Railway passenger station ticket checking task dynamic scheduling method based on ant colony algorithm

Abstract

The invention provides a railway passenger station ticket checking task dynamic scheduling method based on an ant colony algorithm, and the method comprises the steps: compiling related information according to a railway station ticket checker scheduling plan, and determining a working time slice of each ticket checking entrance of a railway station; constructing a continuous space-time network of the working time slices of the ticket checker and the ticket entrance; establishing a railway station ticket checker dynamic scheduling plan optimization model; and solving the optimization model by using an ant colony search algorithm to obtain the optimal dynamic scheduling. Aiming at a dynamic post scheduling arrangement mode of a large-scale railway station ticket entrance, a dynamic post scheduling arrangement plan of a ticket checker is rapidly generated; according to the invention, the working time slices of the ticket checker and the ticket entrance are constructed into the continuous space-time network, so that the establishment of the optimization model is facilitated; according to the invention, the objective function is established by minimizing the comprehensive cost, the constraint condition is considered, and the optimal dynamic scheduling scheme is searched through the ant colony search algorithm, so that the efficiency of the ticket checking task is greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field of railway ticket checking and scheduling, in particular to a method for dynamically scheduling ticket checking tasks at a railway passenger station based on an ant colony algorithm. Background Art

[0002] The railway station ticket inspection scheduling plan is an important part of the passenger management of railway passenger transport stations. Whenever a train departs from the station, the passenger ticket gate corresponding to the train will arrange ticket inspectors to organize and guide passengers to enter the platform in the corresponding time period (usually 15 minutes to 5 minutes before the train departs). The effectiveness of the ticket inspector scheduling plan is closely related to factors such as passenger service efficiency, station operation safety and economic benefits.

[0003] At present, there are great differences in the scheduling and post management mode of ticket inspectors at stations under the jurisdiction of various railway bureaus. Most stations adopt the "fixed post" mode in the scheduling and post management of ticket inspectors, that is, each ticket gate of the station is assigned to a fixed ticket inspector, who performs ticket checking tasks at the ticket gate all day. The advantage of this mode is that the organization is clear and the implementation is simple. The biggest disadvantage is that for stations with a small number of departures, there will be a phenomenon that there are no trains departing from a certain ticket gate for a long time, resulting in a waste of human resources for ticket inspectors to do nothing. In response to this deficiency, a few railway stations that implement intelligent operation management have tried to implement the "dynamic post" mode of ticket inspector scheduling and post management. Under this mode, ticket inspectors are no longer fixed to work at a certain ticket gate, but are flexibly arranged to work at different ticket gates based on the occupancy information of the ticket gate at the departure time of the train. The advantage of this method is that it can effectively reduce the idleness of ticket inspectors, effectively improve the work efficiency of ticket inspectors, and then reduce the number of ticket inspectors to reduce the operating costs of the station.

[0004] At present, there are very few studies on ticket inspection scheduling methods under the "dynamic post" model at home and abroad. The existing methods are usually based on the manual experience of the stationmaster on duty in charge of passenger transportation, that is, before the station is put into operation, the ticket inspector scheduling plan is formulated based on the train departure information of the station. However, the method based on manual experience adjustment is usually inefficient and brings great work pressure to the stationmaster on duty.

[0005] The scheduling and deployment planning problem of railway station ticket inspectors under the "dynamic post" mode is a typical NP-Hard problem. Finding the exact optimal solution based on traditional operations research algorithms requires unacceptable time and cost, which is not feasible in practice. Therefore, scholars in this field often focus on using approximate algorithms or heuristic intelligent optimization methods to quickly obtain solutions, although there is a certain deviation in quality between the obtained solutions and the optimal solutions. At the same time, the existing auxiliary scheduling software is mainly aimed at crew scheduling, and cannot effectively assist the scheduling and deployment management decision of station ticket inspectors. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a dynamic scheduling method for ticket checking tasks in railway passenger stations based on ant colony algorithm, which realizes the rapid generation of dynamic scheduling plans for ticket inspectors for dynamic post scheduling at ticket gates of large railway stations.

[0007] The technical solution of the present invention is: a dynamic scheduling method for ticket checking tasks at a railway passenger station based on an ant colony algorithm, comprising the following steps: S1) Prepare relevant information based on the shift schedule of ticket inspectors at railway stations and establish the working time segments for each ticket gate at railway stations; S2), constructing a spatiotemporal network of the working time segments of ticket inspectors and ticket gates; S3) Establish a dynamic scheduling optimization model for railway station ticket inspectors; S4) Use the ant colony search algorithm to solve the optimization model and obtain the optimal dynamic schedule.

[0008] Preferably, in step S1), the relevant information compiled according to the railway station ticket inspector scheduling plan includes: Railway station departure train information set L, train departure time information set T, ticket gate information set E, ticket inspector information set S; where the departure time of each train is The departure ticket gate for each train is .

[0009] Preferably, in step S1), the working time segment of each ticket gate of the railway station is expressed as follows according to the departure time of each train and the departure ticket gate: ; in, The earliest time to pass the ticket gate before the train departs. The maximum time to pass the ticket gate before the train departs; Departure time for each train Get the set of working time segments of the ticket gates at railway stations , where the duration of each working time segment is .

[0010] As a preferred embodiment, in step S1), if the same ticket gate has two adjacent departure trains, the working time segments determined are and If there is overlap in time segments, the two working time segments will be merged into one working time segment. .

[0011] As a preferred method, in step S2), a spatiotemporal network of the working time segments of the ticket inspector and the ticket gate is constructed based on the operations research graph theory method. , where the space-time nodes Represents the source / sink points and arc segments of the ticket gate working time segments Represents the connection time between the working time segments of the two ticket gates.

[0012] As a preferred embodiment, in step S2), the connection time of the working time segments of the two ticket gates is The calculation is: When the end time of the previous ticket gate working time segment is less than the start time of the next ticket gate working time segment, the continuation time is the difference between the latter and the former; When the end time of the previous gate working time segment is greater than or equal to the start time of the next gate working time segment, the continuation time is infinite; that is: ; In the formula, Indicates i The ticket gate and j The connection time of each ticket gate; For working time segments End time of For working time segments The start time of is the threshold value set; is a sufficiently large positive integer.

[0013] Preferably, in step S3), a dynamic scheduling optimization model for railway station ticket inspectors is established, which specifically includes the following steps: S31) On the constructed spatiotemporal network of the working time segments of the ticket inspectors and the ticket gates, a mixed integer linear integer optimization model is established according to the actual needs of the station ticket inspectors' scheduling; S32) Construct constraints for the optimization model.

[0014] Preferably, in step S31), based on minimizing the comprehensive cost Z, the objective function of the optimization model is constructed as follows: ; The objective function requires minimizing the number of ticket inspectors and the duration of ticket inspectors in working time segments; In the formula, and is a 0-1 decision variable, Ticket inspector Whether to undertake work time fragments The ticket checking task, Ticket inspector Whether it is divided into working time segments Continuation of working time segment ; It is the conversion factor from the number of ticket inspectors to comprehensive fees; It is the conversion coefficient of the connecting time of the working time segment of the ticket gate to the comprehensive fee, and Z is the comprehensive fee.

[0015] Preferably, in step S32), the constraint condition is: Constraint 1: Uniqueness constraint of the working time segment of the ticket gate , ; Constraint 2: Feasibility constraint of the continuation of working time segments at the ticket gate , .

[0016] Preferably, in step S4), the optimization model is solved by using an ant colony search algorithm, which specifically includes the following steps: S41), setting ant colony algorithm parameters; S42), placing all ants in the virtual center, each ant randomly selects the next task node according to the transfer rule, and obtains their own complete path after traversing all time and space nodes; S43), update the pheromones on each path, update the current number of iterations, determine whether the algorithm is finished, and output the optimal solution.

[0017] As a preferred embodiment, in step S41), the ant colony algorithm parameters set include the number of ants , Pheromone importance parameters , parameters of the importance of heuristic factors , pheromone evaporation coefficient , pheromone increases intensity coefficient , Maximum number of algorithm iterations .

[0018] Preferably, step S42) is as follows: S421), all ants are placed at the virtual starting point, each ant selects the working time segment of the ticket gate with the earliest start time as the first space-time node, and randomly selects the next space-time node according to the following formula shift rule. After all the space-time nodes are selected to enter the ant search path, all ants continue to move to the virtual end point, so as to obtain their own completed paths; ; in, Represents ants By Node Transfer to Node The probability of Represents ants At the node A set of feasible selection nodes; For arc segment pheromones; For arc segment Heuristic information; It is a parameter that characterizes the importance of pheromones; is a parameter that characterizes the importance of heuristic information.

[0019] Preferably, step S43) specifically includes the following steps: S431), the pheromone on each path searched by the ants is updated according to the following formula, namely: ; ; in, For the After iterations, the path pheromone concentration on; For the After iterations, the path pheromone concentration on; is the pheromone evaporation coefficient; is the pheromone increment after this iteration, is the objective function value of the optimal path searched by the ant in this round, The average objective function value of the paths searched by all ants in this round; S432), update the current number of iterations , if the current iteration number Less than or equal to the maximum number of iterations set , then continue to repeat S21), otherwise the algorithm ends and outputs the optimal solution.

[0020] The beneficial effects of the present invention are: 1. The present invention aims at the dynamic post scheduling arrangement mode of the ticket gate of a large railway station, and realizes the rapid generation of the dynamic scheduling arrangement plan of the ticket inspectors; 2. The present invention constructs the working time segments of ticket inspectors and ticket gates into a continuous spatiotemporal network to facilitate the establishment of an optimization model; 3. The present invention establishes the objective function by minimizing the comprehensive cost, taking the constraints into consideration, and finds the optimal dynamic scheduling scheme through the ant colony search algorithm, which greatly improves the efficiency of the ticket checking task. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is an overall flow chart of an embodiment of the present invention; Figure 2 A schematic diagram of a railway station topology structure in an embodiment of the present invention; Figure 3 This is an example diagram of working time segments of all ticket gates in a station according to an embodiment of the present invention; Figure 4 It is a schematic diagram of the spatiotemporal network of the working time segments of the ticket gate according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the working time segments of a single ticket inspector in an embodiment of the present invention; Figure 6 This is a schematic diagram of a comparison post for working hours of a ticket inspector according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The specific implementation of the present invention will be further described below in conjunction with the accompanying drawings: like Figure 1 As shown, this embodiment provides a method for dynamic scheduling of ticket inspection tasks at a railway passenger station based on an ant colony algorithm. This embodiment uses the topological structure information of a railway station under the Guangzhou Railway Group and related train information (trains departing from 20:00 to 8:00 on the night shift of May 15, 2024) as data to illustrate the present invention. The schematic diagram of the topological structure of the railway station is shown in Figure 2 As shown in the figure, the station has 13 ticket gates for passengers to board the train. The earliest and latest ticket gate times before the train departs are set according to the station conditions. .

[0023] The method of this implementation specifically includes the following steps: S1) Count the departure trains at the 13 ticket gates and form the ticket gate working time segments , each ticket gate working time segment contains the train number and the corresponding ticket gate working period information; the specific method is as follows: Determine the gate working time segment based on the departure time and departure gate of each train ,in, The earliest time to pass the ticket gate before the train departs. It is the latest time before the train departs to pass the ticket gate. It should be noted that at the same ticket gate, if two adjacent departure trains have a certain working time segment and There is overlap in time segments, e.g. , then merge the above two working time segments into one working time segment .

[0024] Taking Ticket Gate No. 2 as an example, the trains, train departure times and derived ticket checking working hours are shown in Table 1: Table 1 Information about the working time period of ticket checking at Gate 2 The initial working time of Gate 2 is as follows: 19:58~20:09, 20:18~20:29, 20:30~20:41, 20:53~21:04, 21:31~21:42, 21:47~21:58; 22:14~22:25, 1:34~1:45, 4:37~4:48, 6:38~6:49, 6:59~7:18, 7:24~7:50; Setting the Threshold It can be found that in the working time segments of Gate 2, there is only a 1-minute time difference between the end time of the former working time segment and the start time of the latter working time segment in the working time segments of 20:18-20:29 and 20:30-20:41. Therefore, the working time segments 20:18-20:29 and 20:30-20:41 are merged into 20:18-20:41, and the working time segments 21:31-21:42 and 21:47-21:58 are merged into 21:31-21:58. The adjusted working time segments of Gate 2 are as follows. The number of working time segments is reduced, thereby reducing the scale of the problem.

[0025] 19:58~20:09, 20:18~20:41, 20:53~21:04, 21:31~21:58, 22:14~22:25; 1:34-1:45, 4:37-4:48, 6:38-6:49, 6:59-7:18, 7:24-7:50; Similarly, the working time segments of the other 12 ticket gates are obtained, such as Figure 3 As shown, the black bold part is the adjusted working time segment.

[0026] The working hours of Gate 1 are as follows: 20:17~20:28, 21:57~22:08, 22:19~22:30, 23:01~23:12; 0:30~0:41, 1:58~2:09, 6:24~6:50, 7:34~7:45; The working hours of Gate 3 are as follows: 19:53~20:04, 20:13~20:24, 20:56~21:22, 22:47~22:58, 0:12~0:23; 1:53~2:04, 3:05~3:16, 6:04~6:30, 7:12~7:33, 7:50~8:01; The working hours of Gate 4 are as follows: 20:03~20:14, 20:23~20:34, 20:50~21:17, 21:26~21:52, 22:02~22:13, 22:52~23:17; 0:07~0:18, 0:45~0:56, 1:09~1:20, 1:55~2:22, 5:53~6:04, 7:12~7:56; The working hours of Gate 5 are as follows: 20:08~20:19, 20:50~21:01, 21:08~21:19, 21:36~21:47, 22:08~22:19, 22:41~22:52; 23:14~23:25, 23:42~23:53, 0:25~0:36, 3:20~3:44, 6:48~7:11, 7:17~8:06; The working hours of Gate 6 are as follows: 20:45~21:12, 21:52~22:03, 5:58~6:09, 6:33~6:54, 7:21~7:32, 7:56~8:07; The working hours of Gate 7 are as follows: 20:36~20:47, 21:01~21:28, 21:48~22:11, 6:22~7:00; The working hours of Gate 8 are as follows: 20:21~20:32, 20:40~21:36, 22:26~23:02, 5:48~5:59; 6:16~6:27, 6:38~6:49, 6:55~7:06, 7:50~8:01; The working hours of Gate 9 are as follows: 20:49~21:00, 21:14~21:45, 21:54~22:17, 22:24~22:35; 22:45~22:56, 23:20~23:31, 6:27~6:38, 7:18~7:57; The working hours of Gate 10 are as follows: 20:26-20:37, 21:01-21:17, 7:07-7:18; The working hours of Gate 11 are as follows: 20:15~20:26, 20:42~20:53, 21:12~21:23, 21:35~21:46, 22:19~22:30; 5:50-6:01, 6:32-6:58, 7:12-7:33, 7:57-8:08; The working hours of Gate 12 are as follows: 6:15-6:26, 7:52-8:03; The working hours of Gate 13 are as follows: 20:01~20:12, 21:57~22:08, 5:55~6:06, 7:42~7:53; S2) Based on the operations research graph theory method, establish a spatiotemporal network of the working time segments of the ticket gate like Figure 4 As shown, the time and space nodes The source / sink points of the ticket gate working time segments are represented by the number of ticket gate working time segments obtained in step S1) is 96, so the spatiotemporal network The number of nodes in is 96, and the arc segment Represents the duration between two consecutive working time segments, where: ; Taking the working time segment of ticket gate 1 from 20:17 to 20:28 as an example, the connection time with the working time segment from 20:48 to 20:59 is 20 minutes, and the connection time with the working time segment from 19:58 to 20:09 is .

[0027] In this way, the connection time matrix between all working time segments is obtained. It should be noted that the connection time space network of the working time segments In the figure, the working time segment connection time matrix is ​​not a symmetric matrix. Taking the two working time segments 19:58-20:09 and 20:17-20:28 as examples, the connection time between 19:58-20:09 and 20:17-20:28 is 20 minutes, and conversely, the end time between 20:17-20:28 and 19:58-20:09 is .

[0028] S3) Establish a dynamic scheduling optimization model for railway station ticket inspectors, specifically: Based on minimizing the comprehensive cost Z, the objective function and constraints of the optimization model are constructed, namely: ; The objective function requires minimizing the number of ticket inspectors and the duration of ticket inspectors in working time segments; In the formula, and is a 0-1 decision variable, Ticket inspector Whether to undertake work time fragments The ticket checking task, Ticket inspector Whether it is divided into working time segments Continuation of working time segment ; is the conversion coefficient of the gate working time segment connection time to the comprehensive fee, Z is the comprehensive fee, F The set of working time segments of the ticket gate; S is the information set of the ticket inspectors.

[0029] S4) Using the ant colony search algorithm to solve the optimization model, specifically including the following steps: S41), set the algorithm parameters, including the number of ants , pheromone importance parameter , the parameter of the importance of the heuristic factor , pheromone evaporation coefficient , pheromone increases the intensity coefficient , the maximum number of iterations of the algorithm , set the current number of iterations .

[0030] S42) Place all ants at the virtual starting point , each ant selects the working time segment of the ticket gate with the earliest start time as the first space-time node, and randomly selects the next space-time node according to the following formula shift rule. After all the space-time nodes are selected to enter the ant search path, all ants continue to move to the virtual end point, thus obtaining their own completion paths; ; in, For arc segment pheromones; For arc segment Heuristic information; It is a parameter that characterizes the importance of pheromones; is a parameter that characterizes the importance of heuristic information; S43) Update the pheromone on each path searched by the ants according to the following formula: ; ; in, is the pheromone increment after this iteration, is the objective function value of the optimal path searched by the ant in this round, The average objective function value of the paths searched by all ants in this round; Update the current iteration number , if the current iteration number Less than or equal to the maximum number of iterations set , then continue to repeat S2.1, otherwise the algorithm ends and outputs the optimal solution.

[0031] According to the above process, the station ticket inspector scheduling plan is as follows: Note: The values ​​outside the brackets are the time span of the working time segment, and the values ​​in the brackets are the ticket gates.

[0032] The working time segment of Ticket Inspector 1 is: 20:17~20:28(1), 20:40~21:36(8), 21:48~22:11(7), 22:24~22:35(9), 22:45~22:56(9), 23:20~23:31(9), 23:42~23:53(5), 0:07~0:18(4), 0:30~0:41(1), 1:09~ 1:20(4), 1:34~1:45(2), 1:55~2:22(4), 3:05~3:16(3), 4:37~4:48(2), 5:48~5:59(8), 6:16~6:27(8), 6:38~6:49(8), 6:59~7:18(2), 7:34~7:45(1), 7:56~8:07(6) The total working time is 301 minutes.

[0033] Ticket Inspector 2 20:45~20:12(6), 21:26~21:52(4), 22:02~22:13(4), 22:26~23:02(8), 23:14~23:25(5), 0:25~0:36(5), 1:53~2:04(3), 3:20~3:44(5), 6:48~7:11(5), 7:21~7:32(6), 7:42~7:53(13) The total working time is 202 minutes.

[0034] Ticket Inspector 3 20:01~20:12(13), 20:23~20:34(4), 20:50~21:17(4), 21:31~21:58(2), 22:08~22:19(5), 22:41~22:52(5), 0:12~0:24(3), 0:45~0:56(4), 1:58~2:09(1), 6:24~6:50(1), 7:07~7:18(10), 7:50~8:01(8) The total working time is 179 minutes.

[0035] Ticket Inspector 4 20:21~20:32(8), 20:42~20:53(11), 21:08~21:19(5), 21:36~21:47(5), 21:57~22:08(1), 22:19~22:30(1), 23:01~23:12(1), 5:55~6:06(13), 6:22~7:00(7), 7:12~7:56(4) The total working time is 170 minutes.

[0036] Ticket Inspector 5 20:03~20:14(4), 20:26~20:37(10), 21:01~21:17(10), 21:35~21:46(11), 21:57~22:08(13), 22:19~22:30(11), 22:52~23:17(4), 5:53~6:04(4), 6:15~6:26(12), 6:38~6:49(2), 7:24~7:50(2) The total working time is 155 minutes.

[0037] Ticket Inspector 6 19:58~20:09(2), 20:53~21:04(2), 21:14~21:45(9), 22:14~22:25(2), 22:47~22:58(2), 6:04~6:30(3), 7:12~7:33(3), 7:50~8:01(3) The total working time is 133 minutes.

[0038] Ticket Inspector 7 19:53~20:04(3), 20:18~20:41(2), 20:56~21:22(3), 21:54~22:17(9), 6:27~6:38(9), 6:55~7:06(8), 7:18~7:57(9) The total working time is 144 minutes.

[0039] Ticket Inspector 8 20:13~20:24(3), 20:36~20:47(7), 21:01~21:28(7), 21:52~22:03(6), 6:33~6:54(6), 7:17~8:06(5) The total working time is 130 minutes.

[0040] Ticket Inspector 9 20:08~20:19(5), 20:50~21:01(5), 21:12~21:23(11), 5:50~6:01(11), 6:32~6:58(11), 7:12~7:33(11), 7:57~8:08(11) The total working time is 102 minutes.

[0041] Ticket inspector 10 20:15~20:26(11), 20:49~21:00(9), 5:58~6:09(6), 7:52~8:03(12) The total working time is 44 minutes.

[0042] It can be found that in the initial station ticket inspector scheduling plan, there is a large difference in the total working hours between ticket inspector 1 and ticket inspector 10, and between ticket inspector 2 and ticket inspector 9, so they are adjusted as follows: The working time segment of Ticket Inspector 1 is: 20:17~20:28(1), 20:40~21:36(8), 21:48~22:11(7), 23:42~23:53(5), 5:48~5:59(8), 6:16~6:27(8), 6:38~6:49(8), 6:59~7:18(2), 7:34~7:45(1), 7:56~8:07(6) The total working time is 175 minutes.

[0043] Ticket Inspector 2 20:45~20:12(6), 21:26~21:52(4), 22:02~22:13(4), 23:14~23:25(5), 0:25~0:36(5), 1:53~2:04(3), 3:20~3:44(5), 6:48~7:11(5), 7:21~7:32(6), 7:42~7:53(13) The total working time is 166 minutes.

[0044] Ticket Inspector 9 20:08~20:19(5), 20:50~21:01(5), 21:12~21:23(11), 22:26~23:02(8), 5:50~6:01(11), 6:32~6:58(11), 7:12~7:33(11), 7:57~8:08(11) The total working time is 138 minutes.

[0045] Ticket inspector 10 20:15~20:26(11), 20:49~21:00(9), 22:24~22:35(9), 22:45~22:56(9), 23:20~23:31(9), 0:07~0:18(4), 0:30~0:41(1), 1:09~1:20(4), 1:34~1:45(2), 1:55~2:22(4), 3:05~3:16(3), 4:37~4:48(2), 5:58~6:09(6), 7:52~8:03(12) The total working time is 170 minutes.

[0046] Take ticket inspector 2 as an example. The time segment of ticket checking work undertaken by him is as follows: Figure 5 shown.

[0047] The working hours of all ticket inspectors are as follows: Figure 6 As shown, it can be found that the working hours of ticket inspectors vary by no more than 20%.

[0048] The above embodiments and descriptions are only for illustrating the principles and best embodiments of the present invention. Without departing from the spirit and scope of the present invention, the present invention may be subject to various changes and improvements, all of which fall within the scope of the present invention to be protected.

Claims

1. A dynamic scheduling method for ticket checking tasks in railway passenger stations based on ant colony algorithm, characterized in that: The following steps are involved: S1) Prepare relevant information based on the shift schedule of ticket inspectors at railway stations and establish the working time segments for each ticket gate at railway stations; S2), constructing a spatiotemporal network of the working time segments of ticket inspectors and ticket gates; S3) Establish a dynamic scheduling optimization model for railway station ticket inspectors; S4) Use the ant colony search algorithm to solve the optimization model and obtain the optimal dynamic schedule.

2. According to claim 1, a method for dynamic scheduling of ticket checking tasks at a railway passenger station based on an ant colony algorithm is characterized by: In step S1), the working time segment of each ticket gate of the railway station is expressed as follows according to the departure time of each train and the departure ticket gate: ； in, The earliest time to pass the ticket gate before the train departs. The maximum time to pass the ticket gate before the train departs; The departure time of each train; Get the set of working time segments of the ticket gates at railway stations , where the duration of each working time segment is .

3. The method for dynamic scheduling of ticket checking tasks at a railway passenger station based on ant colony algorithm according to claim 2 is characterized by: In step S1), if the same ticket gate has two adjacent departure trains, the working time segments determined and If there is overlap in time segments, the two working time segments will be merged into one working time segment. .

4. The method for dynamic scheduling of ticket checking tasks at a railway passenger station based on ant colony algorithm according to claim 1, characterized in that: In step S2), a spatiotemporal network of the working time segments of the ticket inspectors and the ticket gates is constructed based on the operations research graph theory method. , where the space-time nodes Represents the source / sink points and arc segments of the ticket gate working time segments Represents the connection time between the working time segments of the two ticket gates.

5. The method for dynamic scheduling of ticket checking tasks at a railway passenger station based on ant colony algorithm according to claim 4 is characterized by: In step S2), the connection time of the working time segments of the two ticket gates is The calculation is: When the end time of the previous ticket gate working time segment is less than the start time of the next ticket gate working time segment, the continuation time is the difference between the latter and the former; When the end time of the previous gate working time segment is greater than or equal to the start time of the next gate working time segment, the continuation time is infinite; that is: ； In the formula, Indicates i The ticket gate and j The connection time of each ticket gate; For working time segments End time of For working time segments The start time of is the threshold value set; is a sufficiently large positive integer.

6. The method for dynamic scheduling of ticket checking tasks at a railway passenger station based on ant colony algorithm according to claim 1, characterized in that: In step S3), a dynamic scheduling optimization model for railway station ticket inspectors is established, which specifically includes the following steps: S31) On the constructed spatiotemporal network of the working time segments of the ticket inspectors and the ticket gates, a mixed integer linear integer optimization model is established according to the actual needs of the station ticket inspectors' scheduling; Based on minimizing the comprehensive cost Z, the objective function of the optimization model is: ； The objective function requires minimizing the number of ticket inspectors and the duration of ticket inspectors in working time segments; In the formula, and is a 0-1 decision variable, Indicates the ticket inspector Whether to undertake work time fragments The ticket checking task, Indicates the ticket inspector Whether it is divided into working time segments Continuation of working time segment ; It is the conversion factor from the number of ticket inspectors to comprehensive fees; It is the conversion coefficient of the connecting time of the ticket gate working time segment to the comprehensive fee, and Z is the comprehensive fee; S32), constructing constraints of the optimization model; Constraint 1: Uniqueness constraint of the working time segment of the ticket gate ， ； Constraint 2: Feasibility constraint of the continuation of working time segments at the ticket gate ， ； In the formula, F is the set of working time segments of the ticket gate, and S is the set of ticket inspector information.

7. The method for dynamic scheduling of ticket checking tasks at a railway passenger station based on ant colony algorithm according to claim 1, characterized in that: In step S4), the optimization model is solved by using an ant colony search algorithm, which specifically includes the following steps: S41), setting ant colony algorithm parameters; S42), placing all ants in the virtual center, each ant randomly selects the next task node according to the transfer rule, and obtains their own complete path after traversing all time and space nodes; S43), update the pheromones on each path, update the current number of iterations, determine whether the algorithm is finished, and output the optimal solution.

8. The method for dynamic scheduling of ticket checking tasks at a railway passenger station based on ant colony algorithm according to claim 7, characterized in that: In step S41), the ant colony algorithm parameters set include the number of ants , Pheromone importance parameters , parameters of the importance of heuristic factors , pheromone evaporation coefficient , pheromone increases intensity coefficient , Maximum number of algorithm iterations .

9. The method for dynamic scheduling of ticket checking tasks at a railway passenger station based on ant colony algorithm according to claim 7, characterized in that: Step S42) is as follows: S421), all ants are placed at the virtual starting point, each ant selects the working time segment of the ticket gate with the earliest start time as the first space-time node, and randomly selects the next space-time node according to the following formula shift rule. After all the space-time nodes are selected to enter the ant search path, all ants continue to move to the virtual end point, so as to obtain their own completed paths; ； in, Represents ants By Node Transfer to Node The probability of Represents ants At the node A set of feasible selection nodes; For arc segment pheromones; For arc segment Heuristic information; It is a parameter that characterizes the importance of pheromones; is a parameter that characterizes the importance of heuristic information.

10. The method for dynamic scheduling of ticket checking tasks at a railway passenger station based on ant colony algorithm according to claim 7, characterized in that: Step S43) specifically includes the following steps: S431), the pheromone on each path searched by the ants is updated according to the following formula, namely: ； ； in, For the After iterations, the path pheromone concentration on; For the After iterations, the path pheromone concentration on; is the pheromone evaporation coefficient; is the pheromone increment after this iteration, is the objective function value of the optimal path searched by the ant in this round, The average objective function value of the paths searched by all ants in this round; S432), update the current number of iterations , if the current iteration number Less than or equal to the maximum number of iterations set , then continue to repeat S21), otherwise the algorithm ends and outputs the optimal solution.

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