CN114757618A - Goods distribution method and device, electronic equipment and readable storage medium - Google Patents
Goods distribution method and device, electronic equipment and readable storage medium Download PDFInfo
- Publication number
- CN114757618A CN114757618A CN202210444183.0A CN202210444183A CN114757618A CN 114757618 A CN114757618 A CN 114757618A CN 202210444183 A CN202210444183 A CN 202210444183A CN 114757618 A CN114757618 A CN 114757618A
- Authority
- CN
- China
- Prior art keywords
- distribution
- delivery
- cost
- target
- unmanned aerial
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Operations Research (AREA)
- Theoretical Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides a cargo distribution method, a cargo distribution device, electronic equipment and a readable storage medium, wherein the method comprises the following steps: determining a cargo distribution path of the cargo to be distributed, wherein the cargo distribution path comprises a flying distribution road section for realizing distribution through a target unmanned aerial vehicle and a driving distribution road section for realizing distribution through the target unmanned aerial vehicle; determining the actual distribution time length and the actual distribution cost of goods to be distributed according to the flying distribution road section and the driving distribution road section; building a distribution model with the aim of minimizing the actual distribution time length and the actual distribution cost; and obtaining an objective solution result through the solution of the delivery model, wherein the objective solution result is used for indicating the road distribution of the flying delivery road section and the driving delivery road section in the cargo delivery path. According to the method and the device, the distribution cost can be minimized and the distribution efficiency can be maximized through the distribution model, and the reasonability of road section distribution is improved.
Description
Technical Field
The present application relates to the field of transportation technologies, and in particular, to a cargo distribution method and apparatus, an electronic device, and a readable storage medium.
Background
Along with the development of science and technology, the mode that unmanned aerial vehicle or unmanned car delivery can be adopted in cargo delivery at present. Unmanned aerial vehicle and unmanned vehicle have respective advantage and shortcoming respectively, and unmanned aerial vehicle flying speed is fast, but with high costs and the position of falling to the ground has very big limitation, and unmanned vehicle is with low costs and the position limitation is little, but speed is slower.
And the unmanned aerial vehicle and the unmanned vehicle are adopted to complete the cargo delivery task in a cooperative manner, so that the minimization of delivery cost and the maximization of delivery efficiency can be realized, and therefore the flying delivery road section of the unmanned aerial vehicle and the driving delivery road section of the unmanned vehicle need to be respectively determined in a cargo delivery path.
At present, road section allocation is determined according to manual experience, but inaccurate road section allocation is easily caused, and the minimization of distribution cost and the maximization of distribution efficiency cannot be realized.
Disclosure of Invention
An object of the embodiments of the present application is to provide a cargo distribution method, a cargo distribution device, an electronic device, and a readable storage medium, so as to solve the problem that a manually assigned road segment is inaccurate. The specific technical scheme is as follows:
in a first aspect, a method for distributing goods is provided, the method comprising:
determining a cargo delivery path of the cargo to be delivered, wherein the cargo delivery path comprises a flight delivery road section for realizing delivery through a target unmanned aerial vehicle and a driving delivery road section for realizing delivery through a target unmanned aerial vehicle;
determining the actual delivery time length and the actual delivery cost of the goods to be delivered according to the flying delivery road section and the driving delivery road section;
building a distribution model with the aim of minimizing the actual distribution time length and the actual distribution cost;
and obtaining an objective solution result through the solution of the delivery model, wherein the objective solution result is used for indicating the road distribution of the flying delivery road section and the driving delivery road section in the cargo delivery path.
Optionally, building a delivery model with a goal of minimizing the actual delivery duration and the actual delivery cost includes:
taking the product of a preset duration coefficient and the actual distribution duration as a target distribution duration, and taking the product of a preset cost coefficient and the actual distribution cost as a target distribution cost;
taking the minimum sum of the target distribution duration and the target distribution cost as a target function;
and building a delivery model according to the minimized objective function.
Optionally, before building a delivery model according to the minimized objective function, the method further includes:
taking the actual distribution time length of the goods to be distributed as a distribution time length constraint condition, wherein the actual distribution time length is less than or equal to a distribution time length threshold value;
acquiring a preset flight area, wherein the target unmanned aerial vehicle is allowed to fly in the preset flight area;
taking the route rule that the flight distribution road section of the target unmanned aerial vehicle is located in the preset flight path, and the sum of the flight distribution road section of the target unmanned aerial vehicle and the running distribution road section of the target unmanned aerial vehicle is equal to the cargo distribution path;
and taking the duration constraint condition and the path rule as target constraint conditions of the distribution model.
Optionally, determining the actual delivery duration of the goods to be delivered according to the flight delivery road section and the driving delivery road section includes:
taking the ratio of the flight distribution road section to the speed of the unmanned aerial vehicle as the flight duration of the target unmanned aerial vehicle;
taking the ratio of the running distribution road section to the speed of the unmanned vehicle as the running duration of the target unmanned vehicle;
and taking the sum of the flight time, the driving time and the cargo carrying time as the actual distribution time of the to-be-distributed cargos.
Optionally, determining the actual delivery cost of the goods to be delivered according to the flight delivery road section and the driving delivery road section comprises:
determining the distribution cost of the target unmanned aerial vehicle according to the starting cost of the target unmanned aerial vehicle, the flight distribution road section and the cargo weight of the cargo to be distributed;
determining the target unmanned vehicle distribution cost according to the starting cost, the running distribution road section and the cargo weight of the target unmanned vehicle;
and taking the sum of the target unmanned aerial vehicle distribution cost and the target unmanned aerial vehicle distribution cost as the actual distribution cost of the goods to be distributed.
Optionally, the determining the target unmanned aerial vehicle delivery cost according to the starting cost of the target unmanned aerial vehicle, the flight delivery road section and the cargo weight of the cargo to be delivered includes:
taking the product of the number of the unmanned aerial vehicles and the starting cost of a single unmanned aerial vehicle as the starting cost of the target unmanned aerial vehicle, wherein the target unmanned aerial vehicle comprises at least one unmanned aerial vehicle;
taking the product of a preset unmanned aerial vehicle coefficient, a flight delivery road section and the weight of the goods as the transportation cost of the target unmanned aerial vehicle;
and taking the sum of the starting cost of the target unmanned aerial vehicle and the transportation cost of the target unmanned aerial vehicle as the distribution cost of the target unmanned aerial vehicle.
Optionally, the determining the target unmanned aerial vehicle delivery cost according to the starting cost of the target unmanned aerial vehicle, the flight delivery road section and the cargo weight of the cargo to be delivered includes:
taking the product of the number of the unmanned vehicles and the starting cost of a single unmanned vehicle as the starting cost of the target unmanned vehicle, wherein the target unmanned vehicle comprises at least one unmanned vehicle;
taking the product of a preset unmanned vehicle coefficient, a driving and distribution road section and the weight of the goods as the transportation cost of the target unmanned vehicle;
and taking the sum of the starting cost of the target unmanned vehicle and the transportation cost of the target unmanned vehicle as the distribution cost of the target unmanned vehicle.
Optionally, taking the product of the number of drones and the cost of launch of a single drone as the cost of launch of the target drone, the method further comprises:
determining the distribution weight and the distribution volume of the goods to be distributed;
determining the first unmanned aerial vehicle quantity according to the ratio of the distribution weight to the bearing weight of a single unmanned aerial vehicle;
determining the number of second unmanned aerial vehicles according to the ratio of the distribution volume to the bearing volume of a single unmanned aerial vehicle;
and selecting the maximum number from the first unmanned aerial vehicle number and the second unmanned aerial vehicle number as the number of the unmanned aerial vehicles.
Optionally, before taking the product of the number of unmanned vehicles and the starting cost of a single unmanned vehicle as the starting cost of the target unmanned vehicle, the method further comprises:
determining the distribution weight and the distribution volume of the goods to be distributed;
determining the number of the first unmanned vehicles according to the ratio of the distribution weight to the carrying capacity of the single unmanned vehicle;
determining the number of second unmanned vehicles according to the ratio of the distribution volume to the carrying volume of a single unmanned vehicle;
and selecting the maximum number from the first unmanned vehicle number and the second unmanned vehicle number as the number of the unmanned vehicles.
In a second aspect, there is provided a cargo dispensing device, the device comprising:
the system comprises a first determining module, a second determining module and a control module, wherein the first determining module is used for determining a cargo delivery path of cargos to be delivered, and the cargo delivery path comprises a flying delivery road section for delivering by a target unmanned aerial vehicle and a driving delivery road section for delivering by the target unmanned aerial vehicle;
the second determining module is used for determining the actual delivery time and the actual delivery cost of the goods to be delivered according to the flying delivery road section and the driving delivery road section;
the building module is used for building a distribution model with the aim of minimizing the actual distribution time length and the actual distribution cost;
and the solving module is used for obtaining an objective solving result through solving the distribution model, wherein the objective solving result is used for indicating the road distribution of the flying distribution road section and the driving distribution road section in the cargo distribution path.
In a third aspect, an electronic device is provided, which includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete communication with each other through the communication bus;
a memory for storing a computer program;
and the processor is used for realizing any step of the cargo distribution method when executing the program stored in the memory.
In a fourth aspect, a computer-readable storage medium is provided, in which a computer program is stored, which computer program, when being executed by a processor, carries out any of the cargo distribution method steps.
The embodiment of the application has the following beneficial effects:
the embodiment of the application provides a goods distribution method, wherein a server constructs a distribution model, reasonable distribution of flight distribution road sections and driving distribution road sections is realized by solving the distribution model by taking the minimization of actual distribution time and actual distribution cost as a target, and the minimization of the distribution cost and the maximization of the distribution efficiency are further ensured. Compared with manual road section distribution, the method and the device can achieve minimum distribution cost and maximum distribution efficiency through the distribution model, and improve reasonability of road section distribution.
Of course, not all of the above advantages need be achieved in the practice of any one product or method of the present application.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of a method for cargo distribution according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a cargo distribution device according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all 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 application.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for the convenience of description of the present application, and have no specific meaning in themselves. Thus, "module" and "component" may be used in a mixture.
The cargo distribution method in the embodiment of the application can be executed by the server, and is used for realizing reasonable distribution of flying distribution road sections and driving distribution road sections in a cargo distribution path so as to minimize distribution cost and maximize distribution efficiency.
A detailed description will be given below of a cargo distribution method provided in an embodiment of the present application with reference to a specific embodiment, as shown in fig. 1, the specific steps are as follows:
step 101: and determining a cargo distribution path of the cargo to be distributed.
The goods delivery path comprises a flight delivery road section for realizing delivery through the target unmanned aerial vehicle and a driving delivery road section for realizing delivery through the target unmanned aerial vehicle.
In the embodiment of the application, the server obtains a delivery starting point and a delivery ending point of goods to be delivered, and determines a unique goods delivery path based on the delivery starting point and the delivery ending point, the goods to be delivered are delivered by the target unmanned aerial vehicle and the target unmanned vehicle in a segmented manner, and then the goods delivery path comprises a flying delivery section for delivering through the target unmanned aerial vehicle and a driving delivery section for delivering through the target unmanned vehicle.
Target unmanned aerial vehicle includes an at least unmanned aerial vehicle, and target unmanned vehicle includes an at least unmanned car for target unmanned aerial vehicle and target unmanned vehicle that carry out the goods delivery are all in idle state and electric quantity are sufficient.
Step 102: and determining the actual delivery time length and the actual delivery cost of the goods to be delivered according to the flying delivery road section and the driving delivery road section.
In the embodiment of the application, the server determines the flight delivery time and the flight delivery cost of the target unmanned aerial vehicle according to the flight delivery road section, and determines the driving delivery time and the driving delivery cost of the target unmanned aerial vehicle according to the driving delivery road section. The server determines the actual distribution time length of the goods to be distributed according to the flight distribution time length and the driving distribution time length, and determines the actual distribution cost of the goods to be distributed according to the flight distribution cost and the driving distribution cost.
Step 103: and building a distribution model with the aim of minimizing the actual distribution time length and the actual distribution cost.
In the embodiment of the application, the server builds a delivery model, and the delivery model aims to minimize the actual delivery duration and the actual delivery cost, namely the delivery model aims to minimize the delivery cost and maximize the delivery efficiency.
Step 104: and solving the distribution model to obtain a target solving result.
And the target solving result is used for indicating the road distribution of the flying distribution road section and the driving distribution road section in the cargo distribution path.
In the embodiment of the present application, before the delivery model is solved, both the flight delivery road segments and the travel delivery road segments are unknowns. And the server solves the delivery model to obtain an optimal solution serving as an object solving result, and the object solving result indicates the road distribution of the flying delivery road section and the driving delivery road section in the cargo delivery path.
Further, the solution result of the distribution model includes at least one candidate solution result, and the candidate solution result may have two distribution modes in the same road section, which does not meet the requirement of the segmented distribution, and the server may exclude the solution result. And for the remaining solution results to be selected, the server arranges the minimum values of the functions in the order from small to large, and the solution result corresponding to the minimum function is used as a target solution result.
If the target solution result cannot be adopted due to some emergency situations, the solution result corresponding to the suboptimal solution can be selected, the road distribution of the flight delivery road sections and the road distribution of the driving delivery road sections is carried out, and the like until an implementable cooperative delivery scheme is obtained.
In the application, the server constructs a delivery model, the minimization of the actual delivery time and the actual delivery cost is taken as a target, and the reasonable distribution of the flying delivery road sections and the traveling delivery road sections is realized by solving the delivery model, so that the minimization of the delivery cost and the maximization of the delivery efficiency are ensured. Compared with manual road section distribution, the method and the device can achieve minimum distribution cost and maximum distribution efficiency through the distribution model, and improve reasonability of road section distribution.
As an alternative embodiment, building a delivery model with the goal of minimizing the actual delivery duration and the actual delivery cost includes: taking the product of the preset duration coefficient and the actual distribution duration as a target distribution duration, and taking the product of the preset cost coefficient and the actual distribution cost as a target distribution cost; taking the sum of the target distribution time length and the target distribution cost as a target function; and building a delivery model according to the minimized objective function.
In the embodiment of the application, the server obtains the preset duration coefficient and the preset cost coefficient, the product of the preset duration coefficient and the actual distribution duration is used as the target distribution duration, the product of the preset cost coefficient and the actual distribution cost is used as the target distribution cost, the sum of the target distribution duration and the target distribution cost is used as a target function, and then a distribution model is built according to the minimized target function, so that the distribution model aims to minimize the distribution cost and maximize the distribution efficiency.
The formula of the objective function is:
minQ is c × t + d × s, where Q is a target function, t is an actual delivery duration, c is a preset duration coefficient, s is an actual delivery cost, and d is a preset cost coefficient.
As an optional implementation, before building the delivery model according to the minimized objective function, the method further includes: taking the actual distribution time length of the goods to be distributed less than or equal to the distribution time length threshold as a time length constraint condition; acquiring a preset flight area, wherein the target unmanned aerial vehicle is allowed to fly in the preset flight area; taking the route rule that the flight distribution road section of the target unmanned aerial vehicle is positioned in the preset flight path, and the sum of the flight distribution road section of the target unmanned aerial vehicle and the running distribution road section of the target unmanned vehicle is equal to the cargo distribution path; and taking the duration constraint condition and the path rule as target constraint conditions of the distribution model.
The server can also set target constraint conditions for the distribution model, so that the result obtained by solving the distribution model is more accurate and practical. The target constraints include duration constraints and path rules.
The server takes the actual delivery time of the goods to be delivered as the time constraint condition, wherein the actual delivery time is less than or equal to the delivery time threshold, so that the delivery overtime can be avoided.
The path rules include two types: a flight delivery highway section for target unmanned aerial vehicle is located and predetermines the flight area, wherein, predetermines the flight area and is allowed the flight of target unmanned aerial vehicle in the area. The other is that the sum of the flight delivery road section of the target unmanned aerial vehicle and the travel delivery road section of the target unmanned aerial vehicle is equal to the cargo delivery path.
The formula of the target preset condition is as follows:
whereinT is the actual delivery duration, tThreshold valueFor the time threshold value of delivery, l unmanned aerial vehicle is target unmanned aerial vehicle's flight delivery highway section, and l unmanned vehicle is the delivery highway section of traveling of target unmanned vehicle, and H is for predetermineeing the flight area, and l goods are goods delivery route.
In this application, the server sets up the target constraint condition for the delivery model, can make flight delivery highway section be located predetermines the flight area on the one hand, improves the practicality of solving the result, and on the other hand can guarantee that it is not time-out when actual delivery is long, and target unmanned aerial vehicle and target unmanned vehicle can reach the goods delivery terminal point from the delivery starting point with waiting to deliver the goods, accomplishes the delivery of waiting to deliver the goods when length is long in the regulation.
As an alternative embodiment, the determining the actual delivery duration of the goods to be delivered according to the flight delivery section and the travel delivery section includes: taking the ratio of the flight distribution road section to the speed of the unmanned aerial vehicle as the flight duration of the target unmanned aerial vehicle; taking the ratio of the running distribution road section to the speed of the unmanned vehicle as the running duration of the target unmanned vehicle; and taking the sum of the flight time, the driving time and the cargo carrying time as the actual delivery time of the cargo to be delivered.
In the embodiment of the application, the actual delivery duration of the goods to be delivered comprises the flight duration of the target unmanned aerial vehicle, the running duration of the target unmanned aerial vehicle and the goods carrying duration. The server takes the ratio of the flying delivery road section to the speed of the unmanned aerial vehicle as the flying time of the target unmanned aerial vehicle, and takes the ratio of the driving delivery road section to the speed of the unmanned vehicle as the driving time of the target unmanned vehicle.
In this application, the length of time of flight of target unmanned aerial vehicle and the length of time of traveling of target unmanned vehicle have not only been considered in the length of time of actual delivery, still take into account the category with the length of time of goods transport also, have improved the length of time definite accuracy of actual delivery.
The calculation formula of the actual distribution time length is as follows:
t＝1unmanned vehicle/vUnmanned vehicle+lUnmanned plane/vUnmanned plane+tCargo handling
Wherein, it is long when t is actual delivery, and l unmanned aerial vehicle is flight delivery highway section, and V unmanned aerial vehicle is unmanned aerial vehicle speed, and l unmanned vehicle is the highway section of traveling delivery, and V unmanned vehicle is unmanned vehicle speed, and it is long when V freight is cargo handling.
As an alternative embodiment, determining the actual delivery cost of the cargo to be delivered based on the flight delivery section and the travel delivery section includes: determining the delivery cost of the target unmanned aerial vehicle according to the starting cost of the target unmanned aerial vehicle, the flight delivery road section and the cargo weight of the cargo to be delivered; determining the target unmanned vehicle distribution cost according to the starting cost, the running distribution road section and the cargo weight of the target unmanned vehicle; and taking the sum of the target unmanned aerial vehicle distribution cost and the target unmanned aerial vehicle distribution cost as the actual distribution cost of the goods to be distributed.
The actual delivery cost of the goods to be delivered comprises a target unmanned aerial vehicle delivery cost and a target unmanned vehicle delivery cost, the server determines the transportation cost of the target unmanned aerial vehicle according to the flight delivery road section and the weight of the goods to be delivered, and then the sum of the starting cost of the target unmanned aerial vehicle and the transportation cost of the target unmanned aerial vehicle is used as the target unmanned aerial vehicle delivery cost; the server determines the transportation cost of the target unmanned vehicle according to the running distribution road section and the weight of the goods to be distributed, and then takes the starting cost of the target unmanned vehicle and the transportation cost and value of the target unmanned vehicle as the distribution cost of the target unmanned vehicle. And finally, the server takes the sum of the target unmanned aerial vehicle delivery cost and the target unmanned aerial vehicle delivery cost as the actual delivery cost of the goods to be delivered.
Wherein, under the great condition of quantity or volume of goods waiting to deliver, need many unmanned aerial vehicles and many unmanned vehicles, target unmanned aerial vehicle includes at least one unmanned aerial vehicle promptly, and target unmanned vehicle includes at least one unmanned vehicle.
The server determines the number of the unmanned aerial vehicles and the starting cost of a single unmanned aerial vehicle, and then takes the product value of the starting cost and the starting cost of the target unmanned aerial vehicle. The server acquires a preset unmanned aerial vehicle coefficient, and takes the product of the preset unmanned aerial vehicle coefficient, the flight delivery road section and the cargo weight as the transportation cost of the target unmanned aerial vehicle. And finally, the server takes the sum of the starting cost of the target unmanned aerial vehicle and the transportation cost of the target unmanned aerial vehicle as the distribution cost of the target unmanned aerial vehicle.
The server determines the number of the unmanned vehicles and the starting cost of a single unmanned vehicle, and then takes the product value of the number of the unmanned vehicles and the starting cost of the single unmanned vehicle as the starting cost of the target unmanned vehicle. The server obtains a preset unmanned vehicle coefficient, and takes the product of the preset unmanned vehicle coefficient, the running delivery road section and the cargo weight as the transportation cost of the target unmanned vehicle. And finally, the server takes the sum of the starting cost of the target unmanned vehicle and the transportation cost of the target unmanned vehicle as the distribution cost of the target unmanned vehicle.
The actual distribution cost is calculated by the formula:
s＝(aunmanned vehicle×NUnmanned vehicle+bUnmanned vehicle×m×lUnmanned vehicle)+(aUnmanned plane×NUnmanned plane+bUnmanned plane×m×lUnmanned plane)
Wherein, s is actual delivery cost, and a unmanned vehicle is single unmanned vehicle starting cost, and N unmanned vehicle is the quantity of unmanned vehicle, and b unmanned vehicle is unmanned vehicle coefficient, and m is cargo weight, and l unmanned vehicle is the delivery highway section of traveling, and a unmanned aerial vehicle is single unmanned vehicle starting cost, and N unmanned aerial vehicle is unmanned aerial vehicle's quantity, and b unmanned aerial vehicle is the unmanned aerial vehicle coefficient, and l unmanned aerial vehicle is the flight delivery highway section.
As an alternative embodiment, the server obtains the delivery weight and the delivery volume of the goods to be delivered, and generally, the bearing weight of the target unmanned aerial vehicle and the bearing weight of the target unmanned vehicle should both be greater than the delivery weight of the goods to be delivered, and the bearing volume of the target unmanned aerial vehicle and the bearing volume of the target unmanned vehicle should both be greater than the delivery volume of the goods to be delivered.
If the weight of the goods to be delivered is light and the size of the goods to be delivered is small, only one unmanned aerial vehicle and one unmanned vehicle are needed, and if the goods to be delivered are heavy and large in size, the number of the unmanned aerial vehicles and the number of the unmanned aerial vehicles need to be determined when a single unmanned vehicle or a single unmanned aerial vehicle cannot meet the weight requirement or the size requirement of the goods to be delivered.
The method for determining the number of the unmanned aerial vehicles comprises the following steps: the server determines the distribution weight and the distribution volume of goods to be distributed, then determines the first unmanned aerial vehicle quantity according to the ratio of the distribution weight to the bearing weight of a single unmanned aerial vehicle, determines the second unmanned aerial vehicle quantity according to the ratio of the distribution volume to the bearing volume of the single unmanned aerial vehicle, and selects the maximum quantity from the first unmanned aerial vehicle quantity and the second unmanned aerial vehicle quantity to serve as the quantity of the unmanned aerial vehicles. The unmanned aerial vehicle that the server was selected like this can satisfy the weight requirement of the goods of waiting to deliver and also can satisfy the volume requirement.
The method for determining the number of the unmanned vehicles comprises the following steps: the server determines the distribution weight and the distribution volume of goods to be distributed, then determines the number of first unmanned vehicles according to the ratio of the distribution weight to the bearing weight of a single unmanned vehicle, determines the number of second unmanned vehicles according to the ratio of the distribution volume to the bearing volume of the single unmanned vehicle, and selects the maximum number from the number of the first unmanned vehicles and the number of the second unmanned vehicles as the number of the unmanned vehicles. The unmanned vehicle selected by the server can meet the weight requirement and the volume requirement of goods to be delivered.
The formula for determining the number of unmanned vehicles and the number of unmanned vehicles is as follows:
the number of the N unmanned vehicles is the number of the unmanned vehicles, the weight of goods to be delivered is W, the carrying capacity of a single unmanned vehicle is W, the weight of the goods to be delivered is V, and the carrying volume of the single unmanned vehicle is V;
n unmanned aerial vehicle is unmanned aerial vehicle's quantity, and W is the weight of the goods of waiting to deliver, and W unmanned aerial vehicle bears weight for single unmanned aerial vehicle, and V is the weight of the goods of waiting to deliver, and V unmanned aerial vehicle bears the volume for single unmanned aerial vehicle.
As an optional implementation manner, if an emergency situation that the unmanned aerial vehicle cannot complete the distribution in the distribution process is encountered, the unmanned vehicle or other unmanned aerial vehicles can be called for docking, and the distribution is continued; if the unmanned vehicle can not complete the distribution in emergency, the unmanned vehicle or other unmanned vehicles can be called to carry out butt joint until the distribution task is completed.
Optionally, an embodiment of the present application further provides a processing flow of a cargo distribution method, and the specific steps are as follows.
Step 1: the number of unmanned aerial vehicles and the number of unmanned vehicles are determined.
Step 2: the actual delivery duration of the goods to be delivered is determined.
t＝lUnmanned vehicle/vUnmanned vehicle+lUnmanned plane/vUnmanned plane+tCargo handling
And step 3: the actual delivery cost of the goods to be delivered is determined.
s＝(aUnmanned vehicle×NUnmanned vehicle+bUnmanned vehicle×m×lUnmanned vehicle)+(aUnmanned plane×NUnmanned plane+bUnmanned plane×m×lUnmanned plane)
And 4, step 4: and setting target preset conditions.
And 5: and building a distribution model with the aim of minimizing the actual distribution time length and the actual distribution cost.
minQ＝c×t+d×s
Step 6: and solving the distribution model according to the target preset condition and the target function.
Wherein, step 2 and step 3 are parallel steps, and unmanned aerial vehicle 1 are unknown numbers.
Based on the same technical concept, the embodiment of the present application further provides a cargo distribution apparatus, as shown in fig. 2, the apparatus including:
the first determining module 201 is configured to determine a cargo delivery path for goods to be delivered, where the cargo delivery path includes a flight delivery section for delivering by a target unmanned aerial vehicle and a driving delivery section for delivering by a target unmanned aerial vehicle;
the second determining module 202 is configured to determine an actual delivery duration and an actual delivery cost of goods to be delivered according to the flying delivery road section and the traveling delivery road section;
a building module 203, configured to build a distribution model with a goal of minimizing an actual distribution duration and an actual distribution cost;
and the solving module 204 is configured to obtain an objective solution result through the solution of the delivery model, where the objective solution result is used to indicate road allocation of the flying delivery road section and the traveling delivery road section in the cargo delivery path.
Optionally, the building module 203 is configured to:
taking the product of the preset duration coefficient and the actual distribution duration as a target distribution duration, and taking the product of the preset cost coefficient and the actual distribution cost as a target distribution cost;
taking the minimum sum of the target distribution duration and the target distribution cost as a target function;
and building a delivery model according to the minimized objective function.
Optionally, the apparatus is further configured to:
taking the actual distribution time length of the goods to be distributed less than or equal to the distribution time length threshold as a time length constraint condition;
acquiring a preset flight area, wherein the target unmanned aerial vehicle is allowed to fly in the preset flight area;
taking the rule that the flight distribution road section of the target unmanned aerial vehicle is positioned in the preset flight path, and the sum of the flight distribution road section of the target unmanned aerial vehicle and the running distribution road section of the target unmanned vehicle is equal to the cargo distribution path;
and taking the duration constraint condition and the path rule as target constraint conditions of the distribution model.
Optionally, the second determining module 202 is configured to:
taking the ratio of the flight distribution road section to the speed of the unmanned aerial vehicle as the flight duration of the target unmanned aerial vehicle;
taking the ratio of the running distribution road section to the speed of the unmanned vehicle as the running duration of the target unmanned vehicle;
and taking the sum of the flight time, the driving time and the cargo carrying time as the actual delivery time of the cargos to be delivered.
Optionally, the second determining module 202 includes:
the first determining unit is used for determining the delivery cost of the target unmanned aerial vehicle according to the starting cost of the target unmanned aerial vehicle, the flying delivery road section and the cargo weight of the cargo to be delivered;
the second determining unit is used for determining the target unmanned vehicle distribution cost according to the starting cost, the running distribution road section and the cargo weight of the target unmanned vehicle;
and the unit is used for taking the sum of the target unmanned aerial vehicle distribution cost and the target unmanned aerial vehicle distribution cost as the actual distribution cost of the goods to be distributed.
Optionally, the first determining unit is configured to:
taking the product of the number of the unmanned aerial vehicles and the starting cost of a single unmanned aerial vehicle as the starting cost of a target unmanned aerial vehicle, wherein the target unmanned aerial vehicle comprises at least one unmanned aerial vehicle;
taking the product of the preset unmanned aerial vehicle coefficient, the flight delivery road section and the cargo weight as the transportation cost of the target unmanned aerial vehicle;
and taking the sum of the starting cost of the target unmanned aerial vehicle and the transportation cost of the target unmanned aerial vehicle as the distribution cost of the target unmanned aerial vehicle.
Optionally, the second determining unit is configured to:
taking the product of the number of the unmanned vehicles and the starting cost of a single unmanned vehicle as the starting cost of the target unmanned vehicle, wherein the target unmanned vehicle comprises at least one unmanned vehicle;
taking the product of the preset unmanned vehicle coefficient, the driving and distribution road section and the cargo weight as the transportation cost of the target unmanned vehicle;
and taking the sum of the starting cost of the target unmanned vehicle and the transportation cost of the target unmanned vehicle as the distribution cost of the target unmanned vehicle.
Optionally, the apparatus is further configured to:
determining the distribution weight and the distribution volume of goods to be distributed;
determining the number of the first unmanned aerial vehicles according to the ratio of the distribution weight to the bearing weight of the single unmanned aerial vehicle;
determining the number of second unmanned aerial vehicles according to the ratio of the distribution volume to the bearing volume of a single unmanned aerial vehicle;
and selecting the maximum number from the first unmanned aerial vehicle number and the second unmanned aerial vehicle number as the number of the unmanned aerial vehicles.
Optionally, the apparatus is further configured to:
determining the distribution weight and the distribution volume of goods to be distributed;
determining the number of the first unmanned vehicles according to the ratio of the distribution weight to the carrying weight of the single unmanned vehicle;
determining the number of the second unmanned vehicles according to the ratio of the distribution volume to the carrying volume of the single unmanned vehicle;
and selecting the maximum number from the first unmanned vehicle number and the second unmanned vehicle number as the number of the unmanned vehicles.
According to another aspect of the embodiments of the present application, there is provided an electronic device, as shown in fig. 3, including a memory 303, a processor 301, a communication interface 302, and a communication bus 304, where a computer program operable on the processor 301 is stored in the memory 303, the memory 303 and the processor 301 communicate with each other through the communication interface 302 and the communication bus 304, and the processor 301 implements the steps of the method when executing the computer program.
The memory and the processor in the electronic equipment are communicated with the communication interface through a communication bus. The communication bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc.
The Memory may include a Random Access Memory (RAM), and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
There is also provided, in accordance with yet another aspect of an embodiment of the present application, a computer-readable medium having non-volatile program code executable by a processor.
Optionally, in an embodiment of the present application, a computer readable medium is configured to store program code for the processor to execute the above method.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment is not described herein again.
When the embodiments of the present application are specifically implemented, reference may be made to the above embodiments, and corresponding technical effects are achieved.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For a hardware implementation, the Processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units configured to perform the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented by means of units performing the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk. It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (12)
1. A method of cargo distribution, said method comprising:
determining a cargo delivery path of the cargo to be delivered, wherein the cargo delivery path comprises a flight delivery road section for realizing delivery through a target unmanned aerial vehicle and a driving delivery road section for realizing delivery through a target unmanned aerial vehicle;
determining the actual delivery time length and the actual delivery cost of the goods to be delivered according to the flying delivery road section and the driving delivery road section;
building a distribution model with the aim of minimizing the actual distribution time length and the actual distribution cost;
and obtaining an objective solution result through the solution of the delivery model, wherein the objective solution result is used for indicating the road distribution of the flying delivery road section and the driving delivery road section in the cargo delivery path.
2. The method of claim 1, wherein building a delivery model that aims to minimize the actual delivery duration and actual delivery cost comprises:
taking the product of a preset duration coefficient and the actual distribution duration as a target distribution duration, and taking the product of a preset cost coefficient and the actual distribution cost as a target distribution cost;
taking the minimum sum of the target distribution duration and the target distribution cost as a target function;
and building a delivery model according to the minimized objective function.
3. The method of claim 2, wherein prior to building a delivery model from the minimized objective function, the method further comprises:
taking the actual distribution time length of the goods to be distributed as a distribution time length constraint condition, wherein the actual distribution time length is less than or equal to a distribution time length threshold value;
acquiring a preset flight area, wherein the target unmanned aerial vehicle is allowed to fly in the preset flight area;
taking a route rule that a flight distribution road section of the target unmanned aerial vehicle is located in the preset flight path, and the sum of the flight distribution road section of the target unmanned aerial vehicle and a running distribution road section of the target unmanned vehicle is equal to the cargo distribution path;
and taking the duration constraint condition and the path rule as target constraint conditions of the distribution model.
4. The method of claim 1, wherein determining the actual delivery duration of the goods to be delivered according to the flight delivery road segment and the travel delivery road segment comprises:
taking the ratio of the flight distribution road section to the speed of the unmanned aerial vehicle as the flight duration of the target unmanned aerial vehicle;
taking the ratio of the running distribution road section to the speed of the unmanned vehicle as the running duration of the target unmanned vehicle;
and taking the sum of the flight time length, the traveling time length and the cargo carrying time length as the actual distribution time length of the cargoes to be distributed.
5. The method of claim 1, wherein determining the actual delivery cost of the goods to be delivered based on the flight delivery road segment and the travel delivery road segment comprises:
determining the delivery cost of the target unmanned aerial vehicle according to the starting cost of the target unmanned aerial vehicle, the flight delivery road section and the cargo weight of the cargo to be delivered;
determining the target unmanned vehicle distribution cost according to the starting cost, the running distribution road section and the cargo weight of the target unmanned vehicle;
and taking the sum of the target unmanned aerial vehicle distribution cost and the target unmanned aerial vehicle distribution cost as the actual distribution cost of the goods to be distributed.
6. The method according to claim 5, wherein determining a target drone delivery cost based on a launch cost of a target drone, a flight delivery road segment, and a cargo weight of the cargo to be delivered comprises:
taking the product of the number of the unmanned aerial vehicles and the starting cost of a single unmanned aerial vehicle as the starting cost of the target unmanned aerial vehicle, wherein the target unmanned aerial vehicle comprises at least one unmanned aerial vehicle;
taking the product of a preset unmanned aerial vehicle coefficient, a flight delivery road section and the cargo weight as the transportation cost of the target unmanned aerial vehicle;
and taking the sum of the starting cost of the target unmanned aerial vehicle and the transportation cost of the target unmanned aerial vehicle as the distribution cost of the target unmanned aerial vehicle.
7. The method of claim 5, wherein determining a target drone delivery cost based on a launch cost of a target drone, a flight delivery road segment, and a cargo weight of the cargo to be delivered comprises:
taking the product of the number of the unmanned vehicles and the starting cost of a single unmanned vehicle as the starting cost of the target unmanned vehicle, wherein the target unmanned vehicle comprises at least one unmanned vehicle;
taking the product of a preset unmanned vehicle coefficient, a running distribution road section and the weight of the goods as the transportation cost of the target unmanned vehicle;
and taking the sum of the starting cost of the target unmanned vehicle and the transportation cost of the target unmanned vehicle as the distribution cost of the target unmanned vehicle.
8. The method of claim 6, wherein the product of the number of drones and the cost of launch of a single drone is taken as the cost of launch of the target drone, the method further comprising:
determining the distribution weight and the distribution volume of the goods to be distributed;
determining the first unmanned aerial vehicle quantity according to the ratio of the distribution weight to the bearing weight of a single unmanned aerial vehicle;
determining the number of second unmanned aerial vehicles according to the ratio of the distribution volume to the bearing volume of a single unmanned aerial vehicle;
and selecting the maximum number from the first unmanned aerial vehicle number and the second unmanned aerial vehicle number as the number of the unmanned aerial vehicles.
9. The method of claim 7, wherein the product of the number of unmanned vehicles and the launch cost of a single unmanned vehicle is taken as the launch cost of the target unmanned vehicle, the method further comprising:
determining the distribution weight and the distribution volume of the goods to be distributed;
determining the number of the first unmanned vehicles according to the ratio of the distribution weight to the carrying weight of the single unmanned vehicle;
determining the number of second unmanned vehicles according to the ratio of the distribution volume to the carrying volume of a single unmanned vehicle;
and selecting the maximum number from the first unmanned vehicle number and the second unmanned vehicle number as the number of the unmanned vehicles.
10. A cargo distribution apparatus, comprising:
the system comprises a first determining module, a second determining module and a control module, wherein the first determining module is used for determining a cargo delivery path of cargos to be delivered, and the cargo delivery path comprises a flight delivery road section for realizing delivery through a target unmanned aerial vehicle and a driving delivery road section for realizing delivery through a target unmanned aerial vehicle;
the second determining module is used for determining the actual delivery time and the actual delivery cost of the goods to be delivered according to the flying delivery road section and the driving delivery road section;
the building module is used for building a distribution model with the aim of minimizing the actual distribution time length and the actual distribution cost;
and the solving module is used for obtaining an objective solving result through solving the distribution model, wherein the objective solving result is used for indicating the road distribution of the flying distribution road section and the driving distribution road section in the cargo distribution path.
11. The electronic equipment is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing the communication between the processor and the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1-9 when executing a program stored in the memory.
12. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of the claims 1-9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210444183.0A CN114757618A (en) | 2022-04-25 | 2022-04-25 | Goods distribution method and device, electronic equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210444183.0A CN114757618A (en) | 2022-04-25 | 2022-04-25 | Goods distribution method and device, electronic equipment and readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114757618A true CN114757618A (en) | 2022-07-15 |
Family
ID=82332446
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210444183.0A Pending CN114757618A (en) | 2022-04-25 | 2022-04-25 | Goods distribution method and device, electronic equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114757618A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115602306A (en) * | 2022-12-17 | 2023-01-13 | 苏州维伟思医疗科技有限公司（Cn） | Multi-mode AED (automated guided Equipment) scheduling method, device and equipment and readable storage medium |
-
2022
- 2022-04-25 CN CN202210444183.0A patent/CN114757618A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115602306A (en) * | 2022-12-17 | 2023-01-13 | 苏州维伟思医疗科技有限公司（Cn） | Multi-mode AED (automated guided Equipment) scheduling method, device and equipment and readable storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110678885B (en) | System and method for capacity scheduling | |
| US11099566B2 (en) | Systems and methods for providing a vehicle service via a transportation network for autonomous vehicles | |
| US9746332B2 (en) | Method and system for scheduling vehicles along routes in a transportation system | |
| CN109345091B (en) | Ant colony algorithm-based whole vehicle logistics scheduling method and device, storage medium and terminal | |
| US8401790B2 (en) | Computing-time-efficient route determination along several preset path points with given connecting routes in-between | |
| WO2019015661A1 (en) | Systems and methods for service request allocation | |
| CN108320105A (en) | Logistics prestowage dispatching method, device, storage medium and terminal device | |
| CN114757618A (en) | Goods distribution method and device, electronic equipment and readable storage medium | |
| CN112906949A (en) | Route planning method and device, electronic equipment and readable storage medium | |
| CN106779525A (en) | A kind of freight charges Automatic computing system and method based on airfreight practice | |
| CN108196574B (en) | Unmanned aerial vehicle endurance judgment method and device and computer storage medium | |
| US20210350717A1 (en) | Information processing apparatus | |
| CN109740910A (en) | Haulage vehicle determines method and apparatus | |
| US20210224943A1 (en) | Shared vehicle managing system | |
| CN112529487A (en) | Vehicle scheduling method, device and storage medium | |
| CN115079701A (en) | Unmanned vehicle and unmanned aerial vehicle cooperative path planning method | |
| CN108233445B (en) | Charging control method and device for vehicle | |
| CN109215398B (en) | Unmanned aerial vehicle route planning method and device | |
| CN111694350A (en) | Information processing apparatus, information processing method, and program | |
| CN116307573A (en) | Green energy-saving treatment method, equipment and medium for building site materials | |
| CN110400111A (en) | Article based on automatic driving vehicle sends method, apparatus and system with charge free | |
| CN110254284B (en) | Electric taxi charging reminding method, electronic equipment and storage medium | |
| CN115700670A (en) | Freight transportation goods splicing method | |
| Klochkov et al. | The Prediction of Transport-Logistics Systems Based on Unmanned Aerial Vehicles Creation Efficiency | |
| US20220371735A1 (en) | Method and system for unmanned aerial vehicle to pass through unmanned aerial vehicle airport |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |