A driving and charging cooperative scheduling system and method for a connected heavy vehicle cluster

Abstract

This invention discloses a collaborative scheduling system and method for driving and charging of connected heavy-duty vehicle clusters, relating to the technical field of intelligent management. The system includes the following steps: calculating the driving conversion coefficient and charging conversion coefficient of the vehicle cluster; calculating the vehicle cluster's transport capacity and transport efficiency; establishing a first mapping relationship and a second mapping relationship to obtain discrete transport capacity and discrete transport efficiency; matching collaborative vehicles; verifying the collaborative vehicles; and executing a first operation. This invention, by calculating the driving conversion coefficient and charging conversion coefficient of the vehicle cluster, more accurately predicts and plans the driving and charging needs of vehicles. Based on historical freight data and real-time order information, it allocates vehicle resources more rationally. Through collaborative scheduling, it reduces fuel consumption and electricity costs, reduces vehicle wear and maintenance costs, improves the response capability to emergencies by establishing mapping relationships and discretization analysis, and reduces idle time and improves vehicle utilization efficiency through intelligent scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of a networked heavy vehicle cluster, and in particular to a system and method for coordinated scheduling of driving and charging. Background Technology

[0002] In recent years, heavy-duty vehicle cluster driving planning technology and charging collaborative scheduling technology have been widely applied. Mixed integer programming is used to generate initial schemes, and the Vicsek swarm motion model is introduced to characterize the dynamics of vehicles, roads, and depots. Combined with improved particle swarm optimization and NSGA-II solution, joint optimization of path, queuing, and charging is achieved to reduce conflicts and waiting. The upper layer optimizes the power, electricity price, and carbon quota of charging stations, while the lower layer decides the route and charging amount of individual vehicles. It supports complex constraints such as partial charging along the way, pickup and delivery, and time windows. On the microgrid side, a master-slave game is adopted, with the microgrid operator as the leader and the EV cluster as the follower. Combined with tiered electricity pricing and carbon emission trading revenue, it guides heavy-duty trucks to stagger charging and V2G discharge, thereby reducing user costs and increasing the consumption rate of new energy.

[0003] Currently, Chinese invention patent with publication number CN118396317A discloses an automated port AGV charging optimization scheduling method. This method couples the relationship between logistics scheduling and orderly battery swapping, takes the minimum logistics scheduling time and the minimum electricity purchase cost of the port battery swapping station as the objective function, and solves the problem based on an immune optimization algorithm. This invention designates ship berths through a scheduling system and pre-allocates corresponding quay cranes. AGVs meeting power requirements are assigned to designated quay crane positions to wait. After the quay crane loads and unloads containers onto the AGVs, the AGVs automatically travel to the designated unloading area within the yard and wait for the yard crane to unload the containers to their destination, thus completing the current task. The AGVs then remain stationary, awaiting the next assignment. However, related technologies suffer from AGV idle time, which is detrimental to scheduling efficiency and resource utilization. Sending instructions to AGVs via a single scheduling command can lead to multiple command issuances for large datasets, such as AGV vehicle clusters, potentially causing mis-sending and compromising scheduling accuracy. Furthermore, the lack of coupling of the moving AGV vehicle clusters with charging characteristics hinders rapid scheduling and presents certain limitations. Summary of the Invention

[0004] The technical problem solved by this invention is that: in related technologies, the idle dwell time of AGVs is long, which is not conducive to the efficiency of scheduling and the utilization rate of resources. Sending instructions to AGVs through a single scheduling instruction can easily lead to the problem of mis-sending instructions for large amounts of data, such as AGV vehicle clusters, which is not conducive to the accuracy of scheduling. Furthermore, the lack of coupling between the moving AGV vehicle clusters and charging characteristics to quickly obtain a scheduling scheme is not conducive to the speed of scheduling and has certain limitations.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: Firstly, a method for coordinated scheduling of driving and charging of a networked heavy-duty vehicle cluster, comprising the following steps: Based on historical freight data, calculate the driving conversion coefficient and charging conversion coefficient of the vehicle cluster; Based on the driving conversion coefficient, order information, and warehouse information, calculate the vehicle cluster capacity; and based on the charging conversion coefficient, calculate the vehicle cluster efficiency. Establish a first mapping relationship between the total driving distance of the vehicle cluster, the average road conditions from the origin to the exchange point, and the vehicle cluster's transport capacity. Construct a second mapping relationship based on the total driving distance, the average road conditions from the origin to the exchange point, and the vehicle cluster's transport efficiency. Based on the first and second mapping relationships, discrete transport capacity and discrete transport efficiency are obtained, and collaborative vehicles are matched. Verify the collaborative vehicle and perform a first operation, which is used to dispatch the vehicle or re-match the collaborative vehicle.

[0006] As a preferred embodiment of the method for coordinated scheduling of driving and charging of a networked heavy vehicle cluster as described in this invention, the method involves: retrieving historical freight data and calculating the driving conversion coefficient and charging conversion coefficient of the vehicle cluster based on the historical freight data. There is a first calculation method for configuring the driving conversion coefficient of the vehicle cluster. The first calculation method includes: Select any vehicle, obtain the single-column load weight and the total daily freight distance for the vehicle, calculate the first ratio of the single-column load weight and the total daily freight distance, iterate through the first ratios for each vehicle, calculate the sum of the first ratios for each vehicle, count the number of vehicle numbers, calculate the second ratio of the sum of the first ratios for each vehicle to the number of vehicle numbers, and set the second ratio as the driving conversion coefficient.

[0007] As a preferred embodiment of the driving and charging coordinated scheduling method for a networked heavy vehicle cluster according to the present invention, a second calculation method is configured for the charging conversion coefficient of the vehicle cluster, the second calculation method including: For any vehicle, obtain the remaining battery power before a single charge, the remaining battery power after a single charge, and the charging time for that single charge. Obtain the charging power curve, which represents the factory test charging power curve corresponding to the vehicle model. The horizontal axis of the charging power curve is time, and the vertical axis is power. According to the definite integral calculation method, start calculating the area under the curve from the origin until the difference between the area under the curve and the remaining battery power before a single charge is less than or equal to a first value. Stop calculating the area under the curve and mark the coordinate point corresponding to the integration time at this time. Use the marked point as the starting integration point. Calculate the first sum of the time point corresponding to the starting integration point and the charging time for a single charge. Set the coordinate point corresponding to the first sum as the ending integration point. Perform definite integral calculation to obtain the first area. The first area represents the theoretical charging amount from the remaining battery power before a single charge to the end of the charging time. Calculate the second difference between the remaining power after a single charge and the remaining power before a single charge, and calculate the third ratio between the second difference and the theoretical difference; Calculate the average value of the third ratio for each vehicle per day, iterate through the average value of the third ratio for each vehicle, calculate the average of the average values ​​of the third ratio, and set the average of the average values ​​of the third ratio as the charging conversion coefficient.

[0008] As a preferred embodiment of the method for coordinated scheduling of driving and charging of connected heavy vehicle clusters as described in this invention, the vehicle cluster capacity is calculated based on driving conversion coefficients, order information, and warehouse information. The methods for calculating order timeliness include: Calculate the first quantity of goods for orders delivered within half a day, the second quantity of goods for orders delivered within one day, and the third quantity of goods for orders delivered over multiple days. Calculate the product of the first quantity and 2, the second quantity and 1, and the third quantity and 0.5. Calculate the sum of the products of the first quantity and 2, the second quantity and 1, and the third quantity and 0.5, and record this as the order timeliness. More preferably, the method for calculating order completion includes: Calculate the fourth quantity of remaining goods for half-day delivery, the fifth quantity of remaining goods for one-day delivery, and the sixth quantity of remaining goods for multi-day delivery. Calculate the ratio of the fourth quantity to the first quantity, the fifth quantity to the second quantity, and the sixth quantity to the third quantity. Calculate the first product of the ratio of the fourth quantity to the first quantity and 0.2. Calculate the fifth quantity of remaining goods for one-day delivery and 0.5. Calculate the third product of the ratio of the sixth quantity to the third quantity and 1. Calculate the sum of the first, second, and third products, and record it as the order completion rate. The vehicle cluster capacity is calculated based on the driving conversion coefficient, order timeliness, and order completion rate. The calculation method for vehicle cluster capacity includes: Calculate the sum of order timeliness and order completion rate, calculate the difference between the sum of order timeliness and order completion rate and the driving conversion coefficient, and set the sum of order timeliness and order completion rate and the difference between the driving conversion coefficient as the vehicle cluster capacity.

[0009] As a preferred embodiment of the driving and charging coordinated scheduling method for a networked heavy vehicle cluster described in this invention, the vehicle cluster operation efficiency is calculated based on the charging conversion coefficient. Calculate the sum of order timeliness, order completion rate, and charging conversion coefficient, and set it as the vehicle cluster operation efficiency.

[0010] As a preferred embodiment of the method for coordinated scheduling of driving and charging of connected heavy vehicle clusters as described in this invention, the method involves: obtaining the total driving distance of the vehicle cluster and the average road conditions from the origin to the freight exchange point, and establishing a first mapping relationship between the total driving distance of the vehicle cluster, the average road conditions from the origin to the freight exchange point, and the transport capacity of the vehicle cluster.

[0011] As a preferred embodiment of the method for coordinated scheduling of driving and charging of connected heavy vehicle clusters as described in this invention, the method involves: obtaining the total driving distance of the vehicle cluster and the average road conditions from the origin to the freight exchange point; and constructing a second mapping relationship based on the total driving distance, the average road conditions from the origin to the freight exchange point, and the vehicle cluster's operational efficiency.

[0012] As a preferred embodiment of the driving and charging coordinated scheduling method of the connected heavy vehicle cluster described in this invention, the method involves: jumping to any vehicle number, calculating the average road conditions at the current time point and the previous first time period based on the data from each sensor, and obtaining the real-time vehicle cluster capacity and real-time vehicle cluster efficiency corresponding to the vehicle based on the first mapping relationship and the second mapping relationship, which are denoted as discrete capacity and discrete efficiency. Discrete transport capacity and discrete transport efficiency are set as the first transport information of a vehicle, and the first transport information is bound to the vehicle's code and stored in the vehicle's corresponding radio frequency identification code.

[0013] As a preferred embodiment of the driving and charging collaborative scheduling method for a networked heavy vehicle cluster described in this invention, the method includes: setting a first boundary condition and a second boundary condition; marking the frequency identification code corresponding to the vehicle according to the first boundary condition and the second boundary condition; and matching the collaborative vehicle corresponding to the vehicle according to the first mark. The first boundary condition is expressed as the average value of the discrete transport capacity being equal to the transport capacity of the vehicle cluster; The second boundary condition is expressed as the average value of discrete transportation efficiency being equal to the transportation efficiency of vehicle clusters; The system acquires the operation data of the collaborative vehicles, verifies the first marker based on the operation data, obtains the first verification result, and performs the first operation based on the verification result. The verification result includes valid and invalid. The first operation includes re-matching the collaborative vehicles and sending the first scheduling signal. When the verification result is valid, the first operation is set to send the first scheduling signal; when the verification result is invalid, the first operation is set to re-match the cooperating vehicle.

[0014] Secondly, a driving and charging coordinated scheduling system for a networked heavy vehicle cluster includes a computing module, a construction module, and a verification module. The calculation module calculates the driving conversion coefficient and charging conversion coefficient of the vehicle cluster based on historical freight data, calculates the vehicle cluster capacity based on the driving conversion coefficient, order information, and warehouse information, and calculates the vehicle cluster efficiency based on the charging conversion coefficient. The construction module establishes a first mapping relationship between the total driving distance of the vehicle cluster, the average road conditions from the origin to the exchange point, and the vehicle cluster's transport capacity. Based on the total driving distance, the average road conditions from the origin to the exchange point, and the vehicle cluster's transport efficiency, a second mapping relationship is constructed. The verification module obtains discrete transport capacity and discrete transport efficiency based on the first mapping relationship and the second mapping relationship, matches cooperative vehicles, verifies the cooperative vehicles, and executes the first operation, which is used to schedule vehicles or rematch cooperative vehicles.

[0015] The beneficial effects of this invention are as follows: By calculating the driving conversion coefficient and charging conversion coefficient of the vehicle cluster, the driving and charging needs of vehicles can be predicted and planned more accurately, thereby reducing empty driving and waiting time and improving overall transportation efficiency. Based on historical freight data and real-time order information, vehicle resources can be allocated more rationally to ensure vehicle availability in high-demand areas and time periods, reducing resource waste. Through collaborative scheduling, unnecessary driving distances and charging times can be reduced, thereby reducing fuel consumption and electricity costs, and reducing vehicle wear and maintenance costs. By establishing mapping relationships and discretization analysis, the system can better adapt to different road conditions and order changes, improving the ability to respond to emergencies. Through intelligent scheduling, it can ensure that vehicles are in optimal condition when needed, reducing idle time and improving vehicle utilization efficiency. Attached Figure Description

[0016] Figure 1 This is a basic flowchart illustrating a method for coordinated scheduling of driving and charging of a networked heavy vehicle cluster, as provided in one embodiment of the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0018] Example, refer to Figure 1 As an embodiment of the present invention, a method for coordinated scheduling of driving and charging of connected heavy vehicle clusters is provided, including the following steps: calculating the driving conversion coefficient and charging conversion coefficient of the vehicle cluster based on historical freight data; Based on the driving conversion coefficient, order information, and warehouse information, calculate the vehicle cluster capacity; and based on the charging conversion coefficient, calculate the vehicle cluster efficiency. Establish a first mapping relationship between the total driving distance of the vehicle cluster, the average road conditions from the origin to the exchange point, and the vehicle cluster's transport capacity. Construct a second mapping relationship based on the total driving distance, the average road conditions from the origin to the exchange point, and the vehicle cluster's transport efficiency. Based on the first and second mapping relationships, discrete transport capacity and discrete transport efficiency are obtained, and collaborative vehicles are matched. Verify the collaborative vehicle and perform the first operation, which is used to dispatch the vehicle or re-match the collaborative vehicle.

[0019] More preferably, by calculating the driving conversion coefficient and charging conversion coefficient of the vehicle cluster, the present invention can more accurately predict and plan the driving and charging needs of vehicles, thereby reducing empty driving and waiting time, improving overall transportation efficiency. Based on historical freight data and real-time order information, vehicle resources can be allocated more rationally, ensuring vehicle availability in high-demand areas and time periods, reducing resource waste. Through collaborative scheduling, unnecessary driving distances and charging times can be reduced, thereby reducing fuel consumption and electricity costs, and reducing vehicle wear and maintenance costs. By establishing mapping relationships and discretization analysis, the system can better adapt to different road conditions and order changes, improving the ability to respond to emergencies. Through intelligent scheduling, it can ensure that vehicles are in optimal condition when needed, reducing idle time and improving vehicle utilization efficiency.

[0020] Retrieve historical freight data and calculate the driving conversion coefficient and charging conversion coefficient of the vehicle cluster based on the historical freight data; More preferably, the historical freight data includes vehicle code, freight train code, single train load weight, distance from single train departure to the exchange point, distance from single train exchange point to the charging point, single train charging time, remaining power before single train charging, single train charging time, total freight distance per unit day, remaining power after single train charging, order information, and warehouse information.

[0021] More preferably, the vehicle code and freight train code are obtained through a preset vehicle code method and a preset freight train code method. The single-train load weight is represented as the weight of the goods transported in a single transport from the origin point to the exchange point. The distance from the origin to the exchange point and the distance from the exchange point to the charging point are represented. The total transport distance per unit day is a straight-line distance. The order information is represented as the logistics order information of the warehouse per unit day. The order information includes the total number of goods in the order, the timeliness type of the goods in the order, and the quantity of goods of the timeliness type in the order. The warehouse information is represented as the timeliness type of the remaining goods in the warehouse and the quantity of goods of the remaining timeliness type in the warehouse.

[0022] More preferably, the vehicle coding method includes: Obtain the vehicle license plate number and the time of entry into transportation. The time of entry into transportation is in the format of year, month, and day. Convert the letters in the vehicle license plate number into their corresponding alphabetical order numbers and generate a first numerical sequence based on the vehicle license plate number. Convert the month of the entry into transportation time point into a two-digit number. If the month of the entry into transportation time point is less than two digits, add leading zeros to the month. Convert the day of the entry into transportation time point into a two-digit number. If the month of the entry into transportation time point is less than two digits, add leading zeros to the month. Generate a second numerical sequence based on the order of year, month, and day. Combine the first numerical sequence and the second numerical sequence in sequence. Set the combined first numerical sequence and the second numerical sequence as the vehicle code.

[0023] More preferably, the method for coding freight train numbers includes: Obtain the vehicle code and the number of times the vehicle has arrived at the exchange point. Subtract 1 from the number of times the vehicle has arrived at the exchange point to get the first difference. Set the first difference as the number of deliveries. Convert the first difference into a four-digit number. If the first difference does not meet the requirement of a four-digit number, add 0s to the front of the first difference until the first difference meets the requirement of a four-digit number to obtain the third number sequence. Combine the vehicle code and the third number sequence in order. Set the combined vehicle code and the third number sequence as the delivery column code.

[0024] More preferably, the driving conversion coefficient of the vehicle cluster is calculated based on the load weight of a single train and the total freight distance per unit day, and the charging conversion coefficient of the vehicle cluster is calculated based on the remaining power before a single train charging, the remaining power after a single train charging, and the charging time of a single train charging.

[0025] More preferably, a first calculation method is provided for the driving conversion coefficient of the vehicle cluster, the first calculation method including: Select any vehicle, obtain the single-column load weight and the total daily freight distance for the vehicle, calculate the first ratio of the single-column load weight to the total daily freight distance, iterate through the first ratios for each vehicle, calculate the sum of the first ratios for each vehicle, count the number of vehicle numbers, calculate the second ratio of the sum of the first ratios for each vehicle to the number of vehicle numbers, and set the second ratio as the driving conversion coefficient. The larger the value of the second ratio, the worse the load-bearing capacity of the vehicle cluster.

[0026] More preferably, a second calculation method is configured for the charging conversion coefficient of the vehicle cluster, the second calculation method including: For any vehicle, obtain the remaining battery power before a single charge, the remaining battery power after a single charge, and the charging time for that single charge. Obtain the charging power curve, which represents the factory test charging power curve corresponding to the vehicle model. The horizontal axis of the charging power curve is time, and the vertical axis is power. According to the definite integral calculation method, start calculating the area under the curve from the origin until the difference between the area under the curve and the remaining battery power before a single charge is less than or equal to the first value. Stop calculating the area under the curve and mark the coordinate point corresponding to the integration time at this time. Use the marked point as the starting integration point. Calculate the first sum of the time point corresponding to the starting integration point and the charging time for a single charge. Set the coordinate point corresponding to the first sum as the ending integration point and perform definite integral calculation to obtain the first area. The first area represents the theoretical charging amount from the remaining battery power before a single charge to the end of the charging time. Calculate the second difference between the remaining power after a single charge and the remaining power before a single charge, and calculate the third ratio between the second difference and the theoretical difference; Calculate the average value of the third ratio for each vehicle within a day, iterate through the average value of the third ratio for each vehicle, calculate the average of the average values ​​of the third ratios, and set the average of the average values ​​of the third ratios as the charging conversion coefficient. The larger the value of the charging conversion coefficient, the better the charging conversion capability of the battery in the vehicle cluster.

[0027] The vehicle cluster capacity is calculated based on the driving conversion coefficient, order information, and warehouse information.

[0028] More preferably, the order delivery time types include half-day delivery, one-day delivery, and multi-day delivery. The total number of order goods, the order delivery time type, and the quantity of goods for each delivery time type are obtained. The order timeliness is calculated based on the total number of order goods, the order delivery time type, and the quantity of goods for each delivery time type. The remaining delivery time types and the quantity of goods for each delivery time type in the warehouse are obtained. The order completion rate is calculated based on the remaining delivery time types and the quantity of goods for each delivery time type in the warehouse.

[0029] More preferably, the method for calculating order timeliness includes: Calculate the first quantity of goods for orders delivered within half a day, the second quantity of goods for orders delivered within one day, and the third quantity of goods for orders delivered over multiple days. Calculate the product of the first quantity and 2, the second quantity and 1, and the third quantity and 0.5. Calculate the sum of the products of the first quantity and 2, the second quantity and 1, and the third quantity and 0.5, and record this as the order timeliness. More preferably, the method for calculating order completion includes: Calculate the fourth quantity of remaining goods for half-day delivery, the fifth quantity of remaining goods for one-day delivery, and the sixth quantity of remaining goods for multi-day delivery. Calculate the ratio of the fourth quantity to the first quantity, the fifth quantity to the second quantity, and the sixth quantity to the third quantity. Calculate the first product of the ratio of the fourth quantity to the first quantity and 0.2. Calculate the second product of the fifth quantity of remaining goods for one-day delivery and 0.5. Calculate the third product of the ratio of the sixth quantity to the third quantity and 1. Calculate the sum of the first, second, and third products, and record this as the order completion rate.

[0030] The vehicle cluster capacity is calculated based on the driving conversion coefficient, order timeliness, and order completion rate. The calculation method for vehicle cluster capacity includes: Calculate the sum of order timeliness and order completion rate, and the difference between the sum of order timeliness and order completion rate and the driving conversion coefficient. Set the sum of order timeliness and order completion rate and the difference between the driving conversion coefficient as the vehicle cluster capacity. The larger the value of the vehicle cluster capacity, the better the vehicle cluster transportation capacity.

[0031] The efficiency of vehicle cluster operation is calculated based on the charging conversion coefficient.

[0032] More preferably, the sum of order timeliness, order completion rate, and charging conversion coefficient is calculated and set as the vehicle cluster operation efficiency. The larger the vehicle cluster operation efficiency value, the better the vehicle cluster transportation efficiency.

[0033] Obtain the total travel distance of the vehicle cluster and the average road conditions from the origin to the exchange point, and establish the first mapping relationship between the total travel distance of the vehicle cluster, the average road conditions from the origin to the exchange point, and the transport capacity of the vehicle cluster. More preferably, the total driving distance is expressed as the sum of the distances traveled by each vehicle per unit day, and the average road conditions are expressed as the average friction force of the contact surface, the average temperature of the contact surface, the average hardness of the contact surface, the average impact load of the contact surface, the average lateral force of the tire, the average centrifugal force of the vehicle during turning, the average longitudinal inertial force of the vehicle, and the average acceleration driving force of the vehicle.

[0034] More preferably, the average friction force, average temperature, average hardness, and average impact load of the contact surface are obtained, and a first mapping relationship between the average friction force, average temperature, average hardness, and average impact load of the contact surface and the vehicle cluster capacity is constructed. By inputting the average friction force, average temperature, average hardness, and average impact load of the contact surface into the first mapping relationship, the corresponding vehicle cluster capacity is obtained.

[0035] Obtain the total travel distance of the vehicle cluster and the average road conditions from the origin to the exchange point. Based on the total travel distance, the average road conditions from the origin to the exchange point, and the vehicle cluster's operational efficiency, construct a second mapping relationship. More preferably, the average tire lateral force, average vehicle turning centrifugal force, average vehicle longitudinal inertial force, and average vehicle acceleration driving force are obtained, and a second mapping relationship between the average tire lateral force, average vehicle turning centrifugal force, average vehicle longitudinal inertial force, average vehicle acceleration driving force, and vehicle cluster operation efficiency is constructed. By inputting the average tire lateral force, average vehicle turning centrifugal force, average vehicle longitudinal inertial force, and average vehicle acceleration driving force into the second mapping relationship, the corresponding vehicle cluster operation efficiency is obtained.

[0036] Jump to any vehicle number, calculate the average road conditions at the current time point and the first time period before based on the data from each sensor, and obtain the real-time vehicle cluster capacity and real-time vehicle cluster efficiency corresponding to the vehicle based on the first mapping relationship and the second mapping relationship, which are denoted as discrete capacity and discrete efficiency. Discrete transport capacity and discrete transport efficiency are set as the first transport information of a vehicle, and the first transport information is bound to the vehicle's code and stored in the vehicle's corresponding radio frequency identification code.

[0037] More preferably, the first transportation information is bound to the vehicle's code. The binding means setting the first transportation information as a tag of the vehicle's code, storing the vehicle's code and the vehicle's code tag in the vehicle's corresponding radio frequency identification code, and obtaining the corresponding first transportation information by sensing the vehicle's code through radio frequency identification.

[0038] Set a first boundary condition and a second boundary condition, mark the corresponding frequency radio identification code of the vehicle according to the first boundary condition and the second boundary condition, and match the corresponding cooperative vehicle according to the first mark; The first boundary condition is expressed as the average value of the discrete transport capacity being equal to the transport capacity of the vehicle cluster; The second boundary condition is expressed as the average value of discrete transportation efficiency being equal to the transportation efficiency of vehicle clusters.

[0039] More preferably, the method for first marking the vehicle's corresponding radio frequency identification code based on the first boundary condition and the second boundary condition includes: Obtain the discrete transport capacity and discrete transport efficiency of the current vehicle. Based on the first boundary condition and the discrete transport capacity of the current vehicle, obtain the discrete transport capacity of another vehicle and record it as the first reference value. Based on the second boundary condition and the discrete transport efficiency of the current vehicle, obtain the discrete transport efficiency of another vehicle and record it as the second reference value. Set the first reference value and the second reference value as the first flag of the current vehicle.

[0040] More preferably, the method for matching the cooperative vehicle corresponding to the cooperative vehicle based on the first marker includes: Jump to the next vehicle code, where jumping means jumping in order of increasing straight-line distance from the current vehicle. Calculate the discrete capacity and discrete efficiency corresponding to the next vehicle code. Calculate the difference between the discrete capacity corresponding to the next vehicle code and the first reference value. When the difference between the discrete capacity corresponding to the next vehicle code and the first reference value is less than or equal to the second value, stop jumping and set the corresponding next vehicle code as the cooperative vehicle of the current vehicle. When the difference between the discrete capacity corresponding to the next vehicle code and the first reference value is greater than the second value, the jump continues until the difference between the discrete capacity corresponding to the next vehicle code and the first reference value is less than or equal to the second value. Then the jump stops and the corresponding next vehicle code is set as the cooperative vehicle of the current vehicle.

[0041] The system acquires the operation data of the collaborative vehicles, verifies the first marker based on the operation data, obtains the first verification result, and performs the first operation based on the verification result. The verification result includes valid and invalid. The first operation includes re-matching the collaborative vehicles and sending the first scheduling signal. When the verification result is valid, the first operation is set to send the first scheduling signal; when the verification result is invalid, the first operation is set to re-match the cooperating vehicle.

[0042] More preferably, the running data is represented as a time series of driving directions. The driving direction of the cooperative vehicle is obtained, the road between the current vehicle and the cooperative vehicle is obtained, and an arrow is set along the central axis of the road according to the direction from the cooperative vehicle to the current vehicle to obtain a first trajectory line. The angle between the driving direction of the cooperative vehicle and the tangent of the first trajectory line is obtained, the average value of the angle between the driving direction of the cooperative vehicle and the tangent of the first trajectory line at each time is calculated and recorded as the offset angle. An angle threshold is set for the first angle, and the offset angle is compared with the first angle. When the offset angle is less than or equal to the first angle, the first verification result is set to valid; when the offset angle is greater than the first angle, the first verification result is set to invalid.

[0043] In response to the first action, the cooperating vehicle is re-matched, or the first dispatch signal is sent.

[0044] More preferably, when the first operation is to re-match the cooperating vehicles, in response to the first operation, the cooperating vehicles are re-matched, and the re-matching method includes: Jump to the next vehicle code and repeat the setting method for the collaborative vehicle until the collaborative vehicle is set up. Then repeat the verification method until the verification result is valid, and send the first scheduling signal. When the verification result is invalid, the method of setting up the coordinated vehicle is repeated in a loop until the verification result is valid, at which point the first dispatch signal is sent.

[0045] More preferably, when the first operation is to send a first scheduling signal, in response to the first operation, the current vehicle's position coordinates are sent to the cooperating vehicle, and an instruction to arrive at the current vehicle's position coordinates is sent to the cooperating vehicle.

[0046] More preferably, by calculating the driving conversion coefficient and charging conversion coefficient of the vehicle cluster, the present invention can more accurately predict and plan the driving and charging needs of vehicles, thereby reducing empty driving and waiting time, improving overall transportation efficiency. Based on historical freight data and real-time order information, vehicle resources can be allocated more rationally, ensuring vehicle availability in high-demand areas and time periods, reducing resource waste. Through collaborative scheduling, unnecessary driving distances and charging times can be reduced, thereby reducing fuel consumption and electricity costs, and reducing vehicle wear and maintenance costs. By establishing mapping relationships and discretization analysis, the system can better adapt to different road conditions and order changes, improving the ability to respond to emergencies. Through intelligent scheduling, it can ensure that vehicles are in optimal condition when needed, reducing idle time and improving vehicle utilization efficiency.

[0047] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0048] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for coordinated scheduling of driving and charging of connected heavy-duty vehicle clusters, characterized in that, Includes the following steps: Step 1: Calculate the driving conversion coefficient and charging conversion coefficient of the vehicle cluster based on historical freight data; Step 1 includes: retrieving historical freight data and calculating the driving conversion coefficient and charging conversion coefficient of the vehicle cluster based on the historical freight data; There is a first calculation method for configuring the driving conversion coefficient of the vehicle cluster. The first calculation method includes: Select any vehicle, obtain the single-column load weight and the total freight distance per unit day for the vehicle, calculate the first ratio of the single-column load weight and the total freight distance per unit day, iterate through the first ratios for each vehicle, calculate the sum of the first ratios for each vehicle, count the number of vehicle numbers, calculate the second ratio of the sum of the first ratios for each vehicle to the number of vehicle numbers, and set the second ratio as the driving conversion coefficient. There is a second calculation method for the charging conversion coefficient configuration of the vehicle cluster. The second calculation method includes: For any vehicle, obtain the remaining battery power before a single charge, the remaining battery power after a single charge, and the charging time for that single charge. Obtain the charging power curve, which represents the factory test charging power curve corresponding to the vehicle model. The horizontal axis of the charging power curve is time, and the vertical axis is power. According to the definite integral calculation method, start calculating the area under the curve from the origin until the difference between the area under the curve and the remaining battery power before a single charge is less than or equal to a first value. Stop calculating the area under the curve and mark the coordinate point corresponding to the integration time at this time. Use the marked point as the starting integration point. Calculate the first sum of the time point corresponding to the starting integration point and the charging time for a single charge. Set the coordinate point corresponding to the first sum as the ending integration point. Perform definite integral calculation to obtain the first area. The first area represents the theoretical charging amount from the remaining battery power before a single charge to the end of the charging time. Calculate the second difference between the remaining power after a single charge and the remaining power before a single charge, and calculate the third ratio between the second difference and the theoretical difference; Calculate the average value of the third ratio for each vehicle per day, iterate through the average value of the third ratio for each vehicle, calculate the average of the average values ​​of the third ratio, and set the average of the average values ​​of the third ratio as the charging conversion coefficient. Step 2: Calculate the vehicle cluster capacity based on the driving conversion coefficient, order information, and warehouse information; calculate the vehicle cluster efficiency based on the charging conversion coefficient. Step 2 includes: calculating the vehicle cluster capacity based on the driving conversion coefficient, order information, and warehouse information; The methods for calculating order timeliness include: Calculate the first quantity of goods for orders delivered within half a day, the second quantity of goods for orders delivered within one day, and the third quantity of goods for orders delivered over multiple days. Calculate the product of the first quantity and 2, the second quantity and 1, and the third quantity and 0.

5. Calculate the sum of the products of the first quantity and 2, the second quantity and 1, and the third quantity and 0.5, and record this as the order timeliness. The methods for calculating order completion rate include: Calculate the fourth quantity of remaining goods for half-day delivery, the fifth quantity of remaining goods for one-day delivery, and the sixth quantity of remaining goods for multi-day delivery. Calculate the first ratio of the fourth quantity to the first quantity, the second ratio of the fifth quantity to the second quantity, and the third ratio of the sixth quantity to the third quantity. Calculate the first product of the first ratio and 0.2, the second product of the second ratio and 0.5, and the third product of the third ratio and 1. Calculate the sum of the first, second, and third products and record it as the order completion rate. The vehicle cluster capacity is calculated based on the driving conversion coefficient, order timeliness, and order completion rate. The calculation method for vehicle cluster capacity includes: Calculate the sum of order timeliness and order completion rate, calculate the difference between the sum of order timeliness and order completion rate and the driving conversion coefficient, and set the sum of order timeliness and order completion rate and the difference between the driving conversion coefficient as the vehicle cluster capacity; Calculate the vehicle cluster operation efficiency based on the charging conversion coefficient; Calculate the sum of order timeliness, order completion rate, and charging conversion coefficient, and set it as the vehicle cluster operation efficiency; Step 3: Establish the first mapping relationship between the total driving distance of the vehicle cluster, the average road conditions from the origin to the exchange point, and the vehicle cluster's transport capacity. Based on the total driving distance, the average road conditions from the origin to the exchange point, and the vehicle cluster's transport efficiency, construct the second mapping relationship. Step 4: Obtain discrete transport capacity and discrete transport efficiency based on the first and second mapping relationships, and match cooperative vehicles; Step 4 includes: jumping to any vehicle number, calculating the average road conditions at the current time point and the previous first time period based on the data from each sensor, and obtaining the real-time vehicle cluster capacity and real-time vehicle cluster efficiency corresponding to the vehicle based on the first mapping relationship and the second mapping relationship, which are denoted as discrete capacity and discrete efficiency. Discrete transport capacity and discrete transport efficiency are set as the first transport information of a vehicle, and the first transport information is bound to the vehicle's code and stored in the vehicle's corresponding radio frequency identification code; Set a first boundary condition and a second boundary condition, mark the corresponding frequency radio identification code of the vehicle according to the first boundary condition and the second boundary condition, and match the corresponding cooperative vehicle according to the first mark; The first boundary condition is expressed as the average value of the discrete transport capacity being equal to the transport capacity of the vehicle cluster; The second boundary condition is expressed as the average value of discrete transportation efficiency being equal to the transportation efficiency of vehicle clusters; The system acquires the operation data of the collaborative vehicles, verifies the first marker based on the operation data, obtains the first verification result, and performs the first operation based on the verification result. The verification result includes valid and invalid. The first operation includes re-matching the collaborative vehicles and sending the first scheduling signal. When the verification result is valid, the first operation is set to send the first scheduling signal; when the verification result is invalid, the first operation is set to re-match the cooperating vehicle. Step 5: Verify the collaborative vehicle and perform the first operation, which is used to dispatch the vehicle or re-match the collaborative vehicle.

2. The method for coordinated scheduling of driving and charging of a networked heavy vehicle cluster as described in claim 1, characterized in that: In step 3, the total travel distance of the vehicle cluster and the average road conditions from the origin to the exchange point are obtained, and the first mapping relationship between the total travel distance of the vehicle cluster, the average road conditions from the origin to the exchange point, and the transport capacity of the vehicle cluster is established.

3. The method for coordinated scheduling of driving and charging of a networked heavy vehicle cluster as described in claim 1, characterized in that: In step 3, the total travel distance of the vehicle cluster and the average road conditions from the origin to the exchange point are obtained. Based on the total travel distance, the average road conditions from the origin to the exchange point, and the vehicle cluster's operational efficiency, a second mapping relationship is constructed.

4. A driving and charging coordinated scheduling system for a connected heavy-duty vehicle cluster, the system being used to execute the driving and charging coordinated scheduling method for a connected heavy-duty vehicle cluster as described in claim 1, characterized in that, It includes a calculation module, a construction module, and a verification module; The calculation module calculates the driving conversion coefficient and charging conversion coefficient of the vehicle cluster based on historical freight data, calculates the vehicle cluster capacity based on the driving conversion coefficient, order information, and warehouse information, and calculates the vehicle cluster efficiency based on the charging conversion coefficient. The construction module establishes a first mapping relationship between the total driving distance of the vehicle cluster, the average road conditions from the origin to the exchange point, and the vehicle cluster's transport capacity. Based on the total driving distance, the average road conditions from the origin to the exchange point, and the vehicle cluster's transport efficiency, a second mapping relationship is constructed. The verification module obtains discrete transport capacity and discrete transport efficiency based on the first mapping relationship and the second mapping relationship, matches cooperative vehicles, verifies the cooperative vehicles, and executes the first operation, which is used to schedule vehicles or rematch cooperative vehicles.

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