A vehicle schedule determination method, device, equipment and medium

Abstract

The present invention discloses a method, device, equipment and medium for determining a vehicle schedule. The method comprises: obtaining a driving time period, and dividing the driving time period according to the operating time corresponding to the preset time period and the preset meal time, to obtain a turnover time period; obtaining the predicted number of departures corresponding to each turnover time period, and determining the operating time period corresponding to each turnover time period; arranging each operating time period in the driving time period to form a driving time matrix; obtaining the associated operating time period corresponding to the preset meal time period, and adjusting the associated operating time period in the driving time matrix until the driving time matrix has a preset meal time in the preset meal time period, and determining the vehicle schedule. The embodiment of the present invention can effectively utilize the preset meal time period and vehicle resources, and improve the efficiency and accuracy of determining the vehicle schedule.

Description

Technical Field

[0001] The present invention relates to the technical field of big data processing, and in particular to a vehicle timetable determination method, device, equipment and medium. Background Art

[0002] With the continuous expansion of urban scale, efficient and reasonable vehicle dispatching of ground public transportation becomes particularly important.

[0003] The existing timetables in my country are basically manually arranged, and there are also some automatic arrangement methods for timetables. However, manual arrangement requires a lot of manpower. Automatic arrangement methods mainly set restrictions based on experience, randomly generate results and select the best ones, but the arrangement results cannot effectively utilize vehicle and time resources, and many cannot be used in practice. Summary of the invention

[0004] The present invention provides a vehicle schedule determination method, device, equipment and medium, which can effectively utilize vehicle resources and meal time and improve the accuracy of vehicle schedule generation.

[0005] According to one aspect of the present invention, a method for determining a vehicle schedule is provided, characterized in that it includes:

[0006] Obtaining a driving time period, and dividing the driving time period according to the operating time corresponding to the preset time periods and the preset meal time, to obtain a turnover time period;

[0007] Obtain the predicted number of departures corresponding to each of the turnaround time periods, and determine the operating time periods corresponding to each of the turnaround time periods;

[0008] In the driving time period, arranging each of the running time periods to form a driving time matrix;

[0009] The associated running time period corresponding to the preset meal time period is obtained, and the associated running time period is adjusted in the driving time matrix until the driving time matrix has a preset meal time in the preset meal time period, and the vehicle schedule is determined.

[0010] According to another aspect of the present invention, there is provided a vehicle schedule determination device, characterized in that it comprises:

[0011] A turnover time period acquisition module is used to acquire a driving time period, and divide the driving time period according to the operating time corresponding to the preset time periods and the preset meal time, to obtain a turnover time period;

[0012] An operation time period determination module is used to obtain the predicted number of departures corresponding to each of the turnover time periods, and determine the operation time period corresponding to each of the turnover time periods;

[0013] A driving time matrix determination module, used for arranging each of the operating time periods in the driving time period to form a driving time matrix;

[0014] The vehicle schedule determination module is used to obtain the associated running time period corresponding to the preset meal time period, and adjust the associated running time period in the driving time matrix until the driving time matrix has a preset meal time in the preset meal time period, thereby determining the vehicle schedule.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle schedule determination method described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle schedule determination method described in any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention divides the driving time period according to the operating time and the preset meal time to obtain the turnover time period, determines the corresponding operating time period according to the predicted number of departures corresponding to the turnover time period, arranges the operating time period in the driving time period to form a driving time matrix, and adjusts the associated operating time period corresponding to the preset meal time period in the driving time matrix until the driving time matrix has a preset meal time in the preset meal time period, determines the vehicle schedule, and the meal time is used to generate the vehicle schedule. When dividing the turnover time period, the preset meal time is considered in advance, which reduces the difficulty of retaining the meal time in the driving time matrix. According to the associated operating time period corresponding to the preset meal time period, the associated operating time period is adjusted in the driving time matrix until the driving time matrix has a preset meal time in the preset meal time period, which reduces the probability of long-term vehicle idleness, can effectively utilize the preset meal time period and vehicle resources, and improve the efficiency and accuracy of vehicle schedule determination.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a vehicle schedule determination method provided according to Embodiment 1 of the present invention;

[0024] Figure 2 is a flow chart of a vehicle schedule determination method provided according to Embodiment 2 of the present invention;

[0025] Figure 3 is a flow chart of a vehicle schedule determination method provided according to Embodiment 3 of the present invention;

[0026] Figure 4a is a schematic diagram of an example of a method for determining a vehicle schedule according to a fourth embodiment of the present invention;

[0027] Figure 4b is a schematic diagram of a travel time matrix provided according to a fourth embodiment of the present invention;

[0028] Figure 4c is a schematic diagram of an adjusted travel time matrix provided according to a fourth embodiment of the present invention;

[0029] Figure 5 is a schematic diagram of the structure of a vehicle schedule determination device provided according to Embodiment 3 of the present invention;

[0030] Figure 6 The present invention is a schematic diagram of the structure of an electronic device for implementing the vehicle schedule determination method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the term "comprising" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] Embodiment 1

[0034] Figure 1 A flow chart of a method for determining a vehicle schedule is provided for the first embodiment of the present invention. This embodiment can be applied to generating a vehicle schedule. The method can be executed by a vehicle schedule determination device. The vehicle schedule determination device can be implemented in the form of software. The vehicle schedule determination device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0035] S110, obtaining a driving time period, and dividing the driving time period according to the operating hours corresponding to the preset time periods and the preset meal time to obtain a turnover time period.

[0036] The driving time period refers to the time period between the starting departure time and the last departure time of the vehicle. Usually the starting departure time and the last departure time are preset. In the case where the vehicle has an upward route and / or a downward route, the driving time period may include the driving time period for the vehicle to go up and / or the driving time period for the vehicle to go down. The driving time period for going up and / or going down is preset according to the actual situation, wherein the driving time period for going up and the driving time period for going down can be set to the same time period or different time periods. For example, the driving time period is set according to the season. Exemplarily, the driving time period for going up of the vehicle is 5:30-21:20, and the driving time period for going down of the vehicle is 6:00-22:00.

[0037] The preset time periods refer to the results of the preset time period division. The preset time periods can be set according to actual conditions or habits. For example, according to the driving time period, except for the first minute, a time period is divided every thirty minutes, and the preset time periods are 05:30-05:30, 05:31-06:00, 06:01-06:30, 06:31-07:00... and so on. The operating time refers to the interval between two consecutive departures of the same vehicle. It can also be understood as the sum of the time it takes for the vehicle to travel from the starting point to the end point and the time it stays at the end point. The preset time periods correspond to the operating time. In different preset time periods, the operating time may be different due to the influence of factors such as the number of passengers getting on and off the vehicle during the driving process.

[0038] Exemplarily, various data of historical vehicles in the driving time period can be counted by big data technology to determine the operating time corresponding to the preset time period. Among them, the various data of the vehicle in the driving time period at least include the transportation time data and historical passenger flow data of the time period. The transportation time data of the time period refers to the duration of the behaviors associated with the vehicle during the driving process. The associated behaviors are driving behavior and resting behavior, the duration of driving behavior is the transportation time, and the duration of resting behavior is the stop time. The transportation time data of the time period at least includes the up transportation time, the up stop time, the down transportation time and the down stop time. The up transportation time refers to the driving time of the vehicle from the starting point of the up route to the end point of the up route. The up stop time refers to the time the vehicle stays at the station after arriving at the end point of the up route. The down transportation time refers to the driving time from the starting point of the down route to the end point of the down route. The down stop time refers to the time the vehicle stays at the station after arriving at the end point of the down route. According to the preset time periods and the vehicle's up or down travel situation, the operating time corresponding to the preset time periods can be determined by querying the time period transportation time data. When the vehicle is going up, the operating time is calculated as follows: operating time = up transportation time + up stop time; when the vehicle is going down, the operating time is calculated as follows: operating time = down transportation time + down stop time.

[0039] According to the preset meal time, a certain amount of time can be reserved for the driving user to eat when dividing the turnover time period. The preset meal time can be set according to the actual situation. The preset meal time period refers to the time period for arranging the driver to eat, that is, there is a preset meal time within the preset meal time period. The start and end times and number of the preset meal time periods can be set according to the actual situation. The process of a vehicle driving an upward route and then driving a downward route is regarded as a turnover. The turnover time period refers to the time period between two consecutive departure times of the same vehicle in the same driving direction. It can also be understood that for the same vehicle in the same driving direction, the moment of the previous departure time node in two consecutive departure time nodes is used as the turnover start time, and the moment of the next departure time node is used as the turnover end time, forming a turnover time period. The turnover time period is determined according to the actual situation during the driving process of the vehicle, wherein the preset meal time of the driving user is added to the turnover time period within the preset meal time period, for example, the preset meal time period is from 9:00 to 13:00, and the preset meal time is 20 minutes. When the initially determined turnover time period is 9:03-9:56, and there is no time node between 9:00-9:03, the preset meal time is added to the initially determined turnover time period. For example, the end time of the turnaround time period can be delayed by 20 minutes, and the updated turnover time period is 9:03-10:16.

[0040] Specifically, the operating time corresponding to the preset time period is determined by the driving time period and the time period transportation time data, and then the driving time period is divided in combination with the preset meal time period and the preset meal time, and the turnover marking is determined. The turnover marking refers to the timeline that describes the vehicle turnover time period. The turnover time period for the vehicle to go up and / or down can be determined by the turnover marking. Among them, the earliest departure time in the driving time period is determined as the starting point of the turnover marking, and the latest departure time is determined as the end point of the turnover marking. Other time nodes in the turnover marking can be determined by querying the time period transportation time data. The turnover marking for the vehicle to go up and / or down is determined according to the turnover marking, and then the turnover time period for the up or down is determined.

[0041] For example, the time period for the upward movement of vehicles is 5:30-21:20, and the time period for the downward movement of vehicles is 6:00-22:00. Then the starting point of the turnover line is 5:30, and the end point of the turnover line is 22:00. According to the time-segment transportation time data statistically calculated by big data technology, after adding the preset meal time, the turnover line generated from the starting departure time to the last departure time is determined as follows: 05:30-06:00-06:52-07:48-09:03-10:16-11:19-12:10-13:06-13:59-14:59-15:52-16:56-18:19-19:23-20:17-21:12-22:00-22:48. The turnover line starts at 5:30, which is an upward line. The odd-numbered columns form the upward turnover lines as follows: 05:30-06:52-09:03-11:19-13:06-14:59-16:56-19:23-21:12-22:48. The remaining columns form the downward turnover lines as follows: 06:00-07:48-10:16-12:10-13:59-15:52-18:19-20:17-22:00. The upward turnover time periods are 05:30-06:52, 06:52-09:03, 09:03-11:19, and so on. Among them, the turnover time periods 09:03-10:16 and 16:56-18:19 have preset meal times.

[0042] S120, obtaining the predicted number of departures corresponding to each of the turnover time periods, and determining the operating time periods corresponding to each of the turnover time periods.

[0043] The predicted number of departures refers to the estimated value of the number of departures. The predicted number of departures can be calculated based on historical passenger flow data. Usually, based on the historical passenger flow data, the number of people who need to be carried in the turnover time period is determined, and then the number of departures in the driving direction corresponding to the turnover time period is determined based on the number of people, so that the number of vehicles traveling is sufficient to carry the number of people who need to be carried in the turnover time period. Passenger flow refers to the flow of passengers in a certain direction on a certain transportation route section within a certain period of time, including factors such as flow rate, flow direction and flow time. Historical passenger flow data refers to the data results of statistical analysis of relevant data of passenger flow within a period of time. Historical passenger flow data at least includes data such as full load coefficient, vehicle capacity, upstream passenger flow throughput and downstream passenger flow throughput. Full load coefficient refers to the relative value reflecting the degree of full load of passengers on the vehicles running on the route within a certain period of time. Vehicle capacity refers to the number of passengers that a vehicle can accommodate. Upward passenger flow throughput refers to the number of passenger flows based on vehicles traveling on the upward route. Downward passenger flow throughput refers to the number of passenger flows based on vehicles traveling on the downward route. Historical passenger flow data can be data pre-statisticed by big data technology.

[0044] The operating time period refers to the time period from the start time of the vehicle's departure to the end time of its arrival at the station. The operating time period can be determined based on the predicted number of departures and the transportation time data in different time periods. Specifically, the departure time interval between each vehicle is calculated based on the predicted number of departures corresponding to each turnover time period. The departure time interval refers to the time interval between the departure start times of different vehicles in the same driving direction within a turnover time period. Based on the departure time interval between the vehicles, the departure start time of each vehicle in the turnover time period can be determined, and combined with the transportation time data in different time periods, the operating time period of each vehicle can be determined.

[0045] Specifically, firstly, according to the preset time periods and historical passenger flow data, the departure frequency of the preset time periods corresponding to each turnover time period is calculated, and the predicted departure number of the preset time periods is calculated according to the departure frequency, so as to obtain the predicted departure number corresponding to each turnover time period, and then the departure time interval between the vehicles traveling in each turnover time period is calculated according to the predicted departure number corresponding to the turnover time period, and finally, according to the time period transportation time data, the operation time period of each vehicle is calculated and determined as the operation time period corresponding to each turnover time period. The historical passenger flow data refers to the number of passengers entering the vehicle per unit time. The historical passenger flow data may include the up-going passenger flow and / or the down-going passenger flow according to the driving direction of the vehicle (upward or downward). The departure frequency can be calculated by querying the statistical value in the historical passenger flow data and using the calculation formula, and the calculation formula is: departure frequency = up-going passenger flow (down-going passenger flow) / (vehicle capacity*full load factor) / unit turnover time period duration. Then the calculation formula of the departure number is: departure number = the duration of the preset time period corresponding to the turnover time period*departure frequency.

[0046] Exemplarily, taking the uplink turnover time period 05:30-06:52 as an example, 05:30-06:52 is divided according to the preset time periods, and the division results are 05:30-05:30, 05:31-06:00, 06:01-06:30 and 06:31-06:52. According to the data shown in Table 1, the departure frequency and number of departures in each time period after division are calculated as follows: the departure frequency of 05:30-05:30 is 32 / (0.4*80) / 1=1, and the number of departures is 1*1=1; the departure frequency of 05:31-06:00 is 32 / (0.4*80) / 30=1 / 30, and the number of departures is 1 / 30*30=1; the departure frequency of 06:01-06:30 is 96 / (0.4*80) / 30=3 / 30, and the number of departures is 3 / 30*30=3; the departure frequency of 06:31-06:52 is 180 / (0.75*80) / 30=0.1, and the number of departures is 0.1*28=2.8. The predicted number of vehicles departing during the upward turnover period of 05:30-06:52 is 1+1+3+2.8, rounded to 7 vehicles.

[0047] Table 1 Some historical passenger flow data

[0048]

[0049] The duration of the turnaround time period is 06:52-05:30 = 82 minutes, and 7 shifts need to be dispatched at a certain interval, that is, the predicted number of departures is 7, and the interval between trains = 82 / 7 = 12 minutes. The departure times of the 7 shifts are: 05:30, 05:42, 05:54, 06:06, 06:18, 06:30 and 06:41. According to the data shown in Table 2, the running time periods of the 7 shifts are calculated to be 05:30-06:08, 05:42-06:20, 05:54-06:32, 06:06-06:47, 06:18-06:59, 06:30-07:11 and 06:41-07:29.

[0050] Table 2 Delivery time data by time period

[0051]

[0052] S130: Arrange the operating time periods in the driving time period to form a driving time matrix.

[0053] The driving time matrix refers to a matrix that describes the operating time periods of all vehicles within the driving time period. The driving time matrix can be described in the form of a chart. In the driving time matrix, the operating time periods do not overlap. The horizontal axis of the vehicle driving matrix is ​​time, and the vertical axis is the vehicle number. The driving time matrix is ​​formed by reasonably arranging the operating time periods of each driving vehicle in the driving time period. Exemplarily, the remaining operating time periods can be arranged by enumeration until the arrangement of the driving time matrix is ​​completed.

[0054] S140, obtaining the associated running time period corresponding to the preset meal time period, and adjusting the associated running time period in the driving time matrix until the driving time matrix has a preset meal time in the preset meal time period, and determining the vehicle schedule.

[0055] The preset meal time period refers to the time period for the driver to eat. The preset meal time period can be set according to the actual situation. The associated running time period corresponding to the preset meal time period refers to at least one running time period that departs within the preset meal time period in a row of the driving time matrix, or the running time period of the first departure within the preset meal time period and at least one running time period after the first departure running time period. The vehicle schedule refers to a table that expresses the departure, arrival, passing time or stay time of the vehicle at the station. The vehicle schedule can extract the time corresponding to the running time period according to the driving time matrix to form a vehicle schedule.

[0056] Specifically, according to the preset meal time period, the associated operating time period corresponding to each row in the driving time matrix is ​​determined, and the associated operating time period corresponding to each row is adjusted up and down, while ensuring that there is no overlap between the associated operating time period and other operating time periods, until the operating time period of each row is within the preset meal time period, and the time interval between two operating time periods is greater than or equal to the preset meal time, the vehicle schedule is determined according to the adjusted driving time matrix.

[0057] The technical solution of the embodiment of the present invention divides the driving time period according to the operating time and the preset meal time to obtain the turnover time period, determines the corresponding operating time period according to the predicted number of departures corresponding to the turnover time period, arranges the operating time period in the driving time period to form a driving time matrix, and adjusts the associated operating time period corresponding to the preset meal time period in the driving time matrix until the driving time matrix has a preset meal time in the preset meal time period, determines the vehicle schedule, and the meal time is used to generate the vehicle schedule. When dividing the turnover time period, the preset meal time is considered in advance, which reduces the difficulty of retaining the meal time in the driving time matrix. According to the associated operating time period corresponding to the preset meal time period, the associated operating time period is adjusted in the driving time matrix until the driving time matrix has a preset meal time in the preset meal time period, which reduces the probability of long-term vehicle idleness, can effectively utilize the preset meal time period and vehicle resources, and improve the efficiency and accuracy of vehicle schedule determination.

[0058] On the basis of the above embodiment, the number of the driving time matrices is at least two, and forming a vehicle schedule based on the driving time matrices includes: screening each driving time matrix according to the time interval between adjacent operating time periods of the same driving vehicle in the driving time matrix, the number of each operating time period and the distribution of the operating time periods in the same turnover time period; and determining the vehicle schedule based on the screened driving time matrices.

[0059] The time interval between adjacent operating time periods refers to the time interval between the end time of the previous operating time period and the start time of the next operating time period in the same row of the driving time matrix. The number of operating time periods is used to determine the sum of the number of times each vehicle travels and the number of rows in the driving time matrix. It should be noted that due to the adjustment of the operating time periods of the driving vehicles, the number of rows in the driving time matrix will eventually be greater than the maximum predicted number of departures. The distribution of operating time periods in the same turnover time period refers to the distribution of operating time periods in different rows in the same turnover time period of the driving time matrix, which is used to determine the sum of the time intervals between operating time periods in the same turnover time period in the driving time matrix.

[0060] Specifically, there may be multiple results of arranging the running time periods, and each arrangement result corresponds to a running time matrix, so the number of running time matrices is at least two. For the at least two running time matrices formed, according to the time intervals between adjacent running time periods of the same running vehicle, the number of each running time period, and the distribution of the running time periods in the same turnover time period, an optimal running time matrix is ​​selected therefrom, and the vehicle schedule is determined.

[0061] Exemplarily, based on whether the time intervals between adjacent operating time periods of the same driving vehicle are uniform or greater than 3 minutes, the driving time matrices with uniform time intervals or greater than 3 minutes can be retained; based on whether the sum of the time intervals between adjacent operating time periods in the same turnover time period is the smallest, that is, based on the distribution of the operating time periods in the same turnover time period, the driving time matrix with the smallest sum of time intervals can be retained; based on whether the number of driving vehicles is the smallest, that is, based on the number of operating time periods, the vehicle driving matrix with the smallest number of driving vehicles can be retained; different weights can be set for the time intervals between adjacent operating time periods of the same driving vehicle in the driving time matrix, the number of each operating time period, and the distribution of the operating time periods in the same turnover time period, and each driving time matrix can be scored according to the weight, and the driving time matrix with the highest or lowest score can be selected as needed. Exemplarily, the scoring rule may be that the score of the driving time matrix with uniform time intervals is greater than the score of the driving time matrix with uneven time intervals; the score of the driving time matrix with a time interval greater than or equal to 3 minutes is greater than the score of the driving time matrix with a time interval less than 3 minutes; the score of the driving time matrix with a small sum of time intervals is greater than the score of the driving time matrix with a large sum of time intervals; the score of the vehicle driving matrix with a small number of vehicles is greater than the score of the driving time matrix with a large number of vehicles; the above scores may be combined, and different weights may be configured for different types, and the weighted sum of the weight and the score may be determined as the final score of the driving time matrix, and the driving time matrix with the highest final score may be retained. In the case where there are multiple screening results, a driving time matrix may be randomly or selected to be determined as the optimal driving time matrix, and the vehicle schedule may be determined based on the optimal driving time matrix.

[0062] By screening multiple driving time matrices according to the time intervals between adjacent running time periods of the same driving vehicle, the number of each running time period and the distribution of the running time periods in the same turnover time period, the optimal driving time matrix can be selected therefrom, thereby determining the optimal vehicle schedule, improving the accuracy of vehicle schedule determination, saving labor costs, effectively utilizing vehicle resources, and improving the efficiency of vehicle schedule determination.

[0063] Embodiment 2

[0064] Figure 2This is a flow chart of a vehicle schedule determination method provided in the second embodiment of the present invention. Based on the above embodiment, this embodiment obtains the associated operating time period corresponding to the preset meal time period, and adjusts the associated operating time period in the driving time matrix until the driving time matrix has a preset meal time period. It is specifically implemented as follows: in the driving time matrix, obtain the operating time period of at least one moving vehicle; in the operating time period of each moving vehicle, obtain at least one associated operating time period corresponding to the preset meal time period to form an associated group of each moving vehicle; in the driving time matrix, adjust at least one associated group of the moving vehicles to the idle time period position of other moving vehicles until each moving vehicle has a preset meal time period in the preset meal time period. Figure 2 As shown, the method includes:

[0065] S210: Obtain a driving time period, and divide the driving time period according to the operating hours corresponding to the preset time periods and the preset meal time to obtain a turnover time period.

[0066] S220, obtaining the predicted number of departures corresponding to each of the turnover time periods, and determining the operating time periods corresponding to each of the turnover time periods.

[0067] S230: Arrange the operating time periods in the driving time period to form a driving time matrix.

[0068] S240: Obtain an operating time period of at least one driving vehicle in the driving time matrix.

[0069] The running time period of a running vehicle refers to the running time period corresponding to a row in the running time matrix. The running time matrix contains at least one row of running time periods. Specifically, according to the running time matrix, the running time periods of each row in the matrix are extracted and determined as the running time periods of each running vehicle.

[0070] S250. In the running time period of each of the running vehicles, obtain at least one associated running time period corresponding to the preset meal time period to form an associated group of each of the running vehicles.

[0071] An association group refers to an operating time period corresponding to a preset meal time period, and an association group includes at least one association group consisting of associated operating time periods. Independent associated operating time periods can be integrated through association groups. Specifically, for the operating time period of each running vehicle, the associated operating time period corresponding to the preset meal time period is extracted, and the associated operating time periods corresponding to the preset meal time period are formed into an association group, which is determined as the association group of the running vehicles.

[0072] S260. In the driving time matrix, adjust the associated group of at least one of the driving vehicles to the idle time period of other driving vehicles until each of the driving vehicles has a preset meal time in the preset meal time period.

[0073] The idle time period refers to the time period when the vehicle does not need to work, and can also be understood as the time period in the driving time matrix where there is no running time period distribution. Specifically, in the driving time matrix, the associated groups of each vehicle are translated up and down and adjusted to the idle time period position of other vehicles. Under the condition that the associated groups do not overlap with other running time periods, the time interval between two consecutive running time periods of each vehicle in the preset meal time period is greater than or equal to the preset meal time, so as to form the final driving time matrix.

[0074] S270: Determine the vehicle schedule.

[0075] The technical solution of the embodiment of the present invention determines the associated group of each running vehicle by obtaining the running time period of each running vehicle, and adjusts the associated group of the running vehicle to the idle time period of other running vehicles, so as to realize that each running vehicle has a preset meal time in the preset meal time period. The associated running time period corresponding to the preset meal time period in each running vehicle is regarded as a whole to form an associated group, which can realize the overall adjustment of the associated running time period and improve the adjustment efficiency of the driving time matrix. The associated group of the running vehicle is adjusted to the idle time period of other running vehicles, which reduces the probability of long-term vehicle idleness, can effectively utilize the preset meal time period and vehicle resources, and improve the accuracy of vehicle schedule determination.

[0076] On the basis of the above embodiment, the departure sequence of each of the traveling vehicles in the traveling time matrix and the row sequence of each of the traveling vehicles in the same turnover time period are matched; in the traveling time matrix, at least one associated group of the traveling vehicles is adjusted to the idle time period position of other traveling vehicles, including: in the traveling time matrix, obtaining the associated group of the traveling vehicles that are not in the first row as the current associated group; adjusting the current associated group to the idle time period of the adjusted traveling vehicles in the previous row with an adjacent row sequence within the preset meal time period, and determining the adjusted traveling vehicle corresponding to the current associated group; detecting whether the adjusted traveling vehicle corresponding to the current associated group has a preset meal time in the preset meal time period; and determining whether the adjusted traveling vehicle corresponding to the current associated group has a preset meal time in the preset meal time period. When there is a preset meal time, the current association group is further adjusted to the idle time period of the adjusted vehicle in the previous row adjacent to the row sequence within the preset meal time period, and the adjusted vehicle corresponding to the current association group is updated; the step of detecting whether the adjusted vehicle corresponding to the current association group has the preset meal time period in the preset meal time period is returned to execute until the adjusted vehicle corresponding to the current association group has the preset meal time period; when the adjusted vehicle has the preset meal time in the preset meal time period, the step of obtaining the association group of the non-first row of vehicles as the current association group is returned to execute until the vehicles in all association groups have the preset meal time in the preset meal time period.

[0077] The departure sequence refers to the order in which different vehicles depart in the same turnaround time period in the driving time matrix. The row sequence refers to the order of each row in the driving time matrix. Specifically, the departure sequence of the driving vehicles in the same turnaround time is consistent with the row sequence of the driving vehicles in the driving time matrix. Exemplarily, the row sequence of the driving vehicle 101 in the driving time matrix is ​​the first, and the departure sequence of the driving vehicle 101 in the turnaround time period 05:30-06:08 is the first.

[0078] The associated groups are adjusted one by one. The current associated group refers to the associated group of the non-first row of running vehicles that need to be adjusted. One associated group is selected each time and determined as the current associated group. The running vehicles to be adjusted refer to the running vehicles corresponding to the row where the current associated group is located.

[0079] Specifically, an association group of one of the non-first row of traveling vehicles is taken as the current association group. The current association group is translated upward to the idle time period of the adjacent previous row of adjusted traveling vehicles within the preset meal time period, wherein the idle time period is sufficient to carry the current association group. According to the row sequence of the current association group in the driving time matrix after the translation, the adjusted traveling vehicle corresponding to the row sequence is determined, that is, the adjusted traveling vehicle corresponding to the current association group. Whether there is a preset meal time period can be determined based on the time interval between the first running time period in the current association group and the adjacent running time period before the current association group. For example, the time interval between the first running time period in the current association group and the adjacent running time period before the current association group is calculated, and it is determined whether the time interval is greater than or equal to the preset meal time.

[0080] If the time interval is less than the preset meal time, the adjusted driving vehicle does not have the preset meal time within the preset meal time period, and the current association group is further translated upward to the idle time period of the adjusted driving vehicle in the adjacent previous row within the preset time period, and the adjusted driving vehicle corresponding to the row sequence is determined again according to the row sequence of the current association group in the driving time matrix after the translation, that is, the adjusted driving vehicle corresponding to the current association group is updated. Return to the step of calculating the time interval between the first running time period in the current association group and the adjacent running time period before the current association group, and judging whether the time interval is greater than or equal to the preset meal time, until the time interval is greater than or equal to the preset meal time, that is, the adjusted driving vehicle corresponding to the current association group has the preset meal time in the preset meal time period.

[0081] If the time interval is greater than or equal to the preset meal time, the vehicle is adjusted to have the preset meal time in the preset meal time period, and the process returns to the step of using the associated group of vehicles other than the first row as the current associated group, until all vehicles in the associated group have the preset meal time in the preset meal time period.

[0082] The current associated group is adjusted by cyclic execution, and the step of detecting whether the adjusted vehicle corresponding to the current associated group has a preset meal time in the preset meal time period is detected. After the adjusted vehicle corresponding to the current associated group has a preset meal time in the preset meal time period, the same operation is performed on the remaining associated groups, thereby simplifying the process of adjusting the associated groups, improving the efficiency of adjusting the associated groups, and thereby improving the efficiency of determining the vehicle schedule.

[0083] Embodiment 3

[0084] Figure 3A flowchart of a vehicle schedule determination method provided in the third embodiment of the present invention. Based on the above embodiments, this embodiment obtains at least one associated operating time period corresponding to a preset meal time period in the operating time period of each of the traveling vehicles to form an associated group of the traveling vehicles. The flowchart is specifically as follows: obtaining a target turnover time period with the preset meal time added, and querying the target operating time period included in the target turnover time period in the operating time period of the traveling vehicle; for the operating time period of the traveling vehicle, determining the associated group of the traveling vehicles by taking the next operating time period that is adjacent to the target operating time period and at least one operating time period after the next operating time period. Figure 3 As shown, the method includes:

[0085] S310: Obtain a driving time period, and divide the driving time period according to the operating hours corresponding to the preset time periods and the preset meal time to obtain a turnover time period.

[0086] S320, obtaining the predicted number of departures corresponding to each of the turnaround time periods, and determining the operating time periods corresponding to each of the turnaround time periods.

[0087] S330: Arrange the operating time periods in the driving time period to form a driving time matrix.

[0088] S340: Obtain an operating time period of at least one driving vehicle in the driving time matrix.

[0089] S350: Obtain a target turnover time period with the preset meal time added thereto, and query the target operating time period included in the target turnover time period in the operating time period of the traveling vehicle.

[0090] The target turnover time period refers to the turnover time period that includes the preset meal time when the turnover time period is divided. The target operation time period refers to the first operation time period in the target turnover time period. Specifically, in the driving time matrix, the turnover time period that includes the preset meal time is determined as the target turnover time period, and in the operation time period of each driving vehicle, the first operation time period whose start time is within the target turnover time period is determined as the target operation time period.

[0091] S360: For the running time period of the running vehicle, determine the associated group of the running vehicle by taking the next running time period that is adjacent to the target running time period in time sequence and at least one running time period after the next running time period.

[0092] The next running time period that is sequentially adjacent to the target running time period refers to a running time period that is sequentially adjacent to the target running time period in the driving time matrix. Specifically, among the running time periods of each running vehicle, at least two running time periods that are sequentially adjacent and whose sum of target running times is arranged after the target running time period are determined as an associated group of the running vehicles.

[0093] S370. In the driving time matrix, adjust the associated group of at least one of the driving vehicles to the idle time period of other driving vehicles until each of the driving vehicles has a preset meal time in the preset meal time period.

[0094] S380: Determine the vehicle schedule.

[0095] The technical solution of the embodiment of the present invention determines a target turnover time period, determines a target operating time period based on the target turnover time period, thereby determining an associated group of traveling vehicles, determines an associated group of traveling vehicles based on the target turnover time period, locates the position of the associated group, and improves the accuracy and efficiency of determining the associated group.

[0096] Based on the above embodiment, the preset meal time period includes a morning meal time period and / or an afternoon meal time period, and the associated group of traveling vehicles includes a morning associated group and / or an afternoon associated group.

[0097] Specifically, according to the arrangement of the running time periods of the running vehicles, the preset meal time period is divided into the morning meal time period and / or the afternoon meal time period. Exemplarily, with 13 o'clock as the dividing line, the time before 13 o'clock is set as the morning, and the time after 13 o'clock is set as the afternoon. If the running time period of the running vehicles is only arranged in the morning or the afternoon, the preset meal time period is the morning meal time period or the afternoon meal time period. If the running time period of the running vehicles is arranged in both the morning and the afternoon, the preset meal time period includes the morning meal time period and the afternoon meal time period. The associated group of running vehicles corresponding to the morning meal time period is determined as the morning associated group, and the associated group of running vehicles corresponding to the afternoon meal time period is determined as the afternoon associated group.

[0098] Exemplarily, the morning meal time period is 9:00-11:00, the morning time period is 0:00-13:00, the target operation time period corresponding to the vehicle in the morning meal time period is the first operation time period after the departure time is 9:00, and at least two operation time periods in the morning meal time period whose departure time is after the target operation time period are determined as the morning association group. For another example, the morning association group can be all operation time periods from the start time of the morning meal time period to the end time of the morning time period, specifically, the target operation time period corresponding to the vehicle in the morning meal time period is all operation time periods with departure time between 9:00-13:00, which is determined as the morning association group. The afternoon meal time period is 16:00-20:00, and the afternoon time period is 13:00-24:00, then the target operation time period corresponding to the vehicle in the afternoon meal time period is the first operation time period after the departure time is 16:00, and at least two operation time periods after the target operation time period are determined as the afternoon association group. For another example, the afternoon association group may be all the operating time periods from the start time of the afternoon meal time period to the end time of the afternoon time period, specifically, the target operating time period corresponding to the afternoon meal time period of the driving vehicle is all the operating time periods between the departure time of 16:00-24:00, which is determined as the afternoon association group. When the morning association group is adjusted to the idle time period position of other driving vehicles, it can only be adjusted to the idle time period position corresponding to the morning meal time period. When the afternoon association group is adjusted to the idle time period position of other driving vehicles, it can only be adjusted to the idle time period position corresponding to the afternoon meal time period.

[0099] By dividing the preset meal time period into the morning meal time period and / or the afternoon meal time period, the range of the associated groups of traveling vehicles is narrowed, the adjustment accuracy of the associated groups of traveling vehicles is improved, and thus the accuracy of determining the vehicle schedule is improved.

[0100] On the basis of the above embodiment, the driving time period is divided according to the operating hours corresponding to the preset time periods and the preset meal time to obtain the turnover time period, including: determining the current turnover starting time by the starting time of the driving time period and the ending time of the previous adjacent turnover time period; determining the current turnover operating time according to the time period to which the current turnover starting time belongs and the operating hours corresponding to each of the time periods; determining the current turnover end time and the current turnover time period according to the current turnover starting time, the current turnover operating time, the preset meal time and the ending time of the driving time period.

[0101] The current turnover time period refers to the turnover time period of the time distribution to be determined, such as the next turnover time period that is adjacent in time sequence to the one that has completed the turnover time period division. The current turnover start time refers to the start time of the current turnover time period, and is also the end time of the sequentially adjacent turnover time period. The time period to which the current turnover start time belongs refers to the preset time period corresponding to the current turnover start time. The current turnover operating time refers to the operating time of the current turnover time period. The current turnover end time refers to the end time of the current turnover time period.

[0102] Specifically, when the current turnover time period is the first turnover time period of the driving time period, the current turnover start time is the start time of the driving time period. When the current turnover time period is not the first turnover time period of the driving time period, the current turnover start time is the end time of the previous turnover time period that is adjacent in time sequence. According to the transportation time data of the time period, the preset time period corresponding to the current turnover start time can be queried, and the operation time corresponding to the preset time period can be calculated according to the operation time calculation formula. According to the operation time corresponding to the time period, the operation time corresponding to the time period is added to the current turnover start time to determine the current turnover end time. If the current turnover start time is within the preset meal time period, and there is no other time node between the current turnover start time and the start time of the preset meal time period, the preset meal time needs to be added to the current turnover end time, that is, the operation time corresponding to the time period and the preset meal time are added to the current turnover start time to determine the current turnover end time. If the current turnover end time exceeds the end time of the driving time period, the end time of the driving time period is determined as the current turnover end time. The time period between the current turnover start time and the current turnover end time is determined as the current turnover time period.

[0103] The current turnover start time is determined by the start time of the driving time period and the end time of the previous adjacent turnover time period. The current turnover end time is determined based on the current turnover operation time, the preset meal time and the end time of the driving time period. This clarifies the time range of the turnover time period and improves the accuracy of the turnover time period division.

[0104] On the basis of the above embodiment, the method of obtaining the predicted number of departures corresponding to each of the turnaround time periods includes: smoothing the predicted number of departures corresponding to each of the turnaround time periods according to the temporal distribution data of the predicted number of departures corresponding to each of the turnaround time periods, and updating the predicted number of departures corresponding to each of the turnaround time periods.

[0105] Smoothing refers to reducing the difference between adjacent values. The smoothing method may be a sliding average method, a least square method, a local weighted regression method, etc. Specifically, according to the predicted number of departures corresponding to each turnover time period, the predicted number of departures is smoothed by a smoothing method, and the processed predicted number of departures is determined as the predicted number of departures corresponding to each turnover time period.

[0106] By smoothing the predicted number of departures corresponding to each turnaround time period, the uneven distribution of the predicted number of departures can be avoided, vehicle resources can be used rationally, and the accuracy of vehicle timetable determination can be improved.

[0107] On the basis of the above-mentioned embodiment, the target departure interval of each turnaround time period is determined according to the turnaround time of each turnaround time period and the predicted number of departures corresponding to each turnaround time period, including: for each turnaround time period, according to the turnaround time of the turnaround time period and the predicted number of departures corresponding to the turnaround time period, calculating the alternative departure interval of the turnaround time period; according to the temporal distribution data of the alternative departure interval of the turnaround time period, smoothing the alternative departure interval of each turnaround time period, and updating the alternative departure interval of each turnaround time period to determine the target departure interval of the turnaround time period.

[0108] Turnaround time refers to the time interval between the start time and the end time of a turnaround time period. Specifically, the turnaround time of each turnaround time period can be calculated based on the start time and the end time of each turnaround time period. The calculation formula for the turnaround time is: Turnaround time = end time of the turnaround time period - start time of the turnaround time period. Exemplarily, if the turnaround time period is 05:30-06:52, then the turnaround time = (06:52-05:30) + 1 = 82 minutes.

[0109] The alternative departure interval refers to the initial departure interval determined based on the turnaround time of the turnaround time period and the predicted number of departures corresponding to the turnaround time period. Smoothing refers to reducing the difference between adjacent values. The smoothing method can be a sliding average method, a least squares method, a local weighted regression method, etc. Specifically, the initial departure interval is determined based on the turnaround time of the turnaround time period and the predicted number of departures corresponding to the turnaround time period. Based on the initial departure interval corresponding to each turnaround time period, the initial departure interval is smoothed by a smoothing method, and the processed initial departure interval is determined as the target departure interval corresponding to each turnaround time period.

[0110] For example, taking the turnaround time period of 05:30-06:52 as an example, the alternative departure interval is (06:52-05:30) / 7 rounded up to 12 minutes, then the alternative departure interval between the first and second vehicles in the turnaround time period is 12 minutes, and the alternative departure interval between the second and third vehicles in the turnaround time period is 11 minutes, and so on. The alternative departure intervals in the turnaround time period are 12, 11, 12, 11, 12, 11, 12. The alternative departure intervals in the turnaround time period are smoothed, and data with the same alternative departure interval value can be swapped together, while ensuring that the sum of the alternative departure interval values ​​is the turnaround time of the turnaround time period. The smoothed alternative departure intervals are determined as the target departure intervals, and the target departure intervals are 12, 12, 12, 12, 11, 11, 11.

[0111] By smoothing the initial departure intervals corresponding to each turnaround time period, vehicles can be dispatched at more even time intervals, reducing the probability of continuous fluctuations in departure intervals between vehicles, making rational use of vehicle resources, and improving the accuracy of vehicle timetable determination.

[0112] On the basis of the above embodiment, the number of the turnover time periods is obtained to determine the average driving time per person; the working hours of the preset work type are obtained, and the number of driving time periods corresponding to the work type is determined based on the average driving time per person; the working time table is determined based on the vehicle schedule and the number of driving time periods corresponding to each of the work types.

[0113] The number of turnover time periods refers to the sum of the number of turnover time periods within the vehicle's up and / or down travel time periods. The average driving time per person refers to the average time that each person drives a vehicle once in a working day, which can be expressed as the average time of each turnover time period in a working day. In this application, the average driving time per person can be calculated by the number of turnover time periods, the earliest departure time (starting departure time) in the travel time period, the latest departure time (last bus departure time) in the travel time period, and the preset meal time. The formula for average driving time is: average driving time = (last bus departure time - early bus departure time - preset meal time * 2) / number of turnover time periods.

[0114] For example, based on the above example, the earliest time in the driving time period is 5:30, the latest time in the driving time period is 22:00, the preset meal time is 20 minutes, and the number of turnover time periods determined according to the turnover marking is 17, then the average driving time per person = ((22:00-5:30)-20*2) / 17=56 minutes.

[0115] The preset work type refers to the type of allocation of the driver's working days and rest days within a week. The preset work type can be determined according to the actual situation. Exemplarily, the preset work type can be work five days and rest two days, work one day and rest one day, work six days and rest one day, work four days and rest one day, work three days and rest one day, and work two days and rest one day. The working hours of the preset work type refer to the length of daily working time corresponding to the preset work type. The working hours of the preset work type can be set in advance. The number of driving time periods corresponding to the work type is used to describe the number of times the driver drives the vehicle from the starting point to the end point. The number of driving time periods can be calculated based on the working hours of the preset work type and the average driving time per person, and the calculation formula is: number of driving time periods = (working hours of the preset work type - early maintenance time - late maintenance time - vehicle entry time - vehicle exit time) / average driving time per person.

[0116] For example, the preset work type is five days on, two days off, and the preset work type working hours are 8 hours, that is, 480 minutes. The morning maintenance time is 15 minutes, the evening maintenance time is 15 minutes, the vehicle entry time is 40 minutes, and the vehicle exit time is 15 minutes, then the number of driving time periods = (480-15-15-40-15) / 56=7.

[0117] The timetable refers to the weekly work schedule of the driver. According to the number of driving time periods corresponding to the work type, the vehicle number with the same number of operating time periods as the number of driving time periods is matched in the vehicle schedule. If there is no matching vehicle number, multiple vehicles can be combined. For example, the number of operating time periods of vehicle 1 is 6, the number of operating time periods of vehicle 2 is 10, and the number of driving time periods corresponding to the work types of driver A and driver B is 8. It is impossible to directly match with vehicle 1 and vehicle 2, then driver A can be arranged to drive vehicle 1 and driver B to drive vehicle 2. After completing the work tasks of 8 operating time periods of vehicle 1, driver B can go off work smoothly. After completing the work of the operating time period of vehicle 1, driver A completes the work of the remaining operating time periods of vehicle 2.

[0118] In this application, if there is overtime, it can be adjusted in turn according to the overtime driver or vehicle to ensure that the working hours of each driver remain stable as much as possible. For example, the number of operating time periods of vehicle 1 is 8, the operating time period of vehicle 2 is 10, and the number of driving time periods corresponding to the work types of driver A and driver B is 8. It is impossible to directly match with vehicle 2, then driver A can be arranged to drive vehicle 1 and driver B to drive vehicle 2. Next week, driver A will drive vehicle 2 and driver B will drive vehicle 1. For different timetable scheduling methods, weights can be set for vehicle numbers to calculate the weights of vehicle number matching results, and the best timetable scheduling method can be selected according to the weights.

[0119] By determining the average driving time per person and combining it with the working hours of the preset work type to determine the number of driving time periods corresponding to the work type, the timesheet can be determined, which realizes the automatic generation of the timesheet and improves the efficiency of timesheet determination. While ensuring the reasonable arrangement of the timesheet, it also reduces the workload and work pressure of the staff.

[0120] On the basis of the above embodiment, the working time sheet is determined according to the vehicle schedule and the number of driving time periods corresponding to each of the work types, including: obtaining at least one standard vehicle and the standard operating time period corresponding to each of the standard vehicles according to the vehicle schedule; matching the standard operating time period corresponding to each of the standard vehicles with each of the work types according to the number of driving time periods corresponding to each of the work types, and determining the work type corresponding to each of the standard operating time periods; and forming a working time sheet according to the work type corresponding to each of the standard operating time periods.

[0121] A standard vehicle refers to a vehicle corresponding to each row in the vehicle schedule. The standard operating time period corresponding to a standard vehicle refers to the operating time period contained in the row where each standard vehicle is located in the vehicle schedule. For example, there are 11 rows in the vehicle schedule, and 11 standard vehicles can be determined. The operating time period of each of the 11 rows is the standard operating time period of the standard vehicle corresponding to the row. The work type corresponding to the standard operating time period refers to the work type corresponding to the matching result after matching the standard operating time period. The standard operating time period can correspond to at least one work type.

[0122] Specifically, the operating time periods of a row in the vehicle schedule are regarded as the standard operating time periods of the same standard vehicle. According to the number of driving time periods corresponding to each work type, a suitable standard operating time period is selected from the standard operating time periods corresponding to each standard vehicle, so that the number of standard operating time periods is the same as the number of driving time periods. And the work type corresponding to the number of driving time periods is determined as the work type corresponding to the selected standard operating time period. According to the work type corresponding to each standard operating time period, the standard operating time periods are classified according to the work type to form a work time table.

[0123] By determining standard vehicles and standard operating time periods, matching standard operating time periods with work types according to the number of driving time periods corresponding to the work types, and determining the work types corresponding to the standard operating time periods, a timesheet is formed, thereby improving the efficiency and accuracy of timesheet determination and reducing the workload of staff.

[0124] Embodiment 4

[0125] Figure 4a A schematic diagram of an example of a method for determining a vehicle schedule provided in Embodiment 4 of the present invention.

[0126] S401. Obtain the driving time period, and generate a turnover line according to the transportation time data in each time period.

[0127] The driving time period for the upward movement of vehicles is 5:30-21:20, and the driving time period for the downward movement of vehicles is 6:00-22:00. Then the starting point of the turnover marking is 5:30, and the end point of the turnover marking is 22:00. According to the partial data of the time period transportation data shown in Table 3, the turnover marking is determined by taking the downward driving time period as an example. The starting time of the downward driving time period is 6:00. According to Table 3, the preset time period corresponding to 6:00 is 5:31-6:00, the downward transportation time is 46 minutes, the downward stop time is 6 minutes, and the operating time of the downward vehicle is the sum of the downward transportation time and the downward stop time, that is, 46+6=52 minutes. Then, after the vehicle that departs at 6:00 arrives at the terminal, the time for the next departure is 6:52. At this time, the vehicle that departs at 6:52 is going up, and the corresponding preset time period is 6:31-7:00. The up-going transportation time is 48 minutes, and the up-going stop time is 8 minutes. The operation time of the up-going vehicle is the sum of the up-going transportation time and the up-going stop time, that is, 48+8=56 minutes. Therefore, the 6:52 departure time is the time when the vehicle departs again after arriving at the terminal, which is 7:48. At this time, the vehicle that departs at 7:48 is going down, and so on, until the end of the driving time period, the turnaround time line is as follows: 05:30-06:00-06:52-07:48-09:03-10:16-11:19-12:10-13:06-13:59-14:59-15:52-16:56-18:19-19:23-20:17-21:12-22:00-22:48. Among them, when this turnaround time line is determined, according to the meal time periods of 9:00-11:00 and 16:00-20:00, 20 minutes of meal time has been added. For example, the up-bound vehicle that departs at 09:03 has a preset time period of 9:01-9:30. By looking up the table, it can be obtained that the up-bound transportation time is 45 minutes and the up-bound stop time is 8 minutes. The operating time is the sum of the up-bound transportation time, the up-bound stop time and the meal time, that is, 45+8+20=73 minutes. The up-bound vehicle that departs at 09:03 will depart again after reaching the terminal at 10:16. The vehicle that departs at 10:16 is a down-bound vehicle. Although it is still in the meal time period, since the meal time has been added once in the meal time period of 9:00-11:00, the meal time does not need to be added to this calculation.

[0128] S402: Determine an upward turnaround time period and a downward turnaround time period according to the turnaround time mark.

[0129] The turnover line starts at 5:30, which is an upward movement. The odd-numbered columns form the upward turnover line as follows: 05:30-06:52-09:03-11:19-13:06-14:59-16:56-19:23-21:12-22:48. The remaining columns form the downward turnover line as follows: 06:00-07:48-10:16-12:10-13:59-15:52-18:19-20:17-22:00. The upward turnover time periods are 05:30-06:52, 06:52-09:03, 09:03-11:19, 11:19-13:06, 13:06-14:59, 14:59-16:56, 16:56-19:23, 19:23-21:12 and 21:12-22:48. The downward turnover time periods are 06:00-07:48, 07:48-10:16, 10:16-12:10, 12:10-13:59, 13:59-15:52, 15:52-18:19, 18:19-20:17 and 20:17-22:00.

[0130] S403. Determine the predicted number of departures in each turnover time period according to the turnover time period.

[0131] Taking the turnover time period 05:30-06:52 of the above line as an example, 05:30-06:52 is divided according to the preset time periods, and the division results are 05:30-05:30, 05:31-06:00, 06:01-06:30 and 06:31-06:52. According to the data shown in Table 1, the departure frequency and number of departures in each time period after division are calculated as follows: the departure frequency of 05:30-05:30 is 32 / (0.4*80) / 1=1, and the number of departures is 1*1=1; the departure frequency of 05:31-06:00 is 32 / (0.4*80) / 30=1 / 30, and the number of departures is 1 / 30*30=1; the departure frequency of 06:01-06:30 is 96 / (0.4*80) / 30=3 / 30, and the number of departures is 3 / 30*30=3; the departure frequency of 06:31-06:52 is 180 / (0.75*80) / 30=0.1, and the number of departures is 0.1*28=2.8. The number of vehicles departing during the upward turnover period 05:30-06:52 is 1+1+3+2.8, rounded up to 7 vehicles.

[0132] The number of departures in all upward and downward turnover time periods is calculated using the same calculation method, and statistical data is formed in an upward and downward staggered manner, as shown in Table 3.

[0133] Table 3 Predicted number of departures corresponding to each turnover time period for up and down routes

[0134]

[0135] S404: Perform smoothing processing based on the predicted number of departures, and update the predicted number of departures.

[0136] According to the predicted number of departures corresponding to the uplink and downlink turnover time periods shown in Table 3, the predicted number of departures is smoothed to keep the maximum predicted number of departures in the morning and afternoon consistent, ensuring that there are only two peaks throughout the day. After smoothing, the updated predicted number of departures is shown in Table 4.

[0137] Table 4 Updated predicted number of departures corresponding to each uplink and downlink turnaround time period

[0138]

[0139] S405. Generate the departure time of the turnaround time period according to the updated predicted departure quantity.

[0140] Taking the turnover time period of 05:30-06:52 as an example, the duration of the turnover time period is 06:52-05:30=82 minutes. It is necessary to dispatch 7 shifts according to a certain driving interval. The alternative departure interval is 82 / 7 rounded up to 12 minutes. Then, the alternative departure interval between the first and second vehicles in the turnover time period is 12 minutes, and the alternative departure interval between the second and third vehicles is 11 minutes. By analogy, the alternative departure intervals in the turnover time period are 12, 11, 12, 11, 12, 11, 12. Smoothing the alternative departure intervals in the turnover time period can swap data with the same alternative departure interval value together, while ensuring that the sum of the alternative departure interval values ​​is the turnover time of the turnover time period. The smoothed alternative departure intervals are determined as the target departure intervals, which are 12, 12, 12, 12, 11, 11, 11. The driving interval = 82 / 7 = 12 minutes. The departure times for this turnaround time are as follows: 05:30

[0142] 05:30 + 12 minutes target departure interval = 05:42

[0143] 05:42 + 12 minutes target departure interval = 05:54

[0144] 05:54 + 12 minutes target departure interval = 06:06

[0145] 06:06 + 12 minutes target departure interval = 06:18

[0146] 06:18 + 12 minutes target departure interval = 06:30

[0147] 06:18 + 11 minutes target departure interval = 06:41

[0148] And so on to generate all the departure times.

[0149] S406. Determine the operating time period of the vehicles in each turnover time period according to the departure time of each turnover time period.

[0150] According to the departure time of the turnover time period, query the data corresponding to the departure time in the time-segment transportation time data to determine the operating time period corresponding to the departure time. Take the departure time corresponding to the shift in the above turnover time period 05:30-06:52 as an example. According to the data in Table 3, the arrival time of the shift with a departure time of 05:30 is 5:30+38 minutes=6:08, so the operating time period is 05:30-06:08; the arrival time of the shift with a departure time of 05:42 is 05:42+38 minutes=6:20, so the operating time period is 05:42-06:20; the arrival time of the shift with a departure time of 05:54 is 05:54+38 minutes=06:32, so the operating time period is 05:54-06:32; the arrival time of the shift with a departure time of 06:06 is 06:06 +41 minutes = 06:47, the operating time period is 06:06-06:47; the departure time of 06:18 arrives at 06:18+41 minutes = 06:59, the operating time period is 06:18-06:59; the departure time of 06:30 arrives at 06:30+41 minutes = 07:11, the operating time period is 06:30-07:11; the departure time of 06:41 arrives at 06:41+48 minutes = 07:29, the operating time period is 06:41-07:29. And so on, until the operating time periods of vehicles in all turnover time periods are determined.

[0151] S407: Determine an associated group of a driving time matrix and driving vehicles according to the driving time period.

[0152] The running time periods of each turnaround time period are arranged by enumeration method to form a travel time matrix. Figure 4b Schematic diagram of the travel time matrix provided according to the fourth embodiment of the present invention. Figure 4bAs shown, it is a driving time matrix of a moving vehicle, in which the horizontal axis is time, the vertical axis is the number of the moving vehicle, the rectangular blocks are operating time periods, 1 is the upward operating time period, and 2 is the downward operating time period. Taking the moving vehicle 102 as an example, there are operating time periods arranged in the morning and afternoon, and the preset meal time period includes the morning meal time period and the afternoon meal time period. Taking the morning meal time period as an example, the circled operating time period is the next operating time period adjacent to the target operating time period of the moving vehicle 102. The associated operating time period of the morning meal time period is determined according to the target operating time period. The operating time periods circled by the box constitute the associated group of the moving vehicle 102, that is, the morning associated group of the moving vehicle 102. The associated groups of all moving vehicles are determined by the same method.

[0153] S408, adjusting the associated groups of the traveling vehicles until the traveling time matrix has a preset meal time in the preset meal time period, and determining the vehicle schedule.

[0154] like Figure 4b As shown, taking the moving vehicle 102 as an example, the association group circled by the adjustment box is translated upward and placed in the idle time period of the moving vehicle 101. At this time, there is no overlap between the association group and other running time periods of the moving vehicle 101. And the time interval between the target running time period and the adjacent previous running time period in the association group is 10:16-9:48=28 minutes, which is greater than the preset meal time, and the adjustment of the morning association group of the moving vehicle 102 is completed. The same method is used to adjust other association groups in the driving time matrix to form an adjusted driving time matrix. Figure 4c is a schematic diagram of the adjusted travel time matrix provided according to the fourth embodiment of the present invention. Figure 4c As shown, the idle time period indicated by the two arrows is the time period for the preset meal time.

[0155] According to Figure 4c The driving time matrix shown in the figure extracts the time at both ends of the running time period to generate the vehicle schedule. Tables 5 and 6 are vehicle schedules. In Table 5, C represents meal time, T represents the driver leaving work, and P represents the last bus.

[0156] Table 5 Vehicle timetable

[0157] vehicle Downside Up Downside Up Downside Up Downside Up Downside Up 101 6:00 6:52 7:48 9:03 C10:16 11:19 12:10 13:06 T13:51 102 7:03 8:00 9:17 C10:35 11:36 12:29 13:25 T14:10

[0158] Table 6 Vehicle timetable

[0159] vehicle Downside Up Downside Up Downside Up Downside Up Downside 101 13:59 14:59 15:52 16:56 C18:19 19:23 20:17 21:20 P22:00 102 14:16 15:12 16:08 17:09 C18:33 19:42 20:42 P21:30

[0160] S409: Calculate the average driving time per person, and calculate the number of driving time periods corresponding to the work type according to the preset work type and the average driving time per person.

[0161] The earliest time in the driving time period is 5:30, the latest time in the driving time period is 22:00, the meal time is 20 minutes, and the number of turnaround time periods is 17, then the average driving time per person = ((22:00-5:30)-20*2) / 17 = 56 minutes. According to the calculation formula for the number of driving time periods, the morning maintenance time is 15 minutes, the evening maintenance time is 15 minutes, the vehicle entry time is 40 minutes, and the vehicle exit time is 15 minutes. The calculated number of driving time periods corresponding to the work types is: work five days and rest two days (480-15-15-40-15) / 56=7; work one day and rest one day (690-15-15-40-15) / 56=10; work six days and rest one day (385-15-15-40-15) / 56=5; work four days and rest one day (430-15-15-40-15) / 56=6; work three days and rest one day (445-15-15-40-15) / 56=6; work two days and rest one day (515-15-15-40-15) / 56=7.

[0162] S410: Determine a work schedule according to the vehicle schedule and the number of driving time periods corresponding to each work type.

[0163] According to the number of driving time periods corresponding to the work type, vehicles with the same number of operating time periods as the number of driving time periods are matched in the vehicle schedule. If there is no matching vehicle, multiple vehicles can be combined. Users can set corresponding weights for each vehicle. Vehicles with larger weights are given priority. The closer the vehicle combination is to the number of rows in the vehicle schedule, the larger the default weight. Tables 7 and 8 are partial data of the work timetable. In Tables 7 and 8, arrival refers to the time when the first bus arrives at the terminal, and departure refers to the time when the last bus leaves the terminal.

[0164] Table 7 Working time sheet

[0165] Class number Road Sign 1 Check-in 2 Appearance 3 Direction 4 Off duty 5 Location 6 Arrival 12 Departure 13 1 101 5:05 5:20 Up 13:51 Up 22:40 22:55 2 102 6:33 6:48 Downside 14:10 Up 21:45 22:00

[0166] Table 8 Working time sheet

[0167]

[0168]

[0169] The technical solution of the embodiment of the present invention divides the driving time period according to the operating time and the preset meal time to obtain the turnover time period, determines the corresponding operating time period according to the predicted number of departures corresponding to the turnover time period, arranges the operating time period in the driving time period to form a driving time matrix, and adjusts the associated operating time period corresponding to the preset meal time period in the driving time matrix until the driving time matrix has a preset meal time in the preset meal time period, determines the vehicle schedule, and the meal time is used to generate the vehicle schedule. When dividing the turnover time period, the preset meal time is considered in advance, which reduces the difficulty of retaining the meal time in the driving time matrix. According to the associated operating time period corresponding to the preset meal time period, the associated operating time period is adjusted in the driving time matrix until the driving time matrix has a preset meal time in the preset meal time period, which reduces the probability of long-term vehicle idleness, can effectively utilize the preset meal time period and vehicle resources, and improve the efficiency and accuracy of vehicle schedule determination.

[0170] Embodiment 5

[0171] Figure 5 This is a schematic diagram of the structure of a vehicle schedule determination device provided in Embodiment 5 of the present invention. Figure 5 As shown, the device includes: a turnaround time period acquisition module 501, an operation time period determination module 502, a travel time matrix determination module 503 and a vehicle schedule determination module 504.

[0172] The turnover time period acquisition module 501 is used to acquire the driving time period, and divide the driving time period according to the operation time corresponding to the preset time periods and the preset meal time, to obtain the turnover time period;

[0173] The operation time period determination module 502 is used to obtain the predicted number of departures corresponding to each of the turnover time periods, and determine the operation time period corresponding to each of the turnover time periods;

[0174] A driving time matrix determination module 503 is used to arrange each of the operating time periods in the driving time period to form a driving time matrix;

[0175] The vehicle schedule determination module 504 is used to obtain the associated operating time period corresponding to the preset meal time period, and adjust the associated operating time period in the driving time matrix until the driving time matrix has a preset meal time in the preset meal time period, thereby determining the vehicle schedule.

[0176] The technical solution of the embodiment of the present invention divides the driving time period according to the operating time and the preset meal time to obtain the turnover time period, determines the corresponding operating time period according to the predicted number of departures corresponding to the turnover time period, arranges the operating time period in the driving time period to form a driving time matrix, and adjusts the associated operating time period corresponding to the preset meal time period in the driving time matrix until the driving time matrix has a preset meal time in the preset meal time period, determines the vehicle schedule, and the meal time is used to generate the vehicle schedule. When dividing the turnover time period, the preset meal time is considered in advance, which reduces the difficulty of retaining the meal time in the driving time matrix. According to the associated operating time period corresponding to the preset meal time period, the associated operating time period is adjusted in the driving time matrix until the driving time matrix has a preset meal time in the preset meal time period, which reduces the probability of long-term vehicle idleness, can effectively utilize the preset meal time period and vehicle resources, and improve the efficiency and accuracy of vehicle schedule determination.

[0177] Optionally, the vehicle schedule determination module 504 includes:

[0178] A running vehicle operation time period determination unit, used to obtain an operation time period of at least one running vehicle in the running time matrix;

[0179] An associated group determination unit, used to obtain at least one associated running time period corresponding to a preset meal time period in the running time period of each of the running vehicles, to form an associated group of each of the running vehicles;

[0180] The association group adjustment unit is used to adjust the association group of at least one of the traveling vehicles in the driving time matrix to the idle time period of other traveling vehicles until each of the traveling vehicles has a preset meal time in the preset meal time period.

[0181] Optionally, the association group adjustment unit includes:

[0182] A current association group determination subunit is used to match the departure sequence of each of the running vehicles in the same turnover time period with the row sequence of each of the running vehicles in the running time matrix, and obtain the association group of the running vehicles in the non-first row in the running time matrix as the current association group;

[0183] The first adjustment subunit of the current association group is used to adjust the current association group to the idle time period within the preset meal time period of the adjusted running vehicle in the previous row with an adjacent row sequence, and determine the adjusted running vehicle corresponding to the current association group;

[0184] A meal time detection subunit, used to detect whether the adjusted driving vehicle corresponding to the current association group has a preset meal time in the preset meal time period;

[0185] The second adjustment subunit of the current association group is used for, when the adjusted driving vehicle does not have a preset meal time in the preset meal time period, continuing to adjust the current association group to the idle time period of the adjusted driving vehicle in the previous row adjacent in row sequence within the preset meal time period, and updating the adjusted driving vehicle corresponding to the current association group;

[0186] Return to the meal time detection subunit, which is used to return to the step of detecting whether the adjusted driving vehicle corresponding to the current association group has a preset meal time in the preset meal time period, until the adjusted driving vehicle corresponding to the current association group has a preset meal time in the preset meal time period;

[0187] The current association group updating subunit is used to return to the step of obtaining the association group of the non-first row of traveling vehicles as the current association group when the adjusting traveling vehicles have a preset meal time in the preset meal time period, until the traveling vehicles in all the association groups have a preset meal time in the preset meal time period.

[0188] Optionally, the association group determining unit includes:

[0189] a target operation time period determination subunit, configured to obtain a target turnover time period added with the preset meal time length, and query a target operation time period included in the target turnover time period in the operation time period of the traveling vehicle;

[0190] The association group determination subunit is used to determine the association group of the running vehicle by taking the next running time period adjacent to the target running time period and at least one running time period after the next running time period for the running time period of the running vehicle.

[0191] Optionally, the preset meal time period includes a morning meal time period and / or an afternoon meal time period, and the associated group of traveling vehicles includes a morning associated group and / or an afternoon associated group.

[0192] Optionally, the turnaround time period acquisition module 501 includes:

[0193] A turnover start time determination unit, used to determine the current turnover start time by using the start time of the driving time period and the end time of the previous turnover time period that is adjacent in time sequence;

[0194] A current turnover operation duration determining unit, used to determine the current turnover operation duration according to the time segment to which the current turnover start time belongs and the operation duration corresponding to each of the time segments;

[0195] The current turnover time period determination unit is used to determine the current turnover end time and the current turnover time period according to the current turnover start time, the current turnover operation time, the preset meal time and the end time of the driving time period.

[0196] Optionally, the operating time period determination module 502 is specifically configured to:

[0197] According to the temporal distribution data of the predicted number of departures corresponding to each of the turnover time periods, the predicted number of departures corresponding to each of the turnover time periods is smoothed, and the predicted number of departures corresponding to each of the turnover time periods is updated.

[0198] The vehicle schedule determination device provided in the embodiment of the present invention can execute the vehicle schedule determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0199] Embodiment 6

[0200] Figure 6 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0201] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0202] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0203] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a vehicle schedule determination method.

[0204] In some embodiments, the vehicle schedule determination method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the vehicle schedule determination method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the vehicle schedule determination method in any other appropriate manner (e.g., by means of firmware).

[0205] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0206] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0207] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0208] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0209] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0210] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0211] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0212] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining a vehicle schedule, characterized in that: include: Obtaining a driving time period, and dividing the driving time period according to the operating hours corresponding to the preset time periods and the preset meal time, to obtain a turnover time period; wherein the turnover time period refers to the time period between two consecutive departure times of the same vehicle in the same driving direction; Obtain the predicted number of departures corresponding to each of the turnaround time periods, and determine the operating time periods corresponding to each of the turnaround time periods; In the driving time period, arranging each of the running time periods to form a driving time matrix; Obtaining the associated running time period corresponding to the preset meal time period, and adjusting the associated running time period in the driving time matrix until the driving time matrix has a preset meal time in the preset meal time period, and determining the vehicle schedule; There are at least two driving time matrices, and a vehicle schedule is formed based on the driving time matrices, including: screening each driving time matrix according to the time interval between adjacent running time periods of the same driving vehicle in the driving time matrix, the number of each running time period and the distribution of the running time periods in the same turnover time period; and determining the vehicle schedule based on the screened driving time matrices.

2. The method according to claim 1, characterized in that The step of obtaining the associated running time period corresponding to the preset meal time period and adjusting the associated running time period in the driving time matrix until the driving time matrix has a preset meal time in the preset meal time period includes: In the driving time matrix, obtaining a running time period of at least one driving vehicle; In the running time period of each of the running vehicles, at least one associated running time period corresponding to the preset meal time period is obtained to form an associated group of each of the running vehicles; In the driving time matrix, at least one associated group of the driving vehicles is adjusted to the idle time period of other driving vehicles until each of the driving vehicles has a preset meal time in the preset meal time period.

3. The method according to claim 2, characterized in that The departure sequence of each of the running vehicles in the running time matrix in the same turnaround time period matches the row sequence of each of the running vehicles; In the driving time matrix, adjusting at least one associated group of the driving vehicles to the idle time period of other driving vehicles includes: In the travel time matrix, obtaining an associated group of traveling vehicles that are not in the first row as a current associated group; Adjust the current associated group to the idle time period of the adjusted running vehicle in the previous row adjacent to the row sequence within the preset meal time period, and determine the adjusted running vehicle corresponding to the current associated group; Detecting whether the adjusted driving vehicle corresponding to the current association group has a preset meal time in the preset meal time period; In the case where the adjusted driving vehicle does not have a preset meal time in the preset meal time period, the current association group is further adjusted to the idle time period of the adjusted driving vehicle in the previous row adjacent in row sequence within the preset meal time period, and the adjusted driving vehicle corresponding to the current association group is updated; Return to the step of detecting whether the adjusted driving vehicle corresponding to the current association group has a preset meal time in the preset meal time period, until the adjusted driving vehicle corresponding to the current association group has a preset meal time in the preset meal time period; In the case where the adjusted driving vehicles have a preset meal time in the preset meal time period, the step of returning to execute the step of obtaining the associated group of driving vehicles other than the first row as the current associated group is performed until the driving vehicles in all associated groups have a preset meal time in the preset meal time period.

4. The method according to claim 2, characterized in that: The step of obtaining, in the running time period of each of the running vehicles, at least one associated running time period corresponding to the preset meal time period to form an associated group of each of the running vehicles includes: Acquire a target turnover time period added with the preset meal time length, and query a target running time period included in the target turnover time period in the running time period of the traveling vehicle; For the running time period of the running vehicle, the next running time period that is adjacent to the target running time period in time sequence and at least one running time period after the next running time period are used to determine the associated group of the running vehicles.

5. The method according to claim 2, characterized in that: The preset meal time period includes a morning meal time period and / or an afternoon meal time period, and the associated group of traveling vehicles includes a morning associated group and / or an afternoon associated group.

6. The method according to claim 1, characterized in that The driving time period is divided according to the operating time corresponding to the preset time periods and the preset meal time to obtain the turnover time period, including: Determine the current turnover start time by using the start time of the driving time period and the end time of the previous turnover time period that is adjacent in time sequence; Determine the current turnover operation duration according to the time period to which the current turnover start time belongs and the operation duration corresponding to each of the time periods; According to the current turnover start time, the current turnover operation duration, the preset meal duration and the end time of the driving time period, the current turnover end time is determined, and the current turnover time period is determined.

7. The method according to claim 1, characterized in that The obtaining of the predicted number of departures corresponding to each of the turnaround time periods includes: According to the temporal distribution data of the predicted number of departures corresponding to each of the turnover time periods, the predicted number of departures corresponding to each of the turnover time periods is smoothed, and the predicted number of departures corresponding to each of the turnover time periods is updated.

8. A vehicle schedule determination device, characterized in that: include: The turnover time period acquisition module is used to acquire the driving time period, and divide the driving time period according to the operating time corresponding to the preset time periods and the preset meal time, to obtain the turnover time period; wherein the turnover time period refers to the time period between two consecutive departure times of the same vehicle in the same driving direction; An operation time period determination module is used to obtain the predicted number of departures corresponding to each of the turnover time periods, and determine the operation time period corresponding to each of the turnover time periods; A driving time matrix determination module, used for arranging each of the operating time periods in the driving time period to form a driving time matrix; A vehicle schedule determination module, used for obtaining an associated running time period corresponding to a preset meal time period, and adjusting the associated running time period in the driving time matrix until the driving time matrix has a preset meal time in the preset meal time period, thereby determining a vehicle schedule; There are at least two driving time matrices, and a vehicle schedule is formed based on the driving time matrices, including: screening each driving time matrix according to the time interval between adjacent running time periods of the same driving vehicle in the driving time matrix, the number of each running time period and the distribution of the running time periods in the same turnover time period; and determining the vehicle schedule based on the screened driving time matrices.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the vehicle schedule determination method as described in any one of claims 1-7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the vehicle schedule determination method as described in any one of claims 1 to 7 is implemented.

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