A scheduling control method, system, device and storage medium for a modular vehicle

Abstract

The present application discloses a scheduling control method, system, device and storage medium for modular vehicles. By determining the driving parameters and vehicle parameters of each modular vehicle operating in the road network, road network parameters are obtained; based on the driving parameters, road network parameters and vehicle parameters, a road network multi-module vehicle scheduling model is constructed; scheduling parameters to be solved for the road network multi-module vehicle scheduling model are set; based on the scheduling parameters, an objective function and constraint conditions are established with the goal of minimizing the total operating cost of the road network multi-module vehicle scheduling model; linearization processing is performed to obtain a linear function and the solution is obtained under the constraints of the constraint conditions to obtain the optimal solution of the scheduling parameters; the scheduling control of each modular vehicle is performed according to the optimal solution. This solution performs joint optimization from multiple aspects such as driving routes, average speeds and formations, obtains the minimum total operating cost from a global perspective, determines the scheduling control method for each modular vehicle, and enables each modular vehicle to better play its role.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle dispatch control, and in particular to a dispatch control method, system, device and storage medium for a module vehicle. Background Art

[0002] Modular vehicles are emerging vehicles used in urban transportation. Modular vehicles refer to vehicles that are divided into multiple modules that can be used independently or in combination. They are usually powered by electricity. Each module has the ability to operate independently. At the same time, they can be grouped with other module vehicles through physical connections to form larger vehicles. Modularization of vehicles can achieve flexible control and scheduling of vehicles. For example, in public transportation, buses can be modularized to form modular buses. Modular buses can provide services to passengers on demand and can also respond to different road conditions and passenger needs, improving the efficiency of public transportation.

[0003] However, the existing method of dispatching and controlling modular vehicles is not comprehensive and does not fully consider the various factors that affect the operation of modular vehicles, resulting in a relatively high overall operating cost of multiple modular vehicles operating in a certain area, which prevents modular vehicles from playing their role better. Summary of the invention

[0004] In view of this, the present application provides a scheduling control method, system, device and storage medium for modular vehicles, which are used to solve the problem that the existing scheduling control method for modular vehicles is not comprehensive and does not fully consider the various factors that affect the operation of modular vehicles, resulting in a relatively high overall operating cost of multiple modular vehicles operating in a certain area, making the modular vehicles unable to better play their role.

[0005] To achieve the above objectives, the proposed solution is as follows:

[0006] In a first aspect, a dispatching control method for a module vehicle comprises:

[0007] Determine the driving parameters of each module vehicle operating in a preset road network in the road network, and obtain the road network parameters of the road network and the vehicle parameters of each module vehicle;

[0008] Based on the driving parameters, road network parameters and vehicle parameters, a road network multi-module vehicle scheduling model is constructed;

[0009] Setting the dispatching parameters to be solved of the road network multi-module vehicle dispatching model;

[0010] Based on the dispatching parameters, an objective function and constraint conditions are established with the goal of minimizing the total operating cost of the road network multi-module vehicle dispatching model;

[0011] Linearize the objective function to obtain a linear function;

[0012] Under the constraints of the constraint conditions, solve the linear function to obtain the optimal solution of the scheduling parameters;

[0013] Schedule and control each of the modular vehicles according to the optimal solution of the scheduling parameters.

[0014] In a second aspect, a scheduling control system for modular vehicles includes:

[0015] A parameter determination unit configured to determine the driving parameters of each modular vehicle operating in a preset road network, and obtain the road network parameters of the road network and the vehicle parameters of each modular vehicle;

[0016] A model construction unit configured to construct a road network multi-modular vehicle scheduling model based on the driving parameters, road network parameters, and vehicle parameters;

[0017] A scheduling parameter setting unit configured to set the scheduling parameters to be solved for the road network multi-modular vehicle scheduling model;

[0018] An objective function establishment unit configured to establish an objective function and constraint conditions based on the scheduling parameters with the goal of minimizing the total operating cost of the road network multi-modular vehicle scheduling model;

[0019] A linearization processing unit configured to perform a linear transformation on the objective function to obtain a linear function;

[0020] A linear function solving unit configured to solve the linear function under the constraints of the constraint conditions to obtain the optimal solution of the scheduling parameters;

[0021] A modular vehicle scheduling unit configured to schedule and control each of the modular vehicles according to the optimal solution of the scheduling parameters.

[0022] In a third aspect, a scheduling control device for modular vehicles includes a memory and a processor;

[0023] The memory is configured to store a program;

[0024] The processor is configured to execute the program to implement each step of the scheduling control method for modular vehicles as described in any item of the first aspect.

[0025] In a fourth aspect, a storage medium stores a computer program, and when the computer program is executed by a processor, each step of the scheduling control method for modular vehicles as described in any item of the first aspect is implemented.

[0026] As can be seen from the above technical solution, in this application, the driving parameters of each modular vehicle operating within a preset road network are determined, and the road network parameters of the road network and the vehicle parameters of each modular vehicle are obtained. Based on the driving parameters, road network parameters, and vehicle parameters, a road network multi-module vehicle scheduling model is constructed. The scheduling parameters to be solved for the road network multi-module vehicle scheduling model are set. Based on the scheduling parameters, with the goal of minimizing the total operating cost of the road network multi-module vehicle scheduling model, an objective function and constraint conditions are established. The objective function is linearized to obtain a linear function. Under the constraints of the constraint conditions, the linear function is solved to obtain the optimal solution of the scheduling parameters. Each modular vehicle is scheduled and controlled according to the optimal solution of the scheduling parameters. This solution aims to achieve the optimal and most suitable scheduling and control of all modular vehicles within the road network on the premise of minimizing the total operating cost. Thus, various parameters are determined, including the driving parameters of the modular vehicles, vehicle parameters, and road network parameters of the road network. Based on these parameters, a road network multi-module vehicle scheduling model is constructed, and the scheduling parameters to be solved for this model need to be set. With the minimization of the total operating cost of this model as the optimization goal, an objective function and constraint conditions are established. At the same time, for smooth and efficient solution, the objective function also needs to be linearized to obtain a linear function. Finally, under the constraints of the constraint conditions, the linear function is solved to obtain the optimal solution of the scheduling parameters, and each modular vehicle is scheduled and controlled according to the optimal solution of the scheduling parameters. The road network multi-module vehicle scheduling model constructed in this way can be jointly optimized from multiple aspects such as driving parameters, vehicle parameters, and road network parameters, and the minimum total operating cost can be obtained from a global perspective. Thus, the scheduling and control methods for each modular vehicle within the road network can be determined, enabling each modular vehicle to better play its role. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the provided drawings.

[0028] Figure 1 FIG. is an optional flowchart of a method for scheduling and controlling a modular vehicle provided by an embodiment of the present application;

[0029] Figure 2 FIG. is a schematic diagram of a scenario for forming a formation of modular vehicles provided by an embodiment of the present application;

[0030] Figure 3 FIG. is a schematic diagram of an energy consumption - speed function provided by an embodiment of the present application;

[0031] Figure 4 A schematic diagram of secant approximation linearization provided by an embodiment of the present application;

[0032] Figure 5 and Figure 6 A schematic diagram of a road network and the driving paths of modular vehicles provided by an embodiment of the present application;

[0033] Figure 7 A schematic diagram of the structure of a scheduling control system for modular vehicles provided by an embodiment of the present application;

[0034] Figure 8 A schematic diagram of the structure of a scheduling control device for modular vehicles provided by an embodiment of the present application. Detailed implementation manners

[0035] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0036] In recent years, due to the increasing popularity of transportation modes such as online car-hailing and shared bicycles, the occupancy rate of urban public transportation in all transportation modes around the world has shown a continuous decline. This market change trend challenges the sustainability of the existing bus service system, and emerging modular vehicles can address this challenge. Modular vehicles refer to vehicles that are divided into multiple modular vehicles that can be used independently or in combination, and each modular vehicle has the ability to operate independently. At the same time, it can also be grouped with other modular vehicles through physical connection. For modular buses, modular buses can provide services to passengers on demand to reduce the number of transfers and travel time, provide a better passenger experience, thereby increasing the occupancy rate of public transportation and realizing a low-carbon and green urban transportation mode.

[0037] However, when scheduling and controlling modular vehicles, considering the operation / running costs of the entire modular vehicle system, reasonable scheduling and control of modular vehicles are required. Especially for modular buses, achieving reasonable and low-cost scheduling and control of modular buses is beneficial to the efficiency of public transportation and the experience of passengers. However, the current scheduling and control methods for modular vehicles are usually not comprehensive enough and do not consider the characteristics of modular vehicles during operation. For example, after modular vehicles are connected and marshaled, all modular vehicles in the marshal can only travel at the same speed. However, when modular vehicles are connected and travel together, due to the reduced wind resistance, the energy consumption can be reduced compared to traveling separately; nor do they fully consider various factors affecting the operation of modular vehicles. For example, whether vehicle marshalling can be achieved by changing the driving routes of one or more modular vehicles, such as controlling the speed and driving time of modular vehicles, can reduce energy consumption to a certain extent. Therefore, if specific characteristics and various factors are not comprehensively considered, the overall operation cost of multiple modular vehicles operating in a certain area is relatively high, and modular vehicles cannot play their roles better, even causing problems such as power waste and time waste. Therefore, reasonable scheduling and control of modular vehicles / modular buses are very important.

[0038] To solve the problems of the above-mentioned prior art, an embodiment of the present invention provides a scheduling and control method for modular vehicles. This method can be applied to various computer terminals or intelligent terminals, and its execution subject can be the processor or server of a computer terminal or intelligent terminal. The flowchart of the method is as Figure 1 shown and specifically includes:

[0039] S1: Determine the driving parameters of each modular vehicle operating in a preset road network in the road network, and obtain the road network parameters of the road network and the vehicle parameters of each modular vehicle.

[0040] First, determine the road network for which modular vehicle scheduling and control are required. All modular buses operating in this road network are the modular vehicles in this application. It should be noted that modular buses are not limited to the form of buses. They can be vehicles in modular form that provide public transportation services.

[0041] The road network refers to a network structure formed by the interconnection of various roads (urban roads, highways, rural roads). There are multiple stations in the road network, and each modular vehicle can stop at each station. Any station in the road network can be used as the starting point, ending point, or passing point of any modular vehicle.

[0042] For each modular vehicle, the driving parameters of the modular vehicle in the road network can be considered as parameters related to the driving path of the modular vehicle, such as the starting point, the ending point, the stations passed through, the road segments passed through, etc. The road segment refers to the drivable route composed of every two stations, and these two stations can be adjacent or non - adjacent; the road network parameters can include data information related to the road network, such as the number of stations included in the road network, the location of each station, the positional relationship between every two stations, the road segments composed of every two stations, and the length of each road segment; the vehicle parameters refer to the parameters related to the modular vehicle, which can include the number of modular vehicles in the road network, the energy / electricity parameters related to the modular vehicle, the parameters related to the formation (such as the maximum number of modular vehicles that can be borne in the formation), etc.

[0043] In this step, which parameters to determine and obtain can be selected according to the purpose of model optimization or scheduling control. This application aims to determine the optimal solution of the scheduling parameters for the scheduling control of modular vehicles to minimize the total operating cost of the multi - modular vehicle scheduling model of the road network. Therefore, from the perspective of minimizing the total operating cost, it is determined which parameters are needed.

[0044] By determining and obtaining the above - mentioned various parameters and taking them into account in the scheduling control of modular vehicles, the comprehensiveness, accuracy, and timeliness of the scheduling control can be ensured.

[0045] S2: Based on the driving parameters, road network parameters, and vehicle parameters, construct a multi - modular vehicle scheduling model for the road network.

[0046] After obtaining the driving parameters, road network parameters, and vehicle parameters, a mathematical model can be constructed based on the knowledge of mixed - integer programming and the actual problems proposed in this application, and this mathematical model is used as the multi - modular vehicle scheduling model for the road network. Input the driving parameters, road network parameters, and vehicle parameters into the mathematical model, and other parameters can also be input according to specific requirements.

[0047] Mixed - integer programming is a special mathematical optimization problem that combines the characteristics of linear programming and integer programming. In mixed - integer programming, the decision variables include continuous variables and integer variables. Therefore, it is also necessary to set the decision variables according to the characteristics of the multi - modular vehicle scheduling model for the road network, or according to the driving parameters, road network parameters, vehicle parameters, and other parameters.

[0048] S3: Set the scheduling parameters to be solved for the multi - modular vehicle scheduling model of the road network.

[0049] The scheduling parameters can cover all the modular vehicles in the scheduling control road network. Then, this application aims to solve for the scheduling parameters corresponding to the minimum total operating cost of the road network multi-module vehicle scheduling model. Therefore, it is first necessary to set the scheduling parameters to be solved. The scheduling parameters refer to the parameters related to the scheduling control of the modular vehicles in the road network. Scheduling the modular vehicles involves aspects such as the driving routes, driving speeds, driving times of the modular vehicles, whether to form a formation with other modular vehicles and drive together, whether to wait for other modular vehicles at the current station in order to form a formation, the waiting time, and the amount of energy (which can also refer to the battery power here) shared among each other within the formation. Therefore, the scheduling parameters can be set based on the determined and obtained parameters, as well as the model optimization objective. The mutual sharing of energy within the formation means that when the modular vehicles are driving in formation, they are physically connected, so they can transfer battery power to each other, thus realizing energy sharing when the vehicles are driving in formation.

[0050] S4: Based on the scheduling parameters, with the goal of minimizing the total operating cost of the road network multi-module vehicle scheduling model, establish an objective function and constraint conditions.

[0051] To solve the problem of the relatively high overall operating cost of multiple modular vehicles operating in a region in the prior art, after establishing the road network multi-module vehicle scheduling model, it is necessary to set the optimization objective of this model and optimize it with this objective. That is, to minimize the total operating cost of the road network multi-module vehicle scheduling model as the optimization objective, establish an objective function and solve it. At the same time, it is also necessary to set constraint conditions, and the optimization process needs to be carried out under the constraints of the constraint conditions.

[0052] The total operating cost of the road network multi-module vehicle scheduling model can be considered from multiple aspects of costs, such as time cost, energy cost, revenue cost, etc. And it is necessary to establish constraint conditions based on the driving parameters, road network parameters, and vehicle parameters obtained in the above steps.

[0053] S5: Perform linearization processing on the objective function to obtain a linear function.

[0054] Since there are non-linear terms in the road network multi-module vehicle scheduling model, in order to enable this model to be smoothly and efficiently optimized and solved, it is necessary to perform linearization processing on the non-linear terms in the model. During the linearization processing, it is necessary to control the error to ensure the accuracy of the solution results, and use the objective function after linearization processing as the linear function.

[0055] S6: Under the constraints of the constraint conditions, solve the linear function to obtain the optimal solution of the scheduling parameters.

[0056] Since the objective function and constraint conditions are established with the goal of minimizing the total operating cost of the road network multi-module vehicle scheduling model in step S4 above, the solution result of the objective function is the optimal solution of the scheduling parameters corresponding to the minimum total operating cost.

[0057] S7: Schedule and control each of the module vehicles according to the optimal solution of the scheduling parameters.

[0058] After obtaining the optimal solution of the scheduling parameters, the optimal solution can be used for the scheduling control of the module vehicles. For example, for each module vehicle, the driving speed, driving path, sections where it is grouped with other module vehicles, whether it needs other module vehicles to share energy with it or to share energy with other module vehicles, etc. can be determined.

[0059] The finally obtained optimal solution can be in the form of a matrix, an array or a table, which contains the driving speed of each module vehicle in each section of its driving path, the energy sharing situation with other module vehicles, the grouping situation, etc.

[0060] In this solution, on the premise of minimizing the total operating cost, it is desired to achieve the optimal and most suitable scheduling control of all module vehicles in the road network. Then, parameters in multiple aspects are determined, including the driving parameters of the module vehicles, vehicle parameters, and road network parameters of the road network. Based on these parameters, a road network multi-module vehicle scheduling model is constructed, and the scheduling parameters to be solved in the model need to be set. With the goal of minimizing the total operating cost of the model, an objective function and constraint conditions are established. At the same time, in order to solve smoothly and efficiently, the objective function needs to be linearized to obtain a linear function. Finally, the linear function is solved under the constraints of the constraint conditions to obtain the optimal solution of the scheduling parameters, and the module vehicles are scheduled and controlled according to the optimal solution of the scheduling parameters. The constructed road network multi-module vehicle scheduling model can be jointly optimized from multiple aspects such as driving parameters, vehicle parameters, and road network parameters. Specifically, it includes aspects such as driving path, average speed, and grouping. Starting from a global perspective, the minimum total operating cost is obtained, so that the scheduling control method for each module vehicle can be determined, enabling each module vehicle to better play its role.

[0061] In the method provided by the embodiment of the present invention, for the driving parameters, this embodiment defines that the driving parameters include the starting point, ending point, and driving path of the module vehicle, including the starting point, ending point, and driving path of each module vehicle.

[0062] It can be divided into two cases: one is that the starting point, ending point and driving route of each modular vehicle in the road network have been determined, and each modular vehicle needs to drive according to the set route. In this case, the driving parameters of each modular vehicle can be directly obtained.

[0063] The other case is that the starting point and ending point of each modular vehicle are set, but which sections to pass through from the starting point to reach the ending point need to be determined separately. Then, the shortest path problem can be solved for each modular vehicle first, and the shortest driving path of each modular vehicle can be determined from it. The obtained shortest path is used as the driving path in the driving parameters. It should be noted that this shortest path is only an initial driving path. In the subsequent optimization process, with the starting point and ending point fixed, this shortest path can be changed, so as to obtain the minimum value of the objective function. Among them, methods such as Dijkstra algorithm, Bellman-Ford algorithm or Floyd-Warshall algorithm can be used to solve the shortest path, and this embodiment does not limit this.

[0064] For the road network parameters, this embodiment defines that the road network parameters include the station set of the road network, the section set of the road network and the section length.

[0065] The station set contains all the stations in the road network. Stations can also be called nodes, which are the basic elements constituting the road network. In the transportation field, they generally refer to stations where vehicle modules can stop. The section set contains all the sections in the road network. Each section is composed of two stations and the driving road between the stations. The section length refers to the driving distance between the stations at both ends of the section, not the straight-line distance.

[0066] For the vehicle parameters, this embodiment defines that the vehicle parameters include the vehicle set, the energy-saving rate after the modular vehicles are grouped, the maximum number of modular vehicles in the formation, the maximum power of each modular vehicle, the unit power cost, and the unit time cost.

[0067] Specifically, the vehicle set includes all modular vehicles within the road network, named by numbers; the energy-saving rate after the modular vehicles are grouped refers to that after the modular vehicles are grouped into a vehicle group, the air resistance of the following vehicle is reduced, so compared with the modular vehicles driving alone, a certain amount of energy can be saved. Therefore, the present application sets the energy-saving rate after the modular vehicles are grouped; it should be noted that a group may include multiple modular vehicles, but it cannot include infinitely many. Therefore, the constraint condition of the maximum number of modular vehicles in a group is also set, which refers to the maximum number of modular vehicles belonging to the same group in a section. For example, if the length of a section is 600 meters and the length of the carriage of a modular vehicle is 8 meters, then the maximum number of modular vehicles in the group is set according to the traffic conditions, traffic light conditions, pedestrian or non-motor vehicle conditions, section length and carriage length of the modular vehicle in this section. In the extreme case, the product of the maximum number and the carriage length cannot exceed the section length; the maximum power of each modular vehicle refers to the power when the modular vehicle is fully charged. Generally, it is considered that the power of the modular vehicle at the starting point is the maximum; the unit power cost refers to the total cost required to produce / supply each kilowatt-hour (kWh) of electric energy, which may include multiple aspects, such as battery cost, vehicle design, energy efficiency, charging method, electricity price, etc.; the unit time cost also includes multiple aspects, such as driving time, charging time, waiting time, etc.

[0068] It can be understood that the size of a single modular vehicle is relatively small, and its battery capacity is much lower than that of a conventional vehicle. Therefore, the driving distance of the modular vehicle is limited. Since the modular vehicles can be connected to each other to form a group, the modular vehicle with low power can obtain power from the modular vehicle connected to it (that is, in the same group, the same group means that multiple modular vehicles are incorporated into the same vehicle group) during driving. In this way, the driving distance of some modular vehicles with low power can be increased, or the driving speed of the modular vehicle with low power on some sections can be increased, and the driving time can be reduced. Then, the total operation cost can also be reduced to a certain extent. As Figure 2 shown, modular vehicle 1, modular vehicle 2, modular vehicle 3 and modular vehicle 4 can be physically connected, that is, incorporated into the same vehicle group and drive together, and energy transfer / energy sharing can also be carried out. For example, modular vehicle 1 and modular vehicle 3 are modular vehicles with low power, and modular vehicle 2 and modular vehicle 4 are modular vehicles with high power. Then, modular vehicle 2 can supply power to modular vehicle 1 and / or modular vehicle 3, and modular vehicle 4 can also supply power to modular vehicle 3.

[0069] Specifically, represents the vehicle set, , represents the modular vehicle , represents the total number of modular vehicles operating in the road network, represents the set of stations of the road network, , and respectively represent the stations and station in the road network, represents the total number of stations in the road network, represents the section composed of station and station , , represents the set of sections of the road network, represents the starting point of the modular vehicle , represents the end point of the modular vehicle , and the starting point and the end point are both stations in the set of stations, , represents the length of the section between station and station ; represents the energy-saving rate of the modular vehicle after formation, represents the maximum number of modular vehicles in the formation, represents the maximum power of the vehicle module, represents the cost per unit of power, represents the cost per unit of time.

[0070] For the scheduling parameters, in this embodiment, it is defined that the scheduling parameters include the driving path of each modular vehicle, the average speed of each modular vehicle on each section of its driving path, and the number of other modular vehicles in the same formation as each modular vehicle on each section of its driving path.

[0071] Specifically, it can be understood that for each modular vehicle, the average speed of the modular vehicle on each section of its driving path is uncertain. For example, the average speed is relatively high on some sections and relatively low on some sections. It is also uncertain on which section to form a formation with one or more other modular vehicles. It is also possible to change the originally set driving path for the purpose of forming a formation / reducing energy consumption, etc. Therefore, by setting these three scheduling parameters and performing global optimization in this application, the optimal operation data of each modular vehicle can be obtained, and each modular vehicle can operate according to this optimal operation data.

[0072] Among them, the driving path of each modular vehicle can be considered from each section, and a decision variable is set, and this decision variable is used to specifically represent the driving path of the modular vehicle, is a 0-1 variable, and it is defined that if the modular vehicle The section passed through , then , that is, the driving path of the modular vehicle contains the section . If the modular vehicle does not pass through the section , then , that is, the driving path of the modular vehicle does not contain the section . Since the driving path in the driving parameters has been determined in step S1 of this solution, and during the subsequent optimization process, the driving path may change. Therefore the value of may change, that is, the value in the optimal solution of the finally obtained scheduling parameters and the value in the initially determined driving parameters may not be exactly the same. Of course, it is also possible that the value in the optimal solution of the finally determined scheduling parameters is the same as the value in the initially determined driving parameters; Define the average speed as , which specifically represents the average speed of the modular vehicle on the section . Define as the number of other modular vehicles in the same formation, which specifically represents the number of other modular vehicles in the same formation as the modular vehicle when passing through the section .

[0073] After determining the above-mentioned various aspect parameters, establish a road network multi-modular vehicle scheduling model, and establish an objective function and set constraint conditions with the minimum total operating cost of this model as the goal. Among them, the expression of the objective function is:

[0074] ;

[0075] Among them, represents the objective function, refers to the end point of the modular vehicle , which is also any station in the station set . represents the power consumption per unit mileage corresponding to the average speed of the modular vehicle on the section . represents the time when the modular vehicle arrives at its end point .

[0076] This objective function can be regarded as consisting of two parts, namely the power cost and the time cost. In order to unify the units, use the unit power cost and the unit time cost To normalize these two parts, after solving the objective function, we can obtain , , and the optimal solutions of is related to , is determined by , is related to , , , are all related. Therefore, the scheduling parameters are set to , and .

[0077] While establishing the objective function, constraint conditions are also set. This application sets four constraint conditions, namely, driving path constraint condition, formation constraint condition, driving time constraint condition, and power consumption constraint condition.

[0078] (1) The driving path constraint condition is used to constrain the driving path of the modular vehicle, including constraining the starting point, ending point, and preventing repeated access to sections.

[0079] Specifically, it includes:

[0080] , and , this constraint condition ensures that each modular vehicle starts from its own starting point;

[0081] , and , this constraint condition ensures that each modular vehicle reaches its own ending point;

[0082] , and , this constraint condition prevents the modular vehicle from passing through sections that have already been traveled;

[0083] Among them, , , is expressed as: site is except for the starting point and the ending point of modular vehicle in represents whether section is included in the driving path of modular vehicle . If so, , if not, .

[0084] (2) The formation constraints are used to constrain the formation of modular vehicles, including whether to form a formation and whether to provide energy to each other after formation. Taking two modular vehicles as an example, if these two modular vehicles arrive at a certain station at the same time and the next section or sections of the road they travel are the same, they can be formed at this station. If the distance between the two modular vehicles from a certain station is large or the time difference for arriving at this station is large, and if they want to be formed, it is necessary to greatly change the speed of one of the modular vehicles / both of these modular vehicles, or let one of the modular vehicles wait for the other modular vehicle for a long time. For example, the faster modular vehicle needs to reduce its speed so that the two can enter the station at the same time at a certain station. Therefore, on the premise that the number of formations does not exceed the maximum number of modular vehicles in the same formation, it is necessary to ensure that only two modular vehicles that arrive at a certain station at the same time and enter the same section of the road after this station can be formed. From another perspective, it can also be understood that if two modular vehicles are to be formed at a certain station, then they enter the same section of the road after this station and need to arrive at this station at the same time; on the other hand, it is also necessary to ensure that only modular vehicles in the same formation can provide energy to each other.

[0085] Specifically, it includes:

[0086] , , these two constraint conditions ensure that the vehicles in the same formation arrive at the same station at the same time, that is, modular vehicle and modular vehicle If they are to start forming at station , then they arrive at station at the same time;

[0087] , , these two constraint conditions ensure that the average speeds of the vehicles in the same formation are the same, that is, modular vehicle and modular vehicle If they are in the same formation on section , then their average speeds on section are the same;

[0088] , this constraint condition ensures that two modular vehicles can only be in the same formation when they pass through a section at the same time, that is, modular vehicle and modular vehicle can only be incorporated into the same vehicle group when they pass through section at the same time;

[0089] , this constraint condition is used to calculate the number of modular vehicles in the same formation;

[0090] This constraint is used to ensure that the number of modular vehicles in the same formation does not exceed the maximum number of modular vehicles in the formation, which is a vehicle parameter.

[0091] , , These three constraints together ensure that two modular vehicles can supply energy to each other only when they are in the same formation.

[0092] Among them, represents the modular vehicle , , and , is a parameter, which can be regarded as a large natural number. represents the time when the modular vehicle arrives at the station . represents the time when the modular vehicle arrives at the station . represents whether the modular vehicle and the modular vehicle pass through the section simultaneously and are in the same formation. If so, , if not, . represents the average speed of the modular vehicle on the section . represents whether the driving path of the modular vehicle includes the section . If so, , if not, . represents the average speed of the modular vehicle on the section . represents the maximum number of modular vehicles in the formation. represents the energy provided by the modular vehicle to the modular vehicle when they simultaneously pass through the section , and the modular vehicle provides energy to the modular vehicle . represents the energy provided by the modular vehicle to the modular vehicle when they simultaneously pass through the section , and the modular vehicle provides energy to the modular vehicle .

[0093] (3) The driving time constraint is used to restrict the driving time of the modular vehicle. For example, to form a formation, some modular vehicles are made to wait for other modular vehicles within a certain range.

[0094] Specifically, it includes:

[0095] , , this constraint ensures that each modular vehicle departs at time 0, that is, the modular vehicle departs from its starting point at time 0;

[0096] , this constraint can calculate the driving time on this section according to the average speed and the section length;

[0097] , this constraint ensures that the difference between the time when the modular vehicle arrives at a station and the time when it arrives at the previous station is greater than the driving time of the modular vehicle on the section composed of these two stations; this is considering that there may be a waiting time, and the reason for the formation of the waiting time is: to form a formation, the modular vehicle waits for other modular vehicles.

[0098] Among them, and respectively represent the time when the modular vehicle arrives at station and the time when it arrives at station , represents the driving time of the modular vehicle on the section .

[0099] (4) The power consumption constraint is used to ensure that the power of each modular vehicle is maximized at the initial departure, etc.

[0100] Specifically, it includes:

[0101] ;

[0102] This constraint calculates the power consumption per unit mileage corresponding to a modular vehicle driving at an average speed on a section.

[0103] The energy consumption - speed function (power consumption function) of a standard electric modular vehicle is as Figure 3 shown, Figure 3 where the abscissa is the speed (km / h) and the ordinate is the energy consumption (energy consumption / power consumption W-h / km). It can be seen from the figure that the power consumption per unit mileage first decreases and then increases with the speed of the modular vehicle. Considering multiple factors affecting the power consumption of the modular vehicle, including:

[0104] The self-weight of the modular vehicle , the load of the modular vehicle , air resistance , the frontal area , gravitational constant , road surface angle , aerodynamic drag coefficient , rolling resistance coefficient , transmission coefficient 、 、 、 , the energy consumption of other electrical appliances in the vehicle , the energy consumption of the on-vehicle air conditioner , then the expression of the power consumption function can be determined according to these parameters as:

[0105] ;

[0106] Among them, represents the energy consumption, represents the speed. After converting the above expression and substituting 、 and , we get:

[0107] .

[0108] The power consumption constraint conditions also include:

[0109] , , this constraint condition ensures that the power of the modular vehicle at its own starting point is its maximum power;

[0110] , , ,

[0111] , these four constraint conditions jointly calculate the energy consumption of the modular vehicle when marshaling and not marshaling.

[0112] Among them, , , , are all power consumption coefficients, and respectively represent the power of the modular vehicle at station and the power at station , represents the maximum power of the vehicle module, represents the modular vehicle on the section of the actual power consumption.

[0113] Furthermore, it can be understood that if there are non-linear terms in the objective function, it may be impossible to solve the objective function during the solution process. Therefore, in order to ensure a smooth and efficient solution process, it is also necessary to perform a non-linearization process with controllable error on the non-linear terms. Specifically:

[0114] Perform a linearization process on the objective function to obtain a linear function, including:

[0115] Transform the objective function to obtain:

[0116] ;

[0117] Determine the non-linear terms in the transformed objective function and non-linear term ;

[0118] Perform a linearization process on the non-linear term and at the same time perform a linearization process on the non-linear term to determine the linear function.

[0119] The process of performing a linearization process on the non-linear term may include:

[0120] According to the knowledge of mixed-integer programming, introduce an integer variable , and use to replace the non-linear term , and at the same time set the constraint conditions:

[0121]

[0122] where, , is a parameter.

[0123] The process of performing a linearization process on the non-linear term may include:

[0124] Specifically, linearize the in the non-linear term . According to the knowledge of mixed-integer programming, introduce a continuous variable , an integer variable and a continuous variable , and use to replace , and at the same time set the constraint conditions:

[0125]

[0126] where, is a 0-1 variable, Indicates the number of other modular vehicles in the same formation as the modular vehicle when passing through a section (it can be considered to have the same meaning as ), , is a set of quantities, specifically representing the set of quantities of other modular vehicles in the same formation except itself, , represents the maximum number of modular vehicles in the formation, is a parameter.

[0127] That is to say, restricts that only one has a value of 1, and the rest of the are all 0.

[0128] Furthermore, in the above power consumption constraint condition is a non-linear constraint condition. Therefore, if we want to solve the objective function under this constraint, we also need to linearize this constraint condition. The specific process is as follows:

[0129] The secant approximation method is used for linearization. The secant approximation method approximates the function value at a certain point on the curve by the value at the corresponding point on the secant line of the function. Therefore, using this idea, for and , multiple secant lines can be used for linear approximation. Under reasonable allocation, the more secant lines, the higher the accuracy of the linear approximation. However, the increase in the number of secant lines will lead to an increase in the subsequent calculation amount and increase the calculation burden. Therefore, reasonable secant line allocation is required to ensure that it does not exceed the maximum error and the number of secant lines is the least. The linearization schematic diagram of the secant approximation is as shown in Figure 4 , and the expression is , where the abscissa is the speed , and the ordinate is . Similarly, if is approximated, the ordinate will be replaced with here and the same processing will be carried out.

[0130] Regarding in the non-linear terms and the non-linear term , an intermediate variable is introduced, and , represents the maximum error, and determine the minimum number of secant lines when the maximum error is :

[0131] ;

[0132] Determine respectively when the minimum number of secant lines is : the slope and the intercept of the secant line, and the slope and the intercept of the secant line

[0133]

[0134] Introduce continuous variables to replace the non-linear term ; meanwhile, introduce continuous variables to replace the non-linear term , and set the constraint conditions and to complete the linearization of the non-linear term and the non-linear term ;

[0135] Among them, , , , are all power consumption coefficients; is an intermediate variable, is the number of secant lines, , is the maximum speed selected during the secant line approximation process, is the minimum speed selected during the secant line approximation process. The maximum speed and the minimum speed can be selected as needed.

[0136] In one embodiment, under the constraints of the said constraint conditions, the process of solving the said linear function to obtain the optimal solution of the said scheduling parameters may specifically include:

[0137] After constructing the objective function and the constraint conditions, an optimization solver can be used to solve and obtain the optimal solution of the scheduling parameters. The optimization solver can select open-source software such as OR-Tools, SCIP (Solving Constraint Integer Programs), etc., and this embodiment does not limit this.

[0138] It can also be solved by other methods, such as:

[0139] This solution method is specifically divided into two stages. In the first stage, the initial driving path is calculated first, and the initial driving path is optimized considering the energy consumption problem to obtain an optimized path. In the second stage, based on the initial driving path and the optimized path, the number and average speed of other module vehicles in the same formation are solved. Solving in stages can greatly improve the calculation speed and optimization efficiency. Moreover, this method can ensure the feasibility of the scheduling control result and quickly obtain an optimal (compared with other existing methods) scheduling control scheme.

[0140] Define the scheduling parameters as the driving path of each module vehicle, the average speed of each module vehicle on each section of its driving path, and the number of other module vehicles in the same formation as each module vehicle on each section of its driving path. And use the heuristic algorithm to split this solution into two stages. First, in the first stage, solve the initial driving path of each module vehicle. However, this initial driving path may be fixed from the beginning. Then, in the optimal solution of the three scheduling parameters finally obtained, the driving path is the same as the initial driving path; it is also possible to first determine a shortest path and optimize it to obtain an optimized path. In the second stage, while solving the remaining two scheduling parameters, balance the shortest path and the optimized path, and finally obtain the optimal solution of the three scheduling parameters. For example, in some special cases, even if some module vehicles take a little detour (that is, they do not drive according to the initially determined initial driving path / shortest path) and increase the energy consumption, but they can be grouped with other module vehicles and also save energy. Then, in this case, as long as the increased energy consumption caused by the detour is less than the energy saved by grouping, the detour situation can be considered, and it is also necessary to combine and optimize with other scheduling parameters to find the optimal solution.

[0141] In the first stage, after determining the shortest path, the optimization of the shortest path includes the following two methods:

[0142] (1) First, set the number of iterations according to the total number of module vehicles operating in the road network. For example, if there are module vehicles in the road network, a vehicle set can be set, , and set the number of iterations to . Each time, select 2 module vehicles in the preset order (the preset order can be set according to actual needs or calculation needs), solve the objective function under the constraint conditions, calculate the energy consumption of these two module vehicles after grouping, and at the same time calculate the energy consumption when not grouped, that is, when the two vehicles drive separately, and make a comparison to determine the energy saving rate, so as to determine whether to group. After the iteration is completed, select the grouping information corresponding to the maximum energy saving rate, and then re-plan the driving paths of these two module vehicles to obtain an optimized path.

[0143] (2)Set a threshold for the number of iterations. When the number of iterations equals this threshold, although the iteration is not completed, the calculation should still be aborted, and the optimal path obtained in the current number of iterations is used as the optimized path.

[0144] In addition, in the process of each iteration of methods (1) and (2), the formation combinations of the two modular vehicles in the current iteration can be obtained, and the path is re-planned. Then, in the next iteration, these two modular vehicles will not be selected, which can be understood as removing these two modular vehicles from the vehicle set Therefrom.

[0145] In the second stage, joint optimization is carried out. Considering the power cost and time cost as a whole and making a trade-off. It can be considered that when the unit power cost is relatively large, the modular vehicles can be made to take a detour, form more combinations, and wait more, which can increase the energy-saving rate and achieve power savings in the total operating cost; when the unit time cost is relatively large, the modular vehicles can be made to take fewer detours, form fewer combinations, and wait less, so as to achieve time savings in the total operating cost. And in this process, it is also necessary to balance the shortest path and the optimized path, and they interact with the formation and average speed for joint optimization. Finally, an optimal path (i.e., the driving path in the optimal solution of the final scheduling parameters) and scheduling parameters such as the optimal formation for each section and the optimal average speed for each section are obtained. Especially in complex scenarios, compared with using an optimization solver to solve, the calculation time can be greatly reduced, the calculation speed can be improved, and thus the global scheduling efficiency can be enhanced.

[0146] In an example, according to this solution, driving parameters, road network parameters, and vehicle parameters are obtained; the road network parameters are shown in Tables 1 and 2 as follows:

[0147] Table 1

[0148]

[0149] Table 2

[0150]

[0151] This road network contains 23 stations, and these 23 stations are respectively named 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 69, 70, 71.

[0152] There are a total of 3 modular vehicles operating in this road network, namely modular vehicle 1, modular vehicle 2, and modular vehicle 3. The starting point of modular vehicle 1 is station 52, and the ending point is station 70; the starting point of modular vehicle 2 is station 46, and the ending point is station 71; the starting point of the modular vehicle is station 41, and the ending point is station 69.

[0153] At the same time, the power consumption coefficient obtained is , , , ;

[0154] Determine the data in the linearization process:

[0155] For , Table 3 below shows the secant slopes and secant intercepts when the number of secant lines is from 1 to 10:

[0156] Table 3

[0157]

[0158] For , Table 4 below shows the secant slopes and secant intercepts when the number of secant lines is from 1 to 10:

[0159] Table 4

[0160]

[0161] Obtain the unit power cost corresponding to the road network , the unit time cost ;

[0162] First, with the starting point and the ending point determined, determine the initial driving path / shortest path of each module vehicle:

[0163] Module vehicle 1: 52, 48, 47, 46, 44, 36, 71, 69, 70;

[0164] Module vehicle 2: 46, 44, 36, 71;

[0165] Module vehicle 3: 41, 40, 39, 38, 42, 43, 36, 71, 69;

[0166] The road network and the driving path diagram of the module vehicles are as Figure 5 shown. If there are two arrow symbols between two stations in this diagram, it means that two module vehicles will pass through the section between the two stations one after another. Three arrow symbols mean that these three module vehicles will pass through this section one after another.

[0167] Based on the above parameters, a multi-module vehicle scheduling model for the road network is constructed. The scheduling parameters are set as the driving routes of each module vehicle, the average speed of each module vehicle on each section of its driving route, and the number of other module vehicles in the same formation as each module vehicle on each section of its driving route. Then, the objective function and constraints are established and solved to obtain the driving routes of the three module vehicles, the average speed of each module vehicle on each section of its driving route, and the formation information. In this embodiment, the objective function does not change the driving route during the optimization process. Therefore, finally, the average speed of each module vehicle on each section of its driving route and the formation information are obtained. The scheduling parameter tables corresponding to module vehicle 1, module vehicle 2, and module vehicle 3 are shown in Table 5, Table 6, and Table 7 respectively as follows:

[0168] Table 5

[0169]

[0170] Table 6

[0171]

[0172] Table 7

[0173]

[0174] Among them, module vehicle 2 arrives at station 36 first. However, when module vehicle 2 arrives at station 36, module vehicles 1 and 3 still need a long time / travel a long distance to reach station 36. Under the constraint of the driving time constraint and considering the time cost, module vehicle 2 does not wait for the other two module vehicles to form a formation. By adjusting the average speed, module vehicles 1 and 3 can arrive at station 36 simultaneously and form a formation to reduce part of the energy consumption. Therefore, these three module vehicles are scheduled and controlled according to the scheduling parameters. The driving situation after the scheduling and control can be as Figure 6 shown. It can be seen from the figure that module vehicle 1 and module vehicle 3 are incorporated into the same vehicle group during the process of passing through station 71 and driving to station 69 at station 36.

[0175] It should be noted that in this embodiment, since the energy of each module vehicle is relatively sufficient, energy sharing is not required during the final scheduling and control process. In other more complex embodiments, energy sharing may be required between the module vehicles corresponding to the finally obtained optimal solution to help the module vehicles play their roles better.

[0176] Corresponding to Figure 1 the method described above, an embodiment of the present invention further provides a scheduling control system for module vehicles, which is used to Figure 1For the specific implementation of the method, the scheduling control system of the modular vehicle provided by the embodiments of the present invention can be in a computer terminal or various mobile devices, combined with Figure 7 , the scheduling control system of the modular vehicle will be introduced. As Figure 7 shown, the system may include:

[0177] A parameter determination unit 10, configured to determine the driving parameters of each modular vehicle operating in a preset road network within the road network, and obtain the road network parameters of the road network and the vehicle parameters of each modular vehicle;

[0178] A model construction unit 20, configured to construct a road network multi-modular vehicle scheduling model based on the driving parameters, road network parameters, and vehicle parameters;

[0179] A scheduling parameter setting unit 30, configured to set the scheduling parameters to be solved for the road network multi-modular vehicle scheduling model;

[0180] An objective function establishment unit 40, configured to establish an objective function and constraint conditions based on the scheduling parameters with the goal of minimizing the total operating cost of the road network multi-modular vehicle scheduling model;

[0181] A linearization processing unit 50, configured to perform a linear transformation on the objective function to obtain a linear function;

[0182] A linear function solving unit 60, configured to solve the linear function under the constraints of the constraint conditions to obtain the optimal solution of the scheduling parameters;

[0183] A modular vehicle scheduling unit 70, configured to perform scheduling control on each modular vehicle according to the optimal solution of the scheduling parameters.

[0184] As can be seen from the above technical solution, this solution aims to achieve the optimal and most suitable scheduling control of all modular vehicles in the road network on the premise of minimizing the total operating cost. Then, parameters in multiple aspects are determined, including the driving parameters of modular vehicles, vehicle parameters, and road network parameters of the road network. Based on these parameters, a road network multi-module vehicle scheduling model is constructed, and the scheduling parameters to be solved in this model need to be set. Taking the minimization of the total operating cost of this model as the optimization goal, an objective function and constraint conditions are established. At the same time, in order to solve smoothly and efficiently, the objective function also needs to be linearized to obtain a linear function. Finally, the linear function is solved under the constraints of the constraint conditions to obtain the optimal solution of the scheduling parameters, and the modular vehicles are scheduled and controlled according to the optimal solution of the scheduling parameters. The road network multi-module vehicle scheduling model constructed in this way can be jointly optimized in multiple aspects such as driving routes, average speeds, and formations. Starting from a global perspective, the minimum total operating cost can be obtained, so that the scheduling control method for each modular vehicle can be determined, enabling each modular vehicle to better play its role.

[0185] Furthermore, an embodiment of the present application provides a scheduling control device for modular vehicles. Optionally, Figure 8 shows a hardware structure block diagram of the scheduling control device for modular vehicles. Referring to Figure 8 , the hardware structure of the scheduling control device for modular vehicles may include: at least one processor 01, at least one communication interface 02, at least one memory 03, and at least one communication bus 04.

[0186] In the embodiment of the present application, the number of the processor 01, the communication interface 02, the memory 03, and the communication bus 04 is at least one, and the processor 01, the communication interface 02, and the memory 03 complete mutual communication through the communication bus 04.

[0187] The processor 01 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention, etc.

[0188] The memory 03 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0189] Among them, the memory stores a program, and the processor can call the program stored in the memory. The program is used to execute the following scheduling control method for modular vehicles, including:

[0190] Determine the driving parameters of each modular vehicle operating within a preset road network, and obtain the road network parameters of the road network and the vehicle parameters of each modular vehicle;

[0191] Based on the driving parameters, road network parameters, and vehicle parameters, construct a road network multi-module vehicle scheduling model;

[0192] Set the scheduling parameters to be solved for the road network multi-module vehicle scheduling model;

[0193] Based on the scheduling parameters, with the goal of minimizing the total operating cost of the road network multi-module vehicle scheduling model, establish an objective function and constraint conditions;

[0194] Perform linearization processing on the objective function to obtain a linear function;

[0195] Under the constraints of the constraint conditions, solve the linear function to obtain the optimal solution of the scheduling parameters;

[0196] Schedule and control each modular vehicle according to the optimal solution of the scheduling parameters.

[0197] Optionally, the refinement functions and extension functions of the program can refer to the description of the scheduling control method of the modular vehicle in the method embodiment.

[0198] The embodiment of the present application also provides a storage medium, which can store a program suitable for being executed by a processor. When the program runs, it controls the device where the storage medium is located to execute the following scheduling control method of the modular vehicle, including:

[0199] Determine the driving parameters of each modular vehicle operating within a preset road network, and obtain the road network parameters of the road network and the vehicle parameters of each modular vehicle;

[0200] Based on the driving parameters, road network parameters, and vehicle parameters, construct a road network multi-module vehicle scheduling model;

[0201] Set the scheduling parameters to be solved for the road network multi-module vehicle scheduling model;

[0202] Based on the scheduling parameters, with the goal of minimizing the total operating cost of the road network multi-module vehicle scheduling model, establish an objective function and constraint conditions;

[0203] Perform linearization processing on the objective function to obtain a linear function;

[0204] Under the constraints of the constraint conditions, solve the linear function to obtain the optimal solution of the scheduling parameters;

[0205] Schedule and control each of the modular vehicles according to the optimal solution of the scheduling parameters.

[0206] Specifically, the storage medium may be a computer-readable storage medium, and the computer-readable storage medium may be an electronic memory such as a flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, a hard disk, or a ROM.

[0207] Optionally, the refinement function and the extension function of the program may refer to the description of the scheduling control method of the modular vehicle in the method embodiment.

[0208] In addition, in each of the embodiments of the present disclosure, the functional modules may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part. If the function is implemented in the form of a software functional module and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present disclosure, in essence, or the part that contributes to the prior art, or a part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a live broadcast device, or a network device, etc.) to execute all or part of the steps of the methods in the embodiments of the present disclosure.

[0209] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or device including a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0210] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments may be referred to each other.

[0211] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not intended to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dispatching control method for a module vehicle, characterized in that: include: Determine the driving parameters of each module vehicle operating in a preset road network in the road network, and obtain the road network parameters of the road network and the vehicle parameters of each module vehicle; Based on the driving parameters, road network parameters and vehicle parameters, a road network multi-module vehicle scheduling model is constructed; the driving parameters include the starting point, end point and driving path of the module vehicle; the road network parameters include the site set of the road network, the road section set and the road section length of the road network; the vehicle parameters include the vehicle set, the energy saving rate of the module vehicle after grouping, the maximum number of module vehicles in the grouping, the maximum power of each module vehicle, the unit power cost, and the unit time cost; Setting the scheduling parameters to be solved for the road network multi-module vehicle scheduling model; the scheduling parameters include the driving path of each module vehicle, the average speed of each module vehicle on each section of its driving path, and the number of other module vehicles in the same group as each module vehicle on each section of its driving path; Based on the dispatching parameters, an objective function and constraints are established with the goal of minimizing the total operating cost of the road network multi-module vehicle dispatching model; the objective function is: ； in, represents the objective function, represents the unit electricity cost, represents the unit time cost, represents a collection of vehicles, , Represents a module vehicle , represents the total number of module vehicles operating in the road network, represents the set of sites in the road network, , and Respectively represent the stations in the road network and Site , represents the total number of stations in the network, Indicated by site With site The road segments consist of , represents a set of road segments of the road network, Represents a module vehicle On the road The power consumption per unit mileage corresponding to the average speed is Indicates the site With site The length of the road section between Represents a module vehicle Whether the driving path contains road segments , if so, then , if not, then , Indicates the energy saving rate of module vehicles after marshaling, Represents a module vehicle On the passing road The number of other modular vehicles in the same group, Represents a module vehicle The end point, Represents a module vehicle Reach its end time, ; Performing linearization processing on the objective function to obtain a linear function; Under the constraints of the constraints, solving the linear function to obtain the optimal solution of the scheduling parameters; The modules are controlled according to the optimal solution of the scheduling parameters.

2. The method according to claim 1, characterized in that The constraints include driving path constraints, grouping constraints, driving time constraints, and power consumption constraints.

3. The method according to claim 2, characterized in that The driving path constraints include: ,and ; ,and ; ,and ; in, represents a collection of vehicles, , Represents a module vehicle , represents the total number of module vehicles operating in the road network, represents the set of sites in the road network, , and Respectively represent the stations in the road network and Site , represents the total number of stations in the network, Indicated by site With site The road segments consist of , represents a set of road segments of the road network, Represents a module vehicle Whether the driving path contains road segments , if so, then , if not, then , Represents a module vehicle Whether the driving path contains road segments , if so, then , if not, then , Represents a module vehicle The starting point, Represents a module vehicle The end point, Represented as: site for Modular vehicle Starting point and end point Other sites besides .

4. The method according to claim 2, characterized in that: The grouping constraints include: ； ； ； ； ； ； ； ； ； ； in, represents a collection of vehicles, , Represents a module vehicle , represents the total number of module vehicles operating in the road network, represents the set of sites in the road network, , and Respectively represent the stations in the road network and Site , represents the total number of stations in the network, Indicated by site With site The road segments consist of , represents a set of road segments of the road network, Represents a module vehicle , ,and , is a parameter; Represents a module vehicle Arrival site time, Represents a module vehicle Arrival site time; Represents a module vehicle and modular vehicles Whether to pass through the road section at the same time And the same group, if so, then , if not, then ; Represents a module vehicle On the road The average speed on Represents a module vehicle On the road The average speed on Represents a module vehicle On the passing road The number of other modular vehicles in the same group, Represents a module vehicle Whether the driving path contains road segments , if so, then , if not, then , Represents a module vehicle Whether the driving path contains road segments , if so, then , if not, then , Indicates the maximum number of modular vehicles in the group. Represents a module vehicle and modular vehicles Passing through the road When the module vehicle For module vehicles The energy provided, Represents a module vehicle and modular vehicles Passing through the road When the module vehicle For module vehicles Provides energy.

5. The method according to claim 2, characterized in that: The travel time constraints include: ， ； ； ； in, represents a collection of vehicles, , Represents a module vehicle , represents the total number of module vehicles operating in the road network, represents the set of sites in the road network, , and Respectively represent the stations in the road network and Site , represents the total number of stations in the network, Indicated by site With site The road segments consist of , represents a set of road segments of the road network, and Respectively represent the module vehicle Arrival site and arrival point time, For modular vehicles The starting point, Indicates the site With site The length of the road section between Represents a module vehicle Whether the driving path contains road segments , if so, then , if not, then , is a parameter, Represents a module vehicle On the road The driving time on Represents a module vehicle On the road The average speed on.

6. The method according to claim 2, characterized in that The power consumption constraints include: ； ， ； ； ； ； ； in, represents a collection of vehicles, , Represents a module vehicle , represents the total number of module vehicles operating in the road network, represents the set of sites in the road network, , and Respectively represent the stations in the road network and Site , represents the total number of stations in the network, Indicated by site With site The road segments consist of , represents a set of road segments of the road network, Represents a module vehicle , Represents a module vehicle On the road The power consumption per unit mileage corresponding to the average speed is , , , are power consumption coefficients, Represents a module vehicle On the road The average speed on and Respectively represent the module vehicle On site The power consumption and the of electricity, Indicates the maximum power of the vehicle module. Represents a module vehicle On the road The actual power consumption on Represents a module vehicle Whether the driving path contains road segments , if so, then , if not, then , Represents a module vehicle The starting point, is a parameter, Indicates the site With site The length of the road section between Indicates the energy saving rate of module vehicles after marshaling, Represents a module vehicle On the passing road The number of other modular vehicles in the same group, Represents a module vehicle and modular vehicles Passing through the road When the module vehicle For module vehicles Provides energy.

7. The method according to claim 1, characterized in that The linearizing the objective function to obtain a linear function includes: The objective function is transformed to obtain: ； Determine the nonlinear terms in the transformed objective function and nonlinear terms ; For the nonlinear term Linearization is performed, and the nonlinear term A linearization process is performed to determine a linear function.

8. The method according to claim 7, characterized in that The nonlinear term Perform linearization processing, including: Introducing integer variables , and use Replace the nonlinear term , and set constraints: ； ； ； in, , is a parameter.

9. The method according to claim 7, characterized in that: The nonlinear term Perform linearization processing, including: For the nonlinear term In , introducing continuous variables , integer variables and continuous variables , and use replace , and set constraints: ； ； ； ； ； ； in, Indicates the number of other modular vehicles in the same group as the modular vehicle when it passes through a road section. , is a set of quantities, , Indicates the maximum number of modular vehicles in the group. is a parameter.

10. The method according to claim 6, characterized in that It also includes power consumption constraints Perform linearization processing, including: against The nonlinear term in and nonlinear terms , introducing intermediate variables ,and , determine the maximum error The minimum number of secants when : ； Determine the minimum number of cut lines to be hour, The slope of the secant line and the secant intercept as well as The slope of the secant line and the secant intercept : ； ； Introducing continuous variables To replace the nonlinear term , while introducing continuous variables To replace the nonlinear term , and set constraints and , to complete the nonlinear term and nonlinear terms Linearization processing; in, is the intermediate variable, is the number of secants, , is the maximum speed selected during the secant approximation process, is the minimum speed selected during the secant approximation process.

11. A dispatching control system for a modular vehicle, characterized in that: include: A parameter determination unit, used to determine the driving parameters of each module vehicle operating in a preset road network in the road network, and obtain the road network parameters of the road network and the vehicle parameters of each module vehicle; A model building unit is used to build a road network multi-module vehicle scheduling model based on the driving parameters, road network parameters and vehicle parameters; the driving parameters include the starting point, end point and driving path of the module vehicle; the road network parameters include the site set of the road network, the road section set and the section length of the road network; the vehicle parameters include the vehicle set, the energy saving rate of the module vehicle after grouping, the maximum number of module vehicles in the grouping, the maximum power of each module vehicle, the unit power cost, and the unit time cost; A dispatch parameter setting unit is used to set the dispatch parameters to be solved for the road network multi-module vehicle dispatch model; the dispatch parameters include the driving path of each module vehicle, the average speed of each module vehicle on each section of its driving path, and the number of other module vehicles in the same group as each module vehicle on each section of its driving path; An objective function establishing unit, for establishing an objective function and constraint conditions based on the scheduling parameters and taking minimization of the total operating cost of the road network multi-module vehicle scheduling model as the goal; The objective function is: ;in, represents the objective function, represents the unit electricity cost, represents the unit time cost, represents a collection of vehicles, , Represents a module vehicle , represents the total number of module vehicles operating in the road network, represents the set of sites in the road network, , and Respectively represent the stations in the road network and Site , represents the total number of stations in the network, Indicated by site With site The road segments consist of , represents a set of road segments of the road network, Represents a module vehicle On the road The power consumption per unit mileage corresponding to the average speed is Indicates the site With site The length of the road section between Represents a module vehicle Whether the driving path contains road segments , if so, then , if not, then , Indicates the energy saving rate of module vehicles after marshaling, Represents a module vehicle On the passing road The number of other modular vehicles in the same group, Represents a module vehicle The end point, Represents a module vehicle Reach its end time, ; A linearization processing unit, used for performing a linear transformation on the objective function to obtain a linear function; A linear function solving unit, used to solve the linear function under the constraint of the constraint condition to obtain the optimal solution of the scheduling parameter; The module vehicle dispatching unit is used to dispatch and control each of the module vehicles according to the optimal solution of the dispatching parameters.

12. A dispatching control device for a modular vehicle, characterized in that: including memory and processor; The memory is used to store programs; The processor is used to execute the program to implement the various steps of the scheduling control method for a modular vehicle as described in any one of claims 1-10.

13. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the scheduling control method for a module vehicle as described in any one of claims 1 to 10 is implemented.

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