Early warning method for subway passenger flow volume in rainy days and electronic equipment

Abstract

The invention provides a rainy day subway passenger flow volume early warning method and electronic equipment, and the method comprises the steps: obtaining related subway stations arriving at a target subway station in a target time period and related time periods of the related subway stations, and enabling passengers to arrive at the target subway station in the target time period when entering the related time periods of the related subway stations; according to the meteorological data, the actual umbrella carrying probability of passengers entering the subway station in the related time period is obtained; according to the real-time rainfall and each actual umbrella carrying probability of the target subway station in the target time period, obtaining a station leaving probability; based on the station leaving probability and the number of passengers arriving at the target subway station in the target time period, the total number of detained passengers in the target time period is obtained; and when the total number of the detained passengers is greater than the bearing threshold value, early warning is initiated. Therefore, the total number of the passengers detained in the subway station is predicted in real time, and early warning is performed in a targeted manner, so that the sudden passenger flow can be managed and dredged timely and effectively, the passenger flow management level of the subway station under the sudden weather condition is improved, and the safety risk is reduced.

Description

Technical Field

[0001] The present application relates to the field of data processing, and more specifically, to a method and electronic equipment for warning subway passenger flow on rainy days. Background Art

[0002] With the continuous advancement of urbanization, subways have become one of the most important public transportation tools in metropolises. Its passenger volume and operating efficiency play a vital role in the efficient operation of cities. Due to the impact of global climate change and urban microclimate effects, localized showers frequently occur in large cities. Such weather phenomena are characterized by suddenness and locality, making it difficult for subway passengers to obtain real-time weather information in a timely and comprehensive manner. It is common for subway passengers to encounter sunny weather when entering the station but torrential rain when leaving the station.

[0003] The exit of a subway station is usually directly connected to the ground through an escalator, and the shelter from the rain at the entrance and exit is relatively limited. In showery weather, a large number of passengers will gather near the exit and wait for the rain to subside or stop before leaving. At this time, the space at the exit is not enough to accommodate so many passengers, which affects the passage of passengers and causes congestion in the station. In addition, excessive gathering of passengers can easily cause safety accidents such as trampling. At present, it is difficult to timely and effectively manage and guide sudden passenger flows by relying on manual monitoring and guidance, as well as entrance and exit passenger flow control, and there is a lack of targeted early warning and response mechanisms. Summary of the invention

[0004] In view of this, the purpose of this application is to provide a rainy day subway passenger flow warning method and electronic equipment, which can predict the number of stranded passengers in the subway station in real time and issue targeted warnings, which helps to manage and guide sudden passenger flows in a timely and effective manner.

[0005] In order to achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:

[0006] In a first aspect, the present application provides a method for early warning of subway passenger flow on rainy days, the method comprising:

[0007] Obtain relevant subway stations that reach the target subway station within the target time period, and relevant time periods of the relevant subway stations; wherein passengers who enter the relevant subway stations within the relevant time periods can reach the target subway station within the target time period;

[0008] For each of the relevant subway stations, according to the meteorological data, the actual probability of passengers carrying umbrellas entering the relevant subway station during the relevant period is obtained;

[0009] According to the real-time rainfall at the target subway station during the target period and the actual probability of carrying an umbrella, the probability of passengers leaving the station during the target period is obtained;

[0010] Based on the departure probability and the number of passengers arriving at the target subway station during the target period, obtaining the total number of stranded passengers during the target period;

[0011] When the total number of stranded passengers is greater than the carrying threshold, an early warning is initiated.

[0012] Optionally, the step of obtaining the total number of stranded passengers in the target time period based on the departure probability and the number of passengers arriving at the target subway station in the target time period includes:

[0013] According to the departure probability and the number of passengers arriving at the target subway station during the target period, the number of newly stranded passengers during the target period is obtained;

[0014] The initial total number of stranded passengers is obtained by combining the number of newly stranded passengers until the target period and the number of stranded passengers before the target period;

[0015] The total number of stranded passengers in the target time period is obtained according to the departure probability and the initial total stranded number.

[0016] Optionally, the step of obtaining, based on meteorological data, an actual probability of passengers carrying umbrellas entering the relevant subway station during the relevant period of time comprises:

[0017] Obtaining, based on meteorological data, the cumulative rainfall at the relevant subway station up to the relevant time period, and the real-time rainfall at the relevant subway station during the relevant time period;

[0018] According to the real-time rainfall and the accumulated rainfall at the relevant subway station during the relevant time period, the actual probability of passengers entering the relevant subway station carrying umbrellas is obtained.

[0019] Optionally, the step of obtaining the actual probability of passengers entering the relevant subway station carrying umbrellas according to the real-time rainfall and the accumulated rainfall at the relevant subway station during the relevant period includes:

[0020] When the real-time rainfall at the relevant subway station during the relevant period is greater than the lower limit threshold for rain and less than the rainfall threshold for carrying an umbrella, the probability of carrying an umbrella without rain is obtained according to the accumulated rainfall and the historical maximum rainfall;

[0021] According to the probability of carrying an umbrella when there is no rain and the ratio between the real-time rainfall and the rainfall threshold for carrying an umbrella, the actual probability of carrying an umbrella for passengers entering the relevant subway station during the relevant time period is obtained.

[0022] Optionally, the step of obtaining the probability of wearing an umbrella without rain according to the cumulative rainfall and the historical maximum rainfall of the target duration includes:

[0023] The ratio between the accumulated rainfall and the historical maximum rainfall is taken as the rainfall ratio;

[0024] From the rainfall ratio and one, take the minimum value as the probability of wearing an umbrella without rain in the rainless scenario.

[0025] Optionally, the step of obtaining the probability of passengers leaving the station within the target period according to the real-time rainfall at the target subway station within the target period and each of the actual probabilities of carrying an umbrella comprises:

[0026] According to the real-time rainfall at the target subway station during the target period, the rainfall threshold for light rain, and the passenger's sensitivity to rainfall, a first probability value of a passenger leaving the station without an umbrella is obtained;

[0027] According to the real-time rainfall at the target subway station during the target period, the maximum rainfall that can be tolerated by passengers with umbrellas, and the passengers' sensitivity to rainfall, a second probability value of passengers with umbrellas leaving the station is obtained;

[0028] According to the number of passengers arriving at the target subway station from each of the related subway stations and each of the actual probabilities of carrying umbrellas, the proportion of all passengers arriving at the target subway station carrying umbrellas is obtained;

[0029] The probability of passengers leaving the station within the target time period is obtained according to the proportion of passengers carrying umbrellas, the first probability value, and the second probability value.

[0030] Optionally, the step of obtaining the proportion of all passengers carrying umbrellas arriving at the target subway station based on the number of passengers arriving at the target subway station from each of the relevant subway stations and each of the actual probabilities of carrying umbrellas comprises:

[0031] For each of the relevant subway stations, a number of people carrying umbrellas is obtained according to the number of passengers arriving at the target subway station from the relevant subway station and the actual probability of carrying umbrellas at the relevant subway station;

[0032] The total number of passengers arriving at the target subway station is obtained by summarizing the number of passengers arriving at the target subway station from each of the related subway stations;

[0033] According to the number of people carrying umbrellas and the total number of passengers arriving at the station, the proportion of all passengers carrying umbrellas who arrive at the target subway station is obtained.

[0034] Optionally, the calculation formula for the second probability value of the parachute passenger leaving the station includes:

[0035]

[0036] Among them, P leave_with_umbrella (i, t) represents the second probability value of the target subway station i in the target period t, W i (t) represents the real-time rainfall of the target subway station i in the target period t, β represents the second sensitivity coefficient of passengers to rainfall, W comfort Characterizes the maximum rainfall that an umbrella can withstand.

[0037] Optionally, the calculation formula for the first probability value of the passenger without an umbrella leaving the station includes:

[0038]

[0039] Among them, P leave_no_umbrella (i, t) represents the first probability value of the target subway station i in the target period t, W i (t) represents the real-time rainfall of the target subway station i in the target period t, α represents the first sensitivity coefficient of passengers to rainfall, W small Characterizes the rainfall threshold for light rain days.

[0040] In a second aspect, the present application provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be executed by the processor, and the processor can execute the computer program to implement the method for warning subway passenger flow on rainy days as described in the first aspect.

[0041] In a third aspect, the present application provides a rainy day subway passenger flow warning device, including a preprocessing module, a probability prediction module, a detention prediction module and a warning execution module;

[0042] The preprocessing module is used to obtain relevant subway stations that reach the target subway station within the target time period, and the relevant time period of each relevant subway station; wherein passengers who enter the relevant subway station within the relevant time period can reach the target subway station within the target time period;

[0043] The probability prediction module is used to obtain, for each of the relevant subway stations, the actual probability of passengers carrying umbrellas entering the relevant subway station during the relevant period according to meteorological data;

[0044] The probability prediction module is further used to obtain the probability of passengers leaving the station within the target period according to the real-time rainfall at the target subway station within the target period and the actual probability of carrying an umbrella;

[0045] The detention prediction module is used to obtain the total number of stranded passengers in the target period based on the departure probability and the number of passengers arriving at the target subway station in the target period;

[0046] The warning execution module is used to initiate a warning when the total number of stranded passengers is greater than a carrying threshold.

[0047] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for warning rainy day subway passenger flow as described in the first aspect is implemented.

[0048] The rainy day subway passenger flow early warning method and electronic device provided by the embodiment of the present application include: obtaining the relevant subway stations and their relevant time periods that arrive at the target subway station within the target time period, and the passengers entering the station within the relevant time period of the relevant subway station can arrive at the target subway station within the target time period; for each relevant subway station, according to the meteorological data, the actual probability of carrying an umbrella for the passengers entering the relevant subway station within the relevant time period is obtained; according to the real-time rainfall of the target subway station within the target time period and the actual probability of carrying an umbrella, the probability of passengers leaving the station within the target time period is obtained; based on the probability of leaving the station and the number of passengers arriving at the target subway station within the target time period, the total number of stranded passengers in the target time period is obtained; when the total number of stranded passengers is greater than the carrying threshold, an early warning is initiated. In this way, according to the weather of the relevant subway station and the real-time rainfall of the target subway station, the total number of stranded passengers in the target subway station is predicted in real time, and an early warning is issued in a targeted manner, so as to help timely and effectively manage and guide the sudden passenger flow, improve the passenger flow management level of the subway station under sudden weather conditions, reduce safety risks, and improve the travel experience of passengers.

[0049] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 A schematic diagram of the system architecture of a rainy day subway passenger flow warning system provided in an embodiment of the present application is shown.

[0052] Figure 2 A schematic diagram of the module architecture of an electronic device provided in an embodiment of the present application is shown.

[0053] Figure 3 A schematic diagram of the flow of a method for warning subway passenger flow on rainy days provided in an embodiment of the present application is shown.

[0054] Figure 4 Shows Figure 3 One of the flowchart diagrams of some sub-steps of step 13 in FIG.

[0055] Figure 5 Shows Figure 4 A flowchart of some sub-steps of step 133 in FIG.

[0056] Figure 6 Shows Figure 3 The second flowchart of some sub-steps of step 13.

[0057] Figure 7 Shows Figure 3 One of the flowcharts of some sub-steps of step 15.

[0058] Figure 8 Shows Figure 7 A flowchart of some sub-steps of step 155.

[0059] Fig. 9 Shows Figure 3 One of the flowcharts of some sub-steps of step 17.

[0060] Fig.10 A schematic diagram of the module architecture of the rainy day subway passenger flow warning device provided in an embodiment of the present application is shown.

[0061] Icons: 10-passenger flow warning system; 110-meteorological monitoring equipment; 120-passenger flow monitoring equipment; 130-data service equipment; 140-central service equipment; 150-warning equipment; 20-electronic equipment; 210-memory; 220-processor; 230-communication module; 30-rainy day subway passenger flow warning device; 310-preprocessing module; 320-probability prediction module; 330-detention prediction module; 340-warning execution module. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0063] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0064] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0065] The rainy day subway passenger flow warning method provided in the embodiment of the present application can be applied to Figure 1 In the passenger flow warning system 10 shown, the passenger flow warning system 10 includes a meteorological monitoring device 110, a passenger flow monitoring device 120, a data service device 130, a central service device 140 and a warning device 150. The central service device 140 is respectively connected to the meteorological monitoring device 110, the passenger flow monitoring device 120, the data service device 130 and the warning device 150 in a wired or wireless manner.

[0066] Each subway station is equipped with meteorological monitoring equipment 110 , passenger flow monitoring equipment 120 and early warning equipment 150 .

[0067] The passenger flow monitoring device may be a camera, an infrared sensor or any other device capable of detecting human bodies. The early warning device 150 may be an electronic display screen, a broadcasting system, a station control console or any other device capable of early warning 150. The central service device 140 may be an independent server, a server cluster, a cloud server or any device capable of data processing and calculation. The data service device 130 may be a server, a computer or any device capable of reading data. The meteorological monitoring device 110 may be a rainfall sensor or any other device capable of obtaining information such as rainfall amount and rainfall intensity.

[0068] The meteorological monitoring equipment 110 is installed around the subway station to collect meteorological data in real time, including information such as rainfall amount and rainfall intensity.

[0069] The passenger flow monitoring device 120 is installed at the exit of the subway station to count the number of passengers entering the station in real time. In addition, the number of passengers leaving the station and the number of stranded passengers can also be counted.

[0070] The data service device 130 is used to collect meteorological data in real time through the Internet of Things technology and transmit it to the central server. It is also used to save historical meteorological data and historical passenger flow data of each subway station and parameter configuration of the calculation model.

[0071] The central service device 140 is used to implement the rainy day subway passenger flow warning method provided in the embodiment of the present application, including: obtaining relevant subway stations and their relevant time periods that arrive at the target subway station within the target time period, so that passengers entering the station within the relevant time period of the relevant subway station can arrive at the target subway station within the target time period; for each relevant subway station, based on meteorological data, obtaining the actual umbrella-carrying probability of passengers entering the relevant subway station within the relevant time period; obtaining the departure probability of passengers within the target time period according to the real-time rainfall at the target subway station within the target time period and the actual umbrella-carrying probability; obtaining the total number of stranded passengers in the target time period based on the departure probability and the number of passengers arriving at the target subway station within the target time period; when the total number of stranded passengers is greater than the carrying threshold, initiating a warning to the warning device 150 of the target subway station.

[0072] The early warning device 150 is connected to the control terminal of the subway operation center, and is used to issue early warning information and guiding measures when receiving the early warning transmitted by the central service device 140, and can also implement management emergency plans and passenger flow diversion measures.

[0073] Please refer to Figure 2 , is a block diagram of an electronic device 20, which may be Figure 1 The central service device 140 in the passenger flow warning system 10 is shown. The electronic device 20 includes a memory 210, a processor 220 and a communication module 230. The memory 210, the processor 220 and the communication module 230 are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines.

[0074] The memory 210 is used to store programs or data and can be, but not limited to, a random access memory, a read-only memory, a programmable read-only memory, an erasable read-only memory, an electrically erasable read-only memory, and the like.

[0075] The processor 220 is used to read / write data or programs stored in the memory 210 and execute corresponding functions. For example, Figure 1 In the passenger flow warning system 10 shown, the processor 220 of the central service device 140 executes the computer program stored in the memory 210 to implement the rainy day subway passenger flow warning method provided in the embodiment of the present application.

[0076] The communication module 230 is used to establish a communication connection between the electronic device 20 and other communication terminals through the network, and to send and receive data through the network. Figure 1In the passenger flow warning system 10 shown, the communication module 230 of the central service device 140 communicates with the meteorological monitoring device 110, the passenger flow monitoring device 120, the data service device 130 and the warning device 150 through the network to send and receive data.

[0077] It should be understood that Figure 2 The structure shown is only a schematic diagram of the structure of the electronic device 20. The electronic device 20 may also include Figure 2 More or fewer components as shown, or with Figure 2 Different configurations are shown. Figure 2 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0078] In order to solve the problem that related technologies are difficult to timely and effectively manage and guide the sudden passenger flow of subway stations, and lack targeted early warning and response mechanisms, the embodiment of the present application provides a rainy day subway passenger flow early warning method, referring to Figure 3 , including steps 11 to 19. And, Figure 2 In the passenger flow warning system 10 shown in FIG. 1 , the central service device 140 is used to Figure 2 The structure shown implements steps 11 to 19 when the processor 220 reads the computing program stored in the memory 210 .

[0079] Step 11, obtaining the relevant subway stations that reach the target subway station within the target time period, and the relevant time periods of the relevant subway stations.

[0080] Among them, passengers who enter the relevant subway station during the relevant time period can reach the target subway station within the target time period.

[0081] Step 13, for each relevant subway station, based on the meteorological data, obtain the actual probability of passengers carrying umbrellas entering the relevant subway station during the relevant period.

[0082] Step 15, according to the real-time rainfall at the target subway station during the target period and the actual probability of carrying an umbrella, the probability of passengers leaving the station during the target period is obtained.

[0083] Step 17, based on the departure probability and the number of passengers arriving at the target subway station during the target period, the total number of stranded passengers during the target period is obtained.

[0084] Step 19: When the total number of stranded passengers is greater than the carrying threshold, an early warning is initiated.

[0085] For example, for any city with a subway, there are N subway stations, N is a positive integer and N ≥ 2, and in a day, the operation start time of subway station i is start(i) and the end time is end(i). Therefore, the total operation time of the entire subway network in the city is L = max(end(i))-min(start(i)), in minutes, max(end(i)) represents the latest end time of all subway stations, and min(start(i)) represents the earliest start time of all subway stations.

[0086] The operating hours of the subway station are divided into X operating periods, namely The operating period number is represented by t, where t is a positive integer and 1≤t≤X, where the time range corresponding to t=1 is [min(start(i), min(start(i)+T), the time range corresponding to t=2 is [min(start(i)+T, min(start(i)+2T), the time range corresponding to t=3 is [min(start(i)+2T, min(start(i)+3T), and the same applies to each operating period.

[0087] Combination Figure 1 In the passenger flow warning system 10 shown, the meteorological monitoring equipment 110 at each subway station obtains the real-time rainfall at the location of the subway station, the data service equipment 130 obtains the meteorological data of various locations in the city, and the passenger flow monitoring equipment 120 at each subway station obtains the number of passengers entering the station in real time.

[0088] At the same time, the central service device 140 opens a passenger flow warning task for each subway station, and takes the upcoming operation period as the target period. For each passenger flow warning task, the corresponding subway station is the target subway station. In the passenger flow warning task, the central service device 140 first obtains the relevant subway stations where passengers can reach the target subway station within the target period based on the travel time of passengers from the remaining subway stations to the target subway station, and obtains the relevant period of each relevant subway station. For example, if the travel time of passengers from subway station j to target subway station i is D ji , the target period is t, then the relevant period of subway station j can be expressed as tD ji .

[0089] For each passenger flow warning task, the central service device 140 obtains the actual probability of passengers carrying umbrellas entering the relevant subway station during the relevant period based on meteorological data for each relevant subway station during the target period. At the same time, the central service device 140 obtains the probability of passengers leaving the station during the target period based on the real-time rainfall at the target subway station during the target period and the actual probability of carrying umbrellas. Furthermore, the central server can obtain the total number of stranded passengers during the target period based on the probability of leaving the station and the number of passengers arriving at the target subway station during the target period. Furthermore, when the total number of stranded passengers is greater than the carrying threshold, the central service device 140 initiates a warning to the warning device 150 of the target subway station.

[0090] The early warning device 150 responds to the early warning issued by the central service device 140, issues early warning information and guiding measures, and implements any one or several early warning and guidance mechanisms such as emergency management plans and passenger flow diversion measures to divert passenger flow in the subway station.

[0091] The rainy day subway passenger flow warning method provided in the embodiment of the present application, in the above steps 11 to 19, predicts the total number of stranded passengers at the target subway station in real time according to the weather of the relevant subway station and the real-time rainfall at the target subway station, and issues targeted warnings, thereby facilitating timely and effective management and guidance of sudden passenger flows, improving the passenger flow management level of subway stations under sudden weather conditions, reducing safety risks, and improving passengers' travel experience.

[0092] In the above step 11, the central server can obtain the timetable of each train, and determine the relevant subway stations at which the passenger can reach the target subway station within the target period, and the relevant time periods of the relevant subway stations, based on the time when the train arrives at each subway station and the target period. Alternatively, the travel time D of the passenger from subway station j to target subway station i is ji , the target period is t, and tD ji If there is a train from subway station j to target subway station i, subway station j is regarded as the related subway station of target subway station i.

[0093] Alternatively, the travel time of passengers from various subway stations to the target subway station, the train schedule, and the target time period can be input into a pre-established subway operation model, and the subway operation model can be used to infer the relevant subway stations of the target subway station. The subway operation model can be a model obtained by twin digital means or deep learning technology to simulate the operation scenario of the urban subway.

[0094] The above-mentioned several methods of obtaining related subway stations of the target subway station are only examples, and the implementation methods are not limited.

[0095] After determining the relevant subway stations of the target subway station within the target time period and the relevant time periods of the relevant subway stations in the above manner, in step 13, any feasible manner can be used to obtain the actual probability of carrying an umbrella for passengers entering the relevant subway station within the relevant time period. For example, the rainfall is obtained based on meteorological data, and the actual probability of carrying an umbrella corresponding to the rainfall is queried from the umbrella probability curve of the subway station, wherein the umbrella probability curve is a curve that characterizes the relationship between rainfall and the actual probability of carrying an umbrella after fitting based on the historical data and / or experimental data of the railway station. It can also be that the meteorological data is input into a pre-trained umbrella simulation model, and the actual probability of carrying an umbrella is inferred using the umbrella simulation model. The implementation method is not limited.

[0096] In order to make the actual probability of carrying an umbrella more accurate, the concept of combining the real-time rainfall and the accumulated rainfall to obtain the actual probability of carrying an umbrella is introduced in step 13. Figure 4 The process of obtaining the actual probability of carrying an umbrella in step 13 includes steps 131 to 133.

[0097] Step 131, based on the meteorological data, obtain the cumulative rainfall of the relevant subway station up to the relevant time period and the real-time rainfall of the relevant subway station in the relevant time period.

[0098] Step 133, according to the real-time rainfall and cumulative rainfall of the relevant subway station in the relevant time period, the actual probability of passengers entering the relevant subway station carrying umbrellas is obtained.

[0099] In step 131, the accumulated rainfall may be the accumulated rainfall from the start of raining to the relevant time period, or the accumulated rainfall from the early morning of the same day to the relevant time period.

[0100] When the accumulated rainfall is the accumulated rainfall from the early morning to the relevant period of time, the accumulated rainfall is the sum of the real-time rainfall from the early morning to the real-time rainfall before the relevant period of time, which can be expressed as: T=tD ji , where T represents the relevant time period of the relevant subway station j, R cumulative (j,T-1) represents the cumulative rainfall of the relevant subway station j from the early morning to T-1 period, R0 represents the cumulative value of rainfall from the early morning to the start of operation of the subway station in one day, in millimeters (mm), W j (k) Characterizes the real-time rainfall before the relevant period.

[0101] When the accumulated rainfall is the accumulated rainfall from the start of raining to the relevant period, the calculation method of the accumulated rainfall is consistent with the above method and will not be repeated here.

[0102] After the cumulative rainfall at the relevant subway station up to the relevant period is obtained through the above step 131, in step 133, the ratio between the cumulative rainfall and the umbrella-carrying rainfall threshold can be used as the actual umbrella-carrying probability. The coefficient corresponding to the real-time rainfall in the relevant period can also be used as a correction coefficient, and the ratio between the cumulative rainfall and the umbrella-carrying rainfall threshold is multiplied by the correction coefficient to obtain the actual umbrella-carrying probability. The above implementations are all examples, and the implementations are not limited.

[0103] In order to make the actual probability of carrying an umbrella more consistent with the actual situation, that is, more accurate, in step 133, the idea of ​​obtaining the actual probability of carrying an umbrella based on the accumulated rainfall in a rainless scenario and obtaining the actual probability of carrying an umbrella based on the real-time rainfall and the accumulated rainfall in a rainy scenario is introduced.

[0104] For example, the rain lower limit threshold and the umbrella rainfall threshold are obtained in advance based on historical data or test data. The rain lower limit threshold can be 0 mm of real-time rainfall, or 2 mm, etc. When the real-time rainfall is greater than the rain lower limit threshold, it means that there is rain, otherwise, there is no rain. The umbrella rainfall threshold is the rainfall threshold when all passengers carry umbrellas. When the real-time rainfall is less than the umbrella rainfall threshold, it means that some passengers carry umbrellas and some passengers do not. Otherwise, all passengers wear umbrellas.

[0105] In step 133, when the real-time rainfall at the relevant subway station during the relevant period is greater than the umbrella-carrying rainfall threshold, the actual umbrella-carrying probability of passengers entering the relevant subway station during the relevant period is set to 1. When the real-time rainfall at the relevant subway station during the relevant period is not greater than the rain lower limit threshold, that is, when there is no rain during the relevant period, the probability of carrying an umbrella in the absence of rain is obtained based on the accumulated rainfall and the historical maximum rainfall. At this time, the probability of carrying an umbrella in the absence of rain is the actual probability of carrying an umbrella.

[0106] When the real-time rainfall at the relevant subway station during the relevant period is greater than the lower limit threshold of rain and less than the threshold of carrying an umbrella, the idea of ​​combining the probability of carrying an umbrella without rain, the real-time rainfall and the threshold of carrying an umbrella is introduced to obtain the actual probability of carrying an umbrella. Figure 5 , this process includes steps 1331 to 1333.

[0107] Step 1331, according to the accumulated rainfall and the historical maximum rainfall, obtain the probability of wearing an umbrella without rain.

[0108] Step 1333, based on the probability of carrying an umbrella when there is no rain, and the ratio between the real-time rainfall and the rainfall threshold for carrying an umbrella, obtain the actual probability of passengers entering the relevant subway station with an umbrella during the relevant time period.

[0109] Among them, the maximum historical rainfall refers to the maximum rainfall in a historical period of equal length to the relevant time period.

[0110] In the above process, the probability of wearing an umbrella without rain can be the ratio between the accumulated rainfall and the historical maximum rainfall, or any other feasible calculation method.

[0111] In order to avoid the situation where the probability of not wearing an umbrella for rain exceeds the limit when the accumulated rainfall is too large, the idea of ​​taking the smaller of the ratio between the accumulated rainfall and the historical maximum rainfall is introduced in the calculation of the probability of not wearing an umbrella for rain. Figure 6 In the above process of step 13, the probability of wearing an umbrella without rain is obtained through steps 21 to 23.

[0112] Step 21, taking the ratio between the accumulated rainfall and the historical maximum rainfall as the rainfall ratio.

[0113] Step 23, taking the minimum value from the rainfall ratio and one as the probability of wearing an umbrella without rain in the rainless scenario.

[0114] The process from step 21 to step 23 above can be expressed by the formula: R max Represents the maximum rainfall in history, P umb_no_rain (j,T) represents the probability of an umbrella without rain.

[0115] By taking the smaller value, we can avoid exceeding the probability of wearing an umbrella when there is no rain, and ensure that the probability of wearing an umbrella when there is no rain is consistent with people's actual umbrella-wearing rules, making the probability of wearing an umbrella when there is no rain more accurate.

[0116] Similarly, in order to avoid exceeding the limit and ensure that the probability of carrying an umbrella conforms to people's actual umbrella-carrying rules, in step 1333: obtain the probability difference between one (i.e., the full probability value) and the probability of carrying an umbrella when there is no rain, and take the minimum value from the real-time rainfall and the rainfall threshold for carrying an umbrella; calculate the ratio between the minimum value and the rainfall threshold for carrying an umbrella, and multiply the ratio by the probability difference to obtain the probability of carrying an umbrella when there is rain; combine the probability of wearing an umbrella when there is rain with the probability of wearing an umbrella when there is no rain to obtain the actual probability of carrying an umbrella for passengers entering the relevant subway station during the relevant time period.

[0117] In this process, the calculation formula for the actual probability of carrying an umbrella can be: Among them, P umb_rain (j,T) represents the probability of carrying an umbrella, W th Characterize the umbrella rainfall threshold, P umb_no_rain (j,T) represents the probability of no rain with umbrella, W j (T) represents the real-time rainfall at the relevant subway station during the relevant period.

[0118] Based on the above process, the actual probability of carrying an umbrella can also be expressed as: Among them, W0 represents the lower limit of rain, P umbrella(j,T) represents the actual probability of carrying an umbrella.

[0119] Through the above steps 131 to 133 and their related implementation methods, in the rainless scenario, the actual probability of a passenger carrying an umbrella when entering the station depends on the accumulated rainfall of the day, that is, the greater the accumulated rainfall, the greater the actual probability of carrying an umbrella, and vice versa. In the rainy scenario, the actual probability of carrying an umbrella is determined by the real-time rainfall in the relevant period and the accumulated rainfall of the day. Thus, the actual probability of carrying an umbrella is closer to people's umbrella-carrying rules, greatly improving the accuracy of the actual probability of carrying an umbrella.

[0120] After obtaining the actual probability of carrying an umbrella, any feasible method can be used in step 15 to obtain the probability of passengers leaving the target subway station during the target period. For example, the real-time rainfall of the target subway station and the actual probability of passengers carrying an umbrella at each subway station during the relevant period can be input into a pre-built subway operation model, and the subway operation model can be used to infer the probability of leaving the station. Alternatively, the probability of leaving the station can be obtained after processing according to preset rules.

[0121] In order to make the departure probability closer to the actual situation and more accurate, the process of obtaining the departure probability in step 15 introduces the concept of obtaining the first probability value of the departure of passengers without umbrellas, the second probability value of the departure of passengers with umbrellas, and the proportion of passengers with umbrellas at the station, and then obtaining the departure probability. Figure 7 The process of obtaining the departure probability in step 15 includes steps 151 to 157.

[0122] Step 151, obtaining a first probability value of passengers leaving the station without umbrellas based on the real-time rainfall at the target subway station during the target period, the rainfall threshold for light rain days, and the passengers' sensitivity to rainfall.

[0123] Step 153, obtaining a second probability value of the passengers with umbrellas leaving the station based on the real-time rainfall at the target subway station during the target period, the maximum rainfall that can be tolerated by passengers with umbrellas, and the passengers' sensitivity to rainfall.

[0124] Step 155, according to the number of passengers arriving at the target subway station from the relevant subway station and the actual probability of carrying umbrellas, the proportion of all passengers arriving at the target subway station carrying umbrellas is obtained.

[0125] Step 157, obtaining the probability of passengers leaving the station within the target time period according to the proportion of passengers carrying umbrellas, the first probability value and the second probability value.

[0126] The light rain rainfall threshold represents the maximum value of the rainfall threshold range in light rain weather, and the maximum rainfall tolerance for umbrellas represents the maximum rainfall that passengers carrying umbrellas can accept. The light rain rainfall threshold and the maximum rainfall tolerance are both values ​​obtained after data analysis and statistics based on historical data or test data, and can also be set empirical values. The specific data of the two are not restricted.

[0127] In step 151, the ratio between the real-time rainfall and the rainfall threshold for light rain can be calculated, and the ratio can be multiplied by the coefficient corresponding to the sensitivity to obtain the first probability value of the passenger leaving the station without an umbrella. The first probability value of the passenger leaving the station without an umbrella can also be inferred according to a preset rule or using a deep learning model. Both of these methods are examples, and their implementation methods are not limited.

[0128] Considering that whether the passengers without umbrellas leave the target subway station immediately during the target period is related to the real-time rainfall, for example, when the real-time rainfall is small enough, the passengers leave the station immediately without being affected by the weather, and when the real-time rainfall exceeds the rainfall threshold for light rain, the probability of leaving the station immediately is related to the real-time rainfall and the sensitivity to rainfall. Therefore, in order to make the first probability value of the passengers without umbrellas leaving the station closer to the actual situation, a fitting model representing the relationship between the probability of the passengers without umbrellas leaving the station immediately and the real-time rainfall and the sensitivity to rainfall is introduced in step 151.

[0129] At this time, the calculation formula of the first probability value includes:

[0130] Among them, P leave_no_umbrella (i, t) represents the first probability value of the target subway station i in the target period t (i.e., the probability that passengers without umbrellas leave the station immediately), W i (t) represents the real-time rainfall of the target subway station i in the target period t, α represents the first sensitivity coefficient of passengers to rainfall, W small Characterizes the rainfall threshold for light rain days.

[0131] Similarly, whether the passenger with an umbrella leaves the target subway station immediately during the target period is also related to the size of the real-time rainfall. For example, when the real-time rainfall is less than the maximum rainfall that the passenger with an umbrella can bear, the passenger leaves the station immediately. When the real-time rainfall exceeds the maximum rainfall that the passenger with an umbrella can bear, the probability of leaving the station immediately is related to the real-time rainfall and the sensitivity to rainfall. Therefore, in order to make the second probability value of the passenger with an umbrella leaving the station closer to the actual situation, a fitting model representing the relationship between the probability of the passenger with an umbrella leaving the station immediately and the real-time rainfall and the sensitivity to rainfall is introduced in step 153.

[0132] At this time, the calculation formula of the second probability value includes:

[0133]

[0134] Among them, P leave_with_umbrella (i, t) represents the second probability value of the target subway station i in the target period t, W i (t) represents the real-time rainfall of the target subway station i in the target period t, β represents the second sensitivity coefficient of passengers to rainfall, W comfort Indicates the maximum rainfall an umbrella can withstand.

[0135] Through the above two formulas, we can obtain the first probability and the second probability which are closer to the actual situation and more accurate.

[0136] At the same time, in step 155, the number of passengers arriving at the target subway station from each relevant subway station and the actual probability of carrying an umbrella can be used as inputs of the subway operation model, and the proportion of passengers carrying umbrellas can be inferred using the model, or the proportion of passengers carrying umbrellas can be obtained according to preset rules, and the implementation method is limited by steps.

[0137] In one possible implementation, refer to Figure 8 In step 155, the proportion of all passengers carrying umbrellas arriving at the target subway station can be obtained by following steps 1551 to 1555.

[0138] Step 1551, for each relevant subway station, according to the number of passengers arriving at the target subway station from the relevant subway station and the actual probability of carrying umbrellas at the relevant subway station, obtain a number of people carrying umbrellas.

[0139] Step 1553, the total number of passengers arriving at the target subway station is obtained by summarizing the number of passengers arriving at the target subway station from each related subway station.

[0140] Step 1555, according to the number of people carrying umbrellas and the total number of people arriving at the station, obtain the proportion of all passengers carrying umbrellas who arrive at the target subway station.

[0141] In step 1551, the median or average value of the historical number of passengers in the same time period may be taken as the number of passengers arriving at the target subway station from the relevant subway station. For example, when the target time period is from 17:30 to 18:00, the median or average value of the historical number of passengers in the time period from 17:30 to 18:00 of the previous n days may be taken.

[0142] Alternatively, the number of passengers entering the relevant subway station during the relevant period may be obtained through passenger flow monitoring data, and the value obtained by multiplying the number of passengers entering the station by a preset ratio may be used as the number of passengers arriving at the target subway station from the relevant subway station.

[0143] Alternatively, the number of passengers entering the relevant subway station during the relevant period can be obtained through passenger flow monitoring data, and the number of passengers entering the station can be input into a pre-trained passenger flow simulation model, and the passenger flow simulation model can be used to infer the number of passengers arriving at the target subway station from the relevant subway station.

[0144] The above methods of obtaining the number of passengers arriving at the target subway station from the relevant subway station are all examples, and the implementation methods are not limited.

[0145] Then, in step 1551, the number of passengers arriving at the target subway station from the relevant subway station is multiplied by the actual probability of carrying an umbrella at the relevant subway station to obtain the number of people carrying umbrellas at the relevant subway station. In step 1553, the number of passengers arriving at the target subway station corresponding to each relevant subway station is added together to obtain the total number of people arriving at the station.

[0146] After obtaining the total number of arrivals and the number of people with umbrellas at each relevant subway station, the ratio of the two can be obtained to get the umbrella carrying ratio.

[0147] The umbrella ratio can be expressed by the formula: P umbrella_arrival (i,t) represents the proportion of passengers carrying umbrellas who arrive at the target subway station t within the target period t, tD ji =T represents the relevant time period of the relevant subway station j, P umbrella (j,tD ji )=P umbrella (j,T) is the actual probability of carrying an umbrella, Q ji (tD ji ) represents the number of passengers who can reach the target subway station i from the relevant subway station j within the target period, and N represents the total number of relevant subway stations.

[0148] After obtaining the first probability, the second probability and the proportion of passengers with umbrellas in the above manner, in step 157, the proportion of passengers without umbrellas is multiplied by the first probability, the second probability is multiplied by the proportion of passengers with umbrellas, and the two products are added to obtain the probability of leaving the station.

[0149] At this time, the probability of leaving the station can be expressed as: P leave (i,t)=P umbrella_arrival (i,t)·P leave_with_umbrella (i,t)+[1-P umbrella_arrival (i,t)]·P leave_no_umbrella (i,t).

[0150] In this way, through the above steps 151 to 157, a more accurate departure probability is obtained.

[0151] Furthermore, the method for obtaining the total number of stranded passengers in step 17 can be flexibly set. For example, the total number of passengers arriving at the target subway station within the target period can be added to the original number of stranded passengers, and the sum of the two can be multiplied by the departure probability to obtain the total number of stranded passengers. The total number of stranded passengers can also be inferred by a model, and the implementation method is not restricted.

[0152] In order to make the total number of stranded passengers in the target period more accurate, the idea of ​​simultaneously considering the number of existing stranded passengers and newly arrived passengers leaving the station is introduced in step 17. Fig. 9 In step 17, the total number of stranded passengers in the target time period is obtained through steps 171 to 175.

[0153] Step 171, according to the departure probability and the number of passengers arriving at the target subway station during the target period, the number of newly stranded passengers during the target period is obtained.

[0154] Step 173, the number of newly added stranded passengers in the target period and the number of original stranded passengers before the target period are combined to obtain an initial total stranded number.

[0155] Step 175, obtaining the total number of stranded passengers in the target time period according to the departure probability and the initial total stranded number.

[0156] The total number of stranded passengers during the target period can be expressed as: N stay (i, t) = N′ stay (i, t)-N stay_leave (i, t), N′ stay (i, t) represents the initial total retention number, N stay_leave (i, t) represents the number of passengers who choose to leave among the stranded passengers.

[0157] Among them, N stay_leave (i, t) = N′ stay (i, t)·P leave (i, t), N′ stay (i, t) = N stay (i, t-1)+N stay_new (i,t),N stay_new (i, t) = N arrival (i, t)-N leave (i,t),N leave (i, t) = N arrival (i, t)·P leave (i,t),N stay (i, t-1) represents the original number of stranded passengers, N stay_new (i, t) represents the number of newly stranded passengers, N arrival (i, t) represents the total number of passengers arriving at the target subway station during the target period, N leave (i, t) represents the number of passengers who choose to leave among the passengers who arrive at the target subway station during the target period.

[0158] Through the above steps 171 to 175, the total number of passengers arriving at the target subway station during the target period and the passengers who leave from the original number of stranded passengers are fully considered, so that the total number of stranded passengers during the target period is more accurate.

[0159] Then, in step 19, when N stay (i, t, satisfies N stay When (i, t) ≥ δC(i), the warning is triggered. δ is the warning trigger coefficient, which is a fixed value, and C(i) represents the maximum number of passengers allowed to gather at the exit of the target subway station i on rainy days.

[0160] After issuing an early warning, the subway management department can inform passengers of the congestion at the exit through the station broadcast or electronic screen, or activate the emergency plan and arrange staff to guide the passenger flow, and restrict the passenger flow when necessary. It can also set up guidance signs in the station to guide passengers to other exits or temporary rain shelters.

[0161] Based on the same concept as the rainy day subway passenger flow warning method, refer to Fig.10 The embodiment of the present application also provides a rainy day subway passenger flow warning device 30, including a preprocessing module 310, a probability prediction module 320, a detention prediction module 330 and a warning execution module 340, so as to implement the rainy day subway passenger flow warning method provided above.

[0162] The preprocessing module 310 is used to obtain the relevant subway stations that reach the target subway station within the target time period, and the relevant time periods of the relevant subway stations, wherein passengers who enter the relevant subway stations within the relevant time periods can reach the target subway station within the target time period.

[0163] The probability prediction module 320 is used to obtain, for each relevant subway station, the actual probability of passengers carrying umbrellas entering the relevant subway station during the relevant time period based on meteorological data.

[0164] The probability prediction module 320 is also used to obtain the probability of passengers leaving the station within the target time period according to the real-time rainfall at the target subway station within the target time period and the actual probability of carrying an umbrella.

[0165] The stranded prediction module 330 is used to obtain the total number of stranded passengers in the target period based on the departure probability and the number of passengers arriving at the target subway station in the target period.

[0166] The warning execution module 340 is used to initiate a warning when the total number of stranded passengers is greater than a carrying threshold.

[0167] The above-mentioned rainy day subway passenger flow warning device 30, under the coordinated action of the preprocessing module 310, the probability prediction module 320, the detention prediction module 330 and the warning execution module 340, predicts the total number of stranded passengers at the target subway station in real time according to the weather of the relevant subway station and the real-time rainfall of the target subway station, and issues targeted warnings, thereby helping to manage and guide sudden passenger flows in a timely and effective manner, improving the passenger flow management level of subway stations under sudden weather conditions, reducing safety risks, and improving passengers' travel experience.

[0168] For the specific implementation and effect of the rainy day subway passenger flow warning device 30, please refer to the above description of the implementation of the rainy day subway passenger flow warning method, such as, the specific implementation and effect of the preprocessing module 310 please refer to the above description of the relevant content of step 11, the specific implementation and effect of the probability prediction module 320 please refer to the above description of the relevant content of step 13 and step 15, the specific implementation and effect of the detention prediction module 330 please refer to the above description of the relevant content of step 17, the specific implementation and effect of the warning execution module 340 please refer to the above description of the relevant content of step 17, and will not be repeated here.

[0169] In addition, each module of the above-mentioned rainy day subway passenger flow warning device 30 can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor 220 in the electronic device 20 in the form of hardware, or can be stored in the memory 210 of the electronic device 20 in the form of software, so that the processor 220 can call and execute the operations corresponding to the above-mentioned modules to implement the rainy day subway passenger flow warning method provided above.

[0170] The embodiment of the present application also provides an electronic device 20, including a processor 220 and a memory 210, wherein the memory 210 stores a computer program that can be executed by the processor 220, and the processor 220 can execute the computer program to implement the rainy day subway passenger flow warning method provided above.

[0171] The embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by the processor 220, the rainy day subway passenger flow warning method proposed in the embodiment of the present application is implemented.

[0172] In summary, the rainy day subway passenger flow warning method and electronic device provided in the embodiments of the present application have at least the following beneficial effects:

[0173] (1) Real-time prediction of passenger gathering risk: Establish a warning process that combines real-time weather data, the probability of passengers carrying umbrellas, and passenger exit behavior to calculate and predict in real time the number of passengers gathering at the exit of a specific subway station in the future period;

[0174] (2) Improved accuracy and timeliness of warnings: By quantitatively analyzing key factors that affect passenger behavior, such as real-time rainfall, historical rainfall conditions, and the probability of passengers carrying umbrellas, the accuracy of warnings is greatly improved, ensuring that warnings can be issued in a timely manner before passenger gatherings form;

[0175] (3) Providing scientific passenger flow control strategies: Based on the predicted total number of stranded passengers, corresponding early warning measures and passenger flow diversion plans can be formulated, such as information release, passenger flow guidance, and coordination with ground transportation, to help subway operators effectively manage sudden passenger flows and ensure passenger safety;

[0176] (4) Scalability and adaptability: The rainy day subway passenger flow warning method has good scalability and can adapt to subway networks of different sizes and changing weather conditions. The model parameters can be adjusted according to actual conditions, and the system has extremely high flexibility.

[0177] Through the implementation of this application, it is possible to effectively make up for the deficiencies of existing technologies in terms of real-time, accuracy and pertinence, improve the passenger flow management level of subway stations under sudden weather conditions, reduce safety risks and improve passengers' travel experience.

[0178] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0179] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0180] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.

[0181] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A rainy day subway passenger flow early warning method, characterized in that: The method comprises: Obtain relevant subway stations that reach the target subway station within the target time period, and relevant time periods of the relevant subway stations; wherein passengers who enter the relevant subway stations within the relevant time periods can reach the target subway station within the target time period; For each of the relevant subway stations, according to the meteorological data, the actual probability of passengers carrying umbrellas entering the relevant subway station during the relevant period is obtained; According to the real-time rainfall at the target subway station during the target period and the actual probability of carrying an umbrella, the probability of passengers leaving the station during the target period is obtained; Based on the departure probability and the number of passengers arriving at the target subway station during the target period, obtaining the total number of stranded passengers during the target period; When the total number of stranded passengers is greater than the carrying threshold, an early warning is initiated.

2. The rainy day subway passenger flow early warning method according to claim 1 is characterized in that: The step of obtaining the total number of stranded passengers in the target period based on the departure probability and the number of passengers arriving at the target subway station in the target period comprises: According to the departure probability and the number of passengers arriving at the target subway station during the target period, the number of newly stranded passengers during the target period is obtained; The initial total number of stranded passengers is obtained by combining the number of newly stranded passengers until the target period and the number of stranded passengers before the target period; The total number of stranded passengers in the target time period is obtained according to the departure probability and the initial total stranded number.

3. The rainy day subway passenger flow early warning method according to claim 1 or 2, characterized in that: The step of obtaining the actual probability of passengers carrying umbrellas at the relevant subway station during the relevant period based on the meteorological data includes: Obtaining, based on meteorological data, the cumulative rainfall at the relevant subway station up to the relevant time period, and the real-time rainfall at the relevant subway station during the relevant time period; According to the real-time rainfall and the accumulated rainfall at the relevant subway station during the relevant time period, the actual probability of passengers entering the relevant subway station carrying umbrellas is obtained.

4. The rainy day subway passenger flow early warning method according to claim 3 is characterized in that: The step of obtaining the actual probability of passengers entering the relevant subway station carrying umbrellas according to the real-time rainfall and the accumulated rainfall at the relevant subway station during the relevant period includes: When the real-time rainfall at the relevant subway station during the relevant period is greater than the lower limit threshold for rain and less than the rainfall threshold for carrying an umbrella, the probability of carrying an umbrella without rain is obtained according to the accumulated rainfall and the historical maximum rainfall; According to the probability of carrying an umbrella when there is no rain, and the ratio between the real-time rainfall and the rainfall threshold for carrying an umbrella, the actual probability of carrying an umbrella for passengers entering the relevant subway station during the relevant time period is obtained.

5. The rainy day subway passenger flow early warning method according to claim 4 is characterized in that: The step of obtaining the probability of wearing an umbrella without rain according to the cumulative rainfall and the historical maximum rainfall of the target duration includes: The ratio between the accumulated rainfall and the historical maximum rainfall is taken as the rainfall ratio; From the rainfall ratio and one, take the minimum value as the probability of wearing an umbrella without rain in the rainless scenario.

6. The rainy day subway passenger flow early warning method according to claim 1 or 2, characterized in that: The step of obtaining the probability of passengers leaving the station within the target period according to the real-time rainfall at the target subway station within the target period and the actual probability of carrying an umbrella comprises: According to the real-time rainfall at the target subway station during the target period, the rainfall threshold for light rain, and the passenger's sensitivity to rainfall, a first probability value of a passenger leaving the station without an umbrella is obtained; According to the real-time rainfall at the target subway station during the target period, the maximum rainfall that can be tolerated by passengers with umbrellas, and the passengers' sensitivity to rainfall, a second probability value of passengers with umbrellas leaving the station is obtained; According to the number of passengers arriving at the target subway station from each of the related subway stations and each of the actual probabilities of carrying umbrellas, the proportion of all passengers arriving at the target subway station carrying umbrellas is obtained; The probability of passengers leaving the station within the target time period is obtained according to the proportion of passengers carrying umbrellas, the first probability value, and the second probability value.

7. The rainy day subway passenger flow early warning method according to claim 6 is characterized in that: The step of obtaining the proportion of all passengers carrying umbrellas arriving at the target subway station based on the number of passengers arriving at the target subway station from each of the related subway stations and each of the actual probabilities of carrying umbrellas comprises: For each of the relevant subway stations, a number of people carrying umbrellas is obtained according to the number of passengers arriving at the target subway station from the relevant subway station and the actual probability of carrying umbrellas at the relevant subway station; The total number of passengers arriving at the target subway station is obtained by summarizing the number of passengers arriving at the target subway station from each of the related subway stations; According to the number of people carrying umbrellas and the total number of passengers arriving at the station, the proportion of all passengers carrying umbrellas who arrive at the target subway station is obtained.

8. The rainy day subway passenger flow early warning method according to claim 6 is characterized in that: The calculation formula for the second probability value of the parachute passenger leaving the station includes: Among them, P leave_with_umbrella (i, t) represents the second probability value of the target subway station i in the target period t, W i (t) represents the real-time rainfall of the target subway station i in the target period t, β represents the second sensitivity coefficient of passengers to rainfall, W comfort Indicates the maximum rainfall an umbrella can withstand.

9. The rainy day subway passenger flow early warning method according to claim 6 is characterized in that: The calculation formula of the first probability value of the passenger leaving the station without an umbrella includes: Among them, P leave_no_umbrella (i, t) represents the first probability value of the target subway station i in the target period t, W i (t) represents the real-time rainfall of the target subway station i in the target period t, α represents the first sensitivity coefficient of passengers to rainfall, W small Characterizes the rainfall threshold for light rain days.

10. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores a computer program that can be executed by the processor, and the processor can execute the computer program to implement the rainy day subway passenger flow warning method as described in any one of claims 1 to 9.

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