CN115860289A - Route planning method, device, equipment and storage medium based on carbon emission - Google Patents
Route planning method, device, equipment and storage medium based on carbon emission Download PDFInfo
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Abstract
The application discloses a path planning method, a device, equipment and a storage medium based on carbon emission, which relate to the field of vehicle path planning and comprise the following steps: establishing a fuel consumption expression of the current vehicle about fuel consumption and vehicle running speed; constructing a vehicle path optimization model considering distribution constraint conditions and based on vehicle carbon emission minimization according to the oil consumption expression; and calling a pre-constructed heuristic algorithm, and solving a vehicle path optimization model based on minimum carbon emission of the vehicle by using the heuristic algorithm to obtain a corresponding vehicle distribution path when the carbon emission is minimum. According to the method and the device, the vehicle path problem is solved through the constructed vehicle path optimization model based on the minimum carbon emission of the vehicle, and the corresponding vehicle distribution path when the carbon emission is minimum is obtained by calling the pre-constructed heuristic algorithm to solve the model, so that the energy conservation and emission reduction problems of the vehicle in distribution can be solved under the conditions of distribution constraint and the like, the distribution cost is reduced, and the vehicle distribution efficiency is improved.
Description
Technical Field
The invention relates to the field of vehicle path planning, in particular to a path planning method, a path planning device, path planning equipment and a storage medium based on carbon emission.
Background
At present, the vehicle route problem is the key and difficult problem of the logistics level, and the important practical significance is achieved for meeting the energy and environmental pressure and increasingly severe service requirements and considering both the enterprise benefit and the social benefit and realizing the effective achievement of the material transportation requirement.
Therefore, how to plan the current optimal vehicle running path by considering the running cost and the carbon emission of the vehicle is a problem to be solved in the field.
Disclosure of Invention
In view of the above, the present invention aims to provide a path planning method, a device, a facility and a storage medium based on carbon emission, which can consider conditions such as distribution constraints to solve the problem of energy saving and emission reduction of vehicles in actual logistics distribution, reduce distribution cost and improve vehicle distribution efficiency. The specific scheme is as follows:
in a first aspect, the present application discloses a path planning method based on carbon emissions, comprising:
establishing a fuel consumption expression of the current vehicle about fuel consumption and vehicle running speed;
constructing a vehicle path optimization model considering distribution constraint conditions and based on vehicle carbon emission minimization according to the oil consumption expression;
and calling a pre-constructed heuristic algorithm, and solving the vehicle path optimization model based on the minimization of the carbon emission of the vehicle by using the heuristic algorithm to obtain a corresponding vehicle distribution path when the carbon emission is minimum.
Optionally, the establishing of the fuel consumption expression of the current vehicle about fuel consumption and vehicle running speed includes:
estimating the fuel consumption of the current vehicle to obtain a corresponding fuel consumption estimation result;
and establishing a fuel consumption expression of the current vehicle about the fuel consumption and the running speed of the vehicle according to the fuel consumption estimation result.
Optionally, the constructing a vehicle path optimization model based on minimization of carbon emissions of the vehicle and considering a distribution constraint condition according to the fuel consumption expression includes:
determining a correlation between the fuel consumption expression and vehicle carbon emissions;
and constructing a vehicle path optimization model based on vehicle carbon emission minimization considering distribution constraint conditions according to the correlation.
Optionally, the constructing a vehicle path optimization model based on minimization of carbon emissions of the vehicle and considering a distribution constraint condition according to the fuel consumption expression includes:
and aiming at the vehicle path optimization problem, constructing a mixed integer programming model considering distribution constraint conditions and based on vehicle carbon emission minimization according to the oil consumption expression.
Optionally, the method for path planning based on carbon emission further includes:
acquiring vehicle attribute parameters of the current vehicle, and estimating vehicle operation parameters of the current vehicle;
and determining target parameters required in the vehicle path optimization model based on the minimization of the carbon emission of the vehicle according to the vehicle attribute parameters and the vehicle operation parameters.
Optionally, before the invoking of the pre-constructed heuristic algorithm, the method includes:
improving the genetic algorithm based on data preprocessing and excellent individual evolution to obtain an improved genetic algorithm; the data preprocessing comprises preprocessing for dividing overweight node requirements and priority cart single-point distribution.
Optionally, the invoking a pre-constructed heuristic algorithm and solving the vehicle path optimization model based on minimization of carbon emission of the vehicle by using the heuristic algorithm to obtain a corresponding vehicle distribution path when carbon emission is minimum includes:
and calling the improved genetic algorithm, and solving the vehicle path optimization model based on the minimization of the carbon emission of the vehicle by using the improved genetic algorithm to obtain a corresponding vehicle distribution path when the carbon emission is minimum.
In a second aspect, the present application discloses a path planning device based on carbon emission, comprising:
the expression establishing module is used for establishing a fuel consumption expression of the current vehicle about fuel consumption and vehicle running speed;
the model building module is used for building a vehicle path optimization model which considers distribution constraint conditions and is based on vehicle carbon emission minimization according to the oil consumption expression;
the algorithm calling module is used for calling a pre-constructed heuristic algorithm;
and the model solving module is used for solving the vehicle path optimization model based on the minimization of the carbon emission of the vehicle by utilizing the heuristic algorithm to obtain a corresponding vehicle distribution path when the carbon emission is minimum.
In a third aspect, the present application discloses an electronic device, comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of the carbon emission based path planning method disclosed in the foregoing.
In a fourth aspect, the present application discloses a computer readable storage medium for storing a computer program; wherein the computer program, when being executed by a processor, realizes the steps of the carbon emission based path planning method as disclosed in the foregoing.
Therefore, the application provides a path planning method based on carbon emission, which comprises the following steps: establishing a fuel consumption expression of the current vehicle about fuel consumption and vehicle running speed; constructing a vehicle path optimization model considering distribution constraint conditions and based on vehicle carbon emission minimization according to the oil consumption expression; and calling a pre-constructed heuristic algorithm, and solving the vehicle path optimization model based on the minimization of the carbon emission of the vehicle by using the heuristic algorithm to obtain a corresponding vehicle distribution path when the carbon emission is minimum. Therefore, the vehicle path problem is solved by constructing a vehicle path optimization model based on minimization of vehicle carbon emission, and the vehicle path optimization model based on minimization of vehicle carbon emission is solved by calling a pre-constructed heuristic algorithm, so that a corresponding vehicle distribution path with minimum carbon emission is solved, and distribution is carried out according to the vehicle distribution path, so that the problem of energy conservation and emission reduction of vehicles in actual logistics distribution can be solved by considering conditions such as distribution constraint and the like, the distribution cost is reduced, and the vehicle distribution efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flow chart of a path planning method based on carbon emissions disclosed herein;
FIG. 2 is a schematic diagram of a specific fuel consumption versus speed relationship disclosed herein;
FIG. 3 is a schematic illustration of a material delivery process disclosed herein;
FIG. 4 is a flow chart of a particular carbon emission-based path planning method disclosed herein;
fig. 5 is a schematic structural diagram of a path planning apparatus based on carbon emissions according to the present disclosure;
fig. 6 is a block diagram of an electronic device disclosed in the present application.
Detailed Description
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.
At present, the vehicle route problem is the key and difficult problem of the logistics level, and the important practical significance is achieved for meeting the energy and environmental pressure and increasingly severe service requirements and considering both the enterprise benefit and the social benefit and realizing the effective achievement of the material transportation requirement. Therefore, the distribution path planning scheme is provided, the energy conservation and emission reduction problems of the vehicles in the actual logistics distribution can be solved by taking the conditions such as distribution constraint and the like into consideration, the distribution cost is reduced, and the distribution efficiency of the vehicles is improved.
The embodiment of the invention discloses a path planning method based on carbon emission, which is shown in figure 1 and comprises the following steps:
step S11: and establishing a fuel consumption expression of the current vehicle relative to the fuel consumption and the running speed of the vehicle.
In the embodiment, before a vehicle path optimization model based on minimization of carbon emission of a vehicle is constructed, an oil consumption expression of the current vehicle about fuel consumption and vehicle running speed needs to be established, and it can be understood that the fuel consumption of the current vehicle is estimated to obtain a corresponding oil consumption estimation result; and establishing a fuel consumption expression of the current vehicle about the fuel consumption and the running speed of the vehicle according to the fuel consumption estimation result. For example, the expression for specific fuel consumption may be:
FP＝ξ(kNV+P/η)/κ；
where ξ is the fuel-air mass ratio, k is the engine friction coefficient, N is the engine speed, V is the engine displacement, η is the engine efficiency parameter, and κ is the fuel calorific value. The parameter P is the output power of the engine per second, and the unit is kilowatt, namely:
P＝P tract /η tf +P acc ；
wherein eta tf For vehicle transmission efficiency, P acc For engine running losses and vehicle parts (e.g. air conditioners), the engine power, parameter P, being relevant for the operation tract The unit of traction power on the wheel is kilowatt, namely:
P tract ＝(Mτ+Mgsinθ+0.5C d ρAv 2 +MgC r cosθ)v/1000；
where M is the total vehicle mass in kilograms, and the total vehicle mass may be expressed as:
M＝w+f；
w is the total vehicle mass of an empty vehicle, and f is the load of the vehicle; v is the vehicle speed in meters per second, τ is the acceleration in units of: meters per square second; theta is the road slope angle; g is the acceleration of gravity; c d And C r Respectively is a pneumatic resistance coefficient and a rolling resistance coefficient; ρ is the air density a is the front surface area of the vehicle.
Also, constants λ and γ can be defined and vehicle specific constants α and β can be defined, with the associated equations as follows:
λ＝ξ/κψ；
γ＝1/1000n tf η；
α＝τ+gSinθ+gC r cosθ；
β＝0.5C d ρA；
where ψ is the conversion factor of fuel from grams/second to liters/second.
Thus, the fuel consumption of the vehicle on an arc (i, j) can be expressed as a function of the speed v and the load f, i.e. the fuel consumption expression can be:
F(v，M)＝λ(kNV+wγαv+γαfv+βγv 3 )d/v；
as shown in fig. 2, the oil consumption of the medium-sized vehicle at the empty load and at the full load is shown as a function of the speed using the above-mentioned oil consumption expression, wherein the unit of the oil consumption is liters per hundred kilometers and the unit of the speed is kilometers per hour.
Step S12: and constructing a vehicle path optimization model considering distribution constraint conditions and based on vehicle carbon emission minimization according to the oil consumption expression.
In this embodiment, after the fuel consumption expression of the Vehicle is established, a Vehicle path optimization model based on minimization of Carbon Emissions of the Vehicle, which considers the distribution constraint condition, that is, a VRPCE (Vehicle Routing rules with Carbon Emissions) model may be established according to the fuel consumption expression, and specifically, a correlation between the fuel consumption expression and the Carbon Emissions of the Vehicle is determined; and constructing a vehicle path optimization model based on vehicle carbon emission minimization considering distribution constraint conditions according to the correlation.
In this embodiment, after a vehicle route optimization model based on minimization of carbon emission of a vehicle is constructed in consideration of distribution constraint conditions, target parameters required by the model are obtained, that is, vehicle attribute parameters of the current vehicle are obtained, and vehicle operation parameters of the current vehicle are estimated; and determining target parameters required in the vehicle path optimization model based on the minimization of the carbon emission of the vehicle according to the vehicle attribute parameters and the vehicle running parameters. For example, the actual distributed vehicle is investigated, the actual vehicle attribute parameters used by the vehicle in the model are obtained, the vehicle attribute parameters may include, but are not limited to, vehicle type, engine parameter, fuel parameter, resistance parameter, etc., and the vehicle operation parameters of the distributed vehicle, which may include, but is not limited to, time-varying speed, acceleration, road slope angle, etc., are estimated, so as to obtain the target parameters required in the vehicle path optimization model based on minimizing carbon emissions of the vehicle according to the vehicle attribute parameters and the vehicle operation parameters.
It can be understood that a Vehicle path Problem optimization model aiming at optimal transportation cost and minimum carbon emission and considering constraints of delivery, capacity, time window and the like is established by adding a series of constraint conditions based on a VRP (Vehicle Routing Problem) basic model, and an ecological path navigation system considering fuel consumption and probabilistic travel time budget is developed on the basis of the Vehicle path Problem optimization model. The path optimization is a process of optimizing paths traveled by people or vehicles according to a certain performance index, wherein the performance index can be time, and the path optimization problems can be divided into different categories according to different analysis angles, for example, the path optimization problems can be divided into deterministic and non-deterministic path optimization problems according to the understanding degree of road network information; the optimization problem can be divided into static and dynamic path optimization problems according to whether the road network attribute changes along with time; the optimization problem can be divided into single OD and multi-OD path optimization problems according to the number of starting points and end points, namely, OD (Origin to Destination); the method can be divided into a shortest path problem and a network flow problem according to the supply and demand of network nodes; the method can be divided into a single target and multi-target path optimization problem according to the characteristics of the target function, and the like. It is understood that the route optimization problem is a vehicle route problem (i.e., VRP), which refers to a certain number of customers, each having a different quantity of goods required, and the distribution center provides the customers with goods, and a fleet is responsible for distributing the goods, and the appropriate distribution route is planned in order to meet the customer's requirements and to minimize the distance, cost, time, energy consumption, and the like under certain constraints. Many variations are derived from the basic VRP model with different constraints, such as CVRP (conditioned Vehicle Routing Problem), VRPTW (Vehicle Routing Problem with Time Window constraint), PDVRP (pick up Delivery Vehicle Routing Problem), etc.
For example, for a target problem, a simpler basic model needs to be considered first, the total travel distance is minimized as an objective function, capacity and load constraints of various vehicle types and vehicle types are considered at the same time, and a CVRP model is established, wherein the CVRP model is suitable for a case that one turnover warehouse serves multiple terminal warehouses, namely one-to-many, for example, the turnover warehouse 1 is in charge of the terminal warehouse 2,3,4, and Zhou Zhuaiku is in charge of the terminal warehouse 6,7,8. Therefore, the model considers the one-to-many terminal library set division scenario, that is, each turnover library is only responsible for the corresponding terminal library set, and it is assumed that each turnover library has all SKUs (Stock Keeping units) and transfers and delivers to the corresponding terminal library as required, so that the delivery paths of multiple turnover libraries can be obtained by solving the CVRP model starting from different turnover libraries. For example, in the CVRP model,
the set M is a set of all nodes, the set N is a node set except a warehouse, namely a demand node set, and the set K is a vehicle set; parameter c ij Representing the distance of node i from node j, parameter v j Representing the demanded volume of node j, parameter w j Representing the required weight of node j, parameter V k Representing the volume of the kth vehicle, parameter W k Representing the load of the kth vehicle, the parameter M' represents a significant number; variable x ijk Variable y indicating whether the k-th vehicle is from node i to node j i For eliminating the sub-loop and the objective function (1) of the above described CVRP model is to minimize the total distance traveled. The constraint (2) is node constraint, which means that all the demand nodes have one trolley to pass through; constraints (3) - (5) are flow constraints, and formula (3) indicates that any one vehicle goes from the warehouse to any one node; the formula (4) shows that the vehicle inflow number is equal to the outflow number for any demand node, namely, if a vehicle enters, the vehicle exits; equation (5) indicates that either vehicle is eventually returned to the warehouse; constraints (6) and (7) represent the volume constraint and the load constraint of the vehicle, respectively, and the volume and the load of the total cargo loaded on any one vehicle cannot be exceeded; constraint (8) is to eliminate sub-loop constraint and prevent the formation of closed-loop paths between the required nodes; constraints (9) and (10) represent variable x, respectively ijk Is a variable from 0 to 1 and a variable y i ∈N。
On the basis of the CVRP model, aiming at the target problem to be solved, the aims of energy conservation and emission reduction and efficient transportation are taken, and an objective function and constraint conditions are modified and adjusted in combination with actual business, for example, the aims of energy conservation and emission reduction are taken into consideration, and the objective function in the CVRP model is modified to minimize the total carbon emission of a fleet, so that the influence factors of the carbon emission of vehicles and the derivation and calculation of corresponding formulas need to be considered; in addition to considering the capacity and load constraints of the vehicle, the time window constraints of the vehicle, that is, the time requirement for reaching the terminal bin and the service time at the terminal bin, the travel of the vehicle needs to meet the time window requirement of each terminal bin on the path, need to be further considered; the total travel time of the vehicle needs to be limited to prevent driver fatigue, taking into account the driving time constraints of the driver of the vehicle. Therefore, a green vehicle path optimization model considering constraints such as vehicle carbon emission, capacity, load, time window and the like is finally established, and the vehicle path optimization model can effectively solve the problems of energy conservation and emission reduction in actual logistics distribution and takes distribution requirements, distribution efficiency, energy consumption and the like into consideration. For example, for a vehicle path optimization problem, a mixed integer planning model based on vehicle carbon emission minimization considering a distribution constraint condition is constructed according to the fuel consumption expression. Wherein, in the VRPCE model,
wherein the set M is a warehouse and demand node set M = {0 }. U M c 0 is a delivery warehouse, set M c M is the number of nodes of the set M, the set K is a vehicle set, and h is the number of vehicles of the set K; parameter w k Is the dead weight of the vehicle k, parameter Q k Is the maximum load of the vehicle k, parameter P k For the maximum capacity of the vehicle k, the parameter LT k For the maximum travel time of the vehicle k, the parameter ST k As departure time of vehicle k, parameter d ij Is the travel length of arc (i, j), t ij Time of travel for arc (i, j), parameter v ij For the speed of travel over the arc (i, j), the parameter q i Required material weight for node i, parameter p i Volume of material required for node i, parameter s i Service time for node i, parameter b i Is the latest time of node iA middle window, wherein the parameter phi is a conversion coefficient of oil consumption and carbon emission, and the parameter M' is a large constant; variable x ij The variable k being 0-1 means that if vehicle k chooses from node i to node j it is 1, otherwise it is 0, variable f ik Variable T representing the load of vehicle k at node i for a non-negative integer variable ik Representing the time of arrival of vehicle k at node i for a non-negative integer variable, variable z ik The order of the vehicle k to the node i is represented for non-negative integer variables to eliminate the model's sub-loops.
And, the above-mentioned objective functional formula (1) represents minimizing the total carbon emission; constraint (2) is node constraint, which means that all demand nodes Mc have access to only one vehicle; the constraint (3) is a vehicle number constraint and indicates that the number of vehicles sent out by the distribution warehouse does not exceed an upper limit; constraints (4) and (5) are flow constraints, (4) represents that any vehicle from the delivery warehouse finally returns to the delivery warehouse, and (5) represents that the inflow number of the vehicles is equal to the outflow number of any node in the set Mc; constraints (6) and (7) are respectively the capacity constraint and the load constraint of the vehicle, and the total load of any vehicle cannot exceed the maximum capacity and the maximum load; constraints (8) - (10) are load balance constraints, (8) represents the load of any vehicle from the delivery warehouse as the total required weight of the path, (9) and (10) make the current-carrying capacity of the next road section of the vehicle equal to the current road section current-carrying capacity minus the next node required capacity, wherein M' is used for controlling whether the constraints are effective or not; constraints (11) - (15) are time window constraints, (11) - (14) make the time when the vehicle arrives at the next node equal to the current node arrival time plus the service time and the journey travel time, where M' is used to control whether the constraint is valid, and (15) indicates that the node arrival time cannot exceed its specified latest time; the constraint (16) is a maximum travel time constraint, the time in transit of any vehicle must not exceed the maximum travel time; the constraint (17) is to eliminate the sub-loop constraint and ensure that each path does not form a closed loop between required nodes; the constraints (18) to (21) are variable value constraints.
Step S13: and calling a pre-constructed heuristic algorithm, and solving the vehicle path optimization model based on the minimization of the carbon emission of the vehicle by using the heuristic algorithm to obtain a corresponding vehicle distribution path when the carbon emission is minimum.
It should be noted that the definition of the heuristic algorithm may be: an algorithm based on an intuitive or empirical construct gives, at an acceptable cost (i.e., computation time and space), a feasible solution for each instance of the combinatorial optimization problem to be solved, the degree of deviation of which from the optimal solution is generally unpredictable. For example, in solving the VRP problem, meta-Heuristic, i.e., a Meta-Heuristic, is often used, which is an improvement of the simple Heuristic, combining a stochastic algorithm and a local search algorithm, wherein the Meta-Heuristic commonly used may include, but is not limited to, a genetic algorithm, an ant colony algorithm, a tabu search algorithm, a particle swarm algorithm, a simulated annealing method, and the like. The VRP problem is an NP-hard problem, and if an accurate algorithm is used for solving, only the problem of small scale can be solved, and the time required by the solving process is long. Therefore, at present, a heuristic algorithm is still the main method for solving the vehicle path problem, and although the heuristic algorithm can solve related problems relatively quickly, the quality of the algorithm is often determined by the practical experience of an algorithm designer and the size of a processed sample space. In the actual solving process, the most appropriate solving method is searched according to the application range of various algorithms and the specific situation of the vehicle path optimization problem.
In this embodiment, by adding actual delivery data, which may include but is not limited to warehouse data, fleet data, material information, demand information, and the like, and calling a pre-constructed heuristic algorithm to solve the vehicle route optimization model based on minimization of carbon emission of the vehicle, the minimum carbon emission of the delivery route, the corresponding demand distribution, and the corresponding vehicle delivery route when carbon emission is minimum are obtained, and a vehicle travel route of the delivery result and related navigation information may be presented by calling an Application Program Interface (API) of a Baidu map. For example, as shown in fig. 3, for the transportation process of a certain type of electric power material management unit, a service scenario from Zhou Zhuaiku to a terminal library is selected as a research object, the materials are planned to be uniformly stored in a regional turnover library, the regional turnover library uniformly supplies the materials to all terminal libraries in the province, and the route distribution from the turnover library to the terminal libraries needs to be planned, so that a vehicle distribution route corresponding to the time when the transportation cost is optimal and the carbon emission is minimum is obtained, and the goals of energy conservation and emission reduction can be achieved while paying attention to the cost and the efficiency. That is to say, in order to solve the optimal vehicle running path and take the running cost and the vehicle carbon emission into consideration, a complete vehicle carbon emission optimization model is established, the vehicle carbon emission optimization model is solved based on the minimum carbon emission, the establishment of related constraints and the design of an algorithm, and the planning of the vehicle running path is completed before the delivery of the vehicle, so that the vehicle is informed how to run, the carbon emission can be minimized, and the goals of energy conservation and emission reduction of enterprises are achieved.
Therefore, in the embodiment of the application, the vehicle path problem is solved by constructing the vehicle path optimization model based on the minimization of the carbon emission of the vehicle, and the vehicle path optimization model based on the minimization of the carbon emission of the vehicle is solved by calling the pre-constructed heuristic algorithm, so that the corresponding vehicle distribution path when the carbon emission is minimum is solved, and distribution is carried out according to the vehicle distribution path, so that the problem of energy conservation and emission reduction of the vehicle in the actual logistics distribution can be solved under the conditions of distribution constraint and the like, the distribution cost is reduced, and the vehicle distribution efficiency is improved.
Referring to fig. 4, a specific path planning method based on carbon emission is disclosed in the embodiment of the present invention, and compared with the previous embodiment, the embodiment further describes and optimizes the technical solution.
Step S21: establishing a fuel consumption expression of the current vehicle about fuel consumption and vehicle running speed;
step S22: constructing a vehicle path optimization model considering distribution constraint conditions and based on vehicle carbon emission minimization according to the oil consumption expression;
step S23: improving the genetic algorithm based on data preprocessing and excellent individual evolution to obtain an improved genetic algorithm; the data preprocessing comprises preprocessing for segmenting overweight node requirements and prioritizing cart single-point delivery. It can be understood that the simple genetic algorithm is improved mainly from two directions of data preprocessing and excellent individual evolution, wherein the data preprocessing comprises preprocessing for dividing overweight node requirements and preferential cart single-point distribution, so that the problems of overload requirements and single-point distribution can be solved, and in an initialized population, an initial solution can be constructed by a greedy and random method; judging the feasibility of individuals in the population, namely, eliminating individuals violating the constraints according to the constraints in the model, selecting good individuals in the population through a binary championship selection strategy, then carrying out individual crossing and mutation to obtain new good individuals, then evolving the new good individuals by adopting methods such as a cycle operator, a 0-1 exchange, a 2-opt operator and the like, further evolving the individuals after crossing and mutation to obtain more optimal individuals, improving a simple genetic algorithm through the process, enabling the algorithm to be better suitable for a current optimization model, solving more optimal results more efficiently, finally helping enterprises reduce distribution cost, improving vehicle distribution efficiency, accelerating enterprise greening logistics transformation, and the like
Step S24: and calling the improved genetic algorithm, and solving the vehicle path optimization model based on the minimization of the carbon emission of the vehicle by using the improved genetic algorithm to obtain a corresponding vehicle distribution path when the carbon emission is minimum.
In this embodiment, if the simple genetic algorithm is improved to obtain an improved genetic algorithm, the improved genetic algorithm may be called to solve the vehicle path optimization model based on minimization of the carbon emission of the vehicle, so as to obtain the minimum carbon emission of the distribution path, the corresponding demand distribution, and the vehicle distribution path corresponding to the minimum carbon emission.
For the specific content of the above steps S21 to S22, reference may be made to the corresponding content disclosed in the foregoing embodiments, and details are not repeated herein.
Therefore, in the embodiment of the application, the vehicle path problem is solved by constructing the vehicle path optimization model based on the minimization of the carbon emission of the vehicle, the vehicle path optimization model based on the minimization of the carbon emission of the vehicle is solved by calling the improved genetic algorithm, so that the corresponding vehicle distribution path with the minimum carbon emission is solved, and distribution is performed according to the vehicle distribution path, so that the problem of energy conservation and emission reduction of the vehicle in the actual logistics distribution can be solved by taking the conditions such as distribution constraint and the like into consideration, the distribution cost is reduced, and the vehicle distribution efficiency is improved.
Correspondingly, the embodiment of the present application further discloses a path planning device based on carbon emission, as shown in fig. 5, the device includes:
the expression establishing module 11 is used for establishing a fuel consumption expression of the current vehicle about fuel consumption and vehicle running speed;
the model building module 12 is used for building a vehicle path optimization model which considers distribution constraint conditions and is based on vehicle carbon emission minimization according to the fuel consumption expression;
the algorithm calling module 13 is used for calling a pre-constructed heuristic algorithm;
and the model solving module 14 is configured to solve the vehicle path optimization model based on minimization of carbon emission of the vehicle by using the heuristic algorithm to obtain a corresponding vehicle distribution path when the carbon emission is minimum.
As can be seen from the above, in the embodiment of the application, the vehicle path problem is solved by constructing the vehicle path optimization model based on minimization of vehicle carbon emission, and the vehicle path optimization model based on minimization of vehicle carbon emission is solved by calling a pre-constructed heuristic algorithm, so that the corresponding vehicle distribution path when the carbon emission is minimum is solved, and distribution is performed according to the vehicle distribution path, so that the problem of energy conservation and emission reduction of vehicles in actual logistics distribution can be solved by taking conditions such as distribution constraints into consideration, the distribution cost is reduced, and the vehicle distribution efficiency is improved.
In some specific embodiments, the expression establishing module 11 may specifically include:
the fuel consumption estimation unit is used for estimating the fuel consumption of the current vehicle to obtain a corresponding fuel consumption estimation result;
and the expression establishing unit is used for establishing a fuel consumption expression of the current vehicle about the fuel consumption and the running speed of the vehicle according to the fuel consumption estimation result.
In some specific embodiments, the model building module 12 may specifically include:
a correlation determination unit for determining a correlation between the fuel consumption expression and vehicle carbon emissions;
and a first model construction unit for constructing a vehicle path optimization model based on vehicle carbon emission minimization considering distribution constraint conditions according to the correlation.
In some specific embodiments, the model building module 12 may specifically include:
and the second model building unit is used for building a mixed integer planning model which considers the distribution constraint condition and is based on the minimization of the carbon emission of the vehicle according to the fuel consumption expression aiming at the optimization problem of the vehicle path.
In some specific embodiments, the distribution path planning apparatus may specifically include:
the attribute parameter acquisition module is used for acquiring the vehicle attribute parameters of the current vehicle;
the running parameter estimation module is used for estimating the current vehicle running parameters of the vehicle;
and the target parameter determination module is used for determining the target parameters required in the vehicle path optimization model based on the minimization of the carbon emission of the vehicle according to the vehicle attribute parameters and the vehicle operation parameters.
In some specific embodiments, the distribution path planning apparatus may specifically include:
the algorithm improvement module is used for improving the genetic algorithm based on data preprocessing and excellent individual evolution to obtain an improved genetic algorithm; the data preprocessing comprises preprocessing for segmenting overweight node requirements and prioritizing cart single-point delivery.
In some specific embodiments, the algorithm invoking module 13 may specifically include:
the algorithm calling unit is used for calling the improved genetic algorithm;
in some specific embodiments, the model solving module 14 may specifically include:
and the model solving unit is used for solving the vehicle path optimization model based on the minimization of the carbon emission of the vehicle by using the improved genetic algorithm to obtain a corresponding vehicle distribution path when the carbon emission is minimum.
Further, the embodiment of the application also provides electronic equipment. FIG. 6 is a block diagram illustrating an electronic device 20 according to an exemplary embodiment, and the contents of the diagram should not be construed as limiting the scope of use of the present application in any way.
Fig. 6 is a schematic structural diagram of an electronic device 20 according to an embodiment of the present disclosure. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input output interface 25, and a communication bus 26. Wherein the memory 22 is used for storing a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the carbon emission-based path planning method disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in this embodiment may be specifically an electronic computer.
In this embodiment, the power supply 23 is configured to provide a working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and an external device, and a communication protocol followed by the communication interface is any communication protocol applicable to the technical solution of the present application, and is not specifically limited herein; the input/output interface 25 is configured to obtain external input data or output data to the outside, and a specific interface type thereof may be selected according to specific application requirements, which is not specifically limited herein.
In addition, the storage 22 is used as a carrier for resource storage, and may be a read-only memory, a random access memory, a magnetic disk or an optical disk, etc., and the resources stored thereon may include an operating system 221, a computer program 222, etc., and the storage manner may be a transient storage or a permanent storage.
The operating system 221 is used for managing and controlling each hardware device on the electronic device 20 and the computer program 222, and may be Windows Server, netware, unix, linux, or the like. The computer program 222 may further include a computer program that can be used to perform other specific tasks in addition to the computer program that can be used to perform the carbon emission-based path planning method disclosed in any of the foregoing embodiments and executed by the electronic device 20.
Further, an embodiment of the present application also discloses a computer-readable storage medium, in which a computer program is stored, and when the computer program is loaded and executed by a processor, the steps of the path planning method based on carbon emission disclosed in any of the foregoing embodiments are implemented.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Finally, it should also be noted that, herein, 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 a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
The method, the apparatus, the device and the storage medium for path planning based on carbon emission provided by the present invention are described in detail above, and the principle and the implementation of the present invention are explained in detail herein by applying specific examples, and the description of the above examples is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A path planning method based on carbon emission is characterized by comprising the following steps:
establishing a fuel consumption expression of the current vehicle about fuel consumption and vehicle running speed;
constructing a vehicle path optimization model considering distribution constraint conditions and based on vehicle carbon emission minimization according to the oil consumption expression;
and calling a pre-constructed heuristic algorithm, and solving the vehicle path optimization model based on the minimization of the carbon emission of the vehicle by using the heuristic algorithm to obtain a corresponding vehicle distribution path when the carbon emission is minimum.
2. The carbon emission-based path planning method according to claim 1, wherein the establishing of the fuel consumption expression of the current vehicle with respect to fuel consumption and vehicle running speed comprises:
estimating the fuel consumption of the current vehicle to obtain a corresponding fuel consumption estimation result;
and establishing a fuel consumption expression of the current vehicle about the fuel consumption and the running speed of the vehicle according to the fuel consumption estimation result.
3. The carbon emission-based path planning method according to claim 1, wherein the constructing a vehicle path optimization model considering distribution constraint conditions and based on minimization of carbon emission of a vehicle according to the fuel consumption expression comprises:
determining a correlation between the fuel consumption expression and vehicle carbon emissions;
and constructing a vehicle path optimization model based on vehicle carbon emission minimization considering distribution constraint conditions according to the correlation.
4. The carbon emission-based path planning method according to claim 1, wherein the constructing a vehicle path optimization model considering distribution constraint conditions and based on minimization of carbon emission of a vehicle according to the fuel consumption expression comprises:
and aiming at the vehicle path optimization problem, constructing a mixed integer programming model considering distribution constraint conditions and based on vehicle carbon emission minimization according to the oil consumption expression.
5. The carbon emission-based path planning method according to claim 1, further comprising:
acquiring vehicle attribute parameters of the current vehicle, and estimating vehicle operation parameters of the current vehicle;
and determining target parameters required in the vehicle path optimization model based on the minimization of the carbon emission of the vehicle according to the vehicle attribute parameters and the vehicle operation parameters.
6. The carbon emission-based path planning method according to any one of claims 1 to 5, wherein the invoking of the pre-constructed heuristic algorithm is preceded by:
improving the genetic algorithm based on data preprocessing and excellent individual evolution to obtain an improved genetic algorithm; the data preprocessing comprises preprocessing for dividing overweight node requirements and priority cart single-point distribution.
7. The carbon emission-based path planning method according to claim 6, wherein the invoking a pre-constructed heuristic algorithm and solving the vehicle path optimization model based on minimization of carbon emission of the vehicle by using the heuristic algorithm to obtain a corresponding vehicle distribution path when carbon emission is minimum comprises:
and calling the improved genetic algorithm, and solving the vehicle path optimization model based on the minimization of the carbon emission of the vehicle by using the improved genetic algorithm to obtain a corresponding vehicle distribution path when the carbon emission is minimum.
8. A carbon emission-based path planning apparatus, comprising:
the expression establishing module is used for establishing a fuel consumption expression of the current vehicle about fuel consumption and vehicle running speed;
the model building module is used for building a vehicle path optimization model which considers distribution constraint conditions and is based on vehicle carbon emission minimization according to the oil consumption expression;
the algorithm calling module is used for calling a pre-constructed heuristic algorithm;
and the model solving module is used for solving the vehicle path optimization model based on the minimization of the carbon emission of the vehicle by utilizing the heuristic algorithm to obtain a corresponding vehicle distribution path when the carbon emission is minimum.
9. An electronic device, comprising:
a memory for storing a computer program;
a processor for executing the computer program for carrying out the steps of the carbon emission-based path planning method according to any one of claims 1 to 7.
10. A computer-readable storage medium for storing a computer program; wherein the computer program when executed by a processor implements the steps of the carbon emission based path planning method of any of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116382099A (en) * | 2023-06-02 | 2023-07-04 | 上海数字大脑科技研究院有限公司 | Robot path scheduling planning method and system |
| CN117273255A (en) * | 2023-11-16 | 2023-12-22 | 深圳市大数据研究院 | Cold chain transportation path planning method, device, equipment and medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116382099A (en) * | 2023-06-02 | 2023-07-04 | 上海数字大脑科技研究院有限公司 | Robot path scheduling planning method and system |
| CN117273255A (en) * | 2023-11-16 | 2023-12-22 | 深圳市大数据研究院 | Cold chain transportation path planning method, device, equipment and medium |
| CN117273255B (en) * | 2023-11-16 | 2024-01-30 | 深圳市大数据研究院 | Cold chain transportation path planning method, device, equipment and medium |
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