iteration, w is the inertial weight; c. C1And c2Is a learning factor; r is1And r2Are random numbers uniformly distributed between (0, 1). The d-th dimension has a position variation range of [ -XMaxd，XMaxd]The speed variation range is [ -VMaxXd，VMaxXd]。
For each particle, its current fixness value is compared to the fixness value corresponding to the historically best location (Pbest). If the current fixness value is higher, the current position is updated to Pbest. For each particle, its current fixness value is compared to the fixnessvalue corresponding to the location (Gbest) for which the population history is best. If the current fixness value is higher, the current location is updated to Gbest. The unmanned mine car cloud intelligent scheduling model based on the end edge cloud framework has 2 objective functions, due to different dimensions, normalization is converted into single-objective solution, and the obtained fitness function expression is as follows:
in the formula N1、N2In order to eliminate the artificially selected weight coefficients of different dimensions, i and j represent task points, N represents the total set of all nodes in a mining area,
is the car number of the parking area b, b is the number of the parking area, p is the car number of the parking area,
a collection of unmanned mine cars representing a parking area b,
is a decision variable with the value of 0 or 1, and is positioned in the p-th unmanned mine car in the parking area b
When the loading and unloading task point is from i to j, the value is 1, otherwise, the value is 0, tijRepresenting the travel time from i to j, representing the unmanned tramcar
The time of the loading and unloading task staying at the point j, B is the set of the parking lots in the mining area, T0Is to load a set of task points,
show unmanned mine car
Starting from the parking area b, the vehicle arrives at the loading area j, and the value is 0 or 1. The particle swarm algorithm parameters mainly comprise inertia weight, learning factors, population size, iteration times and penalty coefficients. The value of the inertia weight is generally 0.2 ≦ w ≦ 1, and is usually [0.4, 0.9 ]]The effect is better, and through many experiments, the inertia weight value w is set to be 0.8 and the population size is set to be 50.
The constraints of the model are satisfied during the particle encoding process. The distribution problem of the unmanned mine car is represented by a 2 xM (M is the total number of subtasks dividing each loading area according to the loading capacity) dimensional vector, the first dimension of the particles represents the task distribution and the transportation sequence of the loading areas, and the second dimension represents the unloading area corresponding to each subtask. The first dimension of task allocation and shipping order is generated using a roulette strategy. The second dimension is a random arrangement of the unloading zone. Taking 3 loading zones as an example, the loading capacity can be divided into 9 subtasks. For example, let 4 random numbers be generated from the interval [0, 1 ]:
r1＝0.14，r2＝0.18，r3＝0.57，r4＝0.96
the selection probability table generated by the 4 unmanned mine cars according to the roulette strategy is as follows:
in this example, the set of loading areas T0Load demand Q ═ 25, 5, 5, 10}, which indicates that loading zone 1 needs to complete a demand for transporting 25 tons of coal, loading zone 2 needs to complete a demand for transporting 25 tons of coal, T1With 1, 2, 3 discharge zones, VbThe number of the parking areas is 1, 2, 3 and 4, and each loading area is divided into different task sets by 5 tons of standard load of the unmanned mine car. In this case, there are 5 task sets in zone 1, 1 task set in zone 3, 15 load tasks in total, and 18 load nodes in total, plus three load nodes. Each task is served only once.
The randomly generated 2x 15-dimensional vector by the particle swarm algorithm is as follows:
the adjusted vector is represented as:
subscripts are in the order of the numerical values, xtFor the loaded unloading area, the solution path of the unmanned mine car obtained by the adjusted position vector is shown in the following table:
step three, after the cloud intelligent scheduling platform matches the task point of the loading and unloading area of the mining area with the unmanned mine car, a scheduling instruction is sent to the vehicle, the vehicle is started and starts to execute the task, in the process, the monitoring emergency system of the unmanned mine car is started, and managers can check the real-time working condition of the vehicle;
and step four, in the working process of the unmanned mine car, the driving route and the driving state of the unmanned mine car are mastered by the cloud intelligent scheduling platform, and when a new task is required to be accessed, the cloud intelligent scheduling platform judges whether a vehicle meeting the time exists or not. If so, sending the new task to the unmanned mine car, repeating the third step, and completing the tasks in sequence; if not, after the vehicle completes the original task, the self-service parking module of the unmanned mine car is started. And interacting with an intelligent parking lot system in a mining area based on the current vehicle geographic position to obtain the parking area site and the number of parking spaces, and determining a parking route according to an optimal parking path algorithm so as to control the unmanned mine car to go to the parking point and back to enter the garage.
And fifthly, carrying out a waiting mode after the unmanned mine car backs up and enters the garage, sending final position information to the cloud intelligent scheduling platform and updating the vehicle state.
Claims (3)
1. An unmanned mine car cloud intelligent scheduling method based on an end edge cloud architecture is characterized in that,
the method is realized by adopting unmanned mine car cloud intelligent scheduling based on an end edge cloud architecture, and the unmanned mine car cloud intelligent scheduling system based on the end edge cloud architecture comprises:
the system comprises a monitoring emergency system, an intelligent parking lot system, an unmanned mine car and a cloud intelligent scheduling platform;
the monitoring emergency system is a system loaded on the unmanned mine car and comprises a monitoring module and an emergency management module; the monitoring module is used for monitoring the mine car task completion condition, and comprises a field task condition transmitted back by a vehicle-mounted video sensor of the unmanned mine car and a video transmitted back by monitoring installed in a mining area; the emergency management module is used for controlling the mine car by a manager through the emergency management module when the automatic driving mine car has an emergency problem, and forcing the mine car to stop a task so as to prevent safety accidents;
the intelligent parking lot system is arranged for realizing self-service parking of the unmanned mine car, and is used for publishing the information of the remaining parking places on line in real time and performing information interaction with the unmanned mine car so as to assist the vehicle to be quickly warehoused; the intelligent parking lot system comprises a geomagnetic sensor, a wireless communication module and a real-time information management and release module;
the unmanned mine car comprises a communication module, an environment sensing module, a planning and decision-making module, a positioning module, a self-service parking module and a control module; the communication module is used for receiving a control instruction sent by the cloud intelligent scheduling platform and simultaneously transmitting decision information and real-time running conditions of the unmanned mine car back to the cloud intelligent scheduling platform, wherein the decision information and the real-time running conditions comprise the geographical position, the speed and a planned running route of the mine car; the environment sensing module acquires the mine area environment where the unmanned mine car is located through a sensor and video equipment, acquires the drivable road condition information, is used for passable road surface identification and obstacle identification, and provides a basis for the operation control of the unmanned mine car; the positioning module is used for acquiring the geographical position information of the unmanned mine car; the self-service parking module finishes two key tasks of parking space searching and automatic backing-up and warehousing in a parking area of the unmanned tramcar when a loading task of the unmanned tramcar is finished and no new task is accessed, searches a parking area which is close to the current position of the tramcar by taking the current geographic position of the tramcar and surrounding environment parameters as base points, acquires the information of the number of the remaining parking spaces, and acquires a correct parking strategy and a parking path according to a specific parking algorithm; detecting parking space information through an environment sensing module of a vehicle body, and automatically parking the vehicle into a position through an automatic driving system; the control device controls the unmanned mine car according to the road information, the position information and the control instruction to meet the operation requirement of the unmanned mine car;
the cloud intelligent scheduling platform receives the excavator loading task information and the real-time position and speed information of the unmanned mine car, analyzes the task requirement and the task position point information, comprehensively considers the factors of transportation efficiency, task grade importance and vehicle loading capacity, generates a vehicle scheduling instruction and issues the vehicle scheduling instruction to the unmanned mine car; the cloud intelligent scheduling platform comprises the following modules:
the mining area work task uploading module receives that managers upload mining area work tasks to the cloud intelligent scheduling platform, wherein the information of the work tasks includes: the number of the loading area, the size of the loading capacity and the loading starting time; the cloud intelligent scheduling platform collects task orders and updates the current running/waiting state of the unmanned mine car;
the computing module of the cloud intelligent scheduling platform contains map files of a mining area, and the mine cars matched with the loading area are searched by analyzing the geographic position of the loading area and the required loading capacity in the task demand; the mine car searched by the cloud intelligent scheduling platform is not only a static waiting vehicle in a parking lot of a mining area, but also comprises an unmanned mine car with a loading allowance in a working state; if the unmanned mine car in the working state with the loading allowance can be matched, the vehicle is preferentially selected; if the in-transit vehicle meeting the conditions is not searched in the specified time, searching a static waiting vehicle; the calculation module completes the establishment of a dispatching model and the algorithm solution of the multi-target multi-unmanned mine car;
the method comprises the following steps:
s001, establishing a multi-target multi-unmanned mine car scheduling model;
matching the loading task requirements to a mining area map and marking the loading task requirements as nodes, and setting the running path of the unmanned mine car as a connecting line of each node on the path; the number of unmanned mine cars in the dispatching system is determined, the dispatching system runs at a constant speed in the running process, the dispatching system refreshes once when a new task is accessed, matching is performed once on the basis of an initial allocation scheme, if an unmanned mine car in transit with loading allowance can be found, the mine car is preferentially arranged, and if the unmanned mine car in transit with loading allowance is not found, a new mine car is arranged;
s002, solving the provided multi-target multi-unmanned mine car scheduling model, and finding out a better solution by using a particle swarm algorithm;
in d-dimensional space, there are n particles, where the position X of the particle ii＝(xi1，xi2，...，xid) At a velocity of Vi＝(vi1，vi2，...，vid)，xi1A first dimension parameter, x, representing the position of a particle i in d-dimensional spacei2Representing the i position of a particle in d-dimensional spaceSecond dimension parameter of position, xidD-dimensional parameter, v, representing the position of particle i in d-dimensional spacei1A first dimension parameter, v, representing the velocity of a particle i in d-dimensional spacei2A second dimension parameter, v, representing the velocity of the particle i in d-dimensional spaceidA d-dimension parameter representing the velocity of the particle i in the d-dimension space; best position Pbest searched by particle iid＝(pbesti1，pbesti2，...，pbestid)，pbesti1The first dimension parameter, pbest, representing the best position found by particle i in d-dimensional spacei2A second dimension parameter, pbest, representing the best position found by particle i in d-dimensional spaceidD-dimension parameter representing the best position searched by particle i in d-dimension space, the best position Gbest experienced by the populationid＝(gbesti1，gbesti2，...，gbestid)，gbesti1The first dimension parameter, gbest, representing the best position experienced by the population in d-dimensional spacei2A second dimension parameter, gbest, representing the best position experienced by the population in d-dimensional spacei3A d-dimension parameter representing a best location experienced by the population in the d-dimension space; d-dimension velocity of ith particle in k-th iteration
And position
The update formula of (2) is:
in the formula:
showing the d-dimension position of the ith particle in the (k-1) th iteration,
indicating the best position found by the ith particle at the kth iteration,
representing the best position the population experiences at the kth iteration, w is the inertial weight; c. C1And c2Is a learning factor; r is1And r2Random numbers uniformly distributed among (0, 1); the d-th dimension has a position variation range of [ -XMaxd，XMaxd]The speed variation range is [ -VMaxXd，VMaxXd]；
For each particle, comparing the current fixness value with the fixness value corresponding to the historical best position Pbest; if the current fixness value is higher, updating the current position to Pbest; for each particle, comparing the current fixness value with the fixness value corresponding to the position Gbest in group history; if the current fixness value is higher, updating the current position to Gbest; the unmanned mine car cloud intelligent scheduling model based on the end edge cloud framework has 2 objective functions, due to different dimensions, normalization is converted into single-objective solution, and the obtained fitness function expression is as follows:
in the formula N1、N2In order to eliminate the artificially selected weight coefficients of different dimensions, i and j represent task points, N represents the total set of all nodes in a mining area,
is the car number of the parking area b, b is the number of the parking area, p is the car number of the parking area,
a collection of unmanned mine cars representing a parking area b,
is a decision variable with the value of 0 or 1, and is positioned in the p-th unmanned mine car in the parking area b
When the loading and unloading task point is from i to j, the value is 1, otherwise, the value is 0, tijRepresenting the travel time from i to j, representing the unmanned tramcar
The time of the loading and unloading task staying at the point j, B is the set of the parking lots in the mining area, T0Is to load a set of task points,
show unmanned mine car
Starting from the parking area b to the loading area j, and taking the value as 0 or 1; the particle swarm algorithm parameters mainly comprise inertia weight, learning factors, population size, iteration times and penalty coefficients; the value of the inertia weight is more than or equal to 0.2 and less than or equal to 1, and the population size is set to be 50;
the constraint conditions of the model are satisfied in the particle coding process; adopting a 2 XM dimensional vector to represent the distribution problem of the unmanned mine car, wherein M is the total quantity of subtasks divided by each loading area according to the loading capacity, the first dimension of particles represents the task distribution and the transportation sequence of the loading area, and the second dimension represents the unloading area corresponding to each subtask; the first-dimension task allocation and transportation sequence is generated by adopting a roulette strategy, and the sequence is arranged from large to small according to numerical values; the second dimension is a random arrangement of the unloading area;
s003, after matching a task point of a loading and unloading area of a mining area with an unmanned mine car, a cloud intelligent scheduling platform issues a scheduling instruction to the vehicle, the vehicle starts and starts to execute the task, in the process, a monitoring emergency system of the unmanned mine car starts, and managers can check the real-time working condition of the vehicle;
s004, in the working process of the unmanned mine car, the running route and the running state of the unmanned mine car are mastered by the cloud intelligent scheduling platform, when a new task is required to be accessed, the cloud intelligent scheduling platform judges whether a vehicle meeting the requirement exists, if so, the new task is sent to the unmanned mine car, and the step S003 is repeated to complete the tasks in sequence; if the current vehicle geographic position is not the current vehicle geographic position, the self-service parking module of the unmanned mine car is started, the current vehicle geographic position is interacted with an intelligent parking lot system of the mining area, the parking area location and the number of parking spaces are obtained, and a parking route is determined according to the optimal parking path algorithm, so that the unmanned mine car is controlled to go to the parking point and back to the garage;
and S005, after backing up and warehousing the unmanned mine car, carrying out a waiting mode, sending final position information to the cloud intelligent scheduling platform, and updating the vehicle state.
2. The method of claim 1, wherein the inertial weight w is between [0.4, 0.9 ].
3. The method of claim 1, wherein the inertial weight w is set to 0.8.
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Effective date of registration: 20211122 Address after: 100176 901, 9th floor, building 2, yard 10, KEGU 1st Street, Beijing Economic and Technological Development Zone, Daxing District, Beijing Patentee after: BEIJING TAGE IDRIVER TECHNOLOGY CO.,LTD. Address before: 100191 No. 37, Haidian District, Beijing, Xueyuan Road Patentee before: BEIHANG University |