Charging allocation method and device of logistics vehicle, electronic equipment and storage medium

Abstract

The invention provides a logistics vehicle charging allocation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the system constraint information of a distribution charging system and the current electric quantity of a plurality of new energy logistics vehicles, and determining the average charging queuing waiting time of the new energy logistics vehicles based on the system constraint information and each current electric quantity, a plurality of randomly generated initial individuals are updated based on the distribution task information, the average charging queuing waiting duration and each current electric quantity, and the initial individuals are used for representing initial node access information of each new energy logistics vehicle; determining an initial fitness function value corresponding to each initial individual according to the system constraint information, each current electric quantity and a plurality of preset cost relationships, and performing iterative updating on each initial individual according to each initial fitness function value to obtain a target optimal individual so as to perform charging deployment of the logistics vehicle; according to the invention, by controlling the selection of the charging station and the distribution path, the distribution efficiency is improved, and the cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation and logistics management technology, and in particular to a charging allocation method, device, electronic equipment and storage medium for a logistics vehicle. Background Art

[0002] The charging and dispatching of logistics vehicles aims to achieve optimal management in the distribution process, improve distribution efficiency and reduce costs. In the field of new energy vehicle battery replacement, new energy logistics vehicles have been initially promoted on a large scale, but they still face problems such as low mileage, difficulty in charging and high vehicle maintenance rate, which seriously affect the actual application effect of new energy logistics vehicles.

[0003] Compared with traditional fuel vehicles, the charging problem of new energy logistics vehicles is particularly prominent. At present, the battery capacity of new energy logistics vehicles is limited and the charging time is long. This easily leads to charging demand exceeding the carrying capacity of existing charging facilities in long-term and long-distance delivery tasks, resulting in charging queues and reduced delivery efficiency. Therefore, how to optimize the delivery route and charging strategy of new energy logistics vehicles has become one of the current research hotspots. Summary of the invention

[0004] The present invention provides a charging allocation method, device, electronic equipment and storage medium for a logistics vehicle to solve the technical problem that the above-mentioned new energy logistics vehicles have to wait in line for charging and the distribution efficiency is reduced due to the limited carrying capacity of the charging facilities.

[0005] In one embodiment of the present application, the present application provides a charging deployment method for logistics vehicles, including: obtaining system constraint information of a distribution charging system and current power of multiple new energy logistics vehicles, the system constraint information including node structure information, usage cost information and distribution task information; determining an average charging queue waiting time of the new energy logistics vehicles based on the system constraint information and each current power, and updating a plurality of randomly generated initial individuals based on the distribution task information, the average charging queue waiting time and each current power, the initial individuals being used to characterize initial node access information of multiple new energy logistics vehicles; determining an initial fitness function value corresponding to each of the initial individuals according to the system constraint information, each current power and multiple preset cost relationships, and iteratively updating each of the initial individuals according to each initial fitness function value to obtain a target optimal individual, so as to perform charging deployment for each of the new energy logistics vehicles according to the target optimal individual.

[0006] In one embodiment of the present application, multiple randomly generated initial individuals are updated based on the delivery task information, the average charging queue waiting time and the current power of each, including: randomly generating multiple initial individuals; if there is at least one of the average charging queue waiting time being greater than a preset waiting threshold, the mass of the goods to be delivered at the delivery task point is greater than a preset vehicle loading threshold, and the current power is less than a preset power threshold, then randomly re-generating multiple initial individuals; wherein the delivery task information includes the mass of the goods to be delivered at the delivery task point.

[0007] In one embodiment of the present application, each of the initial individuals is iteratively updated according to each of the initial fitness function values ​​to obtain a target optimal individual, including: determining the initial individual corresponding to the largest initial fitness function value as the current optimal individual; if the preset iteration condition of the iterative update is met, then the current optimal individual is determined as the target optimal individual; if the preset iteration condition of the iterative update is not met, then repeatedly performing the iterative update of each of the initial individuals according to the sorting of each of the initial fitness function values ​​to obtain multiple iterative individuals, determining the iterative fitness function value corresponding to each of the iterative individuals according to the system constraint information, each of the current power and multiple preset cost relationships, updating the current optimal individual based on the largest iterative fitness function value, taking each of the iterative individuals as the initial individual, and taking each of the iterative fitness function values ​​as the initial fitness function values, until the preset iteration condition of the iterative update is met, and the current optimal individual is determined as the target optimal individual.

[0008] In one embodiment of the present application, each of the initial individuals is iteratively updated according to the ranking of each of the initial fitness function values ​​to obtain multiple iterative individuals, including: dividing each of the initial individuals into three parts according to a preset allocation ratio and the ranking of each of the initial fitness function values ​​to obtain multiple elite individuals, multiple inferior individuals and multiple ordinary individuals; performing random mutation on each of the inferior individuals to obtain multiple mutant individuals; performing roulette wheel selection on each of the ordinary individuals to obtain multiple selected individuals; and determining all elite individuals, all mutant individuals and all selected individuals as multiple iterative individuals.

[0009] In one embodiment of the present application, the initial fitness function value corresponding to each of the initial individuals is determined based on the system constraint information, the current power and multiple preset cost relationships, including: determining multiple initial cost values ​​corresponding to each initial individual based on the system constraint information, the current power and multiple preset cost relationships; determining the sum of all initial cost values ​​corresponding to each initial individual as the cost of each initial individual; and determining the inverse of the cost of each initial individual as the initial fitness function value of each initial individual.

[0010] In one embodiment of the present application, multiple initial cost values ​​corresponding to the initial individuals are determined according to the system constraint information, each of the current power and multiple preset cost relationships, including: if the preset cost relationship is a charging station construction cost relationship, a first cost value is determined according to all selected charging stations and all initial charging station construction costs and the charging station construction cost relationship; if the preset cost relationship is a vehicle use cost relationship, a second cost value is determined according to the initial vehicle fixed use cost, the distribution center-node access information and the vehicle use cost relationship; if the preset cost relationship is a distribution transportation cost relationship, a third cost value is determined according to the unit distance transportation cost, the node-node access information, the node distance information of the node-node access information and the distribution transportation cost relationship; if the preset cost relationship is a vehicle charging cost relationship, a fourth cost value is determined according to the unit electricity price, the charging amount information, all the selected charging stations and the vehicle charging cost relationship, and the charging amount information is obtained based on each of the current power; if the preset cost relationship is an environmental cost relationship, a fourth cost value is determined according to the unit carbon emission cost, the electric energy conversion coefficient, the vehicle power consumption coefficient the fifth cost value is determined according to the number, the proportion of thermal power generation and the environmental cost relationship; if the preset cost relationship is a time window penalty cost relationship, the sixth cost value is determined according to the expected earliest arrival time information of the distribution point, the expected latest arrival time information of the distribution point, the actual arrival time information and the time window penalty cost relationship, and the actual arrival time information is obtained based on the distance information between the initial individual and the node; wherein the initial cost value includes at least one of the first cost value, the second cost value, the third cost value, the fourth cost value, the fifth cost value and the sixth cost value, the node structure information includes the distance information between the nodes, the use cost information includes at least one of the initial charging construction cost, the initial vehicle fixed use cost, the unit electricity price, the unit carbon emission cost, the electric energy conversion coefficient, the vehicle power consumption coefficient, and the proportion of thermal power generation, the distribution task information includes the expected earliest arrival time information of the distribution point, the expected latest arrival time information of the distribution point, and the initial individual includes at least one of the selected charging station, the distribution center-node access information and the node-node access information.

[0011] In one embodiment of the present application, the method for determining the average charging queue waiting time includes:

[0012]

[0013] Among them, T is the average waiting time for charging queue, μ is the arrival rate of the new energy logistics vehicle, k0 is the probability that the charging station is idle, is the charging service rate of the charging station, and b is the number of charging piles in the charging station.

[0014] In one embodiment of the present application, the present application provides a charging allocation device for a logistics vehicle, including: an acquisition module, used to acquire system constraint information of a distribution charging system and current power of multiple new energy logistics vehicles, the system constraint information including node structure information, usage cost information and distribution task information; an individual generation module, used to determine the average charging queue waiting time of the new energy logistics vehicles based on the system constraint information and each current power, and update a plurality of randomly generated initial individuals based on the distribution task information, the average charging queue waiting time and each current power, the initial individuals are used to characterize the initial node access information of multiple new energy logistics vehicles; an individual optimization module, used to determine the initial fitness function value corresponding to each of the initial individuals according to the system constraint information, each current power and multiple preset cost relationships, and iteratively update each of the initial individuals according to each initial fitness function value to obtain a target optimal individual, so as to perform charging allocation for each of the new energy logistics vehicles according to the target optimal individual.

[0015] In one embodiment of the present application, the present application provides an electronic device, which includes: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the electronic device implements the charging deployment method for a logistics vehicle as described in any of the above embodiments.

[0016] In one embodiment of the present application, the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor of a computer, the computer executes the charging and dispatching method for a logistics vehicle described in any one of the above embodiments.

[0017] Beneficial effects of embodiments of the present invention: The present invention provides a charging deployment method, device, electronic device and storage medium for logistics vehicles. The embodiments of the present invention constrain the generation of initial individuals through the average charging queue waiting time, current power and delivery task information, and obtain the target optimal individual through iterative optimization of the initial individuals to carry out charging deployment of new energy logistics vehicles. By controlling the selection of charging sites and delivery routes, the delivery efficiency is improved and the cost is reduced.

[0018] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0020] Figure 1 A schematic diagram showing an exemplary system architecture to which the technical solution of the embodiments of the present application can be applied;

[0021] Figure 2 A schematic diagram of a flow chart of a method for charging and allocating a logistics vehicle according to an embodiment of the present application is shown;

[0022] Figure 3 A schematic diagram of a distribution of a new energy logistics vehicle according to an embodiment of the present application is shown;

[0023] Figure 4 A schematic diagram of site selection for charging deployment of a new energy logistics vehicle according to an embodiment of the present application is shown;

[0024] Figure 5 A schematic diagram showing multi-area new energy logistics vehicles queuing for charging according to an embodiment of the present application is shown;

[0025] Figure 6 A schematic diagram of individual optimization according to an embodiment of the present application is shown;

[0026] Figure 7 A block diagram of a charging deployment device for a logistics vehicle according to an embodiment of the present application is shown;

[0027] Figure 8 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0028] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0029] It should be noted that the illustrations provided in the following embodiments are only used to illustrate the basic concept of the present application in a schematic manner. Therefore, the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0030] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present application difficult to understand.

[0031] See also Figure 1 , Figure 1 A schematic diagram of an exemplary system architecture to which the technical solution of the embodiment of the present application can be applied is shown. Figure 1 As shown, the system architecture may include a computer device 101 and a new energy logistics vehicle 102. There are multiple new energy logistics vehicles 102, and the new energy logistics vehicles 102 are in communication connection with the computer device 101. The computer device is used to determine the optimal target individual and then charge and allocate each new energy logistics vehicle.

[0032] Exemplarily, the computer device 101 obtains system constraint information of the distribution charging system and the current power of multiple new energy logistics vehicles, the system constraint information includes node structure information, usage cost information and distribution task information; determines the average charging queue waiting time of the new energy logistics vehicles based on the system constraint information and the current power, and updates the randomly generated multiple initial individuals based on the distribution task information, the average charging queue waiting time and the current power, the initial individuals are used to characterize the initial node access information of the multiple new energy logistics vehicles; determines the initial fitness function value corresponding to each initial individual according to the system constraint information, the current power and multiple preset cost relationships, and iteratively updates each initial individual according to the initial fitness function value to obtain the target optimal individual, so as to perform charging deployment for each of the new energy logistics vehicles according to the target optimal individual.

[0033] In order to solve the above technical problems, the present application provides a charging allocation method, device, electronic device and storage medium for a logistics vehicle. The implementation details of the technical solution of the embodiment of the present application are elaborated in detail below.

[0034] See also Figure 2 , Figure 2 FIG. 1 is a flow chart showing a method for charging and allocating a logistics vehicle according to an embodiment of the present application. Figure 2As shown, in an exemplary embodiment, the charging deployment method of a logistics vehicle includes at least steps S210 to S230, which are described in detail as follows:

[0035] Step S210, obtaining system constraint information of the distribution charging system and current power levels of multiple new energy logistics vehicles.

[0036] Among them, the system constraint information includes node structure information, usage cost information and delivery task information.

[0037] In one embodiment of the present application, a distribution charging system is formed by a distribution center and a plurality of nodes, wherein the nodes include distribution task points and charging stations.

[0038] In one embodiment of the present application, see Figure 3 , Figure 3 FIG. 1 shows a schematic diagram of a distribution system of a new energy logistics vehicle according to an embodiment of the present application. Figure 3 As shown, the square nodes represent the distribution centers, and the circular nodes represent the distribution task points. The nodes are filled with black, and the distribution centers and multiple distribution task points are connected in sequence to determine the distribution area routes.

[0039] In one embodiment of the present application, see Figure 4 , Figure 4 The figure shows a schematic diagram of the site selection for charging and deployment of new energy logistics vehicles according to an embodiment of the present application. Figure 4 As shown, the charging stations are represented by triangular nodes; the triangles are filled in black to indicate that the charging station is selected, and the nodes are connected to the distribution schematic diagram of the above embodiment.

[0040] Step S220, determining the average charging queue waiting time of new energy logistics vehicles based on system constraint information and the current power levels, and updating a plurality of randomly generated initial individuals based on the delivery task information, the average charging queue waiting time and the current power levels.

[0041] Among them, the initial individual is used to represent the initial node access information of multiple new energy logistics vehicles.

[0042] In one embodiment of the present application, route planning for new energy logistics vehicles is performed based on existing distribution centers. Not only is it necessary to consider how to choose the best location for the selected charging station, but it is also necessary to arrange the best route for the vehicle. Considering the current situation, the charging time of new energy logistics vehicles is relatively long, and the layout coverage of charging piles is not perfect, so new energy logistics vehicles often queue up to wait for charging. Therefore, when using logistics vehicles for cargo delivery, in addition to considering the charging time, the waiting time during charging must also be considered.

[0043] In one embodiment of the present application, the charging queue system corresponding to the new energy logistics vehicle is described as follows: the charging station is equipped with b charging piles, and the number of vehicles arriving is a. The law of vehicles arriving at the charging station conforms to the Poisson distribution of the arrival rate μ of the new energy logistics vehicle. If the number of vehicles a is greater than or equal to the number of charging piles b, there will be ab new energy logistics vehicles waiting in line for charging services. The charging queue system is shown as follows:

[0044]

[0045] in, is the charging pile service rate, k1 is the probability that there is a new energy logistics vehicle in the charging queue system, μ is the arrival rate of new energy logistics vehicles, k0 is the probability that the charging queue system is in the initial state, and k a+1 is the probability that there are a+1 new energy logistics vehicles in the charging queue system, k a-1 is the probability that there are a-1 new energy logistics vehicles in the charging queue system, k a is the probability that there are a+1 new energy logistics vehicles in the charging queuing system, and b is the number of charging piles.

[0046] In one embodiment of the present application, the charging queuing system is in an initial state, which means that there is no new energy logistics vehicle charging and the charging station is idle.

[0047] In one embodiment of the present application, see Figure 5 , Figure 5 A schematic diagram of multi-regional new energy logistics vehicles queuing for charging according to an embodiment of the present application is shown. Figure 5 As shown, the task center is located in the computer device to deploy new energy logistics vehicles to the corresponding charging piles in the corresponding areas for charging. Each area includes multiple charging piles, and the charging sites are represented by regions.

[0048] In one embodiment of the present application, the communication connection between the new energy logistics vehicle and the computer device can be realized by a cellular network such as the fourth generation mobile communication technology, the fifth generation mobile communication technology, etc.

[0049] In one embodiment of the present application, the communication connection between the new energy logistics vehicle and the computer device can also be achieved through a wireless local area network.

[0050] In one embodiment of the present application, the average charging queue waiting time of new energy logistics vehicles can be determined by formula (1), as shown in the following formula:

[0051]

[0052] Among them, T is the average waiting time for charging queue, μ is the arrival rate of new energy logistics vehicles, b is the number of charging piles, is the charging pile service rate, and k0 is the probability that the charging station is idle.

[0053] In one embodiment of the present application, multiple randomly generated initial individuals are updated based on delivery task information, average charging queue waiting time and each current power level, including: randomly generating multiple initial individuals; if there is an average charging queue waiting time greater than a preset waiting threshold, the mass of the goods to be delivered at the delivery task point is greater than a preset vehicle loading threshold, and the current power level is less than at least one of the preset power threshold, then randomly re-generating multiple initial individuals; wherein the delivery task information includes the mass of the goods to be delivered at the delivery task point.

[0054] In one embodiment of the present application, a greedy idea is introduced to generate an initial population, thereby improving the generation quality of initial individuals and thus improving the efficiency of optimization.

[0055] In one embodiment of the present application, the maximum load mass constraint is determined according to the delivery task information, as shown in the formula:

[0056]

[0057] Among them, N a is the mass of the goods to be delivered at the delivery task point a, D is the maximum load mass of the new energy logistics vehicle, x ijk is the number of visits of new energy logistics vehicle k at nodes i and j, and A is the set of all nodes.

[0058] In one embodiment of the present application, the preset vehicle loading threshold is the maximum load mass.

[0059] In one embodiment of the present application, if the new energy logistics vehicle k visits node j immediately after visiting node i, then x ijk is 1, otherwise x ijk is 0.

[0060] In one embodiment of the present application, it is stipulated that after the new energy logistics vehicle arrives at the distribution center, if it does not depart immediately, it will be charged immediately, and it is stipulated that it will not be charged at the distribution task point, but only at the charging station. The required charging amount of the new energy logistics vehicle k is determined as shown in the following formula:

[0061] q ηk =y η bu ηk =y η (Q-SOC Aαk ) Formula (4)

[0062] Among them, q ηk is the charging amount of new energy logistics vehicle k at the charging station η, b is the charging coefficient, u ηk is the charging time of the delivery vehicle at the charging station η, yη is the number of visits to the charging station η, Q is the maximum battery capacity of the new energy logistics vehicle, SOC Aαk is the current power of the new energy logistics vehicle k when it arrives at the customer point α to be served.

[0063] In one embodiment of the present application, if the charging site n is selected, it is 1, otherwise it is 0.

[0064] In one embodiment of the present application, the power of the new energy logistics vehicle k when it leaves the distribution center is as follows:

[0065] SOC B0k =Q Formula (5)

[0066] Among them, SOC B0k is the power of the new energy logistics vehicle k when it leaves the distribution center, and Q is the maximum battery capacity of the new energy logistics vehicle.

[0067] In one embodiment of the present application, the power of the new energy logistics vehicle k when leaving the charging station n is expressed as follows:

[0068] SOC Bηk =y η Q formula (6)

[0069] Among them, SOC Bηk is the power of the new energy logistics vehicle k when it leaves the charging station η, y η is the visit mark quantity of the charging station η.

[0070] In one embodiment of the present application, it is stipulated that the new energy logistics vehicle k does not consume power when loading goods, that is, the arrival and departure power are equal, and the current power of the new energy logistics vehicle k is shown as follows:

[0071] SOC Aαk =SOC Bαk Formula (7)

[0072] Among them, SOC Aαk is the current power of the new energy logistics vehicle k when it arrives at the customer point α to be served, SOC Bαk is the current power of the new energy logistics vehicle k when it leaves the customer point α to be served.

[0073] In one embodiment of the present application, the delivery vehicle k needs to check and determine the current power at each delivery task point to ensure that it is greater than a preset power threshold, as shown in the following formula:

[0074]

[0075] Among them, SOC Aαkis the current power of the new energy logistics vehicle k when it arrives at the customer point α to be served, θ is the preset power threshold, N is the set of all customer points to be served, and K is the set of all new energy logistics vehicles.

[0076] In one embodiment of the present application, the customer point to be served is a selected delivery task point.

[0077] Step S230, determine the initial fitness function value corresponding to each initial individual according to the system constraint information, the current power and multiple preset cost relationships, and iteratively update each initial individual according to the initial fitness function value to obtain the target optimal individual, so as to allocate charging for each new energy logistics vehicle according to the target optimal individual.

[0078] In one embodiment of the present application, each new energy logistics vehicle is allocated for charging based on target node access information corresponding to the optimal target individual through the task center.

[0079] In one embodiment of the present application, the initial fitness function value corresponding to each initial individual is determined according to system constraint information, current power and multiple preset cost relationships, including: determining multiple initial cost values ​​corresponding to each initial individual according to system constraint information, current power and multiple preset cost relationships; determining the sum of all initial cost values ​​corresponding to each initial individual as the cost of each initial individual; and determining the inverse of the cost of each initial individual as the initial fitness function value of each initial individual.

[0080] In one embodiment of the present application, the charging station construction cost relationship is as follows:

[0081]

[0082] Among them, L1 is the construction cost of the target charging station, E is the set of all charging stations, and f η is the initial charging station construction cost of charging station η, y η The number of visits to the charging station.

[0083] In one embodiment of the present application, a first cost value is obtained according to a target charging station construction cost.

[0084] In one embodiment of the present application, the vehicle use cost relationship is shown as follows:

[0085]

[0086] Among them, L2 is the fixed cost of the target vehicle, K is the set of all new energy logistics vehicles, N is the set of all customer points to be served, w0 is the initial fixed cost of the vehicle, x 0ik is the number of visits to the new energy logistics vehicle k at the distribution center-node i.

[0087] In one embodiment of the present application, the second cost value is obtained according to the fixed usage cost of the target vehicle.

[0088] In one embodiment of the present application, if the new energy logistics vehicle k visits node i immediately after departing from the distribution center, then x 0ik is 1; if the new energy logistics vehicle k does not visit node i after leaving the distribution center, then x 0ik is 0.

[0089] In one embodiment of the present application, the distribution transportation cost relationship is as follows:

[0090]

[0091] Among them, L3 is the distribution transportation cost, A is the set of all nodes, K is the set of all new energy logistics vehicles, w1 is the unit distance transportation cost, d ij is the distance between node i and node j, x ijk is the number of visits of new energy logistics vehicle k at nodes i and j.

[0092] In one embodiment of the present application, the third cost value is obtained according to the delivery transportation fee.

[0093] In one embodiment of the present application, the vehicle charging cost relationship is as follows:

[0094]

[0095] Among them, L4 is the charging cost of the target vehicle, E is the set of all charging stations, K is the set of all new energy logistics vehicles, w2 is the unit electricity price, q ηk is the charging amount of new energy logistics vehicle k at charging station η, y η The number of visits to the charging station.

[0096] In one embodiment of the present application, the vehicle will not be charged immediately after leaving the distribution center, then q 0ηk is 0.

[0097] In one embodiment of the present application, a fourth cost value is obtained according to the target vehicle charging cost.

[0098] In one embodiment of the present application, the environmental cost relationship is shown as follows:

[0099]

[0100] Among them, L5 is the target environmental cost, ω is the proportion of thermal power generation, w3 is the unit carbon emission cost, γ is the power conversion coefficient, A is the set of all nodes, K is the set of all new energy logistics vehicles, xijk is the number of visits to nodes i and j by new energy logistics vehicle k, d ij is the distance between node i and node j, and H is the vehicle power consumption coefficient.

[0101] In one embodiment of the present application, the fifth cost value is obtained according to the target environment cost.

[0102] In one embodiment of the present application, the time window penalty cost relationship is shown as follows:

[0103]

[0104] Among them, L6 is the penalty cost for violating the time window, N is the set of all customer points to be served, K is the set of all new energy logistics vehicles, w4 is the unit penalty cost for early arrival, E sα is the expected earliest arrival time of customer point α, A Tαk is the actual arrival time of the new energy logistics vehicle k at the customer point α, and w5 is the unit penalty cost for being late.

[0105] In one embodiment of the present application, the sixth cost value is obtained according to the penalty cost of violating the time window.

[0106] In one embodiment of the present application, the cost is as shown in the following formula:

[0107] L=L1+L2+L3+L4+L5+L6 Formula (14)

[0108] In one embodiment of the present application, L is the cost, L1 is the target charging station construction cost, L2 is the target vehicle fixed usage cost, L3 is the distribution and transportation cost, L4 is the target vehicle charging cost, L5 is the environmental cost, and L6 is the penalty cost for violating the time window.

[0109] In one embodiment of the present application, multiple initial cost values ​​corresponding to the initial individuals are determined based on system constraint information, each current power and multiple preset cost relationships, including: if the preset cost relationship is a charging site construction cost relationship, then the first cost value is determined based on all selected charging stations and all initial charging site construction costs and the charging site construction cost relationship; if the preset cost relationship is a vehicle use cost relationship, then the second cost value is determined based on the initial vehicle fixed use cost, the distribution center-node access information and the vehicle use cost relationship; if the preset cost relationship is a distribution transportation cost relationship, then the third cost value is determined based on the unit distance transportation cost, node-node access information, node-node access information, and the node distance information of the node-node access information and the distribution transportation cost relationship; if the preset cost relationship is a vehicle charging cost relationship, then the fourth cost value is determined based on the unit electricity price, the charging amount information, all selected charging stations and the vehicle charging cost relationship, and the charging amount information is obtained based on each current power; if the preset cost relationship is an environmental cost relationship, then the fourth cost value is determined based on the unit carbon emission cost , power conversion coefficient, vehicle power consumption coefficient, thermal power generation ratio and environmental cost relationship to determine the fifth cost value; if the preset cost relationship is a time window penalty cost relationship, the sixth cost value is determined according to the expected earliest arrival time information of the distribution point, the expected latest arrival time information of the distribution point, the actual arrival time information and the time window penalty cost relationship, and the actual arrival time information is obtained based on the initial individual and the distance information between the nodes; wherein the initial cost value includes at least one of the first cost value, the second cost value, the third cost value, the fourth cost value, the fifth cost value and the sixth cost value, the node structure information includes the distance information between the nodes, the use cost information includes at least one of the initial charging construction cost, the initial vehicle fixed use cost, the unit electricity price, the unit carbon emission cost, the power conversion coefficient, the vehicle power consumption coefficient, and the thermal power generation ratio, the delivery task information includes the expected earliest arrival time information of the distribution point, the expected latest arrival time information of the distribution point, and the initial individual includes at least one of the selected charging station, the distribution center-node access information and the node-node access information.

[0110] In one embodiment of the present application, each initial individual is iteratively updated according to each initial fitness function value to obtain a target optimal individual, including: determining the initial individual corresponding to the largest initial fitness function value as the current optimal individual; if the preset iteration condition of the iterative update is met, then the current optimal individual is determined as the target optimal individual; if the preset iteration condition of the iterative update is not met, then repeatedly performing the iterative update of each initial individual according to the sorting of each initial fitness function value to obtain multiple iterative individuals, determining the iterative fitness function value corresponding to each iterative individual according to system constraint information, each current power and multiple preset cost relationships, updating the current optimal individual based on the largest iterative fitness function value, taking each iterative individual as the initial individual, and taking each iterative fitness function value as the initial fitness function value, until the preset iteration condition of the iterative update is met, and the current optimal individual is determined as the target optimal individual.

[0111] In one embodiment of the present application, the current optimal individual is updated based on the maximum iterative fitness function value, including: if the maximum iterative fitness function is greater than the maximum initial fitness function, the current optimal individual is updated to the iterative individual corresponding to the maximum iterative fitness function.

[0112] In one embodiment of the present application, each initial individual is iteratively updated according to the ranking of each initial fitness function value to obtain multiple iterative individuals, including: dividing each initial individual into three parts according to a preset allocation ratio and the ranking of each initial fitness function value to obtain multiple elite individuals, multiple inferior individuals and multiple ordinary individuals; performing random mutation on each inferior individual to obtain multiple mutant individuals; performing roulette wheel selection on each ordinary individual to obtain multiple selected individuals; and determining all elite individuals, all mutant individuals and all selected individuals as multiple iterative individuals.

[0113] In one embodiment of the present application, three individual selection methods are adopted: elite retention strategy, roulette strategy and random mutation strategy to ensure the diversity of the population and the retention of high-quality solutions. 10% of the individuals are selected using the elite retention strategy, the basic idea of ​​which is to copy the best part of the individuals in the population directly to the next generation without crossover mutation operations. 80% of the individuals are selected using the roulette strategy, that is, the individuals with larger fitness function values ​​have greater probabilities of entering the next generation. The remaining 10% of the individuals adopt a random mutation strategy to jump out of the local optimum by reinitializing the chromosomes. The operation process is: sorting in descending order of fitness function values; re-randomly generating the last 10% of the individuals and copying them to the new population.

[0114] In one embodiment of the present application, see Figure 6 , Figure 6 FIG. 2 shows a schematic diagram of individual optimization according to an embodiment of the present application. Figure 6As shown, generate species: randomly generate multiple initial individuals; whether the load capacity, power and time window constraints are met: if not, then randomly generate multiple initial individuals again, if all are met, then enter the step of calculating the individual fitness function; calculate the individual fitness function: determine the initial fitness function value corresponding to each initial individual according to the system constraint information, the current power and multiple preset cost relationships; set the optimal individual and function value to the optimal: determine the initial individual corresponding to the largest initial fitness function value as the current optimal individual, and the function value corresponding to the current optimal individual is the optimal value; whether the iteration is completed: if the iteration is completed, enter the step of outputting the current optimal individual and the optimal solution, if the iteration is not completed, then enter the step of sorting the individual fitness function; sorting the individual fitness function: sort the individual fitness function; individual random mutation: 10 % of the initial individuals undergo random mutation; roulette wheel selection of individuals: 80% of the initial individuals undergo roulette wheel selection; random selection of parents and multi-point crossover: random selection of parents for mutation operation; selection of elite individuals: 10% of the initial individuals directly enter the new population; calculation of the fitness function value of the new population individuals: determination of the iterative fitness function value corresponding to each iterative individual according to the system constraint information, the current power and multiple preset cost relationships; whether the fitness function value of the optimal individual of the new population is better than the current optimal individual: if better, update the current optimal individual and the optimal solution, and enter the step of determining whether the iteration is completed; if not optimized, directly enter the step of determining whether the iteration is completed; output the current optimal individual and the optimal solution: determine the current optimal individual as the target optimal individual, and determine the individual fitness function value corresponding to the target optimal individual as the target function value.

[0115] See also Figure 7 , Figure 7 A block diagram of a charging allocation device for a logistics vehicle according to an embodiment of the present application is shown. The device can be applied to Figure 1 The implementation environment shown in the figure is specifically configured in the computer device 101. The device can also be applied to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the device is applied.

[0116] like Figure 7 As shown, a charging allocation device 700 for a logistics vehicle according to an embodiment of the present application includes: an acquisition module 701, an individual generation module 702 and an individual optimization module 703.

[0117] The acquisition module 701 is used to acquire the system constraint information of the distribution charging system and the current power of multiple new energy logistics vehicles. The system constraint information includes node structure information, usage cost information and distribution task information;

[0118] Individual generation module 702, used to determine the average charging queue waiting time of new energy logistics vehicles based on system constraint information and current power of each vehicle, and update the randomly generated multiple initial individuals based on delivery task information, average charging queue waiting time and current power of each vehicle, where the initial individuals are used to represent initial node access information of multiple new energy logistics vehicles;

[0119] The individual optimization module 703 is used to determine the initial fitness function value corresponding to each initial individual according to the system constraint information, the current power and multiple preset cost relationships, and iteratively update each initial individual according to the initial fitness function value to obtain the target optimal individual, so as to allocate charging for each new energy logistics vehicle according to the target optimal individual.

[0120] It should be noted that the charging coordination device for the logistics vehicle provided in the above embodiment and the charging coordination method for the logistics vehicle provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment and will not be repeated here. In actual application, the charging coordination device for the logistics vehicle provided in the above embodiment can distribute the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0121] An embodiment of the present application also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by one or more processors, the electronic device implements the charging and dispatching method for logistics vehicles provided in the above-mentioned embodiments.

[0122] See also Figure 8 , Figure 8 The structure diagram of the computer system suitable for implementing the electronic device of the embodiment of the present application is shown. It should be noted that: Figure 8 The computer system 800 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0123] like Figure 8As shown, the computer system 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 802 or the program loaded from the storage part 808 to the random access memory (RAM) 803, such as executing the method in the above embodiment. In the RAM 803, various programs and data required for system operation are also stored. The CPU 801, ROM 802 and RAM 803 are connected to each other through a bus 804. The input / output (I / O) interface 805 is also connected to the bus 804.

[0124] The following components are connected to the I / O interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed so that a computer program read therefrom is installed into the storage section 808 as needed.

[0125] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 809, and / or installed from a removable medium 811. When the computer program is executed by a central processing unit (CPU) 801, various functions defined in the system of the present application are executed.

[0126] It should be noted that the computer-readable medium shown in the embodiment of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. This propagated data signal can take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0127] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0128] The units involved in the embodiments described in the present application can be implemented by software or by hardware, and the units described can also be set in a processor. Among them, the names of these units do not constitute a limitation on the units themselves under certain circumstances. Therefore, the technical solution according to the implementation mode of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including a number of instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute a method according to the implementation mode of the present application.

[0129] Another aspect of the present application further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor of a computer, the computer executes the charging and dispatching method for logistics vehicles provided in the above embodiments. The computer-readable storage medium may be included in the electronic device described in the above embodiments, or may exist independently without being assembled into the electronic device.

[0130] In the above embodiments, unless otherwise specified, by using serial numbers such as "first" and "second" to describe common objects, it only means that they refer to different instances of the same object, rather than indicating that the objects being described must adopt a given order, whether in time, space, sorting or any other way.

[0131] The above embodiments are merely illustrative of the principles and effects of the present application, and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A charging deployment method for a logistics vehicle, It is characterized in that The method comprises: Obtaining system constraint information of the distribution charging system and the current power of multiple new energy logistics vehicles, wherein the system constraint information includes node structure information, usage cost information, and distribution task information; Determine the average charging queue waiting time of the new energy logistics vehicles based on the system constraint information and the current power of each, and update the randomly generated multiple initial individuals based on the delivery task information, the average charging queue waiting time and the current power of each, the initial individuals are used to represent the initial node access information of multiple new energy logistics vehicles; The initial fitness function value corresponding to each of the initial individuals is determined according to the system constraint information, the current power and multiple preset cost relationships, and each of the initial individuals is iteratively updated according to the initial fitness function value to obtain the target optimal individual, so as to perform charging deployment for each of the new energy logistics vehicles according to the target optimal individual.

2. The charging and deployment method for logistics vehicles according to claim 1, It is characterized in that The randomly generated multiple initial individuals are updated based on the delivery task information, the average charging queue waiting time and the current power of each, including: Randomly generate multiple initial individuals; If at least one of the average charging queue waiting time is greater than the preset waiting threshold, the mass of the goods to be delivered at the delivery task point is greater than the preset vehicle loading threshold, and the current power is less than the preset power threshold, then a plurality of initial individuals are randomly generated again; The delivery task information includes the quality of the goods to be delivered at the delivery task point.

3. The charging and deployment method for logistics vehicles according to claim 1, It is characterized in that Iteratively updating each of the initial individuals according to each of the initial fitness function values ​​to obtain a target optimal individual includes: The initial individual corresponding to the maximum initial fitness function value is determined as the current optimal individual; If the preset iteration condition of the iterative update is met, the current optimal individual is determined as the target optimal individual; If the preset iteration conditions for iterative update are not met, the iterative update of each of the initial individuals according to the sorting of each of the initial fitness function values ​​is repeated to obtain multiple iterative individuals, the iterative fitness function value corresponding to each of the iterative individuals is determined according to the system constraint information, the current power and multiple preset cost relationships, the current optimal individual is updated based on the maximum iterative fitness function value, each of the iterative individuals is used as the initial individual, and each of the iterative fitness function values ​​is used as the initial fitness function value, until the preset iteration conditions for iterative update are met and the current optimal individual is determined as the target optimal individual.

4. The charging deployment method for a logistics vehicle according to claim 3, It is characterized in that Iteratively updating each of the initial individuals according to the order of the initial fitness function values ​​to obtain a plurality of iterative individuals, including: Dividing each of the initial individuals into three parts according to a preset allocation ratio and the ranking of each of the initial fitness function values ​​to obtain a plurality of elite individuals, a plurality of inferior individuals and a plurality of ordinary individuals; Performing random mutation on each of the inferior individuals to obtain a plurality of mutant individuals; Performing roulette selection on each of the ordinary individuals to obtain a plurality of selected individuals; All elite individuals, all mutant individuals and all selected individuals are determined as multiple iterative individuals.

5. The charging and deployment method for a logistics vehicle according to any one of claims 1 to 4, It is characterized in that Determining the initial fitness function value corresponding to each of the initial individuals according to the system constraint information, each of the current power quantities and a plurality of preset cost relationships includes: Determine multiple initial cost values ​​corresponding to each initial individual according to the system constraint information, the current power and multiple preset cost relationships; The sum of all initial cost values ​​corresponding to each initial individual is determined as the cost of each initial individual; The inverse of the cost of each initial individual is determined as the initial fitness function value of each initial individual.

6. The charging and deployment method for logistics vehicles according to claim 5, It is characterized in that Determining a plurality of initial cost values ​​corresponding to the initial individuals according to the system constraint information, each of the current power quantities and a plurality of preset cost relationships includes: If the preset cost relationship is a charging station construction cost relationship, determining a first cost value according to all selected charging stations and all initial charging station construction costs and the charging station construction cost relationship; If the preset cost relationship is a vehicle use cost relationship, determining a second cost value according to the initial vehicle fixed use cost, the distribution center-node access information and the vehicle use cost relationship; If the preset cost relationship is a distribution transportation cost relationship, determining a third cost value according to the unit distance transportation cost, the node-node access information, the inter-node distance information of the node-node access information, and the distribution transportation cost relationship; If the preset cost relationship is a vehicle charging cost relationship, a fourth cost value is determined according to the unit electricity price, the charging amount information of all selected charging stations and the vehicle charging cost relationship, wherein the charging amount information is obtained based on each of the current electric quantities; If the preset cost relationship is an environmental cost relationship, a fifth cost value is determined according to the unit carbon emission cost, the electric energy conversion coefficient, the vehicle power consumption coefficient, the thermal power generation ratio and the environmental cost relationship; If the preset cost relationship is a time window penalty cost relationship, a sixth cost value is determined according to the expected earliest arrival time information of the delivery point, the expected latest arrival time information of the delivery point, the actual arrival time information and the time window penalty cost relationship, wherein the actual arrival time information is obtained based on the distance information between the initial individual and the node; Among them, the initial cost value includes at least one of the first cost value, the second cost value, the third cost value, the fourth cost value, the fifth cost value and the sixth cost value; the node structure information includes the distance information between the nodes; the usage cost information includes the initial charging construction cost, the initial vehicle fixed usage cost, the unit electricity price, the unit carbon emission cost, the electric energy conversion coefficient, the vehicle power consumption coefficient, and at least one of the thermal power generation ratio; the distribution task information includes the earliest expected arrival time information of the distribution point and the latest expected arrival time information of the distribution point; the initial individual includes at least one of the selected charging station, the distribution center-node access information and the node-node access information.

7. The charging and deployment method for a logistics vehicle according to any one of claims 1 to 4, It is characterized in that The method for determining the average charging queue waiting time includes: Among them, T is the average waiting time for charging queue, μ is the arrival rate of the new energy logistics vehicle, k 0 is the probability that the charging station is idle, is the charging service rate of the charging station, and b is the number of charging piles in the charging station.

8. A charging and dispatching device for a logistics vehicle, It is characterized in that The device comprises: An acquisition module is used to acquire system constraint information of the distribution charging system and the current power of multiple new energy logistics vehicles, wherein the system constraint information includes node structure information, usage cost information and distribution task information; An individual generation module, used to determine the average charging queue waiting time of the new energy logistics vehicles based on the system constraint information and the current power of each, and update a plurality of randomly generated initial individuals based on the delivery task information, the average charging queue waiting time and the current power of each, the initial individuals are used to characterize the initial node access information of the plurality of new energy logistics vehicles; The individual optimization module is used to determine the initial fitness function value corresponding to each of the initial individuals according to the system constraint information, the current power and multiple preset cost relationships, and iteratively update each of the initial individuals according to the initial fitness function values ​​to obtain the target optimal individual, so as to allocate charging for each of the new energy logistics vehicles according to the target optimal individual.

9. An electronic device, It is characterized in that The electronic device comprises: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the electronic device to implement the charging and dispatching method for a logistics vehicle as described in any one of claims 1 to 7.

10. A computer-readable storage medium, It is characterized in that A computer program is stored thereon, and when the computer program is executed by a processor of a computer, the computer is enabled to execute the charging and deployment method for a logistics vehicle according to any one of claims 1 to 7.

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