Traffic risk event identification and analysis method based on AI sensing network

Abstract

The invention discloses a traffic risk event identification and analysis method based on an AI sensing network, and relates to the technical field of traffic control, and the method comprises the steps: obtaining train number data and driving route data of a current train number after a large interval event of the current train number occurs; wherein the train number data comprises state data of each train number of the same driving route; determining the occurrence reason of the large interval event based on the congestion index in the driving route data and the vehicle state and the vehicle load factor of the current train number in the train number data; determining an influence station range of the large interval event based on the occurrence reason of the large interval event, the driving route data and the train number data; and determining a train number scheduling strategy of the large-interval event based on the occurrence reason of the large-interval event and the influence station range of the large-interval event, thereby improving the processing efficiency of the large-interval event, greatly reducing the influence of the large-interval event, and improving the user satisfaction.

Description

Technical Field

[0001] The present application relates to the field of traffic control technology, and in particular to a method for identifying and analyzing traffic risk events based on an AI perception network. Background Art

[0002] Large interval incidents affect passengers' riding experience and are one of the main reasons for complaints. At the same time, the departure interval compliance rate is also an assessment indicator for some cities. Therefore, it is very necessary to eliminate the adverse effects brought about by large interval incidents as soon as possible.

[0003] At the same time, large interval events are a common and unavoidable scenario in public transportation operations. However, in the past, only the departure intervals of each bus could be specified, and it was impossible to assess the arrival times of all stations. In actual operations, special circumstances exist between different stations. Even if the departure intervals of each bus meet the standards, large interval events may still occur. In the existing technology, dispatchers generally rely on manual experience to judge the affected stations, duration, and number of buses, and manually decide to compensate as much as possible through dispatching methods such as U-turns and detours. This places extremely high demands on the dispatcher's dispatching capabilities, is inefficient, and has poor results. Summary of the Invention

[0004] In an exemplary embodiment of the present application, a traffic risk event identification and analysis method based on an AI perception network is provided to improve the processing efficiency and effectiveness of large-interval events, significantly reduce the impact of large-interval events, and improve user satisfaction.

[0005] According to a first aspect of an exemplary embodiment, a method for identifying and analyzing traffic risk events based on an AI perception network is provided, comprising: After a large interval event occurs on the current train, the train number data and the travel route data of the current train are obtained; wherein the train number data includes the status data of each train on the same travel route; Determining a cause of the large interval event based on a congestion index in the driving route data, a vehicle status of a current train in the train number data, and a vehicle load factor; Determining a range of stations affected by the long-interval event based on the cause of the long-interval event, the travel route data, and the train number data; Based on the cause of the large-interval event and the range of stations affected by the large-interval event, a train scheduling strategy for the large-interval event is determined.

[0006] According to a second aspect of an exemplary embodiment, a device for processing a vehicle long-interval event is provided, the device comprising: An acquisition module is used to acquire train number data and the travel route data of the current train number after a large interval event occurs on the current train number; wherein the train number data includes the status data of each train number on the same travel route; a first determining module, configured to determine a cause of the large interval event based on a congestion index in the driving route data, a vehicle status of a current train in the train number data, and a vehicle load factor; A second determining module is configured to determine a range of stations affected by the large interval event based on the cause of the large interval event, the driving route data, and the train number data; The third determining module is configured to determine a train scheduling strategy for the long-interval event based on the cause of the long-interval event and the range of stations affected by the long-interval event.

[0007] According to a third aspect of the exemplary embodiment, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor implements the steps of the above-mentioned traffic intersection problem method by running the executable instructions.

[0008] According to a fourth aspect of the exemplary embodiment, a computer storage medium is provided, in which computer program instructions are stored. When the instructions are executed on a computer, the computer executes the steps of the above-mentioned traffic intersection problem method.

[0009] In an embodiment of the present application, after a long-interval event occurs on the current train, the cause of the long-interval event is determined based on the congestion index in the acquired route data, the vehicle status of the current train in the train data, and the vehicle load factor. Based on the cause of the long-interval event, the affected station range of the long-interval event is determined, thereby improving the accuracy of the determined affected station range. Furthermore, based on the cause of the long-interval event and the affected station range of the long-interval event, the present application determines a train scheduling strategy for the long-interval event, thereby improving the efficiency and effectiveness of handling long-interval events, significantly reducing the impact of the long-interval event, and improving user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0011] Figure 1 Schematic diagram of application scenarios provided by embodiments of the present application; Figure 2A flowchart of a traffic risk event identification and analysis method based on an AI perception network provided in an embodiment of the present application; Figure 3 A specific flow chart of a traffic risk event identification and analysis method based on an AI perception network provided in an embodiment of the present application; Figure 4 A flowchart of a method for determining the cause of a large interval event provided in an embodiment of the present application; Figure 5 A flowchart of a method for determining the range of affected stations of a large-interval event when the cause of the large-interval event is a vehicle failure, provided in an embodiment of the present application; Figure 6 A schematic diagram of the route of the current train provided in an embodiment of the present application; Figure 7 A flowchart of a method for determining the range of affected stations of a large-interval event when the cause of the large-interval event is road congestion is provided in an embodiment of the present application; Figure 8 A flowchart of a method for determining the range of affected stations of a large interval event when the cause of the large interval event is a late vehicle departure is provided in an embodiment of the present application; Figure 9 A schematic diagram of the route of the current train provided in an embodiment of the present application; Figure 10 A flowchart of a method for determining the affected station range of a large interval event when the cause of the large interval event is excessive passenger flow, provided in an embodiment of the present application; Figure 11 A flowchart of a method for determining the range of trains affected by a large interval event when the cause of the large interval event is road congestion provided in an embodiment of the present application; Figure 12 A flowchart of a method for determining the range of trains affected by a large interval event when the cause of the large interval event is a vehicle failure, provided in an embodiment of the present application; Figure 13 A flowchart of a method for determining a train scheduling strategy for a long-interval event when the cause of the long-interval event is road congestion, provided in an embodiment of the present application; Figure 14 A flowchart of a method for determining a corresponding train scheduling strategy based on a relationship between a multiple and a set multiple provided in an embodiment of the present application; Figure 15 A flowchart of a method for determining a train scheduling strategy for a long-interval event when the cause of the long-interval event is a vehicle failure, provided in an embodiment of the present application; Figure 16A flowchart of a method for determining a train scheduling strategy for a long-interval event when the cause of the long-interval event is a late departure of a vehicle, provided in an embodiment of the present application; Figure 17 A schematic diagram of the structure of a device for processing large-interval vehicle events provided in an embodiment of the present application; Figure 18 A schematic diagram of the structure of a device for processing vehicle large interval events provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0013] Large interval incidents affect passengers' riding experience and are one of the main reasons for complaints. At the same time, the departure interval compliance rate is also an assessment indicator for some cities. Therefore, it is very necessary to eliminate the adverse effects brought about by large interval incidents as soon as possible.

[0014] At the same time, large interval events are a common and unavoidable scenario in public transportation operations. However, in the past, only the departure intervals of each bus could be specified, and it was impossible to assess the arrival times of all stations. In actual operations, special circumstances exist between different stations. Even if the departure intervals of each bus meet the standards, large interval events may still occur. In the existing technology, dispatchers generally rely on manual experience to judge the affected stations, duration, and number of buses, and manually decide to compensate as much as possible through dispatching methods such as U-turns and detours. This places extremely high demands on the dispatcher's dispatching capabilities, is inefficient, and has poor results.

[0015] To this end, an embodiment of the present application provides a traffic risk event identification and analysis method based on an AI perception network to improve the processing efficiency and effectiveness of large-interval events, significantly reduce the impact of large-interval events, and improve user satisfaction.

[0016] To further illustrate the technical solutions provided by the embodiments of the present application, the following is a detailed description of the technical solutions in conjunction with the accompanying drawings and specific implementation methods. Although the embodiments of the present application provide the method operation steps as shown in the following embodiments or drawings, more or fewer operation steps may be included in the method based on routine or no creative work. In steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided in the embodiments of the present application.

[0017] First reference Figure 1, which is a schematic diagram of an application scenario of an embodiment of the present application, including a collector 10 and a server 11. The collector 10 can be various traffic detection devices. The collector 10 is used to obtain train data and the current train's route data after a large interval event occurs on the current train. The train data includes status data of each train on the same route. The server 11 is used to determine a train scheduling strategy for the large interval event based on the train data and route data.

[0018] In an embodiment of the present application, one implementation method of the embodiment of the present application is that, after a large interval event occurs on the current train, the server 11 obtains the train data and the travel route data of the current train collected by the collector 10; wherein, the train data includes status data of each train on the same travel route; based on the congestion index in the travel route data, the vehicle status and vehicle load rate of the current train in the train data, the cause of the large interval event is determined; based on the cause of the large interval event, the travel route data and the train data, the affected station range of the large interval event is determined; based on the cause of the large interval event and the affected station range of the large interval event, the train scheduling strategy for the large interval event is determined.

[0019] refer to Figure 2 A flowchart of a traffic risk event identification and analysis method based on an AI perception network is shown to illustrate the technical solution provided in an embodiment of the present application.

[0020] Step 201: After a long interval event occurs on the current train, obtain train data and the travel route data of the current train; Among them, the train data includes the status data of each train on the same route, and the train data includes the stop time of each train at each station, the driving speed between two adjacent stations, the passenger flow data at each station, the vehicle status of each train and other data; the route data includes the location of each station, congestion index, driving direction and other data.

[0021] The above-mentioned method for identifying whether a large interval event has occurred on the current train can be determined by the following two methods: the first method is to determine whether a large interval event has occurred on the current train through passenger feedback; the second method is to determine the arrival time interval between the current train and the previous train based on the arrival time of the current train and the arrival time of the previous train at the same station. If the arrival time interval is greater than the departure interval between the current train and the previous train, a large interval event has occurred on the current train. For example, if the arrival time of the current train at station 3 is 1:20, the arrival time of the previous train at station 3 is 1:00, and the arrival time interval between the current train and the previous train is 20 minutes, if the departure interval between the current train and the previous train is 15 minutes, then a large interval event has occurred on the current train.

[0022] Step 202: determining the cause of the large interval event based on the congestion index in the driving route data, the vehicle status of the current train in the train data, and the vehicle load factor; Causes of these long-interval events include vehicle failure, road congestion, excessive passenger flow, and late departure. Vehicle failures include both current train failures and vehicle accidents. For example, a current train failure could include a flat tire or engine failure, while a vehicle accident could include a scratch or collision with another vehicle.

[0023] Step 203: determining the affected station range of the long-interval event based on the cause of the long-interval event, the driving route data, and the train number data; The affected stations of the above-mentioned large interval events are all stations that affect the arrival time of the current train.

[0024] Step 204 : Determine a train scheduling strategy for the long-interval event based on the cause of the long-interval event and the range of stations affected by the long-interval event.

[0025] The above train scheduling strategies include adjusting the departure time of each train, increasing the number of trains that pass through some stations on the current train's route, prohibiting some trains from stopping at some stations on the current train's route, and other scheduling strategies.

[0026] After a long-interval event occurs on the current train, this application determines the cause of the long-interval event based on the congestion index in the acquired route data, the vehicle status of the current train in the train data, and the vehicle load rate. Based on the cause of the long-interval event, the application also determines the range of stations affected by the long-interval event, thereby improving the accuracy of the determined range of affected stations. Furthermore, based on the cause of the long-interval event and the range of stations affected by the long-interval event, this application determines a train scheduling strategy for the long-interval event, thereby improving the efficiency and effectiveness of handling long-interval events, significantly reducing the impact of the long-interval event, and improving user satisfaction.

[0027] The following is a detailed description of the traffic risk event identification and analysis method based on AI perception network provided above. Figure 3 Shown, including: Step 301: Based on passenger feedback, determine whether a long interval event occurs on the current train; Step 302: Obtain train number data and the current train's route data; Among them, the train data includes the status data of each train on the same route, the train data includes the length of time each train stops at each station, the driving speed between two adjacent stations, the passenger flow data at each station, the vehicle status of each train and other data. The passenger flow data at any station includes the predicted number of people getting on and off the bus at the station; the route data includes data such as the location of each station, congestion index, and driving direction.

[0028] Step 303 : determining the cause of the large interval event based on the congestion index in the driving route data, the vehicle status and vehicle load rate of the current train in the train number data.

[0029] Figure 4 A flowchart of a method for determining the cause of a large interval event provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, the above step 303 at least includes the following steps: Step 401, determine whether the vehicle state is a fault state, if so, execute step 402, otherwise, execute step 403; Step 402, determining that the cause of the large interval event is a vehicle failure; Step 403: determine whether the congestion index is greater than or equal to the set congestion threshold. If so, execute step 404; otherwise, execute step 405. The congestion index is used to represent the degree of road congestion and can be obtained from map data of a map navigation application. The congestion threshold can be set according to actual conditions. For example, the congestion threshold can be set to 4.

[0030] Step 404: determining that the cause of the large interval event is road congestion; Step 405, determining whether the vehicle load factor is greater than or equal to the set load factor, if so, executing step 406, otherwise executing step 407; The vehicle load factor is the ratio of the vehicle's passenger capacity to the vehicle's maximum passenger capacity. The set full load factor can be set according to actual conditions, for example, the set full load factor can be 90%.

[0031] Step 406: Determine that the cause of the large interval event is excessive passenger flow; Step 407 : Determine that the cause of the large interval event is the late departure of the vehicle.

[0032] Step 304 : Determine the range of stations affected by the long-interval event based on the cause of the long-interval event, the driving route data, and the train number data.

[0033] Depending on the cause of the large interval event, the method for determining the affected site range of the large interval event in step 304 specifically includes the following four situations: In the first case, when the cause of the long interval event is a vehicle failure, Figure 5 A flowchart of a method for determining the affected site range of a large interval event when the cause of the large interval event is a vehicle failure is provided in an embodiment of the present application, such as Figure 5 As shown, the affected site range of the large interval event is determined by the following steps: Step 501: Based on the driving route data, determine, in the driving direction of the train, each first impact station located after the occurrence location of the large interval event; Step 502: Determine the affected site range based on the first affected sites.

[0034] For example, Figure 6 A schematic diagram of the current train route provided in the embodiment of the present application, such as Figure 6 As shown, the current train's route includes 10 stops, and the current train's direction of travel is from left to right. Figure 6 The gray rectangle in the figure is the location where the large interval event occurred. The locations of the 5th to 10th stations are after the location where the large interval event occurred. Therefore, the affected station range is the 5th to 10th stations.

[0035] In the second case, when the cause of the large interval event is road congestion, Figure 7 A flowchart of a method for determining the range of affected stations of a large interval event when the cause of the large interval event is road congestion is provided in an embodiment of the present application, such as Figure 7 As shown, the affected site range of the large interval event is determined by the following steps: Step 701, determining the travel time between each two adjacent stations based on the station distance between each two adjacent stations in the driving route data and the travel speed of the train between each two adjacent stations in the train number data; Since the driving route is divided into congested sections and non-congested sections, the driving speed between each adjacent station in the congested section can be directly obtained from the map data of the map navigation application, and the driving speed between each adjacent station in the non-congested section can be the maximum speed limit between the two adjacent stations.

[0036] The present application can determine the travel time between station i and station i+1 by the following method: if the station distance between station i and station i+1 is S, and the travel speed of the vehicle between station i and station i+1 is V, then the travel time between station i and station i+1 is T=S / V.

[0037] Step 702: Determine the travel time between each two adjacent stations based on the travel time between each two adjacent stations and the stop time of the train at each station in the train data; The present application can determine the travel time between station i and station i+1 by the following method: if the travel time between station i and station i+1 is T, and the stop time of the train at station i is TS, then the travel time between station i and station i+1 TI=T+TS.

[0038] Step 703: When the travel time between two adjacent stations is greater than the corresponding standard travel time, the station with the earliest arrival time between the two adjacent stations is used as the second influencing station; The above-mentioned standard travel time can be set according to actual conditions, and the standard travel time between two adjacent stations can be different or the same.

[0039] For example, the travel time between site i and site i+1 is TI, and the standard travel time between site i and site i+1 is ST. If TI>ST, site i is the second affected site.

[0040] Step 704: Determine the affected site range based on each second affected site.

[0041] The third case is when the cause of the large interval event is the late departure of the vehicle. Figure 8 The present invention provides a flowchart of a method for determining the affected station range of a large interval event when the cause of the large interval event is a late vehicle departure, such as Figure 8 As shown, the affected site range of the large interval event is determined by the following steps: Step 801: Based on the driving route data, determine the first stations located after the occurrence location of the large interval event in the driving direction of the train; Step 802: Determine the travel time between each two adjacent first stops based on the distance between each two adjacent first stops in the travel route data and the travel speed of the train between each two adjacent first stops in the train number data. The travel speed of the above-mentioned train between two adjacent first stations may be the maximum speed limit between the two adjacent first stations.

[0042] The site distance between the two adjacent first sites can be expressed as , the driving speed between the two adjacent first stations can be expressed as , the driving time between the two adjacent first stations can be expressed as , this application can determine the driving time between the jth two adjacent first stations by the following formula: : ; Wherein, is the station distance between the j-th adjacent two first stations, is the driving speed between the j-th adjacent two first stations, and the value range of j is from 1 to n, where n is the total number of first stations.

[0043] Step 803: Based on the driving duration between each adjacent two first stations and the station stop duration of the train number at each first station in the train number data, determine the passing duration between each adjacent two first stations; In this application, the passing duration between the first station j and the first station j + 1 can be determined by the following method: If the driving duration between the first station j and the first station j + 1 is T, and the stop duration of the train number at the first station j is TS, then the passing duration TI between the first station j and the first station j + 1 is TI = T + TS.

[0044] Step 804: When the passing duration between each adjacent two first stations is greater than the corresponding standard passing duration, use the first station with the earliest arrival time among the adjacent two first stations as the third influencing station; The above standard passing duration can be set according to the actual situation.

[0045] For example, if the passing duration between the first station j and the first station j + 1 is TI, and the standard passing duration between the first station j and the first station j + 1 is ST, if TI > ST, then the first station j is the third influencing station.

[0046] Step 805: Based on each third influencing station, determine the influencing station range.

[0047] For example, Figure 9 is a schematic diagram of the driving route of the current train number provided by an embodiment of this application. As Figure 9 shown, the driving route of the current train number includes 10 stations, and the driving direction of the current train number is from left to right. Figure 9 The gray rectangles in are the occurrence positions of large interval events. The station positions of the 6th - 10th stations are after the occurrence positions of the large interval events. The passing duration between the 6th - 7th stations is TI1, and the corresponding standard passing duration is ST1. The passing duration between the 7th - 8th stations is TI2, and the corresponding standard passing duration is ST2. The passing duration between the 8th - 9th stations is TI3, and the corresponding standard passing duration is ST3. The passing duration between the 9th - 10th stations is TI4, and the corresponding standard passing duration is ST4. If TI1 > ST1, TI2 > ST2, TI3 > ST3, TI3 < ST3, then the influencing station range is the 6th - 8th stations.

[0048] The fourth case is when the large interval event is caused by excessive passenger flow. Figure 10 A flowchart of a method for determining the affected station range of a large interval event when the cause of the large interval event is excessive passenger flow is provided in an embodiment of the present application, such as Figure 10 As shown, the affected site range of the large interval event is determined by the following steps: Step 101, based on the driving route data, determining each second station located after the occurrence location of the large interval event in the driving direction of the train; Step 102: determining the travel time between each two adjacent second stops based on the distance between each two adjacent second stops in the driving route data and the travel speed of the train between each two adjacent second stops in the train number data; The specific implementation of the above steps 101-102 is the same as the specific implementation of the above steps 801-802, and will not be repeated here.

[0049] Step 103, determining the stop duration at each second stop based on the relationship between the number of passengers getting on and off the train and the stop duration and the predicted number of passengers getting on and off the train at each second stop in the train data; The predicted number of passengers getting on and off the bus at each of the above second stops can be predicted based on the passenger flow model (Origin Destination, OD). The specific prediction process is an existing technology and will not be described in detail here. The predicted number of passengers getting on and off the bus at each of the above second stops can be expressed as , the predicted number of people getting off at each second stop can be expressed as This application can determine the relationship between the number of people getting on and off the bus and the length of time the bus stops at each station based on the historical data, thereby constructing the function TS d = F(Y d ,X d ), where TS d is the duration of the stop at the d-th second station, Y d is the predicted number of people getting on the bus at the d-th second stop, X d is the predicted number of people getting off at the d-th second stop, where d ranges from 1 to D, and D is the total number of second stops.

[0050] Step 104: determining the travel time between each two adjacent second stations based on the travel time between each two adjacent second stations and the stop time at each second station; Step 105: When the travel time between two adjacent second stations is longer than the corresponding standard travel time, the second station with the earliest arrival time among the two adjacent second stations is used as the fourth influencing station; The specific implementation of the above steps 104-105 is the same as the specific implementation of the above steps 803-804, and will not be repeated here.

[0051] Step 106, determining the predicted passenger capacity of each second stop based on the passenger capacity of the train and the predicted number of people getting on and off the bus at each second stop; This application can determine the predicted passenger capacity PT of the d-th second station according to the following formula: d : PT d =P d +Y d -X d ; Among them, P d is the passenger capacity of the d-th second station, Y d is the predicted number of people getting on the bus at the d-th second stop, X d is the predicted number of people getting off at the d-th second stop, where d ranges from 1 to D, and D is the total number of second stops.

[0052] Step 107 , taking the second station with a predicted passenger volume greater than a set passenger volume threshold as the fifth influencing station; The fifth affected station is a station where there is not much gap between the arrival of trains but not all the passengers waiting to board have boarded.

[0053] The above-mentioned set passenger carrying threshold can be set according to actual conditions. For example, the set passenger carrying threshold of the current train can be the maximum passenger capacity of the current train.

[0054] If the passenger capacity of the d-th second station is P d If the passenger load is greater than the set passenger threshold P, the dth second station is the fifth influencing station.

[0055] Step 108: Determine the affected site range based on each fourth affected site and each fifth affected site.

[0056] Since the cause of a long-interval event is excessive passenger flow or a late train departure, it will only affect the current train and will not affect other trains. Therefore, the prediction of the range of affected trains is not involved. The scope of trains affected by the long-interval event determined in this application includes the following two situations: Case 1: When the cause of the large interval event is road congestion, Figure 11 A flowchart of a method for determining the range of trains affected by a large interval event when the cause of the large interval event is road congestion is provided in an embodiment of the present application, such as Figure 11 As shown, the affected train range of the large interval event is determined through the following steps: Step 111: Based on the travel route data and the train number data, determine each first train number whose departure time is later than the departure time of the train number, and determine, in the travel direction of the train number, a third station whose station location is before the location where the large interval event occurs and is closest to the location where the large interval event occurs; like Figure 6 As shown, the current train's route includes 10 stops, and the current train's direction of travel is from left to right. Figure 6 The gray rectangle in the figure is the location where the large interval event occurs. The location of the first station is before the location where the large interval event occurs and is closest to the location where the large interval event occurs, so the first station is the third station.

[0057] Step 112: determining the travel time of each first train from the originating station to the third station based on the travel time of the train from the originating station to the third station and the departure interval between two adjacent trains in the train data; Among them, the travel time of the above-mentioned train from the starting station to the third station is determined according to the station distance between each two adjacent stations, the driving speed between each two adjacent stations and the parking time at each station.

[0058] For example, if the current train is adjacent to the first train A, the interval between them is T1, and the travel time from the starting station to the third station is Tv, then the travel time from the starting station to the third station for the first train A is TH1 = Tv + T1. If the second train B is adjacent to the first train A, and the interval between them is T2, then the travel time from the starting station to the third station for the first train B is TH1 = Tv + T1 + T2.

[0059] Step 113: When the travel time of the first train from the originating station to the third station is less than or equal to the predicted congestion duration, the first train is regarded as the third influencing train; The above-mentioned predicted congestion duration can be directly obtained from the map data of the map navigation application.

[0060] If the predicted congestion duration is TP, and the travel time TH1 of the first train A from the starting station to the third station is ≤ TP, then the first train A is the third affected train.

[0061] Step 114: For each first train, determine the arrival time of the first train at the final stop based on the departure time of the first train, the travel time of the first train from the starting stop to the third stop, and the travel time between each two adjacent stops. Among them, the travel time between each adjacent station is determined based on the station distance between each adjacent station in the driving route data, the driving speed of the train between each adjacent station in the train data, and the stop time of each train at each station.

[0062] If the departure time of the first train A is TO, the travel time from the starting station to the third station is TH1, the travel time from the third station to the terminal station is T, and the sum of the stop times of each station from the third station to the terminal station is TS, then the arrival time of the first train A at the terminal station is TF=TO+ TH1+T+TS.

[0063] Step 115, the first train whose arrival time is later than the next departure time of the corresponding train is respectively taken as the fourth influencing train; The fourth affected train number is one that cannot return to service due to road congestion. This application compares the arrival time of the first train at its final stop with the next departure time of the train corresponding to the first train to determine the fourth affected train number. The next departure time of the train corresponding to the first train is the next departure time after the first train arrives at its final stop.

[0064] For example, the next departure time of the train corresponding to the first train A is 2:00. If the arrival time of the first train A at the terminal station is 2:05, then the first train A is the fourth affected train.

[0065] Step 116: Determine the affected train number range based on each third affected train number and each fourth affected train number.

[0066] Case 2: When the cause of the large interval event is a vehicle failure, Figure 12 A flowchart of a method for determining the range of trains affected by a large interval event when the cause of the large interval event is a vehicle failure is provided in an embodiment of the present application, such as Figure 12 As shown, the affected train range of the large interval event is determined through the following steps: Step 121, determine whether the congestion index is less than the set congestion threshold, if so, execute steps 122-123, otherwise, execute steps 124-126; Step 122, based on the departure time of each train in the train data, determining each first-affected train whose departure time is within the fault prediction and restoration time interval; The above-mentioned fault prediction and recovery time interval can be directly obtained from the map data of the map navigation application.

[0067] Step 123, determining the affected train number range based on the first affected train numbers; For example, the fault prediction recovery time interval is 1:00-1:30, the departure time of train A is 1:00, the departure time of train B is 1:10, the departure time of train C is 1:20, and the departure time of train D is 1:35. The departure times of trains A, B, and C are within the fault prediction recovery time interval, so the affected trains include trains A, B, and C.

[0068] Step 124, based on the departure time of each train in the train data, determining each first affected train whose departure time is within the fault prediction and restoration time interval; Step 125: determining, based on the driving route data and the train number data, that the cause of the large interval event is the second impact train numbers corresponding to the road congestion; The specific process of the above step 125 can be found in the above steps 111-116, which will not be repeated here.

[0069] Step 126: Determine the affected train number range based on the first affected train numbers and the second affected train numbers.

[0070] Step 305 : Determine a train scheduling strategy for the long-interval event based on the cause of the long-interval event and the range of stations affected by the long-interval event.

[0071] According to the cause of the long interval event, the method for determining the train scheduling strategy for the long interval event in step 305 specifically includes the following four cases: Case 1: When the cause of the long-interval event is road congestion, based on the affected station range and affected train range of the long-interval event, the corresponding train scheduling strategy when the cause of the long-interval event is road congestion is determined. Figure 13 A flowchart of a method for determining a train scheduling strategy for a large interval event when the cause of the large interval event is road congestion is provided in an embodiment of the present application, such as Figure 13 As shown, the road congestion of large interval events is determined by the following steps: Step 131, determining whether the congestion direction is bidirectional congestion, if so, executing step 132, otherwise executing step 134; Step 132, determining whether the affected site range is within the set site range, if so, executing step 133, otherwise executing step 134; This application can divide the route according to actual conditions. For example, if the route of train number A includes 12 stations, the range including stations 4-8 can be used as the set station range. If the affected station range includes stations 5-6, the affected station range is within the set station range; if the affected station range includes stations 1-3, the affected station range is outside the set station range; if the affected station range includes stations 8-9, the affected station range is outside the set station range.

[0072] Step 133, controlling each train within the affected train range to perform a bidirectional U-turn; Specifically, the present application can control each train within the affected train range to make a bidirectional U-turn just after arriving at the location where the large interval event occurs.

[0073] Step 134: determining the total standard travel time of the train passing through each station covered by the large interval event based on the driving route data and the train number data; For example, if the route of train A includes 12 stops, the range including stops 4-8 can be used as the set stop range. If the affected stop range includes stops 2-3, then the affected stop range is outside the set stop range, and the total standard travel time of train A through the stops covered by the large-interval event is equal to the sum of the travel times from stop 2 to stop 3, that is, the sum of the travel times of train A through the stops covered by the large-interval event when no large-interval event occurs.

[0074] Step 135 : determining a corresponding train scheduling strategy based on the relationship between the total standard travel time and the multiples of the predicted travel time of the large interval event in the driving route data and the set multiples.

[0075] The predicted travel time for the aforementioned long-interval event can be directly obtained from the map data of the map navigation application. The aforementioned set multiplier can be set according to actual circumstances. The set multiplier includes a first multiplier and a second multiplier, where the first multiplier is smaller than the second multiplier. The first multiplier can be 1, and the second multiplier can be 2.

[0076] Using onboard equipment, station sensors, and external map data (such as congestion index), the system collects key information in real time, including train status (whether it's faulty), load factor, speed, and station passenger flow forecasts. When the system detects an abnormally long time interval between a train and the preceding vehicle (which can be triggered automatically through passenger feedback or arrival time calculations), it identifies it as a significant time interval event and initiates intelligent analysis.

[0077] Based on the preset AI decision-making rules, combined with the real-time perception of vehicle status, congestion index and load rate, the root cause of the incident is quickly identified - vehicle failure, road congestion, excessive passenger flow or late vehicle departure. For example, a high congestion index in a non-fault state indicates road congestion, while low congestion and high load are attributed to excessive passenger flow. Secondly, the impact range is dynamically evaluated for different root causes: if it is a vehicle failure, the impact range covers all stations after the location of the incident; if it is due to congestion or late departure, the travel time needs to be calculated based on the station spacing, real-time speed and station stop time, and the actual delayed station is accurately located, such as the earliest arrival station among the adjacent stations where the travel time exceeds the standard; in the case of excessive passenger flow, it is also necessary to predict whether the passenger capacity exceeds the limit, and comprehensively judge the risk stations of detention.

[0078] In congestion scenarios, the system may trigger vehicle U-turns, detours, or additional shuttle services based on the direction of the congestion (one-way / two-way) and the extent of the impact. In the event of a vehicle breakdown, subsequent train schedules may be adjusted or skipped based on the level of congestion. In response to high passenger flows, additional vehicles may be dispatched and departure intervals optimized. All strategies are precisely matched using quantitative indicators, significantly improving event handling efficiency and reducing passenger wait times.

[0079] Figure 14 A flowchart of a method for determining a corresponding train scheduling strategy based on the relationship between a multiple and a set multiple provided in an embodiment of the present application is shown in FIG. Figure 14 As shown, the above step 135 at least includes the following steps: Step 141, determine whether the multiple is less than or equal to the first multiple, if so, execute step 142, otherwise execute step 143; Step 142: No train scheduling is performed; Step 143, determine whether the multiple is greater than or equal to the second multiple, if so, execute step 144, otherwise, execute step 145; Step 144, controlling each train within the affected train range to take a detour; Step 145: adding the number of trains passing through some of the stations on the route, and / or prohibiting each train within the affected train number range from stopping at some of the stations on the route; For example, if the route includes 12 stations and the large interval event covers stations 4-8, then the number of trains passing through stations 1-3 and stations 9-12 can be increased. Alternatively, the trains within the affected range may not stop at some stations on the route, such as stations where there are no passengers waiting for the train, that is, they may not pick up passengers.

[0080] Step 146: Adjust the departure time of each train within the affected train range.

[0081] This application can adjust the departure time of each train within the affected range after increasing the number of trains and / or prohibiting trains from stopping at some stations.

[0082] Case 2: When the cause of the long-interval event is a vehicle failure, based on the affected station range and the affected train range of the long-interval event, the corresponding train scheduling strategy when the cause of the long-interval event is a vehicle failure is determined. Figure 15 A flowchart of a method for determining a train scheduling strategy for a long-interval event when the cause of the long-interval event is a vehicle failure is provided in an embodiment of the present application, such as Figure 15 As shown, the road congestion of large interval events is determined by the following steps: Step 151, determining whether the congestion index is less than a set congestion threshold, if so, executing step 152, otherwise executing step 155; Step 152, determining whether the size of the affected site range exceeds the set site range size, if so, executing step 153, otherwise executing step 154; The above-mentioned set site range size can be set according to actual conditions. For example, the site range size is set to 1 / 3.

[0083] Step 153: adding the number of trains passing through some stations on the route, and / or prohibiting each train within the affected train number range from stopping at some stations on the route; Step 154, adjusting the departure time of each train within the affected train range; If the departure time of each train within the affected range is still not met after adjusting the departure time of each train, you can first increase the number of trains that pass through some stations in the route or not stop at some stations in the route for each train within the affected range, and then adjust the departure time of each train within the affected range.

[0084] Step 155 : Based on the affected station range and the affected train range of the long-interval event, determine the corresponding train scheduling strategy when the cause of the long-interval event is road congestion.

[0085] The specific implementation of the above step 155 can be found in the above steps 131-135, which will not be repeated here.

[0086] Case 3: When the cause of the large interval event is excessive passenger flow, the train scheduling strategy for the large interval event is determined by the following method: based on the predicted number of people getting on and off the bus at each affected station within the affected station range, the passenger capacity threshold and the passenger capacity of the train are set, the number of trains that need to be increased is determined, the number of vehicles for the said number of trains is increased, and the trains are dispatched according to the set departure interval.

[0087] The above-mentioned set passenger capacity threshold is the maximum passenger capacity of the train.

[0088] Case 4: When the cause of the long interval event is the vehicle’s late departure, Figure 16 A flowchart of a method for determining a train scheduling strategy for a large interval event when the cause of the large interval event is a late departure of a vehicle is provided in an embodiment of the present application, such as Figure 16 As shown, the road congestion of large interval events is determined by the following steps: Step 161, determine whether the size of the affected site range exceeds the set site range size, if so, execute steps 162-163, otherwise, execute step 164; The above-mentioned set site range size can be set according to actual conditions. For example, the site range size is set to 1 / 3.

[0089] Step 162: adding trains that pass through some of the stations along the route, and / or prohibiting trains whose departure times are later than or equal to the departure time of the train from stopping at some of the stations along the route; Step 163, adjusting the departure time of each train whose departure time is later than the departure time of the train; Step 164: No train scheduling is performed.

[0090] Based on the same inventive concept, an embodiment of the present application provides a device for processing large-interval vehicle events. Since the above-mentioned device is the device in the method in the embodiment of the present application, and the principle of solving the problem by the device is similar to that of the method, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0091] like Figure 17 As shown, the above device includes the following modules: The acquisition module 171 is used to acquire the train number data and the travel route data of the current train number after a large interval event occurs on the current train number; wherein the train number data includes the status data of each train number on the same travel route; a first determining module 172 for determining a cause of the large interval event based on a congestion index in the driving route data, a vehicle status of a current train in the train data, and a vehicle load factor; A second determining module 173 is configured to determine a range of stations affected by the large interval event based on the cause of the large interval event, the driving route data, and the train number data; The third determining module 174 is configured to determine a train scheduling strategy for the long-interval event based on the cause of the long-interval event and the range of stations affected by the long-interval event.

[0092] In one possible design, the first determining module 172 is configured to: If the vehicle state is a fault state, the cause of the large interval event is a vehicle fault; If the vehicle state is a non-fault state and the congestion index is greater than or equal to a set congestion threshold, the cause of the large interval event is road congestion; If the vehicle state is a non-fault state, the congestion index is less than the set congestion threshold, and the vehicle load factor is greater than or equal to the set load factor, then the cause of the large interval event is excessive passenger flow; If the vehicle state is a non-fault state, the congestion index is less than the set congestion threshold, and the vehicle load factor of the vehicle is less than the set load factor, the cause of the large interval event is vehicle late departure.

[0093] In one possible design, the second determining module 173 is configured to: If the cause of the large interval event is a vehicle failure, then based on the travel route data, determine each first affected station located after the location where the large interval event occurred in the travel direction of the vehicle; and determine the affected station range based on each first affected station; If the cause of the large interval event is road congestion, the travel time between each two adjacent stations is determined based on the station distance between each two adjacent stations in the driving route data and the driving speed of the train between each two adjacent stations in the train number data; the travel time between each two adjacent stations is determined based on the travel time between each two adjacent stations and the stop time of the train at each station in the train number data; when the travel time between two adjacent stations is greater than the corresponding standard travel time, the station with the earliest arrival time among the two adjacent stations is used as the second affected station; and the affected station range is determined based on each second affected station; If the cause of the large interval event is the late departure of the vehicle, then based on the driving route data, in the driving direction of the train, determine the first stations whose station locations are after the location where the large interval event occurs; based on the station distance between each two adjacent first stations in the driving route data and the driving speed of the train between each two adjacent first stations in the train data, determine the driving time between each two adjacent first stations; based on the driving time between each two adjacent first stations and the stop time of the train at each first station in the train data, determine the travel time between each two adjacent first stations; when the travel time between each two adjacent first stations is greater than the corresponding standard travel time, the first station with the earliest arrival time among the two adjacent first stations is used as the third affected station; based on each third affected station, determine the affected station range.

[0094] In a possible design, the cause of the large interval event is excessive passenger flow, and the second determining module 173 is configured to: Based on the driving route data, determining, in the driving direction of the train, each second station whose station location is after the location where the large interval event occurs; Determining the travel time between each two adjacent second stations based on the station distance between each two adjacent second stations in the driving route data and the travel speed of the train between each two adjacent second stations in the train number data; Determining the stop duration at each second stop based on the relationship between the number of passengers getting on and off the train and the stop duration and the predicted number of passengers getting on and off the train at each second stop in the train data; Determining the travel time between each two adjacent second stations based on the travel time between each two adjacent second stations and the stop time at each second station; When the travel time between two adjacent second stations is longer than the corresponding standard travel time, the second station with the earliest arrival time among the two adjacent second stations is used as the fourth influencing station; Determining the predicted passenger capacity of each second stop based on the passenger capacity of the train and the predicted number of people getting on and off the bus at each second stop; The second station with a predicted passenger volume greater than the set passenger volume threshold is respectively used as the fifth influencing station; The affected site range is determined based on each fourth affected site and each fifth affected site.

[0095] In one possible design, the cause of the large interval event is a vehicle failure. Before determining the train scheduling strategy for the large interval event, the third determination module 174 is further configured to: If the congestion index is less than the set congestion threshold, determining first affected trains whose departure times are within the fault prediction and restoration time interval based on the departure times of the trains in the train data; and determining the affected train range based on the first affected trains; If the congestion index is greater than or equal to the set congestion threshold, then based on the departure time of each train in the train data, determine each first-affecting train whose departure time is within the fault prediction and recovery time interval; based on the driving route data and the train data, determine each second-affecting train corresponding to when the cause of the large-interval event is road congestion; and based on each first-affecting train and each second-affecting train, determine the range of the affected trains; The third determining module 174 is configured to: Based on the cause of the large-interval event, the affected site range, and the affected train range, a train scheduling strategy for the large-interval event is determined.

[0096] In one possible design, the cause of the long-interval event is road congestion. Before determining the train scheduling strategy for the long-interval event, the third determining module 174 is further configured to: Based on the travel route data and the train number data, determining each first train number whose departure time is later than the departure time of the train number, and determining, in the travel direction of the train number, a third station whose station location is before the occurrence location of the large interval event and closest to the occurrence location of the large interval event; Determine the travel time of each first train from the originating station to the third station based on the travel time of the train from the originating station to the third station and the departure interval between two adjacent trains in the train data; When the travel time of the first train from the starting station to the third station is less than or equal to the predicted congestion duration, the first train is regarded as the third influencing train; For each first train, determine the arrival time of the first train at the final stop based on the departure time of the first train, the travel time of the first train from the starting stop to the third stop, and the travel time between each two adjacent stops; The first train whose arrival time is later than the next departure time of the corresponding train is taken as the fourth influencing train; Determining the affected train number range based on each third affected train number and each fourth affected train number; The third determining module 174 is configured to: Based on the cause of the large-interval event, the affected site range, and the affected train range, a train scheduling strategy for the large-interval event is determined.

[0097] In one possible design, the third determining module 174 is configured to: If the congestion index is less than the set congestion threshold and the size of the affected station range does not exceed the set station range size, then adjust the departure time of each train within the affected train range; If the congestion index is less than the set congestion threshold and the size of the affected station range exceeds the set station range size, then the number of trains passing through some stations in the driving route is increased and / or each train within the affected train range is prohibited from stopping at some stations in the driving route, and the departure time of each train within the affected train range is adjusted; If the congestion index is greater than or equal to the set congestion threshold, based on the affected station range and affected train range of the large interval event, it is determined that the corresponding train scheduling strategy when the cause of the large interval event is road congestion.

[0098] In one possible design, the third determining module 174 is configured to: If bidirectional congestion is determined based on the congestion direction of the driving route data, and the affected station range is within the set station range, controlling each affected train within the affected train range to perform a bidirectional U-turn; If two-way congestion is determined based on the congestion direction of the driving route data, and the affected site range is outside the set site range, or one-way congestion is determined based on the congestion direction of the driving route data, then the total standard travel time of the train passing through each site covered by the large-interval event is determined based on the driving route data and the train number data, and the corresponding train scheduling strategy is determined based on the relationship between the total standard travel time and the multiples of the predicted travel time of the large-interval event in the driving route data and the set multiples.

[0099] In a possible design, the set multiple includes a first multiple and a second multiple, the first multiple is smaller than the second multiple, and the third determining module 174 is configured to: If the multiple is less than or equal to the first multiple, no train scheduling is performed; If the multiple is greater than the first multiple and less than the second multiple, then the number of trains passing through some of the stops on the route is increased and / or the trains within the affected range are prohibited from stopping at some of the stops on the route, and the departure time of the trains within the affected range is adjusted; If the multiple is greater than or equal to a second threshold, each train within the affected train range is controlled to take a detour.

[0100] In one possible design, the third determining module 174 is configured to: If the cause of the large interval event is excessive passenger flow, the number of additional buses that need to be added is determined based on the predicted number of passengers getting on and off at each affected station within the affected station range, the set passenger threshold and the passenger capacity of the bus, and the number of buses that need to be added is increased, and buses are dispatched according to the set departure interval; If the cause of the large interval event is the late departure of a vehicle, and the size of the affected station range does not exceed the set station range size, no train scheduling will be performed; If the cause of the large interval event is the late departure of the vehicle, and the size of the affected station range exceeds the set station range size, then the number of trains passing through some stations in the driving route will be increased, and / or the trains with departure times later than or equal to the departure time of the train will be prohibited from stopping at some stations in the driving route, and the departure time of the trains with departure times later than the departure time of the train will be adjusted.

[0101] In some embodiments, based on the same inventive concept, a device for processing a vehicle large interval event is also provided in the embodiment of the present application. The device can realize the processing function of the vehicle large interval event discussed above. Please refer to Figure 18 , the device includes a processor 181 and a memory 182, wherein the memory 182 is used to store program instructions; The processor 181 calls the program instructions stored in the memory and executes the program instructions to implement: After a large interval event occurs on the current train, the train number data and the travel route data of the current train are obtained; wherein the train number data includes the status data of each train on the same travel route; Determining a cause of the large interval event based on a congestion index in the driving route data, a vehicle status of a current train in the train number data, and a vehicle load factor; Determining a range of stations affected by the long-interval event based on the cause of the long-interval event, the travel route data, and the train number data; Based on the cause of the large-interval event and the range of stations affected by the large-interval event, a train scheduling strategy for the large-interval event is determined.

[0102] The processor 181 implements the steps of the above-mentioned traffic risk event identification and analysis method based on the AI perception network by running the executable instructions, and the repeated parts are not repeated here.

[0103] An embodiment of the present application also provides a computer storage medium, which stores computer program instructions. When the instructions are executed on a computer, the computer executes the steps of the above-mentioned traffic risk event identification and analysis method based on the AI perception network.

[0104] In some possible implementations, various aspects of this application may also be implemented in the form of a program product, comprising computer program code that, when executed on a computer, causes the computer to execute any of the aforementioned methods for identifying and analyzing traffic risk events based on an AI perception network. Because the principles underlying the problems solved by these computer program products are similar to those of the methods for identifying and analyzing traffic risk events based on an AI perception network, the implementation of these computer program products can be referenced to the implementation of the methods, and any repetitions will not be repeated.

[0105] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0107] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0109] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include these modifications and variations.

Claims

1. A traffic risk event identification and analysis method based on AI perception network, characterized by: include: After a large interval event occurs on the current train, the train number data and the travel route data of the current train are obtained; wherein the train number data includes the status data of each train on the same travel route; Determining a cause of the large interval event based on a congestion index in the driving route data, a vehicle status of a current train in the train number data, and a vehicle load factor; Determining a range of stations affected by the long-interval event based on the cause of the long-interval event, the travel route data, and the train number data; Based on the cause of the large-interval event and the range of stations affected by the large-interval event, a train scheduling strategy for the large-interval event is determined.

2. The method according to claim 1, characterized in that The determining the cause of the large interval event based on the congestion index in the driving route data, the vehicle status of the current train in the train data, and the vehicle load rate includes: If the vehicle state is a fault state, the cause of the large interval event is a vehicle fault; If the vehicle state is a non-fault state and the congestion index is greater than or equal to a set congestion threshold, the cause of the large interval event is road congestion; If the vehicle state is a non-fault state, the congestion index is less than the set congestion threshold, and the vehicle load factor is greater than or equal to the set load factor, then the cause of the large interval event is excessive passenger flow; If the vehicle state is a non-fault state, the congestion index is less than the set congestion threshold, and the vehicle load factor of the vehicle is less than the set load factor, the cause of the large interval event is vehicle late departure.

3. The method according to claim 2, characterized in that The determining of the affected station range of the long-interval event based on the cause of the long-interval event, the driving route data, and the train number data includes: If the cause of the large interval event is a vehicle failure, then based on the travel route data, determine each first affected station located after the location where the large interval event occurred in the travel direction of the vehicle; and determine the affected station range based on each first affected station; If the cause of the large interval event is road congestion, the travel time between each two adjacent stations is determined based on the station distance between each two adjacent stations in the driving route data and the driving speed of the train between each two adjacent stations in the train number data; the travel time between each two adjacent stations is determined based on the travel time between each two adjacent stations and the stop time of the train at each station in the train number data; when the travel time between two adjacent stations is greater than the corresponding standard travel time, the station with the earliest arrival time among the two adjacent stations is used as the second affected station; and the affected station range is determined based on each second affected station; If the cause of the large interval event is the late departure of the vehicle, then based on the driving route data, in the driving direction of the train, determine the first stations whose station locations are after the location where the large interval event occurs; based on the station distance between each two adjacent first stations in the driving route data and the driving speed of the train between each two adjacent first stations in the train data, determine the driving time between each two adjacent first stations; based on the driving time between each two adjacent first stations and the stop time of the train at each first station in the train data, determine the travel time between each two adjacent first stations; when the travel time between each two adjacent first stations is greater than the corresponding standard travel time, the first station with the earliest arrival time among the two adjacent first stations is used as the third affected station; based on each third affected station, determine the affected station range.

4. The method according to claim 2, characterized in that The cause of the large interval event is excessive passenger flow. The determining of the affected station range of the large interval event based on the cause of the large interval event, the driving route data, and the train number data includes: Based on the driving route data, determining, in the driving direction of the train, each second station whose station location is after the location where the large interval event occurs; Determining the travel time between each two adjacent second stations based on the station distance between each two adjacent second stations in the driving route data and the travel speed of the train between each two adjacent second stations in the train number data; Determining the stop duration at each second stop based on the relationship between the number of passengers getting on and off the train and the stop duration and the predicted number of passengers getting on and off the train at each second stop in the train data; Determining the travel time between each two adjacent second stations based on the travel time between each two adjacent second stations and the stop time at each second station; When the travel time between two adjacent second stations is longer than the corresponding standard travel time, the second station with the earliest arrival time among the two adjacent second stations is used as the fourth influencing station; Determining the predicted passenger capacity of each second stop based on the passenger capacity of the train and the predicted number of people getting on and off the bus at each second stop; The second station with a predicted passenger volume greater than the set passenger volume threshold is respectively used as the fifth influencing station; The affected site range is determined based on each fourth affected site and each fifth affected site.

5. The method according to claim 2, characterized in that The cause of the long-interval event is a vehicle failure. Before determining the train scheduling strategy for the long-interval event, the method further includes: If the congestion index is less than the set congestion threshold, determining first-affected trains whose departure times are within the fault prediction and restoration time interval based on the departure times of each train in the train data; and determining a range of affected trains based on the first-affected trains; If the congestion index is greater than or equal to the set congestion threshold, then based on the departure time of each train in the train data, determine each first-affecting train whose departure time is within the fault prediction and recovery time interval; based on the driving route data and the train data, determine each second-affecting train corresponding to when the cause of the large-interval event is road congestion; and based on each first-affecting train and each second-affecting train, determine the range of the affected trains; The determining of a train scheduling strategy for the long-interval event based on the cause of the long-interval event and the affected station range includes: Based on the cause of the large-interval event, the affected site range, and the affected train range, a train scheduling strategy for the large-interval event is determined.

6. The method according to claim 5, characterized in that The cause of the long-interval event is road congestion. Before determining the train scheduling strategy for the long-interval event, the method further includes: Based on the travel route data and the train number data, determining each first train number whose departure time is later than the departure time of the train number, and determining, in the travel direction of the train number, a third station whose station location is before the occurrence location of the large interval event and closest to the occurrence location of the large interval event; Determine the travel time of each first train from the originating station to the third station based on the travel time of the train from the originating station to the third station and the departure interval between two adjacent trains in the train data; When the travel time of the first train from the starting station to the third station is less than or equal to the predicted congestion duration, the first train is regarded as the third influencing train; For each first train, determine the arrival time of the first train at the final stop based on the departure time of the first train, the travel time of the first train from the starting stop to the third stop, and the travel time between each two adjacent stops; The first train whose arrival time is later than the next departure time of the corresponding train is taken as the fourth influencing train; Determining the affected train number range based on each third affected train number and each fourth affected train number; The determining of a train scheduling strategy for the long-interval event based on the cause of the long-interval event and the affected station range includes: Based on the cause of the large-interval event, the affected site range, and the affected train range, a train scheduling strategy for the large-interval event is determined.

7. The method according to claim 5, characterized in that The determining of a train scheduling strategy for the long-interval event based on the cause of the long-interval event, the affected station range, and the affected train range includes: If the congestion index is less than the set congestion threshold and the size of the affected station range does not exceed the set station range size, then adjust the departure time of each train within the affected train range; If the congestion index is less than the set congestion threshold and the size of the affected station range exceeds the set station range size, then the number of trains passing through some stations in the driving route is increased and / or each train within the affected train range is prohibited from stopping at some stations in the driving route, and the departure time of each train within the affected train range is adjusted; If the congestion index is greater than or equal to the set congestion threshold, based on the affected station range and affected train range of the large interval event, it is determined that the corresponding train scheduling strategy when the cause of the large interval event is road congestion.

8. The method according to claim 6, characterized in that The determining of a train scheduling strategy for the long-interval event based on the cause of the long-interval event, the affected station range, and the affected train range includes: If bidirectional congestion is determined based on the congestion direction of the driving route data, and the affected station range is within the set station range, controlling each train within the affected train range to perform a bidirectional U-turn; If two-way congestion is determined based on the congestion direction of the driving route data, and the affected site range is outside the set site range, or one-way congestion is determined based on the congestion direction of the driving route data, then the total standard travel time of the train passing through each site covered by the large-interval event is determined based on the driving route data and the train number data, and the corresponding train scheduling strategy is determined based on the relationship between the total standard travel time and the multiples of the predicted travel time of the large-interval event in the driving route data and the set multiples.

9. The method according to claim 8, characterized in that The set multiple includes a first multiple and a second multiple, the first multiple is smaller than the second multiple, and the determination of the corresponding train scheduling strategy based on the relationship between the multiple of the total standard travel time and the predicted travel time of the large interval event in the driving route data and the set multiple includes: If the multiple is less than or equal to the first multiple, no train scheduling is performed; If the multiple is greater than the first multiple and less than the second multiple, then the number of trains passing through some of the stops on the route is increased and / or the trains within the affected range are prohibited from stopping at some of the stops on the route, and the departure time of the trains within the affected range is adjusted; If the multiple is greater than or equal to a second threshold, each train within the affected train range is controlled to take a detour.

10. The method according to claim 2, characterized in that The determining of a train scheduling strategy for the long-interval event based on the cause of the long-interval event and the range of stations affected by the long-interval event includes: If the cause of the large interval event is excessive passenger flow, the number of additional buses that need to be added is determined based on the predicted number of passengers getting on and off at each affected station within the affected station range, the set passenger threshold and the passenger capacity of the bus, and the number of buses that need to be added is increased, and buses are dispatched according to the set departure interval; If the cause of the large interval event is the late departure of a vehicle, and the size of the affected station range does not exceed the set station range size, no train scheduling will be performed; If the cause of the large interval event is the late departure of the vehicle, and the size of the affected station range exceeds the set station range size, then the number of trains passing through some stations in the driving route will be increased, and / or the trains with departure times later than or equal to the departure time of the train will be prohibited from stopping at some stations in the driving route, and the departure time of the trains with departure times later than the departure time of the train will be adjusted.

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