CN116777145B - Method for optimizing airport vehicle operating rate based on Internet of vehicles - Google Patents
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Abstract
The invention relates to a method for optimizing the operation rate of airport vehicles based on the Internet of vehicles, which solves the technical problems of how to improve the operation rate of special vehicles at the airport and the utilization rate of the vehicles, and firstly, the electric quantity and the working time of the vehicles are obtained through an Internet of vehicles system; then, designing an electric quantity penalty function and an operated time penalty function according to the acquired vehicle data, wherein the smaller the electric quantity penalty function value is, the more the vehicle is matched with the current work task, and the vehicle cannot work when the value is smaller than zero; judging the penalty function of the working time according to the length of the working time, wherein the penalty function is larger when the working time is longer; and then, combining the two penalty functions to obtain the index of the optimization calculation of the operating rate.
Description
Technical Field
The invention relates to the technical field of airport ground service equipment, in particular to a method for optimizing airport vehicle operating rate based on the Internet of vehicles.
Background
In the technical field of ground service equipment for airports, a series of ground services accepted by flights during airport stops are guaranteed by different types of special vehicles. The special vehicle mainly comprises: ferry truck, tractor, power supply truck, air conditioner truck, air supply truck, lifting platform truck, luggage transfer truck, food truck, passenger elevator truck, clear water truck, sewage truck, garbage truck, luggage mop truck, fuelling truck, deicing truck, fuelling truck, etc
With the continuous development of civil aviation transportation industry, the throughput of passengers and the taking-off and landing of flights are increased year by year, the service time of airport pavement is more and more longer, and the flight delay is more and more serious. The dispatching and starting efficiency of special vehicles in airport ground guarantee work is one of the main factors influencing flight delay.
The operating rate is defined as the ratio of the number of vehicles with an operating time greater than 1h to the total number of all vehicles: (e.g., the number of vehicles whose operating time is greater than the set operating time threshold for 1 hour is 2, the total number of all vehicles is 4, then the operating rate is 2/4).
At present, various special vehicles on an airport pavement are scheduled by taking manpower as a main work mode, and single-vehicle single-flight service exists, the operation rate of the vehicles is strictly limited by operation timeliness based on operation thresholds, and when the vehicles run, the self performance and the operation track of the vehicles can influence the operation rate and lack certain evaluation indexes, so that the operation guarantee time related data of the special vehicles on the ground are timely obtained based on a vehicle networking system, and the operation rate of the special vehicles is optimized and improved, so that the special vehicles are very necessary, and the technical problem to be solved by the technicians in the field is urgently needed.
Disclosure of Invention
The invention provides a method for optimizing the operating rate of an airport vehicle based on the Internet of vehicles, which aims to solve the technical problem of how to improve the operating rate of the airport special vehicle and the vehicle utilization rate.
The invention integrates and optimizes the operation rate of the special vehicles at the airport according to the information of the ground guarantee vehicles at the airport (the information comprises the types and the quantity of the special vehicles and the performance parameters of the vehicles, and the parameters of the operation threshold value, the on-line time, the battery allowance and the like of the special vehicles at the airport).
The actual starting quantity of the current vehicle, the battery electric quantity and the operable time in the actual operation interval of the vehicle are obtained through the vehicle networking platform, and other influencing factors are combined, such as: the airport is used for disposing vehicles, so that a reasonable optimization scheme for the operating rate of the airport vehicles is realized. Firstly, the electric quantity of the vehicle and the working time are obtained through the Internet of vehicles system. Then, designing an electric quantity penalty function and an operated time penalty function according to the acquired vehicle data, wherein the smaller the electric quantity penalty function value is, the more the vehicle is matched with the current work task, and the vehicle cannot work when the value is smaller than zero; the penalty function of the working time is judged according to the length of the working time, and the longer the working time is, the larger the penalty function is. And then, combining the two penalty functions to obtain the index of the optimization calculation of the operating rate.
The operating rate is defined as the ratio of the number of vehicles with an operating time greater than 1h to the total number of all vehicles: (for example, the number of vehicles with the working time being greater than the set working time threshold value for 1 hour is 2, and the total number of all vehicles is 4, then the working rate is 2/4), so that the direction of optimizing the working rate mainly aims at optimizing the matching mode of the electric quantity of the vehicles and the working task, and an optimization scheme of ' field-contraindicated racing ' is adopted, so that the distribution of ' multiple electric power distribution and less working time of the vehicles is reduced, and the working of the vehicles which do not start is optimized as much as possible. And analyzing through a vehicle networking database to obtain a vehicle electric quantity design penalty function: the vehicle function value which can complete the work task through formula calculation is small, the function value which cannot complete the task is large, and when the function value is smaller than 0, the vehicle cannot execute the task is guaranteed. Meanwhile, the penalty function of the vehicle working time is obtained through analysis of the vehicle networking database, the longer the working time is, the larger the penalty function value is, and the shorter the working time is, the smaller the penalty function value is. And finally, the matching degree of the vehicle and the assigned task is reflected to the size of the overall function value, and the reasonable degree of the distribution scheme is reflected. And meanwhile, setting a judging threshold value so as to judge whether the scheme for optimizing the operating rate is reasonable.
The invention discloses a method for optimizing airport vehicle operation rate based on the Internet of vehicles, which comprises the following steps:
firstly, establishing an airport vehicle operating rate optimization index f:
the definition of each parameter in the above formula is as follows:
E i : the single vehicle in standby state displays the residual electric quantity on the current internet of vehicles;
n c : by scheduling the number of vehicles to be dispatched for a guaranteed flight；
L j : the movement distance of the j-th section of the single vehicle in the standby state in the airport guarantee route;
n L : the total number of moving sections of the single vehicle in the standby state on the airport guarantee route;
t: the average value of the moving distance of the current vehicle participating in dispatching and the electric quantity conversion coefficient of the jth section in the airport guarantee route is obtained by the following formula:
n: the number of workers required for single vehicle guarantee;
E k : a single vehicle in a standby state finishes the electricity quantity required to be consumed by the current guarantee task in an operation area;
E a : basic guarantee electric quantity of a single vehicle in a standby state;
n w : the number of tasks to be completed by a single vehicle in a standby state in a designated operation area;
t i : the warehouse stores the working time of single vehicles of the vehicles;
the electrical penalty function is:
the electric quantity is ensured for a driving path in the process that the vehicle reaches a designated working area;
the electric quantity required to be ensured for the vehicle to finish the operation in the task area;
E a basically guaranteeing the electric quantity for the vehicle to run;
the time penalty function is:
and secondly, combining a plurality of vehicles in a warehouse in a standby state, calculating f values of each combination through the airport vehicle operating rate optimization index formula, and selecting the combination with the minimum f value as a dispatching scheme.
The method has the advantages that various data of the vehicle guarantee operation are quantitatively and accurately calculated, the operation rate of the vehicle is analyzed, the vehicle operation rate optimization index is established, the number and the combination scheme of the scheduled vehicles are determined according to the specific numerical value of the vehicle operation rate optimization index, the vehicle utilization rate is improved, and the operation rate of the special vehicle at an airport is improved.
Further features and aspects of the present invention will become apparent from the following description of specific embodiments with reference to the accompanying drawings.
Drawings
FIG. 1 is a schematic diagram of a vehicle networking system, wherein a vehicle information acquisition module and a vehicle information sending module arranged on a special vehicle at an airport send data to a data analysis server through a gateway;
FIG. 2 is a flow chart of a method of airport vehicle operating rate optimization;
FIG. 3 is an example of a tractor stationary travel segment;
FIG. 4 is a graph of speed, mileage, and battery margin for a 201925 tractor operating at 2022, 8 months, and 28 days;
FIG. 5 is a graph of speed, mileage, and battery margin for a 201340 tractor operating at 2022, 8 months, and 28 days;
FIG. 6 is a graph of speed, mileage, and battery margin for a 201301 tractor operating at 2022, 8 months, and 28 days;
FIG. 7 is a graph of speed, mileage, and battery margin for a 192439 tractor operating at 2022, 8 months, and 28 days;
FIG. 8 is actual test values for a vehicle performing a warranty operation;
FIG. 9 is actual test values for a vehicle performing a warranty operation;
fig. 10 is an actual test value of the vehicle performing the safeguard operation.
Detailed Description
The invention will be described in further detail with reference to the accompanying drawings.
The operation rate of the special vehicles in the airport is different from the common household vehicle commute rate, the operation of the special vehicles in the airport has strict time requirements, the driving route is relatively fixed, and the surrounding environment of the route is relatively fixed, so the key point of optimizing the operation rate of the special vehicles in the airport is how to realize the technical scheme of maximizing the utilization rate of one or more special vehicles on the basis of acquiring data on the basis of the Internet of vehicles. The vehicle networking system writes the received data into a database for storage, records the necessary data such as working time, battery allowance and the like, and makes judgment indexes for the cooperative working efficiency of various special vehicles by accumulating a large amount of data and adding a corresponding working rate optimization algorithm, so that the working rate of the special vehicles at the airport is optimized in time, and the optimization of the working rate of the ground vehicles at the airport is realized on the premise of ensuring smooth operation of flights.
The data required to optimize the vehicle operating rate are: distance of vehicle travel section, number of workers, number of vehicles, remaining power of vehicles, working time of vehicles, etc.
Data required for an algorithm to optimize the vehicle operating rate is obtained from the internet of vehicles system.
Referring to fig. 4, 5, 6, 7, 201925, 201340, 201301 and 192439, in the process of executing a task in a current time period, the electric quantity of the battery allowance is gradually reduced, and the driving mileage is gradually increased, namely, the vehicle allowance and related information of a real-time vehicle can be acquired through a vehicle networking platform, but only through the real-time acquisition of data, only how to complete the task in the next step can be roughly estimated, and then the optimal vehicle combination can be obtained through accurate decision to complete the task so as to improve the operation rate.
Calculating an airport vehicle operating rate optimization index f through the following formula:
the definition of each parameter in the above formula is as follows:
E i : the single vehicle in the standby state currently displays the residual electric quantity through the internet of vehicles, and the vehicle in the standby state is a vehicle which is about to work on line in a warehouse.
n c : by scheduling the number of vehicles to be dispatched for the guaranteed flights, the parameters are determined by determining a specific number based on the airport flight situation, such as a total of four homotype tractors at the airport, if three flights land separately during this time period, 3 tractors are required to work simultaneously, so n c The number of the values is 3.
L j : the distance of movement of the individual vehicle in the armed state in the jth segment of the airport guarantee route is fixed by the airport guarantee route (as shown in fig. 3, the guarantee route consists of several segments, the vehicle runs from the armed position to the corridor bridge, and the aircraft is pushed out from the corridor bridge and turned back again). L (L) j Is derived from historical data.
n L : and the total number of moving segments of the single vehicle in the standby state on the airport guarantee route is obtained by determining the guarantee working position through scheduling and then obtaining the reference historical data according to the vehicle network guarantee vehicle moving route map.
T: the average value of the moving distance and the electric quantity conversion coefficient of the jth section of the current vehicle participating in dispatching in the airport guarantee route is obtained by weighting and averaging historical data of the internet of vehicles, and the average value is obtained specifically through the following formula:
n: the individual vehicles guarantee the number of required staff. For example: airport tractor operation requires 2 people: drivers and operators.
E k : single vehicle in standby stateThe operation area finishes the electricity quantity required to be consumed by the current guarantee task, and the data is acquired by the Internet of vehicles, and belongs to historical data.
E a : the basic guarantee electric quantity of the single vehicle in the standby state, namely the electric quantity which meets the condition that the vehicle normally runs to the charging pile or the garage, is a specific value.
n w : the number of tasks to be completed by a single vehicle in a standby state in a designated working area is arranged according to a schedule.
t i : the working time of a single vehicle stored in the warehouse is determined by real-time recording of the today guaranteed working time of the single vehicle through the Internet of vehicles.
Under the real-time condition of a certain day, the main components of the multiple vehicle operation rate optimization index f based on the internet of vehicles are explained as follows:
the first part is the electric quantity penalty function:
the electric quantity penalty function is planned according to the work guarantee electric quantity of the dispatching vehicle, the current residual electric quantity subtracts the running electric quantity of the path required to reach the working area, subtracts the electric quantity consumed by the working area, and subtracts the basic guarantee electric quantity, wherein:
the amount of power (derived from historical data) guaranteed for the travel path of the vehicle during its arrival at the designated work area.
The electric quantity (history data) required to be ensured for the vehicle to complete the operation in the task area.
E a Basically ensures the electric quantity for the vehicle to run (the electric quantity for meeting the normal running of the vehicle to the charging pile or the garage is a specific value).
The second part is a time penalty function:
the time penalty function sets three stages based on the current working time of the standby vehicle: the time penalty function is set to be 5 when the time is greater than 2 hours and is designed to be greater than 2 hours according to the operation rate requirement and calculated from the point 0 on the same day and the operation rate requirement is met when the time is greater than 1 hour, when the vehicle participates in the operation task for more than 2 hours, the time penalty function is too large due to the fact that the time penalty function is too large, the operation rate optimization index is not met when the time penalty function is too large, and the current vehicle does not need to participate in the task operation.
The following examples are provided for illustrative purposes:
assuming that the warehouse has four vehicles in total, three vehicles need to be scheduled for the current task, wherein the working time of one vehicle is from 0 point to the current time for more than two hours, one vehicle is not started today, and the working time of the other two vehicles is within 1-2 hours; according to the manual experience mode, working vehicles meeting the current operating rate are blindly removed, vehicles which do not start are selected, but vehicles which start working but do not meet the operating rate lack a certain evaluation index; in addition, the time is only one measurement index, and the current vehicle guarantee electric quantity is specifically combined, so that calculation is performed according to the operation rate optimization index f.
On the one hand, aiming at the fact that the working time of one vehicle exceeds 2 hours, if the vehicle participates in dispatching work, when the operation rate optimization index is calculated for the vehicle, the numerical value is larger, and the fact that the vehicle meets the operation rate is indicated, then the other vehicle can be selected to participate in the operation task;
on the other hand, if the calculated value of the vehicle which is not started is smaller according to the time penalty function and the value of the optimization index of the operation rate corresponding to the whole is smaller, the vehicle can participate in the current task;
the other two vehicles have little difference under the calculation of the time penalty function, but the current guaranteed electric quantity of each vehicle is different, so that the corresponding calculation can be carried out according to the electric quantity penalty function until the most reasonable vehicle is selected, and the operating rate requirement is improved.
For example: for the vehicle operation data given in table 1, the operation rates of different vehicle formations are calculated according to the vehicle operation rate optimization index calculation formula. Wherein T is calculated by historical data to be 0.316, and 3 flights are required to be guaranteed, so n c The value is 3.
Table 1 four tractor operating data.
In the first case, the operation rate optimization index f calculated by the formation combination of the vehicle numbers 1, 2 and 3 1 = 177.94; the calculation process is as follows:
f 1 ＝[(84-0.316*2.56-13.33-20)+(89-0.316*2.9-12.76-20)+(62-0.316*1.88-12.23-20)]*(1.35*0.9*1.09)＝177.94
analysis of operating Rate optimization index f 1 The starting time of the vehicles with the numbers 1, 2 and 3 is less than 2 hours, and the current three vehicles can participate in the vehicle operation task according to the time penalty function; in consideration of the electric quantity penalty function, it is known from the calculation that the current electric quantity can meet the following task demands, and in summary, the vehicles numbered 1, 2, and 3 can perform task operations, but whether the vehicle is an optimal power on index is not clear, and other power on index values need to be considered again, and the final vehicle scheduling is determined.
In the second case, the work rate optimization index f calculated by the formation combination of the vehicle numbers 1, 2 and 4 2 ＝901.63；
The calculation process comprises the following steps: f (f) 2 ＝[(84-0.316*2.56-13.33-20)+(89-0.316*2.9-12.76-20)+(95-0.316*7.37-29.44-20)]*(1.35*0.9*5)＝901.63
Analysis of operating Rate optimization index f 2 Of the vehicles numbered 1, 2, and 4, the vehicle numbered 4 has been operated for more than 2 hours, i.e., to satisfy the current day operating rate, the larger the time penalty function calculation value corresponding to the vehicle exceeding 2 hours is known from the time penalty function, so the vehicle numbered 4 may not be referred toAnd the current formation task, however, if the number of the current vehicles is limited, most vehicles meet the operation rate, and if the electric quantity penalty function is met, the task can be continuously executed to participate in operation rate optimization index calculation until the optimal vehicle combination is selected.
In the third case, the work rate optimization index f calculated by the formation combination of the vehicle numbers 1, 3 and 4 3 ＝742.78；
The calculation process comprises the following steps: f (f) 3 ＝[(84-0.316*2.56-13.33-20)+(62-0.316*1.88-12.23-20)+(95-0.316*7.37-29.44-20)]*(1.35*0.9*5)＝742.78
Analysis of operating Rate optimization index f 3 Of the vehicles numbered 1, 3, and 4, the vehicle numbered 4 has been operated for more than 2 hours, but is defined by f 2 It can be known that it meets the electric quantity penalty function, i.e. its electric quantity meets the electric quantity required for executing the task, f 3 Can be combined with f 2 Comparing, if the current data is known, f 3 And f 2 The time penalty function of (2) is the same, namely, the starting time of the other two vehicles except the vehicle with the number of 4 is not greatly different, and on the basis, the values of the electric quantity penalty function can be compared to select f 3 And f 2 The vehicles with corresponding numbers are more satisfactory.
Fourth, the work rate optimization index f calculated by the formation combination of the vehicles with the numbers of 2, 3 and 4 is calculated 4 ＝626.52。
The calculation process comprises the following steps: f (f) 4 ＝[(89-0.316*2.9-12.76-20)+(62-0.316*1.88-12.23-20)+(95-0.316*7.37-29.44-20)]*(0.9*1.09*5)＝626.52。
Analysis of operating Rate optimization index f 4 Of the vehicles numbered 2, 3, 4, the vehicle numbered 4 has been operated for more than 2 hours, represented by f 3 And f 2 From the analysis of f 4 Corresponding numbered vehicles and f 3 、f 2 The corresponding numbered vehicles are compared, namely, three scheduling vehicle schemes are compared, which scheme can ensure higher task completion degree and determine the final vehicle scheduling.
To sum up, from f 1 、f 2 、f 3 、f 4 In the analysis of (2), we can obtain the operation rate optimization index mainly considering the electric quantity penalty function and the time penalty function;
firstly, when the vehicle does not meet the operation rate, namely when the running time of the vehicle is smaller than the operation rate threshold, the time penalty function value is smaller, and the smaller the corresponding operation rate optimization index value is, the electric quantity penalty function comparison is needed, namely when the electric quantity penalty function and the time penalty function value are both smaller, the vehicle combination corresponding to the generated operation rate index value meets the task completion requirement;
then, when a vehicle meeting the operation rate exists in the vehicle combination, namely when the running time of the vehicle is larger than the operation rate threshold, the time penalty function value is larger, and the corresponding operation rate optimization index value is larger, at the moment, whether the electric quantity penalty function value can meet the electric quantity required by the task is considered, and under the condition of meeting the requirement, the operation rate optimization value can be calculated, namely the vehicle combination meeting the operation rate is included, and the most-met vehicle combination is obtained by comparing different electric quantity penalty functions.
Finally, comprehensively comparing different task demands, adopting different vehicle combinations, for example, 4 tractors of the same type are adopted in a garage, 3 different flights are guaranteed by only 3 tractors of the same type in the actual guarantee task in the time period, and the different combinations are enumerated through algorithm calculation, f 1 The minimum value, so the formation combination dispatch scheme with the vehicle numbers of 1, 2 and 3 is the best.
For another example, the garage has 5 tractors with the same type, such as 27T tractors, 3 27T tractors are required for a guarantee task, different combinations are enumerated, f values of each combination are calculated, the combination with the minimum f value is the optimal scheme, and the platform sends a dispatching task request to the 3 related vehicles, namely, the dispatching of the vehicles is completed. And similarly, the dispatching task of vehicles of different models can be completed.
The above description is only for the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the scope of the claims of the present invention should fall within the protection scope of the present invention.
Claims (1)
1. The method for optimizing the airport vehicle operating rate based on the Internet of vehicles is characterized by comprising the following steps of:
firstly, establishing an airport vehicle operating rate optimization index f:
the definition of each parameter in the above formula is as follows:
E i : the single vehicle in standby state displays the residual electric quantity on the current internet of vehicles;
n c : the number of vehicles to be dispatched for the guaranteed flights is scheduled;
L j : the movement distance of the j-th section of the single vehicle in the standby state in the airport guarantee route;
n L : the total number of moving sections of the single vehicle in the standby state on the airport guarantee route;
t: the average value of the moving distance of the current vehicle participating in dispatching and the electric quantity conversion coefficient of the jth section in the airport guarantee route is obtained by the following formula:
n: the number of workers required for single vehicle guarantee;
E k : a single vehicle in a standby state finishes the electricity quantity required to be consumed by the current guarantee task in an operation area;
E a : basic guarantee electric quantity of a single vehicle in a standby state;
n w : the number of tasks to be completed by a single vehicle in a standby state in a designated operation area;
t i : the warehouse stores the working time of single vehicles of the vehicles;
the electrical penalty function is:
the electric quantity is ensured for a driving path in the process that the vehicle reaches a designated working area;
the electric quantity required to be ensured for the vehicle to finish the operation in the task area;
E a basically guaranteeing the electric quantity for the vehicle to run;
the time penalty function is:
and secondly, combining a plurality of vehicles in a warehouse in a standby state, calculating f values of each combination through the airport vehicle operating rate optimization index formula, and selecting the combination with the minimum f value as a dispatching scheme.
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| CN113935543A (en) * | 2021-10-28 | 2022-01-14 | 北京航空航天大学 | Urban aerial taxi site selection-path optimization method |
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