WO2023165080A1

WO2023165080A1 - Method and apparatus for scheduling automated vehicles, and electronic device and readable medium

Info

Publication number: WO2023165080A1
Application number: PCT/CN2022/109803
Authority: WO
Inventors: 秦恒乐
Original assignee: 北京京东振世信息技术有限公司
Priority date: 2022-03-01
Filing date: 2022-08-02
Publication date: 2023-09-07
Also published as: CN114565166A

Abstract

Provided in the present disclosure are a method and apparatus for scheduling automated vehicles, and an electronic device and a readable medium. The method for scheduling automated vehicles comprises: determining state records, power usage records, quantity records and battery attenuation records of automated vehicles; constructing a model constraint condition according to the state records, the power usage records, the quantity records and the battery attenuation records; according to the model constraint condition, finding the shortage of automated vehicles within each transportation time period; and performing scheduling processing on the automated vehicles according to the shortage of automated vehicles within each transportation time period, wherein the scheduling processing comprises controlling the automated vehicles to be charged, or controlling the automated vehicles to perform transportation, or controlling the automated vehicles to be vacant. By means of the embodiments of the present disclosure, the accuracy and reliability of scheduling automated vehicles are improved, thereby improving the reliability and efficiency of goods transportation in a warehouse.

Description

Automated vehicle scheduling method, device, electronic equipment and readable medium

This disclosure claims the priority of the Chinese patent application with the application number 202210206499.6 and titled "Scheduling method, device, electronic equipment and readable medium for automated vehicles" filed on March 1, 2022, the entire content of the Chinese patent application Incorporated herein by reference in its entirety.

technical field

The present disclosure relates to the technical field of resource scheduling, and in particular, to a scheduling method, device, electronic equipment, and readable medium for automated vehicles.

Background technique

At present, e-commerce companies (such as JD.com and Amazon) often use AGV (Automated Guided Vehicle, automatic navigation driving) cars to build unmanned warehouses for production. The kinetic energy source of this automated vehicle is the battery, and the battery needs to be charged in time to replenish the power.

In related technologies, due to the limited number of automated vehicles and the limited number of charging pile resources, when scheduling AGV charging, the operating efficiency of the system will inevitably decrease.

However, due to the goal orientation of warehouse production, the number of automated vehicles to meet each generation wave exceeds the demand. As long as automated vehicles work in the warehouse site, it will bring power consumption, so due to unreasonable Scheduling and scheduling will lead to a waste of power and also lead to excessive charging frequency.

It should be noted that the information disclosed in the above background section is only for enhancing the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to those of ordinary skill in the art.

Contents of the invention

The purpose of the present disclosure is to provide an automated vehicle scheduling method, device, electronic device and readable medium, which are used to overcome the problem of unreasonable automated vehicle scheduling caused by limitations and defects of related technologies at least to a certain extent.

According to the first aspect of an embodiment of the present disclosure, there is provided a scheduling method for an automated vehicle, including: determining the state record, power usage record, quantity record, and battery decay record of the automated vehicle; according to the state record, the power usage record , the number records and the battery decay records construct model constraints; solve the shortage of automated vehicles in each transportation period according to the model constraints; The automated vehicle performs scheduling processing, and the scheduling process includes controlling the automated vehicle to charge, or controlling the automated vehicle to perform transportation work, or controlling the automated vehicle to be idle.

In an exemplary embodiment of the present disclosure, constructing model constraint conditions according to the state record, the power usage record, the quantity record and the battery decay record includes: determining the tth The charging state record, working state record and idle state record of the i-th automated vehicle in the transport period; determine the sum of the charge state record, working state record and idle state record of the i-th automated vehicle in the t-th transport period as A fixed value, and recorded as the first constraint condition, the expression of the first constraint condition includes: x _it +y _it + z _it =1, wherein, the x _it represents the number of automated vehicles recorded in the state of charge, The y _it represents the number of automated vehicles recorded in the working state, and the z _it represents the number of automated vehicles recorded in the idle state.

In an exemplary embodiment of the present disclosure, constructing model constraint conditions according to the state record, the power usage record, the quantity record and the battery decay record further includes: determining the t-1th transport The number of automated vehicles that are in short supply during the period, and recorded as L _t-1 ; determine the number of automated vehicles that need to work in the tth transportation period, and record as N _t ; determine the shortage of automated vehicles in the tth transportation period The number of automated vehicles, and recorded as L _t , said L _t ≥ 0; according to the number of automated vehicles that are in short supply in the t-1th transportation period, the number of automated vehicles that need to work in the t-th transportation period , the number of automated vehicles in short supply within the tth transport period and the number of automated vehicles recorded in the working state construct a second constraint condition, the expression of the second constraint condition includes: L _t =L _t-1 +N _t -∑ _i y _it .

In an exemplary embodiment of the present disclosure, constructing model constraint conditions according to the state record, the power usage record, the quantity record and the battery decay record further includes: determining the charging time for the automated vehicle The total number of charging piles, and denoted as S; according to the size relationship between the number of automated vehicles recorded in the charging state and the total number of charging piles, the third constraint condition is constructed, and the expression of the third constraint condition includes: ∑ _i x _it ≤ S.

In an exemplary embodiment of the present disclosure, constructing model constraint conditions according to the state record, the power usage record, the quantity record and the battery decay record further includes: determining the t-th transportation period The electricity quantity at the end of the i-th automated vehicle, and denoted as q _it , said q _it ≥ 0; determine the electricity quantity at the end of the i-th automated vehicle in the t-1th transportation period, and denoted as q _{it- 1} ; determine the charging power of the tth transport period, and record the charging power as R _it ; determine the power consumption of the i automated vehicle work in the t transport period, and record the charging power Denoted as y _it ×W _it , the characterizes the electricity consumption W _it of the i-th automated vehicle in the t-th transportation period; The power consumption at the end of the i-th automated vehicle in the t-1th transportation period, the charging power in the t-th transportation period and the power consumption of the i-th automated vehicle in the t-th transportation period construct the fourth constraint condition, the fourth constraint condition includes: q _it =q _it-1 +R _it −y _it ×W _it .

In an exemplary embodiment of the present disclosure, constructing model constraint conditions according to the state record, the power usage record, the quantity record and the battery decay record further includes: determining the t-th transportation period automatically The corresponding relationship between the charging amount of the vehicle and time; determine the upper limit of the electric quantity of the automated vehicle, denoted as Q; according to the corresponding relationship, the upper limit of the electric quantity of the automated vehicle, the i-th automated The charging power of the vehicle and the power of the i-th automated vehicle at the end of the t-1th transportation period constitute a fifth constraint condition, and the fifth constraint condition includes: 0≤R _it ≤Qq _it-1 .

In an exemplary embodiment of the present disclosure, solving the shortage of automated vehicles in each transportation period according to the model constraints includes: under the model constraints, using the Monte Carlo algorithm to calculate the shortage in each transportation period The number of automated vehicles is subjected to multiple sampling calculations; the minimum value of the number of shortage automated vehicles in each transportation period is determined according to the results of the sampling calculations.

According to the second aspect of the embodiments of the present disclosure, there is provided a scheduling device for automated vehicles, including: a determination module for determining the state record, power usage record, quantity record and battery decay record of the automated vehicle; The state records, the power usage records, the quantity records and the battery decay records construct model constraints; a solution module is used to solve the shortage of automated vehicles in each transportation period according to the model constraints; scheduling A module, configured to schedule the automated vehicles according to the number of shortage automated vehicles in each transportation period, the scheduled processing includes controlling the automated vehicles to charge, or controlling the automated vehicles to carry out transportation work, or The automated vehicle is controlled to idle.

According to a third aspect of the present disclosure, there is provided an electronic device, including: a memory; and a processor coupled to the memory, the processor is configured to execute any one of the above-mentioned operations based on instructions stored in the memory. method described in the item.

According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, the automatic vehicle scheduling method as described in any one of the above is implemented.

In the embodiment of the present disclosure, by determining the state record, power usage record, quantity record and battery decay record of the automated vehicle, and constructing constraint conditions, the optimization model of the automated vehicle is solved based on the constraint conditions to determine the shortage of automated vehicles in each transportation period. The minimum number of vehicles and the optimized and determined scheduling plan make the scheduling plan of the automated vehicles in the warehouse more optimized, which not only reduces the charging amount, but also reduces the charging frequency.

Furthermore, the optimization model of the present disclosure also fits the corresponding relationship between charging power and time, instead of simply linearizing the charging power, which improves the accuracy and reliability of the optimization model, thereby improving the judgment of the remaining power of the automated vehicle The reliability and accuracy of the system further improves the reliability and timeliness of automated vehicle dispatching.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

Description of drawings

Fig. 1 shows a flow chart of a dispatching method of an automated vehicle in an exemplary embodiment of the present disclosure;

FIG. 2 shows a flow chart of a dispatching method of an automated vehicle in another exemplary embodiment of the present disclosure;

FIG. 3 shows a flowchart of a dispatching method of an automated vehicle in another exemplary embodiment of the present disclosure;

FIG. 4 shows a flowchart of a dispatching method of an automated vehicle in another exemplary embodiment of the present disclosure;

Fig. 5 shows a flow chart of a dispatching method of an automated vehicle in another exemplary embodiment of the present disclosure;

Fig. 6 shows a flow chart of a dispatching method of an automated vehicle in another exemplary embodiment of the present disclosure;

Fig. 7 shows a flow chart of a dispatching method of an automated vehicle in another exemplary embodiment of the present disclosure;

Fig. 8 shows a flow chart of a dispatching method of an automated vehicle in another exemplary embodiment of the present disclosure;

Fig. 9 shows a block diagram of a dispatching device for an automated vehicle in an exemplary embodiment of the present disclosure;

FIG. 10 shows a block diagram of an electronic device in an exemplary embodiment of the present disclosure.

Detailed ways

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided in order to give a thorough understanding of embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details being omitted, or other methods, components, devices, steps, etc. may be adopted. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.

In addition, the drawings are only schematic illustrations of the present disclosure, the same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and/or processor means and/or microcontroller means.

Exemplary implementations of the present disclosure will be described in detail below in conjunction with the accompanying drawings.

FIG. 1 is a flow chart of a scheduling method for automated vehicles in an exemplary embodiment of the present disclosure.

Referring to Fig. 1, the scheduling method of automated vehicles may include:

Step S102, determining the state record, power usage record, quantity record and battery decay record of the automated vehicle.

In an exemplary embodiment of the present disclosure, the state records include records of various states of the automated vehicle of a specified number.

In an exemplary embodiment of the present disclosure, if the i-th automated vehicle is in the charging state, the charge state record of the i-th automated vehicle is 1, and if the i-th automated vehicle is in a non-charging state, the i-th The state of charge of the automated vehicle is recorded as 0.

In an exemplary embodiment of the present disclosure, if the i-th automated vehicle is in the working state, the record of the i-th automated vehicle's working state is 1; if the i-th automated vehicle is in the non-working state, the ith The working status of the automated vehicle is recorded as 0.

In an exemplary embodiment of the present disclosure, if the i-th automated vehicle is in an idle state, the record of the i-th automated vehicle's idle state is 1; if the i-th automated vehicle is in a non-idle state, the ith The idle state of the automated vehicle is recorded as 0.

In an exemplary embodiment of the present disclosure, the power usage record includes a record of changes in power of the automated vehicle over time.

In an exemplary embodiment of the present disclosure, the quantity record includes a record of the quantity of the automated vehicle in various states.

In an exemplary embodiment of the present disclosure, the battery decay record includes a record of the full charge of a battery of the automated vehicle over time.

Step S104, constructing model constraint conditions according to the state record, the power usage record, the quantity record and the battery decay record.

In an exemplary embodiment of the present disclosure, a model constraint condition is constructed through the state record, the power usage record, the quantity record and the battery decay record, and the scheduling model of the automated vehicle is optimized based on the model constraint condition .

Step S106, solving the shortage of automated vehicles in each transportation period according to the model constraints.

In an exemplary embodiment of the present disclosure, the transportation period includes each wave period in the warehouse, and the scheduling scheme of the automated vehicles is also executed within the wave period.

Step S108, according to the number of shortage automated vehicles in each transportation period, the scheduling process is performed on the automated vehicles, the scheduling process includes controlling the automated vehicles to charge, or controlling the automated vehicles to carry out transportation work, or controlling The automated vehicle is idle.

In an exemplary embodiment of the present disclosure, the automated vehicle may be an AGV for transportation or an autonomous vehicle, but is not limited thereto.

The steps of the automatic vehicle scheduling method will be described in detail below with reference to FIG. 2 to FIG. 8 .

As shown in Figure 2, constructing model constraints according to the state record, the power usage record, the quantity record and the battery decay record includes:

Step S202, determining the charging state record, working state record and idle state record of the i-th automated vehicle in the t-th transportation period included in the state record.

Step S204, determine that the sum of the number of charging state records, working state records and idle state records of the i-th automated vehicle in the t-th transportation period is a fixed value, and recorded as the first constraint condition, the first constraint condition Expressions include:

x _it +y _it +z _it =1

Wherein, the x _it represents the number of automated vehicles recorded in the state of charge, the y _it represents the number of automated vehicles recorded in the working state, and the z _it represents the number of automated vehicles recorded in the idle state.

In an exemplary embodiment of the present disclosure, the number of automated vehicles in each state is constrained by a first constraint condition, which is used as a condition for solving the required number of automated vehicles for each transportation period.

As shown in FIG. 3 , constructing model constraints according to the state record, the power usage record, the quantity record and the battery decay record also includes:

Step S302, determining the number of shortage automated vehicles in the t-1th transportation period, and denoting it as L _t-1 .

Step S304, determining the number of automated vehicles that need to work in the t-th transportation period, and denoting it as N _t .

Step S306, determining the number of shortage automated vehicles in the t-th transportation period, and denoting it as L _t , where L _t ≥ 0.

Step S308, according to the number of shortage of automated vehicles in the t-1th transportation period, the number of automated vehicles that need to work in the tth transportation period, and the number of shortage of automated vehicles in the tth transportation period and the number of automated vehicles recorded in the working state to construct a second constraint condition, the expression of the second constraint condition includes: L _t =L _t-1 +N _t -∑ _i y _it .

In an exemplary embodiment of the present disclosure, the number of automated vehicles that are in short supply in the t-1th transportation period, the number of automated vehicles that need to work in the tth transportation period, the tth The number of automated vehicles that are in short supply within the transport period and the number of automated vehicles recorded in the working state constitute a second constraint condition,

As shown in Figure 4, constructing model constraints according to the state record, the power usage record, the quantity record and the battery decay record also includes:

Step S402, determine the total number of charging piles for charging the automated vehicle, and denote it as S.

Step S404, constructing a third constraint condition according to the size relationship between the number of automated vehicles recorded in the state of charge and the total number of charging piles, the expression of the third constraint condition includes: Σ _i x _it ≤ S.

In an exemplary embodiment of the present disclosure, the third constraint condition is constructed by using the size relationship between the number of automated vehicles recorded in the state of charge and the total number of charging piles, which limits the maximum value of schedulable automated vehicles .

As shown in Figure 5, the constraining conditions for constructing a model according to the state record, the power usage record, the quantity record and the battery decay record also include:

Step S502, determine the electricity quantity of the i-th automated vehicle at the end of the t-th transportation period, and denote it as q _it , where q _it ≥0.

Step S504, determine the electricity consumption of the i-th automated vehicle at the end time of the t-1-th transportation period, and denote it as q _it-1 .

Step S506, determining the charging quantity for the t-th transportation period, and denoting the charging quantity as R _it .

Step S508, determine the power consumption of the i-th automated vehicle in the t-th transportation period, and record the charging power as y _it ×W _it , which represents the i-th automated vehicle in the t-th transportation period Power consumed by work W _it .

Step S510, according to the electricity quantity at the end time of the i-th automated vehicle in the t-th transportation period, the electricity quantity at the end time of the i-th automated vehicle in the t-1-th transportation period, and the electricity consumption in the t-th transportation period The charging power and the power consumption of the i-th automated vehicle in the t-th transportation period construct a fourth constraint condition, and the fourth constraint condition includes: q _it =q _it-1 +R _it -y _it ×W _it .

In an exemplary embodiment of the present disclosure, the power consumption at the end time of the i-th automated vehicle in the t-th transportation period, the power consumption at the end time of the i-th automated vehicle in the t-1-th transportation period , the charging power of the t-th transportation period and the power consumption of the i-th automated vehicle in the t-th transportation period construct the fourth constraint condition, which not only determines the power usage characteristics of the automated vehicle in each transportation period, The charging characteristics of the automated vehicle during each transport period are also determined, and the charging demand of the automated vehicle is determined based on the fourth constraint of power usage and charging.

As shown in Figure 6, according to described state record, described electric power usage record, described quantity record and described battery decay record construction model constraint condition also includes:

Step S602, determining the corresponding relationship between the charging amount and time of the automated vehicle in the t-th transportation period.

Step S604, determining the upper limit of the electric quantity of the automated vehicle, denoted as Q.

Step S606, according to the corresponding relationship, the upper limit of the power of the automated vehicle, the charging power of the i-th automated vehicle in the t-th transportation period, and the end of the i-th automated vehicle in the t-1-th transportation period A fifth constraint condition is constructed for the electric quantity at time, and the fifth constraint condition includes: 0≤R _it ≤Qq _it-1 .

In an exemplary embodiment of the present disclosure, by determining the corresponding relationship between the charging amount of the automated vehicle and time during the t-th transportation period, the relationship between the charging amount of the automated vehicle and time is not simply calculated as Determined as a linear relationship, by fitting the corresponding relationship between the charging amount and time, the charging characteristics of each automated vehicle can be more accurately determined. The charging characteristics can include charging quantity, charging duration and charging frequency, etc., but are not limited thereto.

As shown in Figure 7, solving the shortage of automated vehicles in each transportation period according to the model constraints includes:

Step S702, under the constraints of the model, the Monte Carlo algorithm is used to perform multiple sampling calculations on the number of shortage automated vehicles in each transportation period.

Step S704, according to the result of the sampling calculation, determine the minimum value of the number of shortage automated vehicles in each transportation period.

In an exemplary embodiment of the present disclosure, the Monte-Carlo algorithm generally refers to a class of algorithms. In these algorithms, the problem to be solved is the probability of a random event or the expectation of a random variable. At this time, by "experiment "method, using frequency instead of probability or obtaining some digital characteristics of random variables as a solution to the problem.

Corresponding to the above method embodiments, the present disclosure further provides an automated vehicle scheduling device, which can be used to execute the above method embodiments.

As shown in FIG. 8 , when an automated vehicle scheduling method in an exemplary embodiment of the present disclosure is applied to an AGV trolley, it includes the following steps:

Step S802, observe the business status in the library.

In an exemplary embodiment of the present disclosure, by observing the business status in the warehouse, it is determined whether the transportation period in the warehouse is busy, if it is busy, then start the scheduling plan of the AGV trolley, if it is idle, Then the scheduling scheme of the AGV trolley may not be started to reduce the scheduling cost.

Step S804, determining the optimization target of the AGV.

In an exemplary embodiment of the present disclosure, the expression of the optimization target of the AGV trolley includes: minimize(∑ _t L _t + μ∑ _{i, ty} _it ), wherein, μ>0, μ is expressed as the sum of charging times The weight of , which can be set to 0.001, makes the optimization result of the objective function focus on the number of AGVs that are in short supply.

Step S806, setting variables and parameters required for building the model.

In an exemplary embodiment of the present disclosure, in addition to μ, the variables and parameters required to construct the model also include:

(1) i∈{1,...,N}: the subscript of the AGV car, the upper limit is N;

(2) t∈{1,...,T}: period subscript, upper line is T;

(3) Q: AGV car power limit;

(4) S: the number of charging piles, which is a fixed value;

(5) I: the duration of the period, which is a fixed value;

(6) N _t : the number of AGVs that need to work in the tth time period;

(7)W _it ～U(L _i , U _i ): In the tth period, the power consumed by the i-th AGV car participating in the work obeys the uniform distribution with parameters L _i and U _i ;

(8) C: The charging value (charging rate) of the AGV car per unit time;

(9) x _it ∈ {0, 1}: In the tth period, whether the i-th AGV is charging, 1 means charging, 0 means not charging;

(10) y _it ∈ {0, 1}: In the tth period, whether the i-th AGV car is working, 1 is working, 0 is not working;

(11) z _it ∈ {0, 1}: In the tth period, whether the i-th AGV is idle, 1 means idle, 0 means not idle;

(12) q _it ≥ 0: In the tth period, the final electricity quantity of the i-th AGV car; q _i0 represents the initial electricity quantity, which is a fixed value;

(13) R _it ≥ 0: In the tth period, the charging power of the i-th AGV car;

(14) L _t ≥ 0: In the tth period, the number of AGVs that the system is short of.

Step S808, constructing or optimizing a model according to constraint conditions.

In an exemplary embodiment of the present disclosure, the constraints include:

Constraint condition (1): In each transportation period, the state of the AGV car is one of charging, working, and idle, recorded as

Constraint condition (2): In each transportation period, the shortage of AGV cars = the shortage of AGV cars in the previous period + the number of AGV cars required in the current period - the number of AGV cars scheduled for work in the current period, recorded as

Constraint condition (3): In each transportation period, the number of AGVs that can be safely charged must be less than or equal to the number of charging piles, denoted as

Constraint condition (4): In each transportation period, the end-of-period power of each AGV car = the expectation of the end-of-period power of the previous period + the current charging power - the current working power consumption, recorded as

Constraint conditions (5) and constraint conditions (6): In each transportation period, the charging amount of the AGV car has a linear relationship with the length of the period, and the upper limit of the amount of electricity is Q, and the charging amount must be greater than or equal to 0, recorded as

0≤R _it ≤x _it ×C×I;

Constraint condition (7): In each transportation period, the end-of-period power expectation of each AGV car must be greater than or equal to 0, denoted as

Constraint condition (8): In each transportation period, the shortage of AGV cars is greater than or equal to 0, recorded as

Constraint condition (9): In each transportation period t, the power consumed by the i-th AGV car participating in the work, obeys the uniform distribution with parameters L _i and U _i , denoted as

Constraint condition (10): In each transportation period, the 0-1 variable representing the charging, working and idle of the AGV car is denoted as

Step S810, determining the solution method of the model.

Step S812, put the model into the actual application scene.

Step S814, scheduling the AGVs according to the optimization results of the model.

Step S816, judging whether the effect of dispatching the AGV car is significant, if yes, then end, if not, then execute step S808.

Fig. 9 is a block diagram of a scheduling device for automated vehicles in an exemplary embodiment of the present disclosure.

Referring to FIG. 9, the dispatching device 900 of an automated vehicle may include:

The determination module 902 is configured to determine the status record, power usage record, quantity record and battery decay record of the automated vehicle.

The construction module 904 is configured to construct a model constraint condition according to the state record, the power usage record, the quantity record and the battery decay record.

The solving module 906 is configured to solve the shortage of automated vehicles in each transportation period according to the model constraints.

Scheduling module 908, configured to perform scheduling processing on the automated vehicles according to the number of shortage automated vehicles in each transportation period, the scheduling processing includes controlling the automated vehicles to charge, or controlling the automated vehicles to carry out transportation work , or control the automated vehicle to idle.

In an exemplary embodiment of the present disclosure, the construction module 904 is further configured to: determine the charging state record, working state record and idle state record of the i-th automated vehicle in the t-th transportation period included in the state record ; Determining the sum of the charge state record, working state record and idle state record of the i-th automated vehicle in the t-th transportation period is a fixed value, and is recorded as the first constraint condition, the expression of the first constraint condition Including: x _it +y _it + z _it = 1 wherein, the x _it represents the number of automated vehicles recorded in the charging state, the y _it represents the number of automated vehicles recorded in the working state, and z _it represents the number of automated vehicles recorded in the idle state number of automated vehicles.

In an exemplary embodiment of the present disclosure, the construction module 904 is also used to: determine the number of short-lived automated vehicles in the t-1th transportation period, and denote it as L _t-1 ; determine the t-th The number of automated vehicles that need to work in the first transportation period, and recorded as N _t ; determine the number of automated vehicles that are in short supply in the tth transportation period, and recorded as L _t , and the L _t ≥ 0; according to the tth transportation period The number of automated vehicles that are in short supply within the t-1 transportation period, the number of automated vehicles that need to work in the t-th transportation period, the number of automated vehicles that are in short supply within the t-th transportation period, and the working status records The number of automated vehicles constitutes a second constraint, the expression of which includes: L _t =L _t-1 +N _t -Σ _i y _it .

In an exemplary embodiment of the present disclosure, the construction module 904 is further configured to: determine the total number of charging piles for charging the automated vehicle, and denote it as S; the number of automated vehicles recorded according to the charging state is related to the The size relationship between the total number of charging piles is used to construct the third constraint condition, and the expression of the third constraint condition includes: ∑ _i x _it ≤ S.

In an exemplary embodiment of the present disclosure, the construction module 904 is further configured to: determine the power consumption of the i-th automated vehicle at the end of the t-th transportation period, and denote it as q _it , where q _it ≥0 ; Determine the electricity at the end of the i-th automated vehicle in the t-1 transport period, and denote it as q _it-1 ; determine the charging power in the t-th transport period, and record the charging power as R _it ; Determine the power consumption of the i-th automated vehicle in the t-th transportation period, and record the charging power as y _it ×W _it , which represents the i-th automated vehicle in the t-th transportation period Work consumption power W _it ; according to the electricity consumption at the end of the i-th automated vehicle in the t-th transportation period, the electricity at the end of the i-th automated vehicle in the t-1-th transportation period, the t-th The charging power of the transportation period and the power consumption of the i-th automated vehicle in the t-th transportation period construct the fourth constraint condition, and the fourth constraint condition includes: q _it =q _it-1 +R _it -y _it ×W _it .

In an exemplary embodiment of the present disclosure, the construction module 904 is further configured to: determine the corresponding relationship between the charging amount of the automated vehicle in the t-th transportation period and time; determine the upper limit of the electrical capacity of the automated vehicle, record Make Q; according to the corresponding relationship, the upper limit of the electric quantity of the automated vehicle, the charging quantity of the i-th automated vehicle in the t-th transportation period and the end of the i-th automated vehicle in the t-1-th transportation period A fifth constraint condition is constructed for the electric quantity at time, and the fifth constraint condition includes: 0≤R _it ≤Qq _it-1 .

In an exemplary embodiment of the present disclosure, the solution module 906 is also used to: under the model constraints, use the Monte Carlo algorithm to perform multiple sampling calculations on the number of shortage automated vehicles in each transportation period; The minimum value of the number of shortage automated vehicles in each transportation period is determined based on the results of the sampling calculation.

Since each function of the apparatus 900 has been described in detail in its corresponding method embodiment, the present disclosure will not repeat them here.

It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. Actually, according to the embodiment of the present disclosure, the features and functions of two or more modules or units described above may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided to be embodied by a plurality of modules or units.

In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

Those skilled in the art can understand that various aspects of the present invention can be implemented as systems, methods or program products. Therefore, various aspects of the present invention can be embodied in the following forms, that is: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which can be collectively referred to herein as "circuit", "module" or "system".

An electronic device 1000 according to this embodiment of the present invention is described below with reference to FIG. 10 . The electronic device 1000 shown in FIG. 10 is only an example, and should not limit the functions and scope of use of this embodiment of the present invention.

As shown in FIG. 10, electronic device 1000 takes the form of a general-purpose computing device. Components of the electronic device 1000 may include but not limited to: at least one processing unit 1010 mentioned above, at least one storage unit 1020 mentioned above, and a bus 1030 connecting different system components (including the storage unit 1020 and the processing unit 1010 ).

Wherein, the storage unit stores program codes, and the program codes can be executed by the processing unit 1010, so that the processing unit 1010 executes various exemplary methods according to the present invention described in the "Exemplary Methods" section of this specification. Implementation steps. For example, the processing unit 1010 may execute the method shown in the embodiment of the present disclosure.

The storage unit 1020 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 10201 and/or a cache storage unit 10202 , and may further include a read-only storage unit (ROM) 10203 .

The storage unit 1020 may also include a program/utility 10204 having a set (at least one) of program modules 10205, such program modules 10205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, Implementations of networked environments may be included in each or some combination of these examples.

Bus 1030 may represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local area using any of a variety of bus structures. bus.

The electronic device 1000 can also communicate with one or more external devices 1040 (such as keyboards, pointing devices, Bluetooth devices, etc.), and can also communicate with one or more devices that enable the user to interact with the electronic device 1000, and/or communicate with Any device (eg, router, modem, etc.) that enables the electronic device 1000 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interface 1050 . Moreover, the electronic device 1000 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and/or a public network such as the Internet) through the network adapter 1060 . As shown, the network adapter 1060 communicates with other modules of the electronic device 1000 through the bus 1030 . It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with electronic device 1000, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives And data backup storage system, etc.

Through the description of the above implementations, those skilled in the art can easily understand that the example implementations described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of software products, and the software products can be stored in a non-volatile storage medium (which can be CD-ROM, U disk, mobile hard disk, etc.) or on the network , including several instructions to make a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) execute the method according to the embodiments of the present disclosure.

In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium on which a program product capable of implementing the above-mentioned method in this specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes program code, and when the program product is run on a terminal device, the program code is used to make the The terminal device executes the steps according to various exemplary embodiments of the present invention described in the "Exemplary Method" section above in this specification.

The program product for implementing the above method according to the embodiment of the present invention may adopt a portable compact disk read-only memory (CD-ROM) and include program codes, and may run on a terminal device such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program, and the program may be used by or in combination with an instruction execution system, apparatus or device.

The program product may reside on any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: electrical connection with one or more conductors, portable disk, hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

A computer readable signal medium may include a data signal carrying readable program code in baseband or as part of a carrier wave. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages—such as Java, C++, etc., as well as conventional procedural programming languages. Programming language - such as "C" or a similar programming language. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server to execute. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (for example, using an Internet service provider). business to connect via the Internet).

In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above figures do not imply or limit the chronological order of these processes. In addition, it is also easy to understand that these processes may be executed synchronously or asynchronously in multiple modules, for example.

Other embodiments of the present disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, and these modifications, uses or adaptations follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not disclosed in the present disclosure . The specification and examples are to be considered exemplary only, with the true scope and concept of the disclosure indicated by the appended claims.

Industrial Applicability

By determining the state record, power usage record, quantity record and battery decay record of the automated vehicle, and constructing constraints, the optimization model of the automated vehicle is solved based on the constraint conditions to determine the minimum number of shortage automated vehicles in each transportation period, As well as optimizing and determining the dispatching plan, the dispatching plan of the automated vehicles in the warehouse is more optimized, which not only reduces the charging amount, but also reduces the charging frequency. Furthermore, the optimization model of the present disclosure also fits the corresponding relationship between charging power and time, instead of simply linearizing the charging power, which improves the accuracy and reliability of the optimization model, thereby improving the judgment of the remaining power of the automated vehicle The reliability and accuracy of the system further improves the reliability and timeliness of automated vehicle dispatching.

Claims

A scheduling method for automated vehicles, characterized in that it comprises:

Determine the state record, power usage record, quantity record and battery decay record of the automated vehicle;

Constructing model constraint conditions according to the state record, the power usage record, the quantity record and the battery decay record;

Solve the number of shortage automated vehicles in each transportation period according to the model constraints;

According to the number of shortage automated vehicles in each transportation period, the automated vehicles are scheduled, and the scheduled processing includes controlling the automated vehicles to charge, or controlling the automated vehicles to carry out transportation work, or controlling the automated Vehicle is idle.
The dispatching method of automated vehicles as claimed in claim 1, is characterized in that, according to described status record, described electric quantity usage record, described quantity record and described battery attenuation record construction model constraint condition comprises:

determining the charge state record, working state record and idle state record of the i-th automated vehicle for the t-th transport period included in the state record;

Determining the sum of the charging state records, working state records and idle state records of the i-th automated vehicle in the t-th transportation period is a fixed value, and is recorded as the first constraint condition, and the expression of the first constraint condition includes :

x it + y it + z it = 1,

Wherein, the x it represents the number of automated vehicles recorded in the state of charge, the y it represents the number of automated vehicles recorded in the working state, and the z it represents the number of automated vehicles recorded in the idle state.
The dispatching method of automated vehicles as claimed in claim 2, is characterized in that, according to described state record, described electric quantity usage record, described quantity record and described battery attenuation record construction model constraint condition also comprises:

Determine the number of automated vehicles that are in short supply during the t-1th transport period, and denote it as L t-1 ;

Determine the number of automated vehicles that need to work in the tth transport period, and record it as N t ;

Determining the number of automated vehicles that are in short supply within the tth transport period, and denoting it as L t , where L t ≥ 0;

According to the number of shortage of automated vehicles in the t-1th transportation period, the number of automated vehicles that need to work in the tth transportation period, the number of shortage of automated vehicles in the tth transportation period and the The number of automated vehicles recorded in the working state builds a second constraint condition, and the expression of the second constraint condition includes:

L t =L t-1 +N t -Σ i y it .
The dispatching method of automated vehicles as claimed in claim 2, is characterized in that, according to described state record, described electric quantity usage record, described quantity record and described battery attenuation record construction model constraint condition also comprises:

Determine the total number of charging piles for charging the automated vehicle, and denote it as S;

According to the size relationship between the number of automated vehicles recorded in the charging state and the total number of charging piles, a third constraint condition is constructed, and the expression of the third constraint condition includes:

Σ i x it ≤ S.
The dispatching method of automated vehicles as claimed in claim 2, is characterized in that, according to described state record, described electric quantity usage record, described quantity record and described battery attenuation record construction model constraint condition also comprises:

Determine the electricity consumption of the i-th automated vehicle at the end of the t-th transportation period, and record it as q it , where q it ≥ 0;

Determine the electricity consumption of the i-th automated vehicle at the end of the t-1th transportation period, and denote it as q it-1 ;

Determining the charging quantity for the t-th transportation period, and denoting the charging quantity as R it ;

Determine the power consumption of the i-th automated vehicle in the t-th transportation period, and record the charging power as y it ×W it , which represents the work consumption of the i-th automated vehicle in the t-th transportation period Electricity W it ;

According to the electricity quantity of the i-th automated vehicle at the end of the t-th transportation period, the electricity quantity of the i-th automated vehicle at the end of the t-1 transportation period, the charging electricity of the t-th transportation period and The power consumed by the i-th automated vehicle in the t-th transportation period constructs a fourth constraint condition, and the fourth constraint condition includes:

q it =q it −1 +R it −y it ×W it .
The dispatching method of automated vehicles as claimed in claim 5, is characterized in that, according to described status record, described electric quantity usage record, described quantity record and described battery attenuation record construction model constraint condition also comprises:

Determining the correspondence between the charging amount of the automated vehicle and time during the tth transport period;

Determine the upper limit of the electric quantity of the automated vehicle, denoted as Q;

According to the corresponding relationship, the upper limit of the electric quantity of the automated vehicle, the charging electric quantity of the i-th automated vehicle in the t-th transportation period, and the end-time electric quantity of the i-th automated vehicle in the t-1-th transportation period The fifth constraint condition, the fifth constraint condition includes:

0≤R it ≤Qq it−1 .
The scheduling method of automated vehicles according to any one of claims 1-6, wherein solving the shortage of automated vehicles in each transport period according to the model constraints comprises:

Under the constraints of the model, the Monte Carlo algorithm is used to perform multiple sampling calculations on the number of shortage automated vehicles in each transportation period;

The minimum value of the number of shortage automated vehicles in each transportation period is determined according to the result of the sampling calculation.
A dispatching device for automated vehicles, characterized in that it comprises:

A determination module is used to determine the status record, power usage record, quantity record and battery decay record of the automated vehicle;

A building module for building model constraints according to the state record, the power usage record, the quantity record and the battery decay record;

A solution module, used to solve the shortage of automated vehicles in each transportation period according to the model constraints;

A scheduling module, configured to schedule the automated vehicles according to the number of shortage automated vehicles in each transportation period, the scheduling process includes controlling the automated vehicles to charge, or controlling the automated vehicles to carry out transportation work, Or control the automated vehicle to idle.
An electronic device, characterized in that it comprises:

storage; and

A processor coupled to the memory, the processor configured to execute the method for dispatching an automated vehicle according to any one of claims 1-7 based on instructions stored in the memory.
A computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, the automatic vehicle scheduling method according to any one of claims 1-7 is realized.