CN117973807B - Charging management scheduling method, system and medium for unmanned port truck collection team - Google Patents
Charging management scheduling method, system and medium for unmanned port truck collection team Download PDFInfo
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Abstract
The invention relates to a charge management scheduling method, a system and a medium for a port unmanned integrated truck team, wherein the method comprises the following steps: in U1. ports, starting an operation task in sequence after a single vehicle receives a task information CAN instruction in an unmanned integrated card fleet, and acquiring vehicle state data information of a current unmanned integrated card and recorded state data information of a charging pile in real time; u2., based on the recorded state data information of the charging piles and the current vehicle state data information of the unmanned aerial vehicle, distributing optimal charging tasks to each vehicle in the unmanned aerial vehicle team by adopting a latest idle charging pile distribution algorithm, and outputting vehicle optimal distribution charging task data information. The invention solves the problems of less charge management scheduling schemes, low efficiency and low engineering application range of the unmanned integrated cards, effectively gives consideration to the operation and charging time of the integrated vehicles and improves the operation efficiency of the integrated vehicle team.
Description
Technical Field
The invention relates to the technical field of port unmanned integrated card dispatching, in particular to a charge management dispatching method, a charge management dispatching system and a charge management dispatching medium for port unmanned integrated card teams.
Background
The efficient and rapid operation of port logistics is essentially guaranteed to be carried out by using unimpeded vehicle running as the premise of improving the loading and unloading speed of a wharf, strengthening the turnover of vehicles and vessels, ensuring the on-time delivery of commodities and shortening the commodity circulation time. The port logistics high-efficiency problem is effectively combined with an informatization system and an unmanned collecting card, and the problem can be effectively solved.
If the unmanned truck collecting team can keep uninterrupted operation for 24 hours, the operation efficiency can be improved to the greatest extent. The associated problems are that an informatization system is required to schedule the vehicles and the charging piles, so as to solve the problems of which vehicle is charged when and which is charged. The operation efficiency of the whole motorcade is affected by the advantages and disadvantages of the charging strategy and the management of the charging piles.
In the industry, a port scheduling system and a port scheduling scheme are adopted, but the method is mainly focused on the aspects of application such as integrated card information management, job task allocation, route planning and the like, and the research and release results are less for the charging management scheme of uninterrupted operation of an integrated card fleet in an operation time period (generally 10 hours).
In the first prior art, patent (application number: 202211057349. X) discloses a method, a device and equipment for charging and scheduling an unmanned set card, and a readable storage medium, and the method for charging and scheduling the unmanned set card comprises: when the vehicle is determined to be charged, judging whether a charging pile in an idle state exists or not; if the charging pile in the idle state exists, driving the vehicle into the charging pile in the idle state closest to the vehicle, and charging the vehicle in a preset mode; if no charging piles in the idle state exist, calculating to obtain the waiting time of each charging pile buffer zone; and queuing and waiting the vehicle in a charging pile buffer zone with the shortest waiting time, and charging the vehicle in a preset mode when the vehicle is polled. The invention mainly aims at scheduling and charging management of a single vehicle, and the waiting time in a queue is needed in the middle, so that the time of operation and charging of the truck collecting vehicle cannot be effectively considered, and the operation efficiency of the truck collecting team is reduced.
In the second prior art, patent (application number: 202110194973.3) discloses a charging management method and device for an unmanned electric container truck, and receives charging management data corresponding to each target set card, wherein the charging management data corresponding to the target set card comprises the targets
Residual electric quantity and position information corresponding to the collector card; and controlling a plurality of target set cards to charge according to the residual electric quantity and the position information corresponding to each target set card. The invention combines the charging time and the working time of the motorcade, but needs to carry out charging management according to the charging threshold value. The key for determining the implementation efficiency of the charging management scheme is the rationality of the number of charging piles and a charging threshold value; the more the number of the charging piles is, the higher the efficiency of charging management is, and the lower the efficiency of charging management is when the number of the charging piles is smaller; when the number of the charging piles is less than a certain number, the scheme cannot be implemented
In addition, according to the intermittent charging scheme of the charging threshold, the invention is easy to cause: in the operation starting period, a large number of charging piles are idle, so that charging resources are wasted; after a period of operation, a large number of vehicles are queued and charged, and the operation efficiency is reduced. When the two phenomena are alternated, the operation efficiency of the whole motorcade is greatly reduced; therefore, the scheme of the invention has limited practical engineering application range and is difficult to popularize.
Disclosure of Invention
In view of the defects of the prior art, the invention provides a charge management scheduling method, a charge management scheduling system and a charge management scheduling medium for an unmanned truck team in a port, which not only solve the problems of fewer charge management scheduling schemes, low efficiency and low engineering application range of the unmanned truck, but also effectively give consideration to the operation and charging time of the truck, and improve the operation efficiency of the truck team.
In order to achieve the above object and other related objects, the present invention provides the following technical solutions:
a method for charge management scheduling of an unmanned integrated circuit board fleet at a port, the method comprising:
In U1. ports, starting an operation task in sequence after a single vehicle receives a task information CAN instruction in an unmanned integrated card fleet, and acquiring vehicle state data information of a current unmanned integrated card and recorded state data information of a charging pile in real time;
U2., based on the recorded state data information of the charging piles and the current vehicle state data information of the unmanned aerial vehicle, distributing optimal charging tasks to each vehicle in the unmanned aerial vehicle team by adopting a latest idle charging pile distribution algorithm, and outputting vehicle optimal distribution charging task data information;
u3. optimizing whether the vehicle executes the charging task or not by adopting an optimal charging task execution allocation algorithm based on the optimal allocation charging task data information of the vehicle to obtain optimal execution charging task data information of the vehicle at the current moment;
U4. repeating the steps U2-U3, and alternately charging the unmanned integrated circuit vehicle team under the condition of uninterrupted operation to obtain the optimal running state data information of the vehicle.
Further, the vehicle state data information of the current unmanned set card comprises residual electric quantity data information of a vehicle, operation task state data information of the vehicle and position data information of the vehicle, and the recorded state data information of the charging pile comprises idle time data information of the charging pile, position data information of the charging pile and charging state data information of the charging pile.
Further, in step U2, the allocating an optimal charging task to each vehicle in the unmanned truck fleet by using the latest idle charging pile allocation algorithm includes:
U21. based on the recorded state data information of the charging pile, acquiring an idle time set t= { T 1,T2,...,Ti,...Tn }, and establishing a maximum idle time function T i of the charging pile,
Ti=max{T1,T2,...,Ti,...Tn},
Wherein T i is the idle time of the ith charging pile, n is the number of the charging piles, and the maximum idle time data information of the charging piles is obtained;
U22 based on the maximum idle time data information of the charging pile and the state data information of the current unmanned aerial vehicle, establishing a charging cost function S ij of the unmanned aerial vehicle,
,
Dij=distance(Vehiclej,Chargeri)，
Wherein, alpha and beta are fitness factors of the charging cost of the unmanned collector card, T i is the maximum idle time data information of the charging pile, D ij is the distance from the jth Vehicle to the ith charging pile, vehicle j is the position data information of the jth unmanned collector card, charger i is the position information of the ith charging pile, and P i is the power of the ith charging pile to obtain the charging cost data information of the unmanned collector card;
U23. based on the charging cost data information of the unmanned set card, a minimum charging cost function C is established,
C=min{S11,S12,...Sij,...,Snm}，
And m is the number of the unmanned set cards, and the allocation charging tasks of the unmanned set cards are optimized to obtain the data information of the optimal execution charging tasks of the vehicle.
Further, in step U22, the constraint conditions of the fitness factors a and β of the unmanned ic card charging cost are that,
。
Further, in step U3, the optimizing whether the vehicle performs the charging task by using the optimal charging task performing and allocating algorithm includes:
U31. based on the optimally assigned charging task data information of the vehicle, a charging task execution state function Q (a t,rt, x) of the vehicle is established,
，
Wherein a t is a charging task action function of the vehicle, r t is a rewarding function of the charging task of the vehicle, x is charging task data information optimally distributed to the vehicle, ω is a rewarding factor, σ is a cost factor, and the task execution state of the vehicle is calculated to obtain task execution state data information of the vehicle;
U32. based on the task execution status data information of the vehicle, a vehicle charging task objective function W is established,
，
Q t is task execution state data information of the vehicle, and calculates a vehicle charging task target value to obtain vehicle charging task target value data information;
u33. based on the vehicle charging mission target value data information, establishing a vehicle charging mission target maximum value function V,
V=max{Wt1,Wt2,...Wtk,...,Wtm}，
W tk is the vehicle charging task target value of the kth unmanned set card, m is the number of unmanned set cards, the maximum value of the vehicle charging task target is obtained, whether the vehicle executes a charging task is optimized, and the data information of the optimal execution of the charging task of the vehicle at the current moment is obtained.
Further, the charging task action function a t of the vehicle is,
；
The reward function r t of the charging task of the vehicle is,
。
Further, constraints of the bonus factor omega and the cost factor sigma are that,
。
In order to achieve the above and other related objects, the present invention also provides a system for implementing the charge management scheduling method of the unmanned collection truck team in any port, including a TOS system, a TMC system, a 4G/5G mobile communication terminal, an autopilot system, and a 4G/5G mobile communication network, where the TOS system is a dock task planning and distribution system in a port, and is a computer management system for managing and controlling each link of dock operation, issuing a cargo ship berth, a loading and unloading task and a container transportation loading and unloading task in the dock, and communicating with the TMC system to forward the operation task of the unmanned collection truck;
The TMC system is a port unmanned integrated card informatization system, is a system integrating functions of command, dispatch, remote control, simulation, operation and maintenance monitoring, fault processing, big data analysis and network security policy setting, and is used for displaying tasks/states/maps/resources;
The 4G/5G mobile communication terminal is mounted on each card of the unmanned card collection vehicle team, receives the issued charge state information or the charge stopping state information of the TMC system, and forwards a charge instruction and charge stake position state information to the automatic driving system;
The automatic driving system controller is used for receiving the instruction of the 5G mobile communication terminal, pausing the current automatic driving operation task to execute the charging task, ending the charging task and recovering the current automatic driving operation task;
the 4G/5G mobile communication network is a wireless communication network of each component of each system.
Further, the TMC system comprises a job task management module and a charging management module, wherein the charging management module is used for distributing optimal charging tasks to each trolley in the unmanned truck collection team by adopting a latest idle charging pile distribution algorithm and outputting optimal charging task distribution data information of the vehicles, and the job task management module is used for optimizing whether the vehicles execute the charging tasks by adopting an optimal charging task execution distribution algorithm to obtain optimal charging task execution data information of the vehicles at the current moment.
To achieve the above and other related objects, the present invention also provides a computer-readable storage medium having stored thereon a computer program programmed or configured to perform the charge management scheduling method of the port unmanned integrated circuit fleet of any one of the above.
The invention has the following positive effects:
1. Aiming at the problem of charge management scheduling of the unmanned collection card in the port, according to the task information of the unmanned collection card fleet of the TOS, the map path information of the execution task and the vehicle information reported by the unmanned collection card, a charge management module and a task management module of a TMC system operate a latest idle charge pile allocation algorithm and an optimal charge task execution allocation algorithm, and the charge tasks are executed by calculating and matching vehicles in an optimal charge scheme and vehicles in an optimal charge task execution scheme in real time (generally at intervals of 10 min), or vehicles in unmanned collection cards with different numbers continue to execute operation tasks if the numbers are the same.
2. According to the invention, the cloud deck is used as a management basis, and the charging rules are extracted according to the actual field charging environment, so that the charging tasks are automatically issued to the vehicles needing to be charged, the operation efficiency of a vehicle team is considered, the management of the charging piles is considered, the optimal scheme of when and where the vehicles are to be charged is calculated in real time, the problems of fewer charging management scheduling schemes, low efficiency and low engineering applicability of the unmanned truck are solved, the operation and charging time of the truck collecting vehicle is effectively considered, and the operation efficiency of the truck collecting team is improved.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention;
FIG. 2 is a flow chart of the present invention of a nearest idle charging pile allocation algorithm;
FIG. 3 is a flow chart of an optimal charging task execution allocation algorithm according to the present invention;
FIG. 4 is a schematic diagram of a system framework of the present invention;
Fig. 5 is a schematic diagram of an unmanned set card charging management scheduling job according to the present invention.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Example 1: as shown in fig. 1, a charge management scheduling method for a port unmanned integrated vehicle team, the method includes:
In U1. ports, starting an operation task in sequence after a single vehicle receives a task information CAN instruction in an unmanned integrated card fleet, and acquiring vehicle state data information of a current unmanned integrated card and recorded state data information of a charging pile in real time;
U2., based on the recorded state data information of the charging piles and the current vehicle state data information of the unmanned aerial vehicle, distributing optimal charging tasks to each vehicle in the unmanned aerial vehicle team by adopting a latest idle charging pile distribution algorithm, and outputting vehicle optimal distribution charging task data information;
u3. optimizing whether the vehicle executes the charging task or not by adopting an optimal charging task execution allocation algorithm based on the optimal allocation charging task data information of the vehicle to obtain optimal execution charging task data information of the vehicle at the current moment;
U4. repeating the steps U2-U3, and alternately charging the unmanned integrated circuit vehicle team under the condition of uninterrupted operation to obtain the optimal running state data information of the vehicle.
In this embodiment, the vehicle state data information of the current unmanned set card includes remaining power data information of the vehicle, operation task state data information of the vehicle, and position data information of the vehicle, and the recorded state data information of the charging pile includes charging pile idle time data information, charging pile position data information, and charging state data information of the charging pile.
In this embodiment, as shown in fig. 2, in step U2, the allocating an optimal charging task to each vehicle in the unmanned truck fleet by using the latest idle charging pile allocation algorithm includes:
U21. based on the recorded state data information of the charging pile, acquiring an idle time set t= { T 1,T2,...,Ti,...Tn }, and establishing a maximum idle time function T i of the charging pile,
Ti=max{T1,T2,...,Ti,...Tn},
Wherein T i is the idle time of the ith charging pile, n is the number of the charging piles, and the maximum idle time data information of the charging piles is obtained;
U22 based on the maximum idle time data information of the charging pile and the state data information of the current unmanned aerial vehicle, establishing a charging cost function S ij of the unmanned aerial vehicle,
,
Dij=distance(Vehiclej,Chargeri)，
Wherein, alpha and beta are fitness factors of the charging cost of the unmanned collector card, T i is the maximum idle time data information of the charging pile, D ij is the distance from the jth Vehicle to the ith charging pile, vehicle j is the position data information of the jth unmanned collector card, charger i is the position information of the ith charging pile, and P i is the power of the ith charging pile to obtain the charging cost data information of the unmanned collector card;
U23. based on the charging cost data information of the unmanned set card, a minimum charging cost function C is established,
C=min{S11,S12,...Sij,...,Snm}，
And m is the number of the unmanned set cards, and the allocation charging tasks of the unmanned set cards are optimized to obtain the data information of the optimal execution charging tasks of the vehicle.
In this embodiment, in step U22, the constraint conditions of the fitness factors a and β of the unmanned set card charging cost are that,
。
Example 2: the present invention is further illustrated and described below based on a charge management scheduling method for a port unmanned integrated vehicle fleet of embodiment 1.
As shown in fig. 1, a charge management scheduling method for a port unmanned integrated vehicle team, the method includes:
In U1. ports, starting an operation task in sequence after a single vehicle receives a task information CAN instruction in an unmanned integrated card fleet, and acquiring vehicle state data information of a current unmanned integrated card and recorded state data information of a charging pile in real time;
U2., based on the recorded state data information of the charging piles and the current vehicle state data information of the unmanned aerial vehicle, distributing optimal charging tasks to each vehicle in the unmanned aerial vehicle team by adopting a latest idle charging pile distribution algorithm, and outputting vehicle optimal distribution charging task data information;
u3. optimizing whether the vehicle executes the charging task or not by adopting an optimal charging task execution allocation algorithm based on the optimal allocation charging task data information of the vehicle to obtain optimal execution charging task data information of the vehicle at the current moment;
U4. repeating the steps U2-U3, and alternately charging the unmanned integrated circuit vehicle team under the condition of uninterrupted operation to obtain the optimal running state data information of the vehicle.
In this embodiment, as shown in fig. 3, in step U3, the optimizing whether the vehicle performs the charging task by using the optimal charging task performing and allocating algorithm includes:
U31. based on the optimally assigned charging task data information of the vehicle, a charging task execution state function Q (a t,rt, x) of the vehicle is established,
，
Wherein a t is a charging task action function of the vehicle, r t is a rewarding function of the charging task of the vehicle, x is charging task data information optimally distributed to the vehicle, ω is a rewarding factor, σ is a cost factor, and the task execution state of the vehicle is calculated to obtain task execution state data information of the vehicle;
U32. based on the task execution status data information of the vehicle, a vehicle charging task objective function W is established,
，
Q t is task execution state data information of the vehicle, and calculates a vehicle charging task target value to obtain vehicle charging task target value data information;
u33. based on the vehicle charging mission target value data information, establishing a vehicle charging mission target maximum value function V,
V=max{Wt1,Wt2,...Wtk,...,Wtm}，
W tk is the vehicle charging task target value of the kth unmanned set card, m is the number of unmanned set cards, the maximum value of the vehicle charging task target is obtained, whether the vehicle executes a charging task is optimized, and the data information of the optimal execution of the charging task of the vehicle at the current moment is obtained.
In this embodiment, the charging task action function a t of the vehicle is,
；
The reward function r t of the charging task of the vehicle is,
。
In this embodiment, the constraint conditions of the bonus factor omega and the cost factor sigma are that,
。
In this embodiment, as shown in fig. 4 or fig. 5, the present invention provides a system for implementing a charge management scheduling method for an unmanned collection truck team in a port, where the system includes a TOS system, a TMC system, a 4G/5G mobile communication terminal, an autopilot system, and a 4G/5G mobile communication network, where the TOS system is a dock task planning and distribution system in a port, and is a computer management system for managing and controlling each link of dock operation, and issues a cargo ship berthing, a loading and unloading task and a container transportation loading and unloading task in the dock, and is communicatively connected with the TMC system to forward an operation task of an unmanned collection truck;
The TMC system is a port unmanned integrated card informatization system, is a system integrating functions of command, dispatch, remote control, simulation, operation and maintenance monitoring, fault processing, big data analysis and network security policy setting, and is used for displaying tasks/states/maps/resources;
The 4G/5G mobile communication terminal is mounted on each card of the unmanned card collection vehicle team, receives the issued charge state information or the charge stopping state information of the TMC system, and forwards a charge instruction and charge stake position state information to the automatic driving system;
The automatic driving system controller is used for receiving the instruction of the 5G mobile communication terminal, pausing the current automatic driving operation task to execute the charging task, ending the charging task and recovering the current automatic driving operation task;
the 4G/5G mobile communication network is a wireless communication network of each component of each system.
In this embodiment, the TMC system includes a job task management module and a charging management module, where the charging management module is configured to allocate an optimal charging task to each vehicle in the unmanned truck-collecting fleet by using a latest idle charging pile allocation algorithm, and output data information of the optimal allocation of the charging task to the vehicle, and the job task management module is configured to optimize whether the vehicle performs the charging task by using an optimal charging task allocation algorithm, so as to obtain data information of the optimal execution of the charging task to the vehicle at the current moment.
In this embodiment, the present invention further provides a computer readable storage medium having stored thereon a computer program programmed or configured to perform the charge management scheduling method of the unmanned port truck crew of any one of the above.
Any reference to memory, storage, database, or other medium used in embodiments of the application may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
In summary, the invention solves the problems of less charge management scheduling schemes, low efficiency and low engineering application range of the unmanned integrated circuit card, effectively considers the operation and charging time of the integrated circuit vehicle and improves the operation efficiency of the integrated circuit vehicle team.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (8)
1. A method for charge management scheduling of an unmanned integrated circuit board fleet at a port, the method comprising:
In U1. ports, starting an operation task in sequence after a single vehicle receives a task information CAN instruction in an unmanned integrated card fleet, and acquiring vehicle state data information of a current unmanned integrated card and recorded state data information of a charging pile in real time;
U2., based on the recorded state data information of the charging piles and the current vehicle state data information of the unmanned aerial vehicle, distributing optimal charging tasks to each vehicle in the unmanned aerial vehicle team by adopting a latest idle charging pile distribution algorithm, and outputting vehicle optimal distribution charging task data information;
u3. optimizing whether the vehicle executes the charging task or not by adopting an optimal charging task execution allocation algorithm based on the optimal allocation charging task data information of the vehicle to obtain optimal execution charging task data information of the vehicle at the current moment;
u4. repeating the steps U2-U3, and alternately charging the unmanned integrated circuit vehicle team under the condition of uninterrupted operation to obtain the optimal running state data information of the vehicle;
In step U3, the optimizing whether the vehicle executes the charging task by using the optimal charging task execution allocation algorithm includes:
U31. based on the optimally assigned charging task data information of the vehicle, a charging task execution state function Q (a t,rt, x) of the vehicle is established,
，
Wherein a t is a charging task action function of the vehicle, r t is a rewarding function of the charging task of the vehicle, x is charging task data information optimally distributed to the vehicle, ω is a rewarding factor, σ is a cost factor, and the task execution state of the vehicle is calculated to obtain task execution state data information of the vehicle;
U32. based on the task execution status data information of the vehicle, a vehicle charging task objective function W is established,
，
Q t is task execution state data information of the vehicle, and calculates a vehicle charging task target value to obtain vehicle charging task target value data information;
u33. based on the vehicle charging mission target value data information, establishing a vehicle charging mission target maximum value function V,
V=max{Wt1,Wt2,...Wtk,...,Wtm}，
W tk is a vehicle charging task target value of the kth unmanned set card, m is the number of unmanned set cards, the maximum value of the vehicle charging task target is obtained, whether the vehicle executes a charging task is optimized, and data information of the optimal execution of the charging task of the vehicle at the current moment is obtained;
In step U2, the allocating an optimal charging task to each vehicle in the unmanned truck fleet by using the latest idle charging pile allocation algorithm includes:
U21. based on the recorded state data information of the charging pile, acquiring an idle time set t= { T 1,T2,...,Ti,...Tn }, and establishing a maximum idle time function T i of the charging pile,
Ti=max{T1,T2,...,Ti,...Tn},
Wherein T i is the idle time of the ith charging pile, n is the number of the charging piles, and the maximum idle time data information of the charging piles is obtained;
U22 based on the maximum idle time data information of the charging pile and the state data information of the current unmanned aerial vehicle, establishing a charging cost function S ij of the unmanned aerial vehicle,
,
Dij=distance(Vehiclej,Chargeri)，
Wherein, alpha and beta are fitness factors of the charging cost of the unmanned collector card, T i is the maximum idle time data information of the charging pile, D ij is the distance from the jth Vehicle to the ith charging pile, vehicle j is the position data information of the jth unmanned collector card, charger i is the position information of the ith charging pile, and P i is the power of the ith charging pile to obtain the charging cost data information of the unmanned collector card;
U23. based on the charging cost data information of the unmanned set card, a minimum charging cost function C is established,
C=min{S11,S12,...Sij,...,Snm}，
And m is the number of the unmanned set cards, and the allocation charging tasks of the unmanned set cards are optimized to obtain the data information of the optimal execution charging tasks of the vehicle.
2. The charge management scheduling method of a port unmanned integrated circuit board (unmanned integrated circuit board) according to claim 1, wherein: the vehicle state data information of the current unmanned integrated card comprises the remaining electric quantity data information of the vehicle, the operation task state data information of the vehicle and the position data information of the vehicle, and the recorded state data information of the charging pile comprises the idle time data information of the charging pile, the position data information of the charging pile and the charging state data information of the charging pile.
3. The method for managing and scheduling the charging of an unmanned collection card fleet at a port according to claim 1, wherein in step U22, the constraint conditions of the fitness factors alpha and beta of the unmanned collection card charging cost are,
。
4. The charge management scheduling method of a port unmanned integrated circuit board (unmanned integrated circuit board) according to claim 1, wherein: the charging task action function a t of the vehicle is,
；
The reward function r t of the charging task of the vehicle is,
。
5. The charge management scheduling method of a port unmanned integrated circuit board (unmanned integrated circuit board) according to claim 1, wherein: the constraint of the bonus factor omega and the cost factor sigma is that,
。
6. A system for implementing the charge management scheduling method of the port unmanned integrated card fleet according to any one of claims 1 to 5, comprising a TOS system, a TMC system, a 4G/5G mobile communication terminal, an autopilot system, and a 4G/5G mobile communication network, characterized in that:
The TOS system is a wharf task planning and distribution system of a port and is used for managing and controlling all links of wharf operation, issuing cargo ship berthing, loading and unloading tasks and container transportation loading and unloading tasks of the wharf, and the TOS system is in communication connection with the TMC system to forward operation tasks of unmanned collection cards;
The TMC system is a port unmanned integrated card informatization system, is a system integrating functions of command, dispatch, remote control, simulation, operation and maintenance monitoring, fault processing, big data analysis and network security policy setting, and is used for displaying tasks/states/maps/resources;
The 4G/5G mobile communication terminal is mounted on each card of the unmanned card collection vehicle team, receives the issued charge state information or the charge stopping state information of the TMC system, and forwards a charge instruction and charge stake position state information to the automatic driving system;
The automatic driving system controller is used for receiving the instruction of the 5G mobile communication terminal, pausing the current automatic driving operation task to execute the charging task, ending the charging task and recovering the current automatic driving operation task;
the 4G/5G mobile communication network is a wireless communication network of each component of each system.
7. The system according to claim 6, wherein: the TMC system comprises a job task management module and a charging management module, wherein the charging management module is used for distributing optimal charging tasks to each vehicle in the unmanned truck collection team by adopting a latest idle charging pile distribution algorithm and outputting optimal charging task distribution data information of the vehicles, and the job task management module is used for optimizing whether the vehicles execute the charging tasks by adopting an optimal charging task execution distribution algorithm to obtain optimal charging task execution data information of the vehicles at the current moment.
8. A computer readable storage medium having stored thereon a computer program programmed or configured to perform the method of charge management scheduling for a port unmanned integrated circuit vehicle fleet of any one of claims 1-5.
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