Collaborative simulation optimization method, equipment and medium for multi-line bus dynamic timetable
Through the analysis and optimization algorithm of bus route historical data, a dynamic timetable that meets passenger needs is generated, which solves the problem that fixed timetables cannot adapt to complex environments and improves the service quality and passenger experience of the bus system.
Patent Information
- Application Number
- CN202210913339.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-08-01
- Publication Date
- 2025-09-05
- Estimated Expiration
- 2042-08-01
AI Technical Summary
In the existing bus system, fixed timetables cannot adapt to the complex bus environment, resulting in long wait times for passengers, difficult to meet transfer needs, and limited ride comfort.
By processing the historical operation data of multiple bus lines, a dynamic departure timetable optimization model is built, the departure intervals of each station are optimized to minimize the total waiting time for passengers, and optimization algorithms such as genetic algorithms are used to iterate the optimal timetable.
The generated dynamic timetable can meet the needs of passengers' coordinated transfers at the transfer station, maintain ride comfort, and adapt to the complex random environment of the bus system and improve the quality of bus services.
Smart Images
Figure CN115481777B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of urban public transportation and timetable optimization decision-making, and in particular to a collaborative simulation optimization method, device and medium for a dynamic timetable of multi-line public transportation. Background Art
[0002] Prioritizing the development of urban public transportation is a key measure to alleviate urban traffic congestion and promote sustainable, green, and healthy urban development. However, due to inadequate public transportation infrastructure and a lack of scientific public transportation operation planning by public transportation companies, most urban public transportation systems still face problems such as low service levels and operational efficiency. Therefore, improving public transportation service levels through effective management has become a pressing issue. Designing a public transportation schedule is a complex and crucial sub-problem within the public transportation scheduling process. A reliable and flexible schedule not only meets the uneven temporal and spatial needs of passengers and adapts to complex road traffic conditions, but also helps reduce passenger wait times and improve public transportation service quality. Therefore, developing a scientific timetable has become a key tool for resolving public transportation operational issues.
[0003] Previous research and use of timetables often remained at the stage of fixed departure intervals and long-term, unchanging, repeated use. However, since vehicles are affected by the real environment when traveling between stations, the passenger capacity of vehicles is limited by the influence of riding comfort, and passengers also need to transfer between buses on different lines, fixed timetables cannot adapt to this complex public transportation system. Summary of the Invention
[0004] Based on the problems existing in the existing bus route schedule, the present invention is committed to providing a collaborative simulation optimization method, device and medium for multi-line bus dynamic timetable. It can simulate and optimize the historical passenger flow arrival patterns of multiple bus routes to produce a dynamic bus departure timetable that meets the passengers' collaborative transfer needs at transfer stations and maintains the comfort of the ride.
[0005] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:
[0006] A collaborative simulation optimization method for multi-line bus dynamic timetables, comprising:
[0007] S1, processes the historical operation data of multiple bus routes and extracts the passenger flow distribution patterns of all bus stops;
[0008] S2, considering the random travel time between stations and the vehicle load factor, constructs a dynamic departure schedule optimization model with the goal of minimizing the total waiting time of passengers at all stations. The variables to be optimized include the departure intervals between adjacent trains on all lines;
[0009] S3, based on the passenger flow distribution pattern of each station, simulate and calculate the total waiting time of passengers at all stations in the optimization model;
[0010] S4, taking the departure intervals between adjacent trains on all lines as the variable to be optimized and the simulation results as the fitness value, iterate the optimization algorithm;
[0011] S5, judging whether the iteration termination condition is met: if the iteration termination condition is met, generating the optimal timetable solution; otherwise, returning to step S3.
[0012] Furthermore, the historical operation data of bus routes include: bus stop location information, bus route information, bus GPS trajectory data, and passenger card swiping data.
[0013] Furthermore, it is characterized in that the total waiting time of passengers at all stations includes: the waiting time of passengers at all non-transfer stations, the transfer waiting time of passengers at all transfer stations and the waiting time of non-transfer passengers.
[0014] Furthermore, the dynamic departure schedule optimization model is:
[0015]
[0016] st
[0017]
[0018]
[0019]
[0020]
[0021]
[0022]
[0023]
[0024]
[0025] In the formula, l represents any bus route whose departure schedule is to be optimized, r is the number of bus routes, a and b are any two of the bus routes; j represents any bus number of the bus route, m l represents the total number of departures of line l during the optimization period; i represents any station, n l represents the total number of one-way stations on line l; K ab represents the set of transfer stations between two lines a and b, represents the total number of transfer stations between two routes a and b, and k represents any transfer station in the route; represents the time when the train number j of line l arrives at any non-transfer station i, f li (t) represents the distribution of passengers arriving at station i on route l over time, i.e., the passenger flow distribution law; represents the number of passengers stranded at any non-transfer station i after the train number j of line l arrives at the transfer station; NO represents the order of vehicles arriving at the transfer station k. are the number of passengers waiting for the vehicle on line a at the transfer station k, the number of passengers not waiting for the vehicle, the additional waiting time of passengers waiting for the vehicle, and the normal waiting time of all passengers, are the number of passengers waiting for the bus on route b at the transfer station k, the number of passengers not waiting for the bus, the additional waiting time for passengers waiting for the bus, and the normal waiting time for all passengers; the logical variable δ ab =1 means there is a transfer station between lines a and b, otherwise δ ab =0;
[0026] represents the departure interval between train number j and train number j+1 on line l, which is the variable to be optimized;
[0027] represents the slack time for train number j of line l to arrive at station i, definition The value range of ; S represents the bus stop set, L represents the bus route set, and J represents the route departure set; represents the initial launch time of line l, C represents the optimization period, It represents the maximum cross-sectional passenger flow of line l in a certain period of time, Q l,v represents the rated passenger capacity of the vehicle on line l, μ l represents the planned vehicle load factor of line l; N is the interval between any trains on line l; * Represents a positive integer.
[0028] Furthermore, the simulation calculation method of step S3 is:
[0029] (1) Calculation of passenger waiting time at non-transfer stations:
[0030] Calculate the number of passengers who get off the vehicle at any station i and the remaining passenger capacity after the passengers get off; update the calculation of the number of passengers who are stranded at non-transfer stations after the passengers get on, the remaining passenger capacity in the vehicle, and the waiting time of passengers;
[0031] (2) Calculate the waiting time for passengers at transfer stations:
[0032] Generate the vehicle arrival order schedule Order_k at transfer station k; calculate the average passenger transfer ratio of vehicles on routes a and b at station k and According to the arrival vehicle category corresponding to the NOth element in Order_k, update the calculation of the transfer station k and
[0033] (3) Calculate the total waiting time for all passengers on all routes and all trains arriving at all stops.
[0034] Furthermore, the update calculation includes the number of passengers stranded at non-transfer stations after the passengers board the bus, the remaining passenger capacity in the bus, and the passenger waiting time. The calculation and update are specifically divided into the following three situations:
[0035] when hour:
[0036] when hour:
[0037] when hour:
[0038] Where, represents the remaining passenger capacity of bus j on line l after passengers get off and get on at non-transfer station i, represents the additional waiting time of passengers on train number j of line l at non-transfer station i, It represents the normal waiting time of all passengers of bus number j of line l at non-transfer station i.
[0039] Furthermore, the update calculation of the transfer station k and Specifically, the calculation and update are performed based on two types: route a and route b:
[0040] (1) The jth vehicle of route a departs from the initial station Arrival at site k:
[0041] when hour:
[0042] when hour:
[0043] when hour:
[0044] renew:
[0045] (2) The jth vehicle of route b departs from the initial station Arrival at site k:
[0046] when hour:
[0047] when hour:
[0048] when hour:
[0049] renew:
[0050] Furthermore, the optimization algorithm is a genetic algorithm, a simulated annealing algorithm or a particle swarm algorithm.
[0051] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor implements the collaborative simulation optimization method for a multi-line bus dynamic timetable as described in any of the above technical solutions.
[0052] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the collaborative simulation optimization method for a multi-line bus dynamic timetable as described in any one of the above technical solutions.
[0053] Beneficial effects
[0054] The collaborative simulation optimization method for multi-line bus schedules, constructed in this paper, can optimize a dynamic bus departure schedule based on the historical passenger arrival patterns of multiple bus routes, meeting passengers' needs for coordinated transfers at transfer stations while maintaining a comfortable ride, thereby improving bus service quality. Furthermore, this method considers the random travel times between bus stops, enabling the optimized dynamic departure schedule to withstand and adapt to the complex and random operating environment of the bus system, making it more practical. BRIEF DESCRIPTION OF THE DRAWINGS
[0055] Figure 1 It is a schematic flow chart of the method of the present invention;
[0056] Figure 2 This is a schematic diagram of the multi-line bus structure of the present invention;
[0057] Figure 3Schematic diagram of vehicle arrival and passenger waiting status at non-transfer station i on route a of the present invention;
[0058] Figure 4 Schematic diagram of the vehicle arrival situation and passenger waiting status at the transfer station k between route a and route b of the present invention. DETAILED DESCRIPTION
[0059] The following is a detailed description of an embodiment of the present invention. This embodiment is based on the technical solution of the present invention, provides a detailed implementation method and a specific operation process, and further explains the technical solution of the present invention.
[0060] This embodiment provides a collaborative simulation optimization method for a multi-line bus dynamic timetable. Figure 1 As shown, the following steps are included:
[0061] Step 1: Process historical bus operation data from multiple bus routes to extract passenger flow distribution patterns at all bus stops. This historical bus operation data includes bus stop location information, bus route information, bus GPS trajectory data, and passenger card swipe data. A Poisson distribution is used to fit the distribution of passenger stop counts, a normal distribution is used to fit the passenger arrival density function, and a lognormal distribution is used to fit the distribution of slack time between bus stops.
[0062] Step 2: Considering the random travel time between stations and the vehicle load factor, a dynamic departure schedule optimization model is constructed with the goal of minimizing the total waiting time of passengers at all stations. The variables to be optimized include the departure intervals between adjacent trains on all lines. This includes:
[0063] Step 2.1: Construct a scenario for the problem of compiling a dynamic bus departure schedule in a random environment. Figure 2 As shown, there are r bus routes, and there may be a transfer relationship between them, n l Indicates the total number of one-way stations on the line, m l Represents the total number of departures of the route during the optimization period. Assume that route a and route b are any two of the r routes, K ab represents the set of transfer stations between two lines, represents the total number of transfer stations between two lines, k represents any transfer station in the line,
[0064] Step 2.2: Build a dynamic departure schedule optimization model, which includes:
[0065]
[0066] st
[0067]
[0068]
[0069]
[0070]
[0071]
[0072]
[0073]
[0074]
[0075] In the formula, l represents any bus route whose departure schedule is to be optimized, r is the number of bus routes, a and b are any two of the bus routes; j represents any bus number of the bus route, m l represents the total number of departures of line l during the optimization period; i represents any station, n l represents the total number of one-way stations on line l; K ab represents the set of transfer stations between two lines a and b, represents the total number of transfer stations between two routes a and b, and k represents any transfer station in the route; represents the time when the train number j of line l arrives at any non-transfer station i, f li (t) represents the distribution pattern of passengers arriving at station i on line l over time, that is, the passenger flow distribution pattern. The distribution pattern here should be determined based on specific investigation and statistics of specific actual cases. It can generally be considered that passengers arrive evenly during peak, off-peak and flat periods, or the passenger density may be normally distributed within a certain period of time. represents the number of passengers stranded at any non-transfer station i after the train number j of line l arrives at the transfer station; NO represents the order of vehicles arriving at the transfer station k. are the number of passengers waiting for the vehicle on line a at the transfer station k, the number of passengers not waiting for the vehicle, the additional waiting time of passengers waiting for the vehicle, and the normal waiting time of all passengers, are the number of passengers waiting for the bus on route b at the transfer station k, the number of passengers not waiting for the bus, the additional waiting time for passengers waiting for the bus, and the normal waiting time for all passengers; the logical variable δ ab =1 means there is a transfer station between lines a and b, otherwise δ ab =0;
[0076] represents the departure interval between train number j and train number j+1 on line l, which is the variable to be optimized;
[0077] represents the slack time for train number j of line l to arrive at station i, definition The value range of ; S represents the bus stop set, L represents the bus route set, and J represents the route departure set; represents the initial launch time of line l, C represents the optimization period, It represents the maximum cross-sectional passenger flow of line l in a certain period of time, Q l,v represents the rated passenger capacity of the vehicle on line l, μ l represents the planned vehicle load factor of line l; N is the interval between any trains on line l; * represents a positive integer; Formula (1) is the objective function, which represents the minimum total waiting time for passengers. It consists of two parts: Indicates the total waiting time of passengers at non-transfer stops on the bus route. = represents the total waiting time of passengers at the transfer station between bus lines. Formula (2) represents the range of the departure interval between adjacent buses on the same line. This constraint can ensure that when the departure interval and slack time are random, the order in which adjacent buses on the same line arrive at the transfer station complies with the departure order, thus preventing overtaking within the station. Formula (3) represents the time when bus j arrives at the i-th station, which is determined by the departure time of the starting bus of line l. The sum of all departure intervals before train number j Average travel time between stations T l i and relaxation time Formula (4) is used to deal with extreme situations: if the slack time of the j-1th vehicle on line l arriving at each station is All The slack time for the jth vehicle on line l to arrive at each station is All When the departure interval of these two vehicles is greater than or equal to Only when the jth car does not overtake the j-1th car at a certain station will the jth car not occur. . Formula (5) defines the range of the departure time of the first car of the line from the starting station. Formula (6) indicates that the departure time of the last car of the line from the starting station must be within the optimization period of the model. Formula (7) indicates that the departure time of the first car and the departure interval are both positive integer values. Formulas (8) and (9) respectively specify the calculation methods of the line departure frequency and optimization period.
[0078] Step 3: Based on the passenger flow distribution pattern of each station, simulate and calculate the total waiting time of passengers at all stations in the optimization model. The content includes:
[0079] Step 3.1, calculate the waiting time of passengers at non-transfer stations:
[0080] refer to Figure 3 As shown in Figure 1, based on the passenger flow distribution pattern of each station and the average travel time between bus stations obtained in step 1, the waiting time of passengers at non-transfer stations of the bus line in the total waiting time of passengers in step 2 is calculated using the numerical simulation method: first, the number of passengers getting off the bus when the bus arrives at any station i and the remaining passenger capacity in the bus after the passengers get off are calculated, and then the number of passengers staying at the non-transfer station after the passengers get on the bus, the remaining passenger capacity in the bus and the waiting time of the passengers are updated and calculated, which can be described as Equations (10), (11) and (12):
[0081] when hour:
[0082] when hour:
[0083] when hour:
[0084] Where, represents the remaining passenger capacity of bus j on line l after passengers get off and get on at non-transfer station i, represents the additional waiting time of passengers on train number j of line l at non-transfer station i, represents the normal waiting time for all passengers of bus number j on line l at non-transfer station i;
[0085] Formula (10) means that the remaining passenger capacity of the current train is less than the number of passengers stranded at station i after the previous train leaves the station. At this time, some passengers stranded at station i after the previous train leaves the station. All passengers who arrive at the station in succession during this period are stranded. Formula (11) means that the remaining passenger capacity of the current train is greater than or equal to the number of passengers remaining at station i after the previous train leaves the station, but During this period, some passengers who arrive at the station one after another are stranded. Formula (12) means that the remaining passenger capacity of the current train is greater than or equal to the number of passengers stranded at station i after the previous train leaves the station plus The number of passengers arriving at the station during this period, and there are no stranded passengers in the station at this time.
[0086] Step 3.2, calculate the passenger waiting time at the transfer station:
[0087] refer to Figure 4As shown in the figure, the numerical simulation method is used to calculate the waiting time of passengers at transfer stations between bus lines in the total waiting time of passengers in step 2: first, generate the vehicle arrival order schedule Order_k at transfer station k, and then calculate the average passenger transfer ratio of vehicles on lines a and b at station k and Then, according to the arrival vehicle category corresponding to the NOth element in Order_k, the transfer station k is updated and calculated. and It can be described as formula (13-20).
[0088] (1) The jth vehicle of route a departs from the initial station Arrival at site k:
[0089] when hour:
[0090] when hour:
[0091] when hour:
[0092] renew:
[0093] Formula (13-16) means that when the NOth element in Order_k corresponds to vehicle Arrived at the station. Formula (13) means that the remaining passenger capacity of the current train is less than the sum of the number of remaining passengers waiting for the train on line a after the previous train left the station and the number of non-delayed passengers. At this time, there are still some stranded passengers waiting for the train on line a after the previous train left the station. During this period, all passengers who have arrived at the station and are waiting for the bus on line a are stranded. Formula (14) means that the remaining passenger capacity of the current bus is greater than or equal to the sum of the number of passengers who are waiting for the bus on line a after the previous bus leaves the station and the number of passengers who are not stranded. During this period, there are passengers who arrive at the station and wait for the bus on line a. Formula (15) means that the remaining passenger capacity of the current bus is greater than or equal to the number of remaining passengers waiting for the bus on line a after the previous bus leaves the station and the number of non-delayed passengers plus The sum of the number of passengers who arrived at the station during this period and waited for the vehicle on line a.
[0094] (2) The jth vehicle of route b departs from the initial station Arrival at site k:
[0095] when hour:
[0096] when hour:
[0097] when hour:
[0098] renew:
[0099] Formula (17-20) means that when the NOth element in Order_k corresponds to vehicle NO b j arrives at the station. Formula (17) means that the remaining passenger capacity of the current train is less than the sum of the number of remaining passengers waiting for the train on route b after the previous train leaves the station and the number of non-delayed passengers. At this time, there are still some stranded passengers waiting for the train on route b after the previous train leaves the station. During this period, all passengers who have arrived at the station and are waiting for the bus on line b are stranded. Formula (18) means that the remaining passenger capacity of the current bus is greater than or equal to the sum of the number of passengers who are stranded and waiting for the bus on line b after the previous bus leaves the station, but there is a part of the remaining passengers. During this period, there are passengers who arrive at the station and wait for the bus on route b. Formula (19) means that the remaining passenger capacity of the current bus is greater than or equal to the number of remaining passengers waiting for the bus on route b after the previous bus leaves the station and the number of non-delayed passengers plus The sum of the number of passengers who arrived at the station during this period to wait for the vehicle on line b.
[0100] In step 4, the optimization algorithm iterates, using the intervals between adjacent trains on all routes as the variable to be optimized and the simulation results as the fitness value. Each iteration of the optimization algorithm calculates the total passenger waiting time based on a set of intervals. This total waiting time serves as the fitness value for that iteration and is then used in the optimization algorithm's selection transformation. The optimization algorithm can be a heuristic algorithm such as a genetic algorithm, simulated annealing algorithm, or particle swarm optimization algorithm. In this embodiment, a genetic algorithm is preferably used to perform selection, crossover, and mutation operations on different chromosome solutions based on their fitness values.
[0101] Step 5, determine whether the iteration termination condition is met (which can be set according to the maximum number of iterations and / or the convergence threshold): If the iteration termination condition is met, generate the optimal timetable plan, that is, the departure interval plan of each bus trip on all bus lines within a specific time period; otherwise, return to step 3 and perform a new round of simulation calculation of passenger waiting time at each station.
[0102] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor implements the collaborative simulation optimization method for multi-line bus dynamic timetables described in the above embodiment.
[0103] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the collaborative simulation optimization method for a multi-line bus dynamic timetable as described in the above embodiment.
[0104] The above embodiments are preferred embodiments of the present application. Ordinary technicians in this field can also make various changes or improvements on this basis. Without departing from the overall concept of the present application, these changes or improvements should fall within the scope of protection required by the present application.
Claims
1. A collaborative simulation optimization method for multi-line bus dynamic timetable, characterized by: include: S1, processes the historical operation data of multiple bus routes and extracts the passenger flow distribution patterns of all bus stops; S2, considering the random travel time between stations and the vehicle load factor, constructs a dynamic departure schedule optimization model with the goal of minimizing the total waiting time of passengers at all stations. The variables to be optimized include the departure intervals between adjacent trains on all lines; The dynamic departure schedule optimization model is: st and In the formula, l represents any bus route whose departure schedule is to be optimized, r is the number of bus routes, a and b are any two of the bus routes; j represents any bus number of the bus route, m l represents the total number of departures of route l during the optimization period; i represents any site, n l represents the total number of one-way stations on line l; K ab represents the set of transfer stations between two lines a and b, represents the total number of transfer stations between two routes a and b, and k represents any transfer station in the route; represents the time when the train number j of line l arrives at any non-transfer station i, f li (t) represents the distribution of passengers arriving at station i on route l over time, i.e., the passenger flow distribution law; represents the number of passengers stranded at any non-transfer station i after the train number j of line l arrives at the transfer station; NO represents the order of vehicles arriving at the transfer station k. are the number of passengers waiting for the vehicle on line a at the transfer station k, the number of passengers not waiting for the vehicle, the additional waiting time of passengers waiting for the vehicle, and the normal waiting time of all passengers, are the number of stranded passengers waiting for the bus on route b at transfer station k, the number of non-stranded passengers, the additional waiting time of stranded passengers, and the normal waiting time of all passengers; Logical variable δ ab =1 means there is a transfer station between lines a and b, otherwise δ ab =0; represents the departure interval between train number j and train number j+1 on line l, which is the variable to be optimized; represents the slack time for train number j of line l to arrive at station i, definition The value range of S represents the bus stop set, L represents the bus route set, and J represents the bus departure frequency set; represents the initial launch time of line l, C represents the optimization period, It represents the maximum cross-sectional passenger flow of line l in a certain period of time, Q l,v represents the rated passenger capacity of the vehicle on line l, μ l represents the planned vehicle load factor of line l; N is the interval between any trains on line l; * represents a positive integer; S3, based on the passenger flow distribution pattern of each station, simulate and calculate the total waiting time of passengers at all stations in the optimization model; S4, taking the departure intervals between adjacent trains on all lines as the variable to be optimized and the simulation results as the fitness value, iterate the optimization algorithm; S5, judging whether the iteration termination condition is met: if the iteration termination condition is met, generating the optimal timetable solution; otherwise, returning to step S3.
2. The method according to claim 1, characterized in that The historical operation data of bus routes include: bus stop location information, bus route information, bus GPS trajectory data, and passenger card swiping data.
3. The method according to claim 1, characterized in that The total waiting time for passengers at all stations includes: the waiting time for passengers at all non-transfer stations, the waiting time for passengers at all transfer stations and the waiting time for non-transfer passengers.
4. The method according to claim 1, wherein The simulation calculation method of step S3 is: (1) Calculation of passenger waiting time at non-transfer stations: Calculate the number of passengers who get off the vehicle at any station i and the remaining passenger capacity after the passengers get off; update the calculation of the number of passengers who are stranded at non-transfer stations after the passengers get on, the remaining passenger capacity in the vehicle, and the waiting time of passengers; (2) Calculate the waiting time for passengers at transfer stations: Generate the vehicle arrival order schedule Order_k at transfer station k; calculate the average passenger transfer ratio of vehicles on routes a and b at station k and According to the arrival vehicle category corresponding to the NOth element in Order_k, update the calculation of the transfer station k and (3) Calculate the total waiting time for all passengers on all routes and all trains arriving at all stops.
5. The method according to claim 4, characterized in that The update calculates the number of passengers stranded at non-transfer stations after passengers board the bus, the remaining passenger capacity in the bus, and the passenger waiting time. The update is calculated and updated in the following three cases: when hour: when hour: when hour: Where, represents the remaining passenger capacity of bus j on line l after passengers get off and get on at non-transfer station i, represents the additional waiting time of passengers on train number j of line l at non-transfer station i, It represents the normal waiting time of all passengers of bus number j of line l at non-transfer station i.
6. The method according to claim 4, characterized in that The update calculation of the transfer station k and Specifically, the calculation and update are performed based on two types: route a and route b: (1) The jth vehicle of route a departs from the initial station Arrival at site k: when hour: when hour: when hour: renew: (2) The jth vehicle of route b departs from the initial station Arrival at site k: when hour: when hour: when hour: renew:
7. The method according to claim 4, characterized in that The optimization algorithm is a genetic algorithm, a simulated annealing algorithm or a particle swarm algorithm.
8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the computer program is executed by the processor, the processor is caused to implement the method according to any one of claims 1 to 7.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.