CN114596042A - Cargo transportation method and device, electronic equipment and storage medium - Google Patents
Cargo transportation method and device, electronic equipment and storage medium Download PDFInfo
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- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0835—Relationships between shipper or supplier and carriers
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Abstract
The invention discloses a method and a device for transporting goods, electronic equipment and a storage medium. The method comprises the following steps: acquiring a to-be-transported goods set and a candidate transport vehicle set, wherein the to-be-transported goods set comprises at least two to-be-transported goods, and the candidate transport vehicle set comprises at least two candidate transport vehicles; inputting a to-be-transported goods set and a candidate transport vehicle set into a preset model to obtain a transportation scheme; and transporting the goods to be transported according to the transportation scheme. According to the technical scheme of the embodiment of the invention, the goods to be transported and the candidate transport vehicle set are input into the preset model to obtain the transportation scheme, so that the goods can be transported comprehensively and efficiently at low cost.
Description
Technical Field
The embodiment of the invention relates to the technical field of goods transportation, in particular to a goods transportation method, a goods transportation device, electronic equipment and a storage medium.
Background
With the development of the intelligent industry, the speed, efficiency, accuracy and the like of cargo handling are becoming important points of attention.
At present, when goods in a warehouse are transported, a transport path is usually calculated according to positions of a target starting point and a target ending point by using a greedy algorithm, and then the goods are transported by adopting a manual transportation method. However, manpower cost is excessively consumed by pushing the trolley manually, and a large amount of calculation power is required to calculate a path according to the positions of the target starting point and the target ending point by using a greedy algorithm, so that a large amount of time and calculation cost are consumed.
Disclosure of Invention
The invention provides a method and a device for transporting goods, electronic equipment and a storage medium, which are used for transporting goods comprehensively, efficiently and at low cost.
According to an aspect of the present invention, there is provided a method of cargo transportation, the method comprising:
acquiring a to-be-transported goods set and a candidate transport vehicle set, wherein the to-be-transported goods set comprises at least two to-be-transported goods, and the candidate transport vehicle set comprises at least two candidate transport vehicles;
inputting a to-be-transported goods set and a candidate transport vehicle set into a preset model to obtain a transportation scheme;
and transporting the goods to be transported according to the transportation scheme.
Optionally, the number of the goods to be transported in the set of goods to be transported is M, the number of the candidate transport vehicles in the set of candidate transport vehicles is N, M is greater than or equal to 2 and less than or equal to N, and M is an integer.
Optionally, a transportation scheme is obtained, comprising: acquiring M candidate transport vehicles from the N candidate transport vehicles as target transport vehicles according to the initial positions of the M goods to be transported, the real-time states of the N candidate transport vehicles and the real-time positions of the N candidate transport vehicles, wherein one target transport vehicle corresponds to one goods to be transported; and sending a transportation instruction to the target transport vehicle so that the target transport vehicle transports the goods to be transported corresponding to the target transport vehicle from the starting position to the target position according to the transportation instruction.
Optionally, the transportation instruction satisfies at least one of the following rules: the target transport vehicle determines the advancing direction according to the current position and the initial position of the target transport vehicle or according to the initial position and the target position; and if the obstacle appears in the preset range of the target transport vehicle, determining an obstacle avoidance route by the target transport vehicle.
Optionally, the cargo transportation method further includes: determining a transport vehicle learning environment and a multi-agent reinforcement learning model; and training to obtain a preset model according to the transport vehicle learning environment and the multi-agent reinforcement learning model.
Optionally, according to the transportation vehicle learning environment and the multi-agent reinforcement learning model, a preset model is obtained through training, and the method comprises the following steps: determining reward values of the transport vehicle under different running paths according to the transport vehicle learning environment and the multi-agent reinforcement learning model; and training to obtain a preset model according to different running paths of the transport vehicle and corresponding reward values.
Optionally, the reward value includes a base reward value, a time reward value and an obstacle avoidance reward value.
Optionally, the reward value = basic reward value a + time reward value b + obstacle avoidance reward value c; wherein, a is the weight of the basic reward value, b is the weight of the time reward value, and c is the weight of the obstacle avoidance reward value.
According to another aspect of the present invention, there is provided a device for the transportation of goods, the device comprising:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring a to-be-transported goods set and a candidate transport vehicle set, the to-be-transported goods set comprises at least two to-be-transported goods, and the candidate transport vehicle set comprises at least two candidate transport vehicles;
the determining module is used for inputting the to-be-transported goods set and the candidate transport vehicle set into a preset model to obtain a transportation scheme;
and the transportation module is used for transporting the goods to be transported according to the transportation scheme.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform a method of cargo transportation according to any of the embodiments of the invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to perform a method of cargo transportation according to any one of the embodiments of the present invention when the computer instructions are executed.
According to the technical scheme of the embodiment of the invention, a to-be-transported goods set and a candidate transport vehicle set are obtained, wherein the to-be-transported goods set comprises at least two to-be-transported goods, and the candidate transport vehicle set comprises at least two candidate transport vehicles; inputting a to-be-transported goods set and a candidate transport vehicle set into a preset model to obtain a transportation scheme; and transporting the goods to be transported according to the transportation scheme. According to the technical scheme of the embodiment of the invention, the goods to be transported and the candidate transport vehicle set are input into the preset model to obtain the transportation scheme, so that the goods can be transported comprehensively and efficiently at low cost. The problems of high labor cost, high calculation cost, high time and high calculation cost caused by manually pushing the trolley and calculating the path according to the positions of the target starting point and the target terminal point by utilizing a greedy algorithm are solved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for transporting goods according to an embodiment of the present invention;
FIG. 2a is a schematic diagram of a cargo transportation process according to an embodiment of the present invention;
FIG. 2b is a diagram illustrating a process for calculating a prize value according to an embodiment of the invention;
fig. 3 is a schematic structural diagram of a cargo transportation device according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a schematic flow chart of a cargo transportation method according to an embodiment of the present invention, where the present embodiment is applicable to a situation where multiple transport vehicles transport multiple cargos, and the method may be performed by a cargo transportation apparatus according to an embodiment of the present invention, where the apparatus may be implemented in software and/or hardware, and in a specific embodiment, the apparatus may be integrated in an electronic device. The following embodiments will be described by taking as an example that the apparatus is integrated in an electronic device, and referring to fig. 1, the method specifically includes the following steps:
s101, a to-be-transported goods set and a candidate transport vehicle set are obtained, wherein the to-be-transported goods set comprises at least two to-be-transported goods, and the candidate transport vehicle set comprises at least two candidate transport vehicles.
The set of the goods to be transported is a data set of all the goods to be transported in the system, and the set of the candidate transport vehicles is a data set of all the candidate transport vehicles in the system. Specifically, the goods to be transported include goods in the warehouse which need to be transported, and the candidate transport vehicles include transport vehicles in the warehouse. Further, the multiple transport vehicles are used for transporting multiple goods in a coordinated mode, so that the set of goods to be transported comprises at least two goods to be transported, and the set of candidate transport vehicles comprises at least two candidate transport vehicles.
Illustratively, there are 5 candidate carriers in the warehouse, respectively carrier a, carrier B, carrier C, carrier D and carrier E, and there are 5 to-be-transported goods, respectively cargo 1, cargo 2, cargo 3, cargo 4 and cargo 5, so the to-be-transported goods set includes cargo 1, cargo 2, cargo 3, cargo 4 and cargo 5, and the candidate carrier set includes carrier a, carrier B, carrier C, carrier D and carrier E.
S102, inputting the to-be-transported goods set and the candidate transport vehicle set into a preset model to obtain a transportation scheme.
The preset model comprises a trained model applied to the cooperative transportation of multiple cargos by multiple transport vehicles; the transportation scheme is a method for operating each cargo by each transport vehicle. Specifically, the to-be-transported goods set and the candidate transport vehicle set are input into the preset model, and the scheme for the candidate transport vehicle to transport the to-be-transported goods can be obtained.
Optionally, the number of the goods to be transported in the goods to be transported set is M, the number of the candidate transport vehicles in the candidate transport vehicle set is N, M is greater than or equal to 2 and less than or equal to N, and M is an integer; obtaining a transportation solution comprising: acquiring M candidate transport vehicles from the N candidate transport vehicles as target transport vehicles according to the initial positions of the M goods to be transported, the real-time states of the N candidate transport vehicles and the real-time positions of the N candidate transport vehicles, wherein one target transport vehicle corresponds to one goods to be transported; and sending a transportation instruction to the target transport vehicle so that the target transport vehicle transports the goods to be transported corresponding to the target transport vehicle from the starting position to the target position according to the transportation instruction.
The starting position of the goods to be transported is the current position of the goods to be transported, the target position is the target position of the goods to be transported, the real-time state of the candidate transport vehicle comprises whether the candidate transport vehicle bears the goods to be transported currently or not, the real-time position of the candidate transport vehicle comprises the current position of the candidate transport vehicle, the target transport vehicle is a transport vehicle used for transporting the goods to be transported in the candidate transport vehicle, and the transport instruction is an instruction for driving the transport vehicle.
Fig. 2a is a schematic diagram of a cargo transportation process according to an embodiment of the present invention, and as can be seen from the diagram, 4 candidate transport vehicles, 3 to-be-transported cargos, and 2 obstacles are arranged in a warehouse, where the candidate transport vehicles are transport vehicle 1, transport vehicle 2, transport vehicle 3, and transport vehicle 4, the to-be-transported cargos are cargo 1, cargo 2, and cargo 3, respectively, and each to-be-transported cargo is placed at a start position. Assume that all 4 candidate transport vehicles do not carry cargo to be transported. According to the starting positions of the M goods to be transported, the real-time states of the N candidate transport vehicles and the real-time positions of the N candidate transport vehicles, the M candidate transport vehicles are obtained from the N candidate transport vehicles and are used as target transport vehicles, namely, the transport vehicle 1, the transport vehicle 2 and the transport vehicle 4 are selected from the 4 candidate transport vehicles and are used as target transport vehicles according to the starting positions of the 3 goods to be transported and the real-time positions of the 4 candidate transport vehicles. Wherein, transport vechicle 1 is used for transporting goods 2, and transport vechicle 2 is used for transporting goods 1, and transport vechicle 4 is used for transporting goods 3. Then, a transport instruction is sent to the target transport vehicle, so that the target transport vehicle transports the goods to be transported corresponding to the target transport vehicle from the starting position to the target position according to the transport instruction, for example, an instruction for transporting the goods 2 is sent to the transport vehicle 1, so that the transport vehicle 1 transports the goods 2 from the starting position 2 to the target position 2.
Optionally, when the number of the goods to be transported in the set of goods to be transported is M, the number of the candidate transport vehicles in the set of candidate transport vehicles is N, N is greater than or equal to 2 and is less than or equal to M, and M is an integer, the goods to be transported in the warehouse are larger than the candidate transport vehicles, at this time, according to the starting positions of the M goods to be transported, the real-time states of the N candidate transport vehicles and the real-time positions of the N candidate transport vehicles, the N goods to be transported are obtained from the M goods to be transported as target goods, the appropriate candidate transport vehicles are driven to transport the target goods, the states and the real-time positions of the candidate transport vehicles are obtained, and the appropriate candidate transport vehicles are selected to transport the remaining M-N goods to be transported.
Optionally, the transportation instruction satisfies at least one of the following rules: the target transport vehicle determines the advancing direction according to the current position and the initial position of the target transport vehicle or according to the initial position and the target position; and if the obstacle appears in the preset range of the target transport vehicle, determining an obstacle avoidance route by the target transport vehicle.
The moving direction is the action direction of the target transport vehicle, and comprises easting, southward, westward, northward, warp stop, southeast, northeast, southwest, northwest and the like, and is used for indicating the next step of the target transport vehicle. The obstacle is an object in the traveling direction of the target transport vehicle, and the existence of the obstacle affects the movement of the target transport vehicle, including a rack, a wall, other transport vehicles, and the like, which is not limited in the embodiment of the present invention.
For example, as shown in fig. 2a, a transport vehicle 2 transports a cargo 1 to be transported, and the transport vehicle 1 transports the cargo 2 to be transported, as can be seen from the figure, the transport vehicle 2 determines a traveling direction according to a current position thereof and a start position of the cargo 1 to be transported, and after receiving the cargo 1, determines a direction according to the start position and a target position of the cargo 1 to be transported, and transports the cargo. The transport vehicle 1 firstly determines the advancing direction according to the current position and the initial position of the goods 2 to be transported, after receiving the goods 2, the direction is determined according to the initial position and the target position of the goods 2 to be transported for transporting the goods, but in the transporting process, the transport vehicle touches obstacles, so that the transport vehicle 1 determines the advancing direction again according to the obstacles appearing in the preset range, and transports the goods to the target position through the obstacle avoidance route.
S103, transporting the goods to be transported according to the transportation scheme.
Specifically, after the transportation scheme is obtained, the cargo transportation can be performed according to the instruction of the transportation scheme, and further, the transportation scheme includes which transport vehicle transports which cargo to be transported, what the next route of the transport vehicle is, and the like, which is not limited in the embodiment of the present invention.
For example, assuming that the transportation scheme is that the target transport vehicle 1 transports the goods 8 to be transported, and the next path is north, the target transport vehicle 1 is driven to perform the action of transporting the goods 8 to be transported, and the next path of the target transport vehicle 1 is north, and meanwhile, the target transport vehicle determines and adjusts the next path in real time according to the current position of the target transport vehicle, the obstacle information in the preset environment, the initial position of the goods 8 to be transported, and the target position transport vehicle.
Optionally, the cargo transportation method further comprises: determining a transport vehicle learning environment and a multi-agent reinforcement learning model; and training to obtain a preset model according to the transport vehicle learning environment and the multi-agent reinforcement learning model.
The transport vehicle learning environment comprises a built warehouse simulation environment, the multi-agent reinforcement learning model is a sample model, and the preset model is a trained model applied to the cargo transportation method.
Specifically, the preset model may be constructed based on a digital twin technology, or may be constructed based on technologies such as polygon modeling, parametric modeling, inverse modeling, surface modeling, and the like, which are not limited in the embodiment of the present invention.
Furthermore, the digital twin is the digital expression of the actual physical product, and the simulation processes of multiple disciplines, multiple physical quantities, multiple scales and multiple probabilities are integrated by utilizing data such as a physical model, sensor updating, operation history and the like, so that mapping is completed in a virtual space, and the full life cycle process of the corresponding actual physical product is reflected in a digital expression mode.
In one particular example, the construction of a transporter learning environment includes: determining the dimension of the constructed environment relative to the Action accepted by each intelligent vehicle, corresponding the Action modes of the intelligent vehicle corresponding to each Action one to one, and coding the actions for being used as the input accepted by the environment; determining the dimension of output information (observer) of the constructed environment, wherein the observer is the corresponding Observation output of the environment to the intelligent vehicle according to the position of the vehicle and is also the input of a model for constructing the intelligent vehicle; and constructing an environment reward mechanism, setting a corresponding reward mechanism according to the position of the target point, the action direction of the trolley and other conditions such as whether the trolley collides with a wall or a vehicle, and designing the reward of target starting point goods taking and the overall final reward of route time after achieving the goal of unloading goods at the target end point by using the output of each step of environment iteration. The model building comprises building a hybrid QMIX reinforcement learning model based on multiple transport vehicles according to the output dimension and the input dimension of the existing learning environment, determining the interaction mode of the model and the environment, the data storage mode and the data sampling mode, and designing relevant hyper-parameters such as the learning rate of the model. The method for Training the transport vehicle reinforcement learning model comprises the steps of combining a learning environment with a QMIX algorithm, setting corresponding hyper-parameters, adopting a Centralized Training Centralized Execution (CTDE) method, combining local value functions of single intelligent bodies (transport vehicles) through a mixed network, adding global state information assistance in the Training and learning process to improve the algorithm performance, and accelerating the algorithm Training model by using a Graphics Processing Unit (GPU). The model training comprises changing the position of the obstacle, changing the positions of the starting point and the ending point and testing the complete path from the starting point to the starting point and then to the ending point of the intelligent agent in the testing environment according to the trained model.
Optionally, according to the transportation vehicle learning environment and the multi-agent reinforcement learning model, a preset model is obtained through training, and the method comprises the following steps: determining reward values of the transport vehicle under different running paths according to the transport vehicle learning environment and the multi-agent reinforcement learning model; and training to obtain a preset model according to different running paths of the transport vehicle and corresponding reward values.
Wherein the reward value is used to measure the performance and stability of the model. Specifically, the reward values under different running paths are different, and the shorter the path is, the shorter the transportation time is, and the higher the reward value is. And when a model which can finish the transportation task in the shortest time and can avoid the obstacles is trained, determining that the training process is finished, wherein the trained model is the preset model.
Optionally, the reward value includes a base reward value, a time reward value and an obstacle avoidance reward value.
The basic reward value is a reward value for completing a transportation task, and when the transport vehicle transports goods to be transported from an initial position to a target position, the basic reward value is rewarded; the time reward value and the obstacle avoidance reward value are additional reward values, specifically, the time reward value is related to the time of the transport vehicle for transporting the goods to be transported, and the shorter the time for transporting the goods is, the higher the time reward value is; the obstacle avoidance reward value has positive and negative values, when the transport vehicle transports the goods to be transported from the initial position to the target position without any collision, a positive obstacle avoidance reward value is given, if the transport vehicle collides in the process of transporting the goods to be transported, the obstacle avoidance is considered to fail, and a negative obstacle avoidance reward value is given.
Specifically, the reward value = basic reward value a + time reward value b + obstacle avoidance reward value c; wherein, a is the weight of the basic reward value, b is the weight of the time reward value, and c is the weight of the obstacle avoidance reward value. Furthermore, the model which meets the requirements and has the highest performance can be determined according to the reward value, and the higher the reward value is, the better the performance of the trained model is. Further, fig. 2b is a schematic diagram of a process of calculating an incentive value according to an embodiment of the present invention, where each transport vehicle transports goods according to the environment information, the transport vehicle information, and the goods information, and obtains an incentive value, and the incentive values of all transport vehicles are integrated for evaluation, and the transportation scheme with the highest integrated incentive value is the optimal one.
In an exemplary embodiment of the invention, the basic reward value occupies the largest weight, and the purpose is to transport goods to be transported from an initial position to a target position.
According to the technical scheme, a to-be-transported goods set and a candidate transport vehicle set are obtained, wherein the to-be-transported goods set comprises at least two to-be-transported goods, and the candidate transport vehicle set comprises at least two candidate transport vehicles; inputting a to-be-transported goods set and a candidate transport vehicle set into a preset model to obtain a transportation scheme; and transporting the goods to be transported according to the transportation scheme. According to the technical scheme of the embodiment of the invention, the goods set to be transported and the candidate transport vehicle set are input into the preset model to obtain the transport scheme, so that the goods can be transported comprehensively and efficiently at low cost, the data can be automatically produced in batches for model training, the developed model is small and fast, the transport vehicles can be subjected to real-time strain according to a real-time path, for example, the next-step path selection is determined according to the change of the environment in real time, the global optimization is pursued instead of the local optimization, and the system performance is improved. The problems of high labor cost, high calculation cost, high time and high calculation cost caused by manually pushing the trolley and calculating the path according to the positions of the target starting point and the target terminal point by utilizing a greedy algorithm are solved.
Example two
The cargo transportation device provided by the embodiment of the invention can execute the cargo transportation method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Fig. 3 is a schematic structural diagram of a cargo transportation apparatus according to an embodiment of the present invention, as shown in fig. 3, including: an acquisition module 301, a determination module 302, and a transport module 303.
The acquiring module 301 is configured to acquire a to-be-transported goods set and a candidate transport vehicle set, where the to-be-transported goods set includes at least two to-be-transported goods, and the candidate transport vehicle set includes at least two candidate transport vehicles.
The determining module 302 is configured to input the to-be-transported cargo set and the candidate transport vehicle set into a preset model, so as to obtain a transportation scheme.
And the transportation module 303 is configured to transport the goods to be transported according to the transportation scheme.
The cargo transportation device provided by the embodiment is a method for realizing cargo transportation in the above embodiments, and the realization principle and technical effect of the cargo transportation device provided by the embodiment are similar to those of the above embodiments, and are not described herein again.
Optionally, the number of the goods to be transported in the set of goods to be transported is M, the number of the candidate transport vehicles in the set of candidate transport vehicles is N, M is greater than or equal to 2 and less than or equal to N, and M is an integer.
Optionally, the determining module 302 is specifically configured to obtain M candidate transport vehicles from the N candidate transport vehicles as target transport vehicles according to the starting positions of the M to-be-transported goods, the real-time states of the N candidate transport vehicles, and the real-time positions of the N candidate transport vehicles, where one target transport vehicle corresponds to one to-be-transported goods; and sending a transportation instruction to the target transport vehicle so that the target transport vehicle transports the goods to be transported corresponding to the target transport vehicle from the starting position to the target position according to the transportation instruction.
Optionally, the transportation instruction satisfies at least one of the following rules: the target transport vehicle determines the advancing direction according to the current position and the initial position of the target transport vehicle or according to the initial position and the target position; and if the obstacle appears in the preset range of the target transport vehicle, determining an obstacle avoidance route by the target transport vehicle.
Optionally, the apparatus further comprises a training module for determining a transporter learning environment and a multi-agent reinforcement learning model; and training to obtain a preset model according to the transport vehicle learning environment and the multi-agent reinforcement learning model.
Optionally, the training module is specifically configured to determine reward values of the transport vehicle under different running paths according to the transport vehicle learning environment and the multi-agent reinforcement learning model; and training to obtain a preset model according to different running paths of the transport vehicle and corresponding reward values.
Optionally, the reward value includes a base reward value, a time reward value and an obstacle avoidance reward value.
Optionally, the reward value = basic reward value a + time reward value b + obstacle avoidance reward value c; wherein, a is the weight of the basic reward value, b is the weight of the time reward value, and c is the weight of the obstacle avoidance reward value.
EXAMPLE III
FIG. 4 shows a schematic block diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The processor 11 performs the various methods and processes described above, such as methods of cargo transportation.
In some embodiments, the method of cargo transportation may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method of cargo transportation described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the method of cargo transportation by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method of cargo transportation, comprising:
acquiring a to-be-transported goods set and a candidate transport vehicle set, wherein the to-be-transported goods set comprises at least two to-be-transported goods, and the candidate transport vehicle set comprises at least two candidate transport vehicles;
inputting the set of goods to be transported and the set of candidate transport vehicles into a preset model to obtain a transportation scheme;
and transporting the goods to be transported according to the transportation scheme.
2. The method according to claim 1, wherein the number of the goods to be transported in the set of goods to be transported is M, the number of the candidate transport vehicles in the set of candidate transport vehicles is N, M is greater than or equal to 2 and less than or equal to N, and M and N are integers;
the obtaining of the transportation scheme comprises:
acquiring M candidate transport vehicles from the N candidate transport vehicles as target transport vehicles according to the starting positions of the M to-be-transported goods, the real-time states of the N candidate transport vehicles and the real-time positions of the N candidate transport vehicles, wherein one target transport vehicle corresponds to one to-be-transported goods;
and sending a transportation instruction to the target transport vehicle so that the target transport vehicle transports the goods to be transported corresponding to the target transport vehicle from the starting position to the target position according to the transportation instruction.
3. The method of claim 2, wherein the transport instruction satisfies at least one of the following rules:
the target transport vehicle determines the advancing direction according to the current position and the starting position of the target transport vehicle or according to the starting position and the target position;
and if the obstacle appears in the preset range of the target transport vehicle, determining an obstacle avoidance route by the target transport vehicle.
4. The method of claim 1, further comprising:
determining a transport vehicle learning environment and a multi-agent reinforcement learning model;
and training to obtain the preset model according to the transport vehicle learning environment and the multi-agent reinforcement learning model.
5. The method of claim 4, wherein said training said pre-set model from said transporter learning environment and said multi-agent reinforcement learning model comprises:
determining reward values of the transport vehicle under different running paths according to the transport vehicle learning environment and the multi-agent reinforcement learning model;
and training to obtain the preset model according to different running paths of the transport vehicle and corresponding reward values.
6. The method of claim 5, wherein the reward values comprise a base reward value, a time reward value, and an obstacle avoidance reward value.
7. The method of claim 6,
the reward value = basic reward value a + time reward value b + obstacle avoidance reward value c; wherein, a is the weight of the basic reward value, b is the weight of the time reward value, and c is the weight of the obstacle avoidance reward value.
8. A device for the transportation of goods, comprising:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring a to-be-transported goods set and a candidate transport vehicle set, the to-be-transported goods set comprises at least two to-be-transported goods, and the candidate transport vehicle set comprises at least two candidate transport vehicles;
the determining module is used for inputting the to-be-transported goods set and the candidate transport vehicle set into a preset model to obtain a transportation scheme;
and the transportation module is used for transporting the goods to be transported according to the transportation scheme.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the method of cargo transportation of any of claims 1 to 7.
10. A computer-readable storage medium storing computer instructions for causing a processor to perform the method of cargo transportation of any one of claims 1 to 7 when executed.
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