Driving and charging cooperative scheduling system and method for networked heavy vehicle cluster

Abstract

The invention discloses a driving and charging cooperative scheduling system and method for a networked heavy vehicle cluster, and relates to the technical field of intelligent management, and the method comprises the following steps: calculating a driving conversion coefficient and a charging conversion coefficient of a vehicle cluster, calculating the transport capacity and the transport efficiency of the vehicle cluster, building a first mapping relation and a second mapping relation, the discrete transport capacity and the discrete transport efficiency are obtained, a cooperative vehicle is matched, the cooperative vehicle is verified, and the first operation is executed. By calculating the driving conversion coefficient and the charging conversion coefficient of the vehicle cluster, the driving and charging demands of the vehicles are predicted and planned more accurately, the vehicle resources are allocated more reasonably according to the historical freight data and the real-time order information, and the fuel consumption and the electric power cost are reduced, the wear and maintenance cost of the vehicles is reduced through collaborative scheduling, so that the vehicle transportation efficiency is improved. Through establishment of a mapping relation and discretization analysis, the capability of responding to emergencies is improved, and through intelligent scheduling, idle time is reduced, and the use efficiency of vehicles is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of a net-connected heavy vehicle cluster driving and charging coordination scheduling system and method. BACKGROUND

[0002] In recent years, heavy vehicle cluster driving planning technology and charging coordination scheduling technology are widely used. The initial scheme is generated by using mixed integer programming, the Vicsek group motion model is introduced to depict the dynamics of vehicles, roads and stations, the improved particle swarm and NSGA-II are combined to solve, the path, queuing and charging joint optimization is realized, the conflicts and waiting are reduced, the upper optimization charging station power, electricity price and carbon quota are used, the lower layer decision single vehicle path and charging capacity are used to support complex constraints such as on-the-way partial charging, pick-up and delivery, time window, etc. The master-slave game is used on the micro-grid side, the micro-grid operator is the leader and the EV cluster is the follower, the step electricity price and carbon emission trading income are combined to guide the heavy truck peak shifting charging and V2G discharging, the user cost is reduced, and the new energy consumption rate is improved.

[0003] At present, in the Chinese patent with publication number CN118396317A, an automatic port AGV charging optimization scheduling method is disclosed. The method couples the relationship between logistics scheduling and orderly battery replacement, takes the minimum logistics scheduling time of the port battery replacement station and the battery replacement station electricity purchase fee as the objective function, and solves based on the immune optimization algorithm. The present application specifies the ship berthing position by the scheduling system, and allocates the corresponding shore bridge in advance, assigns the AGV that meets the power requirement to the specified shore bridge position to wait, after the shore bridge loads or unloads the container to the AGV, the AGV automatically drives to the specified unloading area in the yard, and waits for the yard bridge to unload the container to the destination, and then completes the task, the AGV remains in place, and waits for the next round of assignment. However, in the related art, the AGV idle stay time is not conducive to the efficiency of the scheduling, and the utilization ratio of resources is not conducive to the accuracy of the scheduling. Through a single scheduling instruction, the AGV is sent an instruction, for a large amount of data, such as an AGV vehicle cluster, there are multiple instructions, which can easily cause the problem of incorrect instruction transmission, and the driving AGV vehicle cluster and charging characteristics are not coupled to quickly obtain a scheduling scheme, which is not conducive to the rapidity of the scheduling, and has certain limitations. SUMMARY

[0004] The technical problem solved by the present application is that in the related art, the AGV idle stay time is not conducive to the efficiency of the scheduling, and the utilization ratio of resources is not conducive to the accuracy of the scheduling. Through a single scheduling instruction, the AGV is sent an instruction, for a large amount of data, such as an AGV vehicle cluster, there are multiple instructions, which can easily cause the problem of incorrect instruction transmission, and the driving AGV vehicle cluster and charging characteristics are not coupled to quickly obtain a scheduling scheme, which is not conducive to the rapidity of the scheduling, and has certain limitations.

[0005] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, a driving and charging coordination scheduling method for a network-connected heavy vehicle cluster includes the following steps: According to historical freight data, calculate the driving conversion coefficient and the charging conversion coefficient of the vehicle cluster; According to the driving conversion coefficient, order information, and warehouse information, calculate the vehicle cluster's carrying capacity, and according to the charging conversion coefficient, calculate the vehicle cluster's carrying efficiency; Establish a first mapping relationship between the total driving distance of the vehicle cluster and the average road conditions from the origin to the exchange point and the vehicle cluster's carrying capacity, and according to the total driving distance and the average road conditions from the origin to the exchange point and the vehicle cluster's carrying efficiency, construct a second mapping relationship; According to the first mapping relationship and the second mapping relationship, obtain discrete carrying capacity and discrete carrying efficiency, and match the collaborative vehicles; Verify the collaborative vehicles and perform a first operation, which is used to schedule the vehicles or re-match the collaborative vehicles.

[0006] As a preferred scheme of the driving and charging coordination scheduling method for a network-connected heavy vehicle cluster, the historical freight data is retrieved, and according to the historical freight data, the driving conversion coefficient and the charging conversion coefficient of the vehicle cluster are calculated; The vehicle cluster's driving conversion coefficient is configured with a first calculation method, which includes: Select any vehicle, obtain the single-column secondary load gravity and the total distance of unit-day freight transport corresponding to the vehicle, calculate the first ratio of the single-column secondary load gravity and the total distance of unit-day freight transport, traverse the first ratio corresponding to each vehicle, calculate the sum value of the first ratio corresponding to each vehicle, count the number of vehicle numbers, calculate the second ratio of the sum value of the first ratio corresponding to each vehicle and the number of vehicle numbers, and set the second ratio as the driving conversion coefficient.

[0007] As a preferred scheme of the driving and charging coordination scheduling method for a network-connected heavy vehicle cluster, the vehicle cluster's charging conversion coefficient is configured with a second calculation method, which includes: Any vehicle is obtained, the single column secondary charging remaining electric quantity of the vehicle corresponding to the vehicle, the single column secondary charging remaining electric quantity and the single column secondary charging time length are obtained, the charging power curve is obtained, the charging power curve is represented as the charging power curve of the factory test corresponding to the model of the vehicle, the horizontal axis of the charging power curve is time, the vertical axis is power, according to the definite integral calculation method, the area under the curve is calculated from the origin, until the difference between the area under the curve and the single column secondary charging remaining electric quantity is less than or equal to the first value, stop calculating the area under the curve, and mark the coordinate point corresponding to the integral time at this time as a starting integral point, calculate the first sum value of the time point corresponding to the starting integral point and the single column secondary charging time length, set the coordinate point corresponding to the first sum value as the end integral point, and carry out the definite integral calculation to obtain the first area, the first area represents the theoretical charging quantity from the single column secondary charging remaining electric quantity to the end of the single column secondary charging time length; The second difference value between the single column secondary charging remaining electric quantity and the single column secondary charging remaining electric quantity is calculated, and the third ratio value of the second difference value and the theoretical difference value is calculated. The average value of the third ratio value in the unit day of the vehicle is calculated, the average value of the average value of the third ratio value is calculated by traversing the average value of the third ratio value corresponding to each vehicle, and the average value of the average value of the third ratio value is set as the charging conversion coefficient.

[0008] As a preferred scheme of the driving and charging cooperative scheduling method of the networked heavy vehicle cluster, wherein: according to the driving conversion coefficient, the order information and the warehouse information, the vehicle cluster carrying capacity is calculated. The order timeliness calculation method comprises: The first quantity of order goods of half-day, the second quantity of order goods of one-day, and the third quantity of order goods of multi-day are counted, the product of the first quantity and 2, the product of the second quantity and 1, and the product of the third quantity and 0.5 are calculated, and the sum of the product of the first quantity and 2, the product of the second quantity and 1, and the product of the third quantity and 0.5 is calculated, which is recorded as the order timeliness. Further preferably, the order completion degree calculation method comprises: The fourth quantity of residual goods of half-day, the fifth quantity of residual goods of one-day, and the sixth quantity of residual goods of multi-day are counted, the ratio of the fourth quantity to the first quantity, the ratio of the fifth quantity to the second quantity, and the ratio of the sixth quantity to the third quantity are calculated, the first product of the ratio of the fourth quantity to the first quantity and 0.2 is calculated, the second product of the fifth quantity of residual goods of one-day and 0.5 is calculated, the third product of the ratio of the sixth quantity to the third quantity and 1 is calculated, and the sum of the first product, the second product and the third product is calculated, which is recorded as the order completion degree. According to the driving conversion coefficient, the order timeliness and the order completion degree, the vehicle cluster carrying capacity is calculated, and the vehicle cluster carrying capacity calculation method comprises: The sum of the order timeliness and the order completion degree is calculated, the sum of the order timeliness and the order completion degree and the difference of the driving conversion coefficient are calculated, and the sum of the order timeliness and the order completion degree and the difference of the driving conversion coefficient are set as the vehicle cluster carrying capacity.

[0009] As a preferred scheme of the driving and charging collaborative scheduling method of the networked heavy vehicle cluster, the vehicle cluster carrying capacity is calculated according to the charging conversion coefficient. The sum of the order timeliness, the order completion degree and the charging conversion coefficient is calculated, and the sum is set as the vehicle cluster carrying capacity.

[0010] As a preferred scheme of the driving and charging collaborative scheduling method of the networked heavy vehicle cluster, the driving total distance of the vehicle cluster and the average road condition from the starting point to the goods exchange point are obtained, and a first mapping relationship between the driving total distance of the vehicle cluster and the average road condition from the starting point to the goods exchange point and the vehicle cluster carrying capacity is established.

[0011] As a preferred scheme of the driving and charging collaborative scheduling method of the networked heavy vehicle cluster, the driving total distance of the vehicle cluster and the average road condition from the starting point to the goods exchange point are obtained, and a second mapping relationship is constructed according to the driving total distance, the average road condition from the starting point to the goods exchange point and the vehicle cluster carrying capacity.

[0012] As a preferred scheme of the driving and charging collaborative scheduling method of the networked heavy vehicle cluster, any numbered vehicle is jumped, the average road condition in the current time point and the first time period before the current time point is calculated according to the sensor data, the real-time vehicle cluster carrying capacity and the real-time vehicle cluster carrying capacity corresponding to the vehicle are obtained according to the first mapping relationship and the second mapping relationship, and are recorded as discrete carrying capacity and discrete carrying capacity. The discrete carrying capacity and the discrete carrying capacity are set as the first transportation information of the vehicle, the first transportation information is bound with the code of the vehicle, and is stored in the corresponding frequency identification code of the vehicle.

[0013] As a preferred scheme of the driving and charging collaborative scheduling method of the networked heavy vehicle cluster, the first boundary condition and the second boundary condition are set, the corresponding frequency identification code of the vehicle is first marked according to the first boundary condition and the second boundary condition, and the collaborative vehicle corresponding to the vehicle is matched according to the first mark. The first boundary condition is that the average value of the discrete carrying capacity is equal to the vehicle cluster carrying capacity. The second boundary condition is that the average value of the discrete carrying capacity is equal to the vehicle cluster carrying capacity. Obtain operation data of the cooperative vehicle, verify the first mark according to the operation data, obtain a first verification result, perform a first operation according to the verification result, the verification result includes valid and invalid, the first operation includes re-matching the cooperative vehicle, and send a first scheduling signal; When the verification result is valid, the first operation is set to send the first scheduling signal, and when the verification result is invalid, the first operation is set to re-match the cooperative vehicle.

[0014] In a second aspect, a driving and charging cooperative scheduling system for a networked heavy vehicle cluster includes a calculation module, a construction module, and a verification module. The calculation module calculates a driving conversion coefficient and a charging conversion coefficient of the vehicle cluster according to historical freight data, calculates vehicle cluster capacity according to the driving conversion coefficient, order information, and warehouse information, and calculates vehicle cluster efficiency according to the charging conversion coefficient. The construction module establishes a first mapping relationship between the driving total distance of the vehicle cluster and the average road condition from the origin to the goods exchange point and the vehicle cluster capacity, and constructs a second mapping relationship according to the driving total distance and the average road condition from the origin to the goods exchange point and the vehicle cluster efficiency. The verification module obtains discrete capacity and discrete efficiency according to the first mapping relationship and the second mapping relationship, matches the cooperative vehicle, verifies the cooperative vehicle, and performs a first operation, which is used to schedule the vehicle or re-match the cooperative vehicle.

[0015] The present application has the following advantages: by calculating the driving conversion coefficient and the charging conversion coefficient of the vehicle cluster, the driving and charging requirements of the vehicle can be more accurately predicted and planned, thereby reducing empty running and waiting time, improving overall transportation efficiency, and more reasonably allocating vehicle resources according to historical freight data and real-time order information to ensure the availability of vehicles in high-demand areas and time periods and reduce resource waste. Through cooperative scheduling, unnecessary driving distance and charging frequency can be reduced, thereby reducing fuel consumption and power cost and reducing vehicle wear and maintenance cost. Through the establishment of mapping relationship and discretization analysis, the system can better adapt to different road conditions and order changes and improve the response capability to sudden situations. Through intelligent scheduling, the vehicle can be ensured to be in the best state when needed, reducing idle time and improving the use efficiency of the vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A basic flowchart of a driving and charging cooperative scheduling method for a networked heavy vehicle cluster is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments.

[0018] Embodiments, with reference to Figure 1 For an embodiment of the present application, a driving and charging cooperative scheduling method for a network-connected heavy vehicle cluster is provided, comprising the following steps: calculating a driving conversion coefficient and a charging conversion coefficient of the vehicle cluster according to historical freight data; According to the driving conversion coefficient, order information and warehouse information, the vehicle cluster carrying capacity is calculated, and the vehicle cluster carrying efficiency is calculated according to the charging conversion coefficient; A first mapping relationship between the total driving distance of the vehicle cluster and the average road condition from the origin to the exchange point and the vehicle cluster carrying capacity is established, and a second mapping relationship is constructed according to the total driving distance and the average road condition from the origin to the exchange point and the vehicle cluster carrying efficiency; According to the first mapping relationship and the second mapping relationship, discrete carrying capacity and discrete carrying efficiency are obtained, and cooperative vehicles are matched; The cooperative vehicles are verified, and a first operation is performed, which is used to schedule vehicles or re-match cooperative vehicles.

[0019] Further preferably, by calculating the driving conversion coefficient and the charging conversion coefficient of the vehicle cluster, the present application can more accurately predict and plan the driving and charging needs of the vehicle, thereby reducing empty running and waiting time, improving overall transportation efficiency, and more reasonably allocating vehicle resources according to historical freight data and real-time order information, ensuring the availability of vehicles in high-demand areas and time periods, reducing resource waste, and reducing unnecessary driving distance and charging frequency through cooperative scheduling, thereby reducing fuel consumption and power cost, reducing vehicle wear and maintenance cost, and improving the response capability to sudden situations through mapping relationship establishment and discretization analysis, the system can better adapt to different road conditions and order changes, and improve the response capability to sudden situations through intelligent scheduling, which can ensure that the vehicle is in the best state when needed, reduce idle time, and improve the use efficiency of the vehicle.

[0020] The historical freight data is retrieved, and the driving conversion coefficient and the charging conversion coefficient of the vehicle cluster are calculated according to the historical freight data; Further preferably, the historical freight data includes vehicle code, freight train number code, single train number load gravity, single train number distance from origin to exchange point, single train number distance from exchange point to charging point, single train number charging time, single train number remaining power before charging, single train number charging time, total freight distance per day, single train number remaining power after charging, order information, warehouse information.

[0021] Further preferably, the vehicle code, the freight train code, the single train load gravity, the single train distance from the starting point to the freight point, the single train distance from the freight point to the charging point, the total freight distance per unit day, the order information, and the warehouse information are obtained by preset vehicle coding methods and preset freight train coding methods.

[0022] Further preferably, the vehicle coding method comprises: obtaining the vehicle license plate number and the input transportation time point, the time format of the input transportation time point being year, month, and day, converting the letters in the vehicle license plate number into the corresponding English letters in the order of the alphabet, generating a first digital sequence according to the vehicle license plate number, converting the month of the input transportation time point into two digits, and supplementing 0 in front of the month of the input transportation time point when the month of the input transportation time point is less than two digits, converting the day of the input transportation time point into two digits, and supplementing 0 in front of the month of the input transportation time point when the month of the input transportation time point is less than two digits, generating a second digital sequence according to the order of the year, month, and day, sequentially combining the first digital sequence and the second digital sequence, and setting the sequentially combined first digital sequence and second digital sequence as the vehicle code.

[0023] Further preferably, the freight train coding method comprises: obtaining the vehicle code, obtaining the number of times the vehicle currently arrives at the freight point, subtracting 1 from the number of times the vehicle currently arrives at the freight point to obtain a first difference, setting the first difference as the freight number, converting the first difference into four digits, and supplementing 0 in front of the first difference until the first difference meets four digits when the first difference does not meet four digits, obtaining a third digital sequence, sequentially combining the vehicle code and the third digital sequence, and setting the sequentially combined vehicle code and third digital sequence as the freight train code.

[0024] Further preferably, the driving conversion coefficient of the vehicle cluster is calculated according to the single train load gravity and the total freight distance per unit day, and the charging conversion coefficient of the vehicle cluster is calculated according to the single train remaining charge before charging, the single train remaining charge after charging, and the single train charging duration.

[0025] Further preferably, the driving conversion coefficient of the vehicle cluster is configured with a first calculation method, and the first calculation method comprises: Select any vehicle, get the single column secondary load gravity and unit day total distance corresponding to the vehicle, calculate the first ratio of the single column secondary load gravity and unit day total distance, traverse the first ratio corresponding to each vehicle, calculate the sum value of the first ratio corresponding to each vehicle, count the number of vehicle numbers, calculate the second ratio of the sum value of the first ratio corresponding to each vehicle and the number of vehicle numbers, set the second ratio as the driving conversion coefficient, the larger the value of the second ratio, the worse the heavy transport capacity of the vehicle cluster.

[0026] Further preferably, the charging conversion coefficient of the vehicle cluster is configured with a second calculation method, and the second calculation method comprises: Get any vehicle, get the single column secondary charging remaining capacity, single column secondary charging remaining capacity and single column secondary charging time corresponding to the vehicle, get the charging power curve, the charging power curve represents the charging power curve of the factory test corresponding to the model of the vehicle, the horizontal axis of the charging power curve is time, and the vertical axis is power, according to the definite integral calculation method, calculate the area under the curve from the origin, until the difference between the area under the curve and the single column secondary charging remaining capacity is less than or equal to the first value, stop calculating the area under the curve, and mark the coordinate point corresponding to the integral time at this time as the starting integral point, calculate the first sum value of the time point corresponding to the starting integral point and the single column secondary charging time, and set the coordinate point corresponding to the first sum value as the end integral point. The first area is obtained by performing definite integral calculation, which represents the theoretical charging capacity from the single column secondary charging remaining capacity to the end of the single column secondary charging time; Calculate the second difference between the single column secondary charging remaining capacity and the single column secondary charging remaining capacity, and calculate the third ratio of the second difference and the theoretical difference; Calculate the average value of the third ratio in the unit day of the vehicle, traverse the average value of the third ratio corresponding to each vehicle, calculate the average value of the average value of the third ratio, and set the average value of the average value of the third ratio as the charging conversion coefficient. The larger the value of the charging conversion coefficient, the better the charging conversion capacity of the battery of the vehicle cluster.

[0027] According to the driving conversion coefficient, the order information and the warehouse information, the vehicle cluster capacity is calculated.

[0028] Further preferably, the order goods time limit type includes half-day delivery, one-day delivery and multi-day delivery, the total number of order goods, the order goods time limit type and the number of goods of the order goods time limit type are obtained, the order time limit degree is calculated according to the total number of order goods, the order goods time limit type and the number of goods of the order goods time limit type, and the order completion degree is calculated according to the remaining goods time limit type of the warehouse and the number of goods of the remaining goods time limit type of the warehouse.

[0029] Further preferably, the calculation method of the order timeliness comprises: counting the first quantity of the ordered goods delivered within half a day, the second quantity of the ordered goods delivered within a day, and the third quantity of the ordered goods delivered within multiple days, calculating the product of the first quantity and 2, the product of the second quantity and 1, and the product of the third quantity and 0.5, calculating the sum of the product of the first quantity and 2, the product of the second quantity and 1, and the product of the third quantity and 0.5, and recording the sum as the order timeliness. Further preferably, the calculation method of the order completion degree comprises: counting the fourth quantity of the remaining goods delivered within half a day, the fifth quantity of the remaining goods delivered within a day, and the sixth quantity of the remaining goods delivered within multiple days, calculating the ratio of the fourth quantity to the first quantity, the ratio of the fifth quantity to the second quantity, and the ratio of the sixth quantity to the third quantity, calculating the first product of the ratio of the fourth quantity to the first quantity and 0.2, calculating the second product of the fifth quantity and 0.5, calculating the third product of the ratio of the sixth quantity to the third quantity and 1, and calculating the sum of the first product, the second product, and the third product, and recording the sum as the order completion degree.

[0030] According to the driving conversion coefficient, the order timeliness, and the order completion degree, the vehicle cluster transport capacity is calculated, and the calculation method of the vehicle cluster transport capacity comprises: calculating the sum of the order timeliness and the order completion degree, calculating the difference between the sum of the order timeliness and the order completion degree and the driving conversion coefficient, setting the difference between the sum of the order timeliness and the order completion degree and the driving conversion coefficient as the vehicle cluster transport capacity, and the larger the value of the vehicle cluster transport capacity, the better the vehicle cluster transport capacity.

[0031] According to the charging conversion coefficient, the vehicle cluster transport efficiency is calculated.

[0032] Further preferably, the sum of the order timeliness, the order completion degree, and the charging conversion coefficient is calculated, and the sum is set as the vehicle cluster transport efficiency, and the larger the value of the vehicle cluster transport efficiency, the better the vehicle cluster transport efficiency.

[0033] The total driving distance of the vehicle cluster and the average road condition from the origin to the goods exchange point are obtained, and a first mapping relationship between the total driving distance of the vehicle cluster and the average road condition from the origin to the goods exchange point and the vehicle cluster transport capacity is established. Further preferably, the total driving distance represents the sum of the distances driven by each vehicle per unit day, and the average road condition represents 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 side 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] Further preferably, the average friction force of the contact surface, the average temperature of the contact surface, the average hardness of the contact surface, and the average impact load of the contact surface are obtained, a first mapping relationship between the average friction force of the contact surface, the average temperature of the contact surface, the average hardness of the contact surface, and the average impact load of the contact surface and the vehicle cluster carrying capacity is constructed, and the corresponding vehicle cluster carrying capacity is obtained by inputting the average friction force of the contact surface, the average temperature of the contact surface, the average hardness of the contact surface, and the average impact load of the contact surface into the first mapping relationship.

[0035] The total driving distance of the vehicle cluster and the average road condition from the origin to the exchange point are obtained, and a second mapping relationship is constructed according to the total driving distance, the average road condition from the origin to the exchange point, and the vehicle cluster carrying efficiency. Further preferably, the average side force of the tire, the average turning centrifugal force of the vehicle, the average longitudinal inertial force of the vehicle, and the average acceleration driving force of the vehicle are obtained, and a second mapping relationship between the average side force of the tire, the average turning centrifugal force of the vehicle, the average longitudinal inertial force of the vehicle, and the average acceleration driving force of the vehicle and the vehicle cluster carrying efficiency is constructed, and the corresponding vehicle cluster carrying efficiency is obtained by inputting the average side force of the tire, the average turning centrifugal force of the vehicle, the average longitudinal inertial force of the vehicle, and the average acceleration driving force of the vehicle into the second mapping relationship.

[0036] Any numbered vehicle is jumped, the average road condition in the first time period is calculated according to the sensor data at the current time point, and the real-time vehicle cluster carrying capacity and the real-time vehicle cluster carrying efficiency corresponding to the vehicle are obtained according to the first mapping relationship and the second mapping relationship, which are recorded as discrete carrying capacity and discrete carrying efficiency. The discrete carrying capacity and the discrete carrying efficiency are set as the first transportation information of the vehicle, and the first transportation information is bound with the code of the vehicle and stored in the corresponding radio frequency identification code of the vehicle.

[0037] Further preferably, the first transportation information is bound with the code of the vehicle, which is represented by setting the first transportation information as the label of the code of the vehicle, storing the code of the vehicle and the label of the code of the vehicle in the corresponding radio frequency identification code of the vehicle, and obtaining the corresponding first transportation information by sensing the code of the vehicle through the radio frequency identification.

[0038] The first boundary condition and the second boundary condition are set, the corresponding radio frequency identification code of the vehicle is first marked according to the first boundary condition and the second boundary condition, and the cooperative vehicle corresponding to the vehicle is matched according to the first marking. The first boundary condition is that the average value of the discrete carrying capacity is equal to the vehicle cluster carrying capacity. The second boundary condition is that the average value of the discrete carrying efficiency is equal to the vehicle cluster carrying efficiency.

[0039] Further preferably, the method for first marking the corresponding radio frequency identification code of the vehicle according to the first boundary condition and the second boundary condition comprises: obtaining discrete capacity and discrete efficiency of the current vehicle, obtaining discrete capacity of another vehicle according to the first boundary condition and the discrete capacity of the current vehicle, and recording the discrete capacity of the another vehicle as a first reference value, obtaining discrete efficiency of the another vehicle according to the second boundary condition and the discrete efficiency of the current vehicle, and recording the discrete efficiency of the another vehicle as a second reference value, and setting the first reference value and the second reference value as the first mark of the current vehicle.

[0040] Further preferably, the method of matching the cooperative vehicle corresponding to the cooperative vehicle according to the first mark comprises: jumping to the next vehicle code, wherein the jumping is represented as jumping in the order of the straight-line distance from the current vehicle from small to large, calculating the discrete capacity and the discrete efficiency corresponding to the next vehicle code, calculating the difference between the discrete capacity corresponding to the next vehicle code and the first reference value, stopping the jumping 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, and setting the corresponding next vehicle code as the cooperative vehicle of the current vehicle; continuing the jumping when the difference between the discrete capacity corresponding to the next vehicle code and the first reference value is greater than the second value, and stopping the jumping 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, and setting the corresponding next vehicle code as the cooperative vehicle of the current vehicle.

[0041] obtaining the running data of the cooperative vehicle, verifying the first mark according to the running data to obtain a first verification result, and performing a first operation according to the verification result, wherein the verification result comprises valid and invalid, and the first operation comprises re-matching the cooperative vehicle and sending a first scheduling signal; setting the first operation as sending the first scheduling signal when the verification result is valid, and setting the first operation as re-matching the cooperative vehicle when the verification result is invalid.

[0042] Further preferably, the running data is represented as a time sequence of the driving direction, obtaining the driving direction of the cooperative vehicle, obtaining the road between the current vehicle and the cooperative vehicle, and setting an arrow according to the direction of the cooperative vehicle to the current vehicle along the central axis of the road to obtain a first trajectory line, obtaining the included angle between the driving direction of the cooperative vehicle and the tangent of the first trajectory line, calculating the average value of the included angle between the driving direction of the cooperative vehicle and the tangent of the first trajectory line at each time, recording the average value as an offset angle, setting an angle threshold value for the first angle, and comparing the offset angle with the first angle; setting the first verification result as valid when the offset angle is less than or equal to the first angle, and setting the first verification result as invalid when the offset angle is greater than the first angle.

[0043] responding to the first operation, re-matching the cooperative vehicle or sending the first scheduling signal.

[0044] Further preferably, when the first operation is to re-match the cooperative vehicle, in response to the first operation, the cooperative vehicle is re-matched, and the re-matching method comprises: Jumping to the next vehicle code and repeating the cooperative vehicle setting method until the cooperative vehicle is set up, and repeating the verification method until the verification result is valid, and then sending the first scheduling signal; When the verification result is invalid, the cooperative vehicle setting method is repeatedly cycled until the verification result is valid, and then the first scheduling signal is sent.

[0045] Further preferably, when the first operation is to send the first scheduling signal, in response to the first operation, the position coordinates of the current vehicle are sent to the cooperative vehicle, and instructions to reach the position coordinates of the current vehicle are sent to the cooperative vehicle.

[0046] Further preferably, the present application can more accurately predict and plan the driving and charging needs of vehicles by calculating the driving conversion coefficient and the charging conversion coefficient of the vehicle cluster, thereby reducing empty running and waiting time, improving overall transportation efficiency, and more reasonably allocating vehicle resources according to historical freight data and real-time order information to ensure the availability of vehicles in high-demand areas and time periods, reduce resource waste, reduce unnecessary driving distance and charging frequency through cooperative scheduling, thereby reducing fuel consumption and power costs, reducing vehicle wear and maintenance costs, and improving the system's ability to adapt to different road conditions and order changes through mapping and discrete analysis, improving the response to sudden situations, and ensuring that vehicles are in the best condition when needed through intelligent scheduling, reducing idle time, and improving vehicle utilization efficiency.

[0047] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (or computer- readable storage media) having computer-usable program code embodied in the medium. The medium can be any available storage media that can be accessed by a computer. By way of example, and not limitation, such computer-usable storage media can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other storage medium(s) that can be used to carry or store desired computer program code in the form of instructions or data structures and that can be accessed by a computer. Also, the present application can be embodied in a computer program product that can be traded as goods or merchandise, through the based on any such medium, transactional medium, or physical medium. Figure 1 one or more functions specified in the flow or flows and / or blocks Figure 1 one or more functions specified in the flow or flows and / or blocks

[0048] It should be noted that the above-mentioned embodiments are only used to illustrate but not to limit the technical solutions of the present application, and although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A method for coordinated scheduling of driving and charging of connected heavy-duty vehicle clusters, characterized in that, Includes 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.

2. The method for coordinated scheduling of driving and charging of a networked heavy vehicle cluster as described in claim 1, characterized in that: Retrieve historical freight data and calculate 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.

3. The method for coordinated scheduling of driving and charging of a networked heavy vehicle cluster as described in claim 2, characterized in that: 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.

4. The method for coordinated scheduling of driving and charging of a networked heavy vehicle cluster as described in claim 2, characterized in that: Calculate 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. 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.

5. The method for coordinated scheduling of driving and charging of a networked heavy vehicle cluster as described in claim 3, characterized in that: 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.

6. The method for coordinated scheduling of driving and charging of a networked heavy vehicle cluster as described in claim 4, characterized in that: 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.

7. The method for coordinated scheduling of driving and charging of a connected heavy-duty vehicle cluster as described in claim 5, characterized in that: 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.

8. The method for coordinated scheduling of driving and charging of a networked heavy vehicle cluster as described in claim 7, characterized in that: 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.

9. The method for coordinated scheduling of driving and charging of a networked heavy vehicle cluster as described in claim 8, characterized in that: 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.

10. 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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