CN105740979B - Intelligent dispatching system and method for multiple automatic guided vehicles of automatic wharf - Google Patents
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Abstract
The invention relates to an intelligent dispatching system and method for multiple automatic guided vehicles of an automatic wharf, which realize a two-stage distribution control dispatching mode with operation planning and dispatching decoupling separation through a wharf management information system and an AGV group intelligent dispatching system; according to the method, a knowledge learning method is adopted, an AGV dispatching knowledge management module is established, an AGV task instance base, a path instance base and a knowledge base are enriched and optimized continuously, and the self-learning, self-organizing and self-decision-making capabilities of the AGV are improved; according to the method, the task scheduling and the path planning of the AGV are cooperatively optimized by rolling prediction of the traffic flow in the road network space of the intelligent wharf and calling of the knowledge base, the AGV task instance base and the path instance base. The invention provides an AGV path planning strategy comprising an optional path and a free path mode, which can ensure path planning under a conventional task and provide a quick response measure for an emergency task.
Description
Technical Field
The invention relates to an intelligent dispatching system and method for multiple automatic guided vehicles of an automatic wharf.
Background
The automatic wharf operation scheduling mainly comprises three links of shore bridge operation, horizontal transportation operation and yard operation. Automatic Guided Vehicle (AGV) horizontal transportation is an important link for connecting shore bridge operation and yard bridge operation, and is the mainstream mode of horizontal transportation of the current automated wharf. Different from the bottlenecks of the traditional wharf shore bridge and yard bridge type operation systems, on one hand, the operation amount and the horizontal transportation distance of the containers in the modern automatic wharf are greatly increased, and the loading and unloading capacities of the shore bridge and the yard bridge are greatly improved; on the other hand, due to uncertain disturbance of task information, road network information and state information, the multiple AGVs generate node conflict, traffic jam, deadlock and other null interference under the complex operating environments with multiple working conditions, multiple tasks and multiple targets. Therefore, the horizontal transportation link becomes a new bottleneck for improving the operation efficiency of the automatic wharf.
The automatic wharf operation process has uncertainty characteristics of dynamic, random, burst and the like. The uncertainty of the loading/unloading time of the shore bridge and the unloading/loading time of the yard bridge can cause the uncertainty of the queuing waiting time of the AGV; the average AGV traveling time is influenced by inherent uncertain parameters of an AGV system and wharf real-time traffic conditions (namely uncertainty of traffic flow); in addition, emergency tasks and the burstiness of failures are also included; and so on.
The current wharf operation planning and scheduling management adopts a centralized decision-making mechanism, and considering the complexity of the system and various uncertain information influences, the global dynamic optimization is impossible or cannot be controlled in real time due to overlong decision-making time to meet the efficient operation requirement under the high safety of the future automatic wharf. Because the AGV lacks a brain, has no autonomous decision, self-learning and self-organizing capabilities and can not predict the wharf traffic flow condition, the following problems mainly exist in the aspect of AGV operation scheduling:
(1) the static scheduling of AGVs by the centralized decision mechanism makes it unable to adapt to uncertainty. At present, the horizontal transportation link of the AGV does not have group intelligence and cannot self-organize and coordinate task allocation, and each AGV passively receives tasks and path instructions from a wharf operation management system. The task allocation of the AGVs assumes that the arrival time and the operation time of each task can be accurately predicted, and in fact, the loading and unloading operation time of a shore bridge or a yard bridge has uncertainty, so the waiting time of the AGVs in line is too long in the static task allocation mode. In a word, the scheduling mode is static, the decision efficiency is low, and the improvement of the work efficiency is prevented.
(2) Static path planning is difficult to avoid space-time interference, and traffic jam and deadlock are easily caused. Because the AGV only passively receives global and static path planning, the fixed path planning does not consider the influence of real-time traffic state on the driving of the AGV, and the dynamic optimization and adjustment of the path can not be carried out. Because the AGV lacks self-learning capabilities and self-organizing mechanisms, its adaptive and behavior-adjusting capabilities are not sufficient to avoid conflict interference. Therefore, it is urgently needed to improve the intelligence level of the AGVs and establish a self-consistent mechanism for information interaction and conflict coordination between the AGVs.
(3) And the emergency task priority distribution mechanism is lacked, and the emergency response capability of faults and emergencies is insufficient. At present, the collision is mainly prevented by a hard measure of decelerating in advance and stopping advancing after ultrasonic ranging or laser detection and installing a buffer protection device. For faults and emergency events, mainly manual emergency treatment is performed after the accident, an emergency task priority mechanism is lacked, and autonomous cooperative avoidance of the AGV and rapid emergency task execution measures (such as free path running) are not realized in the aspect of functions.
Disclosure of Invention
The invention aims to provide an intelligent scheduling system and method for multiple automatic guided vehicles of an automatic wharf, which realize the safety, real-time performance and accuracy of AGV operation, shorten the operation waiting time of the AGV operation, improve the working efficiency of the AGV operation, enable the AGV to have self-decision, self-learning and self-organization (intelligent) capabilities adaptive to uncertain information disturbance in a complex system environment, and solve the technical problem of dynamic scheduling of multiple AGV cooperative operation (namely the cooperative optimization problem of dynamic task allocation and dynamic path planning).
In order to achieve the above object, one technical solution of the present invention is to provide an intelligent scheduling method for multiple automatic guided vehicles of an automated wharf, wherein:
the wharf management information system sends the operation plan to an AGV group intelligent scheduling system in a task set mode according to the operation real-time state information of each AGV individual, sends emergency tasks according to the situations and receives task completion information fed back by the AGV group intelligent scheduling system;
the AGV group intelligent scheduling system dynamically schedules each AGV according to task allocation or task reallocation according to the real-time operation state information of each AGV and task completion information of one task on each AGV, so that a two-stage distributed control scheduling mode with operation planning and scheduling decoupling separated is realized;
the AGV group intelligent scheduling system generates real-time traffic flow distribution information according to the operation real-time state information and provides the real-time traffic flow distribution information for each AGV individual, and generates rolling prediction information of future traffic flow distribution by performing rolling prediction on traffic flow in a limited operation space of an intelligent wharf so as to realize cooperative optimization of task allocation and path planning of multiple AGVs;
when the AGV individual deals with a conventional task according to an optional path mode, the AGV group intelligent scheduling system calls a past scheduling path example from an example library to form an optional path set for the AGV individual to finish the current task, and rolling prediction information distributed by future traffic flow is provided for the AGV individual at one time for planning the path of the current task, so that the AGV individual can obtain an effective path capable of finishing the conventional task;
or when the AGV individual deals with the emergency task according to the free path mode, the process of planning the free path for the AGV individual, dynamically predicting the traffic flow by the AGV group intelligent scheduling system and providing the prediction result for the AGV individual is repeatedly executed for a plurality of times until the AGV individual obtains the free running path capable of completing the emergency task;
the AGV group intelligent scheduling system receives task completion information fed back by each AGV individual to the AGV group intelligent scheduling system, and optimizes the instance base and the knowledge base according to path optimization information when each AGV individual completes the task.
Preferably, the sampling time interval for acquiring the real-time status information of the operation from each AGV ist 2；
AGV group intelligent scheduling system collects, transmits, stores and fuses real-time state informationGenerating the current timeT 0Real-time traffic flow distribution information;
AGV group intelligent scheduling system is to futureT fLong-duration traffic flow distribution information predictionpNext to and inT 0+△T 1Providing the generated optional path set to each AGV individual before the moment, wherein △T 1=t 1×p，t 1Is the time when traffic flow is predicted once; and the number of the first and second electrodes,t 2＜t 1；
in the alternative path mode, the AGV is individually inT 0+△T 1+△T 2Feeding back the path information of the current task to the AGV group intelligent scheduling system at the moment, wherein △T 2Calculating time for planning individual paths of the AGV;
in thatT 0+△T 1+△T 2+△T 3The AGV individual feeds back the task completion information of the current task to the AGV group intelligent scheduling system at the moment △T 3Is the walking time for the AGV individual to complete the current task.
Preferably, whenT f≠△T 3For a predicted durationT fAnd the AGV group intelligent scheduling system generates a virtual path by calling historical similar tasks and traffic condition instances to complete the rolling prediction of the future traffic flow distribution.
Preferably, in the free path mode, individual AGVs assigned emergency tasks are at a timeT 0Performing first free path planning; the iterative computation time of the AGV individual free path planning is deltat 2；
At T0+ deltat 2Executing first traffic flow prediction at the moment; the sampling interval of traffic flow prediction is the time when the traffic flow is predicted oncet 1；
And then, alternately executing the processes of free path planning and traffic flow prediction for a plurality of times until the AGV individuals allocated with the emergency tasks obtain the required free running paths.
Preferably, a variable communication topology network based on a bionic self-organizing self-negotiation mechanism is established, and real-time scheduling and decentralized control among multiple AGV individuals are realized:
taking one AGV individual in the AGV group as a manager, taking a plurality of AGV individuals as leaders, and taking the other AGV individuals as followers;
the manager is responsible for information interaction between the wharf operation management system and the AGV group intelligent scheduling system, dynamic task scheduling of the AGV group, real-time scheduling of a cooperative control task, and management of entering or exiting of a single AGV individual from a communication topology network;
the manager communicates with the leader in a two-way manner; the leader communicates with the follower periodically; and carrying out real-time communication between the follower and the leader.
Preferably, AGV traffic rules and an anti-collision autonomous safety control strategy are established, so that each AGV individual in an AGV group adjusts the priority of each AGV individual in the following four categories according to task completion and real-time state feedback information of fault emergency conditions;
four categories of priorities, from high to low, comprise:
the first priority is an AGV individual executing an emergency task;
the second priority is the AGV individual which is executing the task and is loaded;
the third priority is an AGV individual which is executing a task and is unloaded;
the fourth priority is an AGV individual without a task and a load;
when conflict occurs, the AGV individual with low priority gives way to the AGV individual with high priority; when the AGV individuals with the same priority conflict, the AGV individuals with low speed give way to the AGV individuals with high speed.
Another technical solution of the present invention is to provide an intelligent dispatching system for multiple automated guided vehicles in an automated dock, comprising:
the wharf management information system receives the real-time operation state information of each AGV, issues an operation plan in a task set mode, and issues emergency tasks according to the situations;
the AGV group intelligent scheduling system receives the task set and the emergency task issued by the wharf management information system, performs task allocation or task reallocation on a plurality of AGV individuals, realizes the cooperative optimization of dynamic scheduling and path planning, and feeds back the task set completion condition to the wharf operation management system;
the AGV group intelligent scheduling system further comprises a task dynamic scheduling module, a road network traffic flow prediction module, a scheduling knowledge management module and an AGV path planning module;
the scheduling knowledge management module is provided with a path instance library and a task instance library which are called in the past, and a knowledge base which is used for mining the calling data in the past to obtain evolution data;
the task dynamic scheduling module calls task instances and knowledge from the scheduling knowledge management module and sends task allocation information to the AGV path planning module;
the road network traffic flow prediction module generates real-time traffic flow distribution information according to the operation real-time state information and then rolls to predict future road network traffic flow distribution information;
based on the predicted road network traffic flow distribution information and the optional path set and knowledge provided by the scheduling knowledge management module, the AGV path planning module realizes the optional path planning and obtains an effective path which enables the AGV individuals allocated with the conventional tasks to complete the current tasks;
or the AGV path planning module alternately executes the processes of free path planning and traffic flow prediction for a plurality of times to realize the real-time rolling optimization of the free path and obtain a free running path which enables the AGV individuals allocated with emergency tasks to complete the current tasks;
the AGV path planning module feeds back information whether the tasks can be executed to the task dynamic scheduling module: if the task executable information is fed back, the task dynamic scheduling module schedules the corresponding AGV individuals to walk according to the planned path to complete the distributed tasks; and if the information that the tasks are not executable is fed back, the task dynamic scheduling module and the path planning module interactively redistribute the tasks.
Preferably, the AGV path planning module sends the planned path optimization information of the completed task to the scheduling knowledge management module, and the scheduling knowledge management module performs data optimization on the instance base and the knowledge base.
Preferably, for AGV individuals with uncertain task completion conditions and paths, the scheduling knowledge management module calls historical similar tasks and traffic condition instances to generate virtual paths to the road network traffic flow prediction module, and the road network traffic flow prediction module completes rolling prediction of future traffic flow distribution.
Compared with the prior art, the invention has the advantages that:
(1) a centralized decision-making mechanism is changed, a complex system decoupling method is adopted, a new scheduling mode of two-stage distributed control of a wharf operation management information system (an upper layer system) and an AGV group intelligent scheduling system (namely a brain of the AGV) is constructed, and separation of operation planning and scheduling is achieved.
(2) By adopting a knowledge learning method, an AGV dispatching knowledge management system is established, an AGV task instance base, a path instance base and a knowledge base are enriched and optimized continuously, and the self-learning, self-organizing and self-decision-making capabilities of the AGV are improved.
(3) And performing collaborative optimization on the task scheduling and path planning of the AGV by performing rolling prediction on the traffic flow in the road network space of the intelligent wharf and calling a knowledge base, an AGV task instance base and a path instance base. An AGV path planning strategy comprising an optional path and a free path mode is provided, so that path planning under a conventional task can be ensured, and quick response measures can be provided for an emergency task.
Drawings
FIG. 1 is an architecture diagram of an AGV group intelligent dispatching system according to the present invention;
FIG. 2 is a schematic diagram of the information interaction mechanism of the multiple AGV task and path collaboration.
Detailed Description
In view of the complex coupling interaction relationship between the task allocation and the path planning (in time, space and information) of multiple AGVs, the method decomposes each operation link module by using the decoupling idea of a complex system, separates the planning from the scheduling, lowers an AGV operation task set through a new two-stage scheduling mode, feeds back AGV operation state information in real time and predicts the rolling of traffic flow, and eliminates or reduces the influence of uncertain information of a wharf complex operation environment. The AGV intelligent level is improved through self-decision, self-organization and self-learning, and multi-AGV cooperative operation dynamic scheduling (cooperative optimization of dynamic task allocation and dynamic path planning) is achieved, so that the problems of multi-AGV space-time interference, operation task priority, fault emergency and the like, which cannot be solved by the existing centralized decision mechanism, static task allocation and static path planning, such as node conflict, traffic jam, deadlock and the like, are solved.
Specifically, the wharf operation management system cooperatively optimizes the operation plans of the AGV and other links such as a shore bridge and a yard bridge, and then issues a task set to the AGV group, and issues an emergency task according to the situation (emergency situations such as dangerous goods container accidents and the like, which cannot be completed due to AGV failure or long-term congestion); the invention provides an AGV group intelligent scheduling system which is responsible for the dynamic scheduling of AGV and feeds back the completion condition of a task set to a wharf operation management system.
The AGV group intelligent scheduling system mainly comprises four modules of task dynamic scheduling, road network traffic flow prediction, scheduling knowledge management and AGV path planning, and the system architecture is as shown in FIG. 1:
in order to establish cooperative optimization of multi-AGV task scheduling and path planning, the task dynamic scheduling module calls task instances and knowledge from the scheduling knowledge management module and distributes task allocation information to the AGV path planning module. And the road network traffic flow prediction module generates real-time traffic flow distribution information according to the AGV real-time state information and then rolls to predict the future road network traffic flow distribution information. Under the condition of a conventional task, based on predicted road network traffic flow information, an AGV path planning module realizes the planning of selectable paths according to a selectable path set and knowledge provided by a scheduling knowledge management module, and feeds back information about whether the task can be executed to a task dynamic scheduling module, and if the task can be executed, the AGV executes corresponding path planning; otherwise, the task dynamic scheduling module and the path planning module interactively redistribute the tasks. In the case of an emergency task, unlike a conventional task, the free path real-time rolling optimization of the AGV, traffic flow prediction, free planning, re-prediction and re-planning, is realized through traffic flow dynamic prediction. After the AGV finishes the task, the task finishing information is fed back to the AGV group intelligent scheduling system in time, and the path optimization information is provided for the scheduling knowledge management module.
As shown in fig. 2, the intelligent scheduling system and method of the present invention mainly have the following features:
1) a centralized decision-making mechanism is changed, a complex system decoupling method is adopted, a new scheduling mode of two-stage distributed control of a wharf operation management information system (an upper layer system) and an AGV group intelligent scheduling system (namely a brain of the AGV) is constructed, and operation planning and scheduling separation is achieved.
The overall dynamic real-time optimization of all operation links is considered to be almost impossible, the operation links are modularized, and the operation plan and the scheduling are decoupled through a two-stage scheduling new mode. Reallocating the functions and the authority of a wharf operation management information system and an AGV group intelligent scheduling system, and establishing a task information (task set distribution and task feedback) interaction mechanism between two levels of systems: the upper-layer system issues emergency tasks according to situations (AGV faults, safety accidents and emergency situations) only by considering operation plans and task set distribution, so that the operation plans can be better optimized in cooperation with other operation links; the AGV group intelligent scheduling system becomes an AGV task allocation (reallocation) and scheduling command center, and actually endows the AGV with self-decision-making capability, so that the real-time dynamic allocation of tasks such as task priority, fault and emergency and the like can be realized, and a framework foundation is laid for uncertainty-oriented dynamic scheduling.
2) The method comprises the steps of calling an AGV (automatic guided vehicle) conventional scheduling path example through rolling prediction of traffic flow in a limited operation space of an intelligent wharf, and performing collaborative optimization on task allocation and path planning of the AGV based on an intelligent optimization algorithm to realize real-time optimization of a dynamic path.
And generating real-time traffic flow distribution information and rolling prediction information of future traffic flow distribution according to the operation real-time state information (including the direction, speed, task state, fault, safety alarm and the like of each AGV), and calling the conventional path example from the scheduling management knowledge system to form an optional path set through data mining. The path dynamic optimization problem is converted into a static optimization problem which can be solved in real time (fast) in discrete time, and the AGV individual carries out fast path planning respectively aiming at a conventional task and an emergency task according to an optional path mode and a free path mode. Under a conventional task (optional path) mode, rolling prediction information distributed by future traffic flow is provided for an AGV individual at one time for the AGV individual to plan a current task path; in an emergency task (free path) mode, the free path real-time optimization of the AGV 'traffic flow prediction-free planning-re-prediction-re-planning' is realized through dynamic prediction of the traffic flow.
3) By adopting a knowledge learning method, an AGV dispatching knowledge management system is established, an AGV dispatching instance base and a knowledge base are enriched and optimized continuously, and the self-learning, self-organization and self-decision-making capabilities of the AGV are improved.
Because it is extremely difficult to establish a dynamic task allocation and dynamic path planning combined optimization model of multiple AGVs, the intelligent capability of the AGVs is continuously trained mainly by a data acquisition and knowledge learning method. And constructing an instance base by using the path optimization information of the completed task, and performing data mining to generate and supplement a knowledge base, so that the AGV has self-learning capability. By using the bionic self-organizing mechanism for reference, the operation priority is divided according to whether the AGV has a task, is loaded or not and whether the AGV is in emergency or not (for example, the emergency task can be endowed with the highest operation priority). And providing self-consistent traffic rules and autonomous safety control strategies based on the collision prevention and self-locking prevention of the AGV with the high and low operation priority level, so that the AGV has the self-organization capability.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.
Claims (8)
1. An intelligent dispatching method for a plurality of automatic guided vehicles of an automatic wharf is characterized in that,
the wharf management information system sends the operation plan to an AGV group intelligent scheduling system in a task set mode according to the operation real-time state information of each AGV individual, sends emergency tasks according to the situations and receives task completion information fed back by the AGV group intelligent scheduling system;
the AGV group intelligent scheduling system dynamically schedules each AGV according to task allocation or task reallocation according to the real-time operation state information of each AGV and task completion information of one task on each AGV, so that a two-stage distributed control scheduling mode with operation planning and scheduling decoupling separated is realized;
the AGV group intelligent scheduling system generates real-time traffic flow distribution information according to the operation real-time state information and provides the real-time traffic flow distribution information for each AGV individual, and generates rolling prediction information of future traffic flow distribution by performing rolling prediction on traffic flow in a limited operation space of an intelligent wharf so as to realize cooperative optimization of task allocation and path planning of multiple AGVs;
when the AGV individual deals with a conventional task according to an optional path mode, the AGV group intelligent scheduling system calls a past scheduling path example from an example library to form an optional path set for the AGV individual to finish the current task, and rolling prediction information distributed by future traffic flow is provided for the AGV individual at one time for planning the path of the current task, so that the AGV individual can obtain an effective path capable of finishing the conventional task;
when the AGV individual deals with the emergency task according to the free path mode, the process of planning the free path for the AGV individual, dynamically predicting the traffic flow by the AGV group intelligent scheduling system and providing the prediction result for the AGV individual is repeatedly executed until the AGV individual obtains the free running path capable of completing the emergency task;
the AGV group intelligent scheduling system receives task completion information fed back by each AGV individual to the AGV group intelligent scheduling system, and optimizes the instance base and the knowledge base according to path optimization information when each AGV individual completes the task;
wherein, the sampling time of the real-time status information of the operation is obtained from each AGV individualInterval t2；
The AGV group intelligent scheduling system collects, transmits, stores and fuses real-time state information to generate a current time T0Real-time traffic flow distribution information;
AGV group intelligent scheduling system for future TfPredicting the traffic flow distribution information p times in a long time and obtaining the time T0+△T1Providing the generated optional path set for each AGV individual before the moment; wherein the content of the first and second substances,△T1＝t1×p，t1is the time when traffic flow is predicted once; and, t2＜t1；
In the alternate path mode, the AGV individual is at T0+△T1+△T2Feeding back path information for completing the current task to the AGV group intelligent scheduling system at the moment; wherein the content of the first and second substances,△T2calculating time for planning individual paths of the AGV;
at T0+△T1+△T2+△T3At the moment, the AGV individual feeds back task completion information of the current task to the AGV group intelligent scheduling system;△T3is the walking time for the AGV individual to complete the current task.
2. The intelligent scheduling method of claim 1,
when T isf≠△T3For a predicted time duration TfAnd the AGV group intelligent scheduling system generates a virtual path by calling historical similar tasks and traffic condition instances to complete the rolling prediction of the future traffic flow distribution.
3. The intelligent scheduling method of claim 1,
in free path mode, AGV individuals assigned with emergency tasks are at time T0Performing first free path planning; the iterative computation time of the AGV individual free path planning is delta t2；
At T0+ δ T2Executing first traffic flow prediction at the moment; the sampling interval of the traffic flow prediction is the time t of one time of traffic flow prediction1；
And then, alternately executing the processes of free path planning and traffic flow prediction until the AGV individuals allocated with the emergency tasks obtain the required free running paths.
4. The intelligent scheduling method of claim 1,
establishing a variable communication topology network based on a bionic self-organizing self-negotiation mechanism, and realizing real-time scheduling and decentralized control among multiple AGV individuals:
taking one AGV individual in the AGV group as a manager, taking a plurality of AGV individuals as leaders, and taking the other AGV individuals in the AGV group as followers;
the manager is responsible for information interaction between the wharf operation management system and the AGV group intelligent scheduling system, dynamic task scheduling of the AGV group, real-time scheduling of a cooperative control task, and management of entering or exiting of a single AGV individual from a communication topology network;
the manager communicates with the leader in a two-way manner;
the leader communicates with the follower periodically; and carrying out real-time communication between the follower and the leader.
5. The intelligent scheduling method of claim 1,
establishing AGV traffic rules and an anti-collision autonomous safety control strategy, so that each AGV in an AGV group adjusts the priority of each AGV according to real-time state feedback information of task completion and fault emergency conditions in the following four categories;
four categories of priorities, from high to low, comprise:
the first priority is an AGV individual executing an emergency task;
the second priority is the AGV individual which is executing the task and is loaded;
the third priority is an AGV individual which is executing a task and is unloaded;
the fourth priority is an AGV individual without a task and a load;
when conflict occurs, the AGV individual with low priority gives way to the AGV individual with high priority; when the AGV individuals with the same priority conflict, the AGV individuals with low speed give way to the AGV individuals with high speed.
6. The utility model provides an intelligent scheduling system of many automated guidance cars of automatic pier which characterized in that contains:
the wharf management information system receives the real-time operation state information of each AGV, issues an operation plan in a task set mode, and issues emergency tasks according to the situations;
the AGV group intelligent scheduling system receives the task set and the emergency task issued by the wharf management information system, performs task allocation or task reallocation on a plurality of AGV individuals, realizes the cooperative optimization of dynamic scheduling and path planning, and feeds back the task set completion condition to the wharf operation management system;
the AGV group intelligent scheduling system further comprises a task dynamic scheduling module, a road network traffic flow prediction module, a scheduling knowledge management module and an AGV path planning module;
the scheduling knowledge management module is provided with a path instance library and a task instance library which are called in the past, and a knowledge base which is used for mining the calling data in the past to obtain evolution data;
the task dynamic scheduling module calls task instances and knowledge from the scheduling knowledge management module and sends task allocation information to the AGV path planning module;
the road network traffic flow prediction module generates real-time traffic flow distribution information according to the operation real-time state information and then rolls to predict future road network traffic flow distribution information;
based on the predicted road network traffic flow distribution information and the optional path set and knowledge provided by the scheduling knowledge management module, the AGV path planning module realizes the optional path planning and obtains an effective path which enables the AGV individuals allocated with the conventional tasks to complete the current tasks;
the AGV path planning module is also used for realizing the process of alternately executing free path planning and traffic flow prediction, realizing the real-time rolling optimization of the free path and obtaining a free running path which enables the AGV individuals allocated with emergency tasks to complete the current tasks;
the AGV path planning module feeds back information whether the tasks can be executed to the task dynamic scheduling module: if the task executable information is fed back, the task dynamic scheduling module schedules the corresponding AGV individuals to walk according to the planned path to complete the distributed tasks; if the information that the tasks are not executable is fed back, the tasks are redistributed by the interaction of the dynamic task scheduling module and the path planning module;
wherein, the sampling time interval for acquiring the real-time status information of the operation from each AGV individual is t2；
The AGV group intelligent scheduling system collects, transmits, stores and fuses real-time state information to generate a current time T0Real-time traffic flow distribution information;
AGV group intelligent scheduling system for future TfPredicting the traffic flow distribution information p times in a long time and obtaining the time T0+△T1Providing the generated optional path set for each AGV individual before the moment; wherein the content of the first and second substances,△T1＝t1×p，t1is the time when traffic flow is predicted once; and, t2＜t1；
In the alternate path mode, the AGV individual is at T0+△T1+△T2Feeding back path information for completing the current task to the AGV group intelligent scheduling system at the moment; wherein the content of the first and second substances,△T2calculating time for planning individual paths of the AGV;
at T0+△T1+△T2+△T3At the moment, the AGV individual feeds back task completion information of the current task to the AGV group intelligent scheduling system;△T3is the walking time for the AGV individual to complete the current task.
7. The intelligent scheduling system of claim 6 wherein,
and the AGV path planning module sends the path optimization information of the completed task obtained by planning to the scheduling knowledge management module, and the scheduling knowledge management module performs data optimization on the instance base and the knowledge base.
8. The intelligent scheduling system of claim 6 wherein,
for the AGV individuals with uncertain task completion conditions and paths, the scheduling knowledge management module calls historical similar tasks and traffic condition instances to generate virtual paths to the road network traffic flow prediction module, and the road network traffic flow prediction module is used for completing the rolling prediction of the future traffic flow distribution.
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