Urban rail transit transport capacity adjusting method, device and equipment and storage medium

Abstract

The invention provides an urban rail transit transport capacity adjustment method and device, computer equipment and a computer storage medium, and relates to the technical field of operation management of urban rail transit. Comprising the following steps: determining passenger retention factors corresponding to a target station and a linear relationship constructed among the passenger retention factors; inputting the passenger retention factor and the linear relationship into a pre-constructed station scene simulation model to obtain a station passenger flow volume simulation scene in the current transport capacity configuration mode, and further calculating passenger retention parameters of the target station in the current transport capacity configuration mode; if the passenger retention parameter of the target station is greater than a preset passenger retention threshold, adjusting a current transport capacity configuration mode to obtain a target transport capacity configuration mode; wherein the passenger retention parameter of the target station corresponding to the target transport capacity configuration mode is not greater than a preset passenger retention threshold value. According to the method, the accuracy of traffic capacity resource allocation can be improved, and a quantifiable decision basis is provided for dispatching personnel to carry out passenger transport organization allocation.

Description

Technical Field

[0001] The present application relates to the technical field of operation management of urban rail transit, and in particular, to an urban rail transit capacity adjustment method, an urban rail transit capacity adjustment device, a computer device and a computer storage medium. Background Art

[0002] With the increasing advancement of rail transit construction and the development of urban rail transit networks, urban rail transit such as subways have gradually become one of the main choices for people to travel. Correspondingly, the contradiction between supply and demand caused by the rapid growth of station passenger flow and insufficient rail transit capacity has led to large passenger flow gathering in stations, which in turn affects the travel needs of both supply and demand sides.

[0003] In order to solve the problem of large passenger flow gathering in the station, in the relevant technical solutions, the operating organization usually adopts methods such as adding temporary trains to increase traffic capacity, setting up obstacles to guide passengers to walk into the station to reduce the number of passengers on the platform within a certain period of time, and limiting the flow at the entrance of the paid area to reduce the number of passengers entering the station within a certain period of time.

[0004] However, the above-mentioned technical solutions only rely on the manual experience of train dispatchers and station operators to judge whether the number of additional trains, obstacle setting time, and flow control time at the paid area entrance are reasonable, making it difficult for operating organizations to obtain the optimal capacity adjustment plan based on the actual operating scenarios of the station, which in turn makes it difficult to accurately allocate transportation capacity resources. Summary of the invention

[0005] The embodiments of the present application provide a method for adjusting the capacity of urban rail transit, a device for adjusting the capacity of urban rail transit, a computer device and a computer storage medium, thereby overcoming, at least to a certain extent, the technical problem that it is difficult for operating organizations to obtain the optimal capacity adjustment plan based on the actual operating scenarios of the station due to the limitations and defects of related technologies, thereby making it difficult to accurately allocate transportation capacity resources.

[0006] A first aspect of an embodiment of the present application provides a method for adjusting the capacity of urban rail transit, the method comprising: determining a passenger retention factor corresponding to a target platform, and determining a linear relationship between each passenger retention factor; inputting the passenger retention factor corresponding to the target platform and the linear relationship between each passenger retention factor into a pre-constructed station scene simulation model to obtain a station passenger flow simulation scene under a current capacity configuration mode, wherein the station passenger flow simulation scene at least includes the real-time motion status of a virtual character model corresponding to each individual passenger in the station passenger flow simulation scene; calculating the passenger retention parameter of the target platform under the current capacity configuration mode based on the real-time motion status of the virtual character model corresponding to each individual passenger in the station passenger flow simulation scene; if the passenger retention parameter of the target platform is greater than a preset passenger retention threshold, adjusting the current capacity configuration mode to obtain a target capacity configuration mode; wherein the passenger retention parameter of the target platform corresponding to the target capacity configuration mode is not greater than the preset passenger retention threshold.

[0007] According to a second aspect of an embodiment of the present application, there is provided an urban rail transit capacity adjustment device, the device comprising: a determination module, configured to determine a passenger retention factor corresponding to a target platform and determine a linear relationship between each passenger retention factor; a data input module, configured to input the passenger retention factor corresponding to the target platform and the linear relationship between each passenger retention factor into a pre-constructed station scene simulation model to obtain a station passenger flow simulation scene under a current capacity configuration mode, wherein the station passenger flow simulation scene at least includes the real-time motion state of each individual passenger corresponding to a virtual character model; a parameter calculation module, configured to calculate the passenger retention parameter of the target platform under the current capacity configuration mode based on the real-time motion state of each individual passenger corresponding to the virtual character model in the station passenger flow simulation scene; a mode adjustment module, configured to adjust the current capacity configuration mode to obtain a target capacity configuration mode if the passenger retention parameter of the target platform is greater than a preset passenger retention threshold; wherein the passenger retention parameter of the target platform corresponding to the target capacity configuration mode is not greater than the preset passenger retention threshold.

[0008] A third aspect of an embodiment of the present application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any of the above urban rail transit capacity adjustment methods are implemented.

[0009] According to a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above methods for adjusting the capacity of urban rail transit are implemented.

[0010] A fifth aspect of the embodiments of the present application provides a computer program product, including a computer program, which is executed by a processor to implement the steps of any one of the above methods for adjusting the capacity of urban rail transit.

[0011] The technical solution of this application has the following beneficial effects:

[0012] Through the urban rail transit capacity adjustment method, the passenger retention factor corresponding to the target platform is determined, and the linear relationship between the passenger retention factors is determined; the passenger retention factor corresponding to the target platform and the linear relationship between the passenger retention factors are input into a pre-constructed station scene simulation model to obtain a station passenger flow simulation scene under a current capacity configuration mode, and the station passenger flow simulation scene at least includes the real-time motion state of the virtual role model corresponding to each individual passenger in the station passenger flow simulation scene; based on the real-time motion state of the virtual role model corresponding to each individual passenger in the station passenger flow simulation scene, the passenger retention parameter of the target platform under the current capacity configuration mode is calculated; if the passenger retention parameter of the target platform is greater than a preset passenger retention threshold, the current capacity configuration mode is adjusted to obtain a target capacity configuration mode; wherein the passenger retention parameter of the target platform corresponding to the target capacity configuration mode is not greater than the preset passenger retention threshold.

[0013] On the one hand, this method analyzes the factors that affect the retention of passengers at the target platform and the linear relationship between the factors, and uses them as model input parameters to construct the real-time motion state of the virtual role model corresponding to each passenger individual, so as to intuitively simulate the actual passenger flow in the station operation process, and then calculate the number and number of passengers stranded in the current operation simulation scenario as a standard for judging whether the current passenger organization configuration has reached the capacity and volume matching, and then adjust the current capacity configuration mode in a timely manner from the overall perspective, overcoming the technical problem that the decision-making separation between station personnel and trains in the existing technical solutions makes it difficult to accurately determine the configuration mode. On the other hand, if the number of passenger strandings is too many, it is necessary to change the parameter settings to adjust the passenger organization and deployment mode, and then obtain the optimal deployment plan for passenger flow evacuation passenger organization under different scenarios. This method can be combined with the actual operation scenario of the station to provide a quantifiable decision basis for dispatching personnel to organize and deploy passenger transportation, avoiding the technical problems of long time and low accuracy caused by manual decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0015] Figure 1An architecture diagram of an urban rail transit capacity adjustment system in one of the application scenarios provided in an embodiment of the present application;

[0016] Figure 2 A flowchart of a method for adjusting urban rail transit capacity provided in one embodiment of the present application;

[0017] Figure 3 A flowchart of a method for determining a linear relationship between passenger retention factors provided in one embodiment of the present application;

[0018] Figure 4 A flow chart of a method for constructing a station scene simulation model provided in one embodiment of the present application;

[0019] Figure 5 A schematic diagram of the structure of a device for adjusting the capacity of urban rail transit provided in one embodiment of the present application;

[0020] Figure 6 A schematic diagram of a computer device structure provided in accordance with an embodiment of the present application. DETAILED DESCRIPTION

[0021] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as being limited to the examples set forth herein; on the contrary, these embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concepts of the example embodiments are fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0022] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0023] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the steps. For example, some steps may be decomposed, while some steps may be combined or partially combined, so the actual execution order may change according to the actual situation.

[0024] With the increasing advancement of my country's rail transit construction and the increasing development of urban rail transit networks, urban rail transit such as subways have gradually become one of the main choices for people to travel. Correspondingly, it is easy to encounter a contradiction between the supply and demand caused by the rapid growth of station passenger flow and the insufficient rail transit capacity. Usually, the large passenger flow at the station is mainly caused by the following two situations: morning and evening peaks and emergencies. Among them, morning and evening peaks are usually caused by the large passenger flow in and out of the station during people's commute, while emergencies are congestion caused by the lack of local distance capacity due to sudden events.

[0025] For the phenomenon of large passenger flow gathering in the station, the existing solutions of the operation organization usually include: adding temporary trains, setting up obstacles to guide passengers to enter the station and limiting the flow of passengers at the entrance of the paid area. Among them, adding temporary trains can increase traffic capacity; setting up obstacles to guide passengers to enter the station can reduce the number of passengers on the platform within a certain period of time; limiting the flow of passengers at the entrance of the paid area can reduce the number of passengers entering the station within a certain period of time, thereby solving the above-mentioned large passenger flow gathering phenomenon.

[0026] For example, taking the transfer station B on any subway line in area A as an example, the congestion level in transfer station B will reach its peak from 17:00 to 20:00 on a working day. During this period, the operation organization will usually add shuttle trains to cut off the route, set up obstacles at transfer station B, and stop the gates from working and keeping them open to guide passengers to walk and increase the walking time of passengers in the station hall to alleviate the congestion on the platform.

[0027] However, the inventors found that the above existing technical solutions basically rely on the experience of train dispatchers and station operators to judge whether the number of additional trains, the time for setting up obstacles, and the time for limiting the flow at the entrance of the paid area are reasonable, and there is no quantitative standard to measure whether the decision made to adjust the capacity or reduce the demand for transportation is the optimal solution. In addition, the above additional trains are line network decisions, and the increase in passenger platform walking time is a station decision. The two are not organically combined to generate a capacity adjustment plan as a whole, and it is difficult to accurately allocate transportation capacity resources. Insufficient allocation of transportation capacity resources makes it difficult to solve the phenomenon of large passenger flow gathering in the station, and excessive allocation of transportation capacity resources leads to waste of capacity resources.

[0028] Therefore, there is an urgent need for a method that can combine the actual operation scenarios of the station to provide dispatchers with a quantifiable decision-making basis for passenger resource allocation, and then obtain the optimal deployment plan for passenger evacuation organization under different operation scenarios based on the passenger stranded situation on the platform.

[0029] In order to solve the above problems, an embodiment of the present application provides a method for adjusting the capacity of urban rail transit. The method uses a pre-constructed station simulation model to analyze the factors affecting the detention of passengers on the platform and the specific relationship between the factors, and sets them as the input parameters of the station simulation model, thereby simulating the passenger detention situation on the station platform under different operating scenarios, thereby calculating the number and number of passenger detentions under different operating scenarios, so as to determine whether the current passenger organization configuration has achieved capacity and volume matching. If the number of passenger detentions is too many, it is necessary to change the parameter settings to adjust the passenger organization and deployment mode, and then derive the optimal passenger evacuation and passenger organization deployment plan for the platform passenger detention situation under different scenarios. This method can be combined with the actual operation scenario of the station to provide a quantifiable decision-making basis for dispatching personnel to organize and deploy passenger transportation.

[0030] System Architecture :

[0031] Based on the above description, the present disclosure proposes a method and device for adjusting the capacity of urban rail transit, which can be applied to Figure 1 In the system architecture of the exemplary application environment shown.

[0032] Figure 1 This is an architecture diagram of an urban rail transit capacity adjustment system in one of the application scenarios provided in the embodiment of the present application, refer to Figure 1 As shown, in this scenario, the system architecture 100 may include a terminal device 101 , a server 102 , and a network 103 .

[0033] Among them, the terminal device 101 can be, for example, a mobile phone, a tablet computer (PAD), a laptop computer, a desktop computer, a smart TV, a smart vehicle-mounted device, a smart wearable device, a smart TV, and an aircraft, and any other device involved in the capacity adjustment of urban rail transit. The terminal device 101 can be installed with a target application, and the target application can have the function of presenting a capacity adjustment plan. The application involved in the embodiment of the present application can be a software client, or a client such as a web page or a small program. The server 102 is a server corresponding to the software or a web page, a small program, etc., and does not limit the specific type of the client.

[0034] In the embodiment of the present application, the terminal device 101 and the server 102 can be directly or indirectly connected to each other through one or more networks 103. The network 103 can be a wired network or a wireless network, for example, the wireless network can be a mobile cellular network, or a wireless fidelity (Wireless-Fidelity, WIFI) network, and of course, it can also be other possible networks, which are not limited in the embodiment of the present application.

[0035] The server 102 may be a background server of the target application, and is used to provide corresponding background services for the target application, such as a rail transit capacity adjustment plan generation service.

[0036] It should be understood that Figure 1 The number of terminal devices, networks and servers in the example is only illustrative. Any number of terminal devices, networks and servers may be provided as required. For example, server 106 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0037] The urban rail transit capacity adjustment method provided in the embodiment of the present disclosure can be executed on the server 102, and accordingly, the urban rail transit capacity adjustment device is generally arranged in the server 102. The urban rail transit capacity adjustment method provided in the embodiment of the present disclosure can also be executed in the terminal device, and accordingly, the urban rail transit capacity adjustment device can also be arranged in the terminal device. The urban rail transit capacity adjustment method provided in the embodiment of the present disclosure can also be partially executed in the server 102 and partially executed in the terminal device, and accordingly, some modules of the urban rail transit capacity adjustment device can be arranged in the server 102, and some modules can be arranged in the terminal device.

[0038] Among them, the server 102 and the terminal device 101 may include one or more processors, memories, and interactive I / O interfaces, etc. In addition, the server 102 may also be configured with a database, which may be used to store the model parameters obtained by training, etc. Among them, the memories of the server 102 and the terminal device 101 may also store program instructions required to be executed in the urban rail transit capacity adjustment method provided in the embodiment of the present application, and these program instructions, when executed by the processor, can be used to implement the generation process of the urban rail transit capacity adjustment plan provided in the embodiment of the present application.

[0039] It should be noted that when the urban rail transit capacity adjustment method provided in the embodiment of the present application is executed by the server 102 or the terminal device 101 alone, the above-mentioned application scenario may also only include a single device of the server 102 or the terminal device 101, or the server 102 and the terminal device 101 may also be considered to be the same device. Of course, in actual application, when the urban rail transit capacity adjustment method provided in the embodiment of the present application is jointly executed by the server 102 and the terminal device 101, the server 102 and the terminal device 101 may also be the same device, that is, the server 102 and the terminal device 101 may be different functional modules of the same device, or virtual devices virtualized by the same physical device.

[0040] For example, in an exemplary embodiment, the operation management personnel triggers the simulation start instruction through the terminal device 101, and the server 102 determines the passenger retention factor corresponding to the target platform and determines the linear relationship between each passenger retention factor; the passenger retention factor corresponding to the target platform and the linear relationship between each passenger retention factor are input into the pre-constructed station scene simulation model to obtain the station passenger flow simulation scene under the current capacity configuration mode, and the station passenger flow simulation scene at least includes the real-time motion state of each individual passenger corresponding to the virtual role model; based on the real-time motion state of each individual passenger corresponding to the virtual role model in the station passenger flow simulation scene, the passenger retention parameter of the target platform under the current capacity configuration mode is calculated; if the passenger retention parameter of the target platform is greater than the preset passenger retention threshold, the current capacity configuration mode is adjusted to obtain the target capacity configuration mode; wherein the passenger retention parameter of the target platform corresponding to the target capacity configuration mode is not greater than the preset passenger retention threshold. Finally, the server sends the target capacity configuration mode to the terminal device 101 for display.

[0041] However, it is easy for those skilled in the art to understand that the above application scenarios are only used as examples and are not limited to this in the present exemplary embodiment.

[0042] Below, in combination with the application scenarios described above, the method provided by the exemplary implementation of the present application is described with reference to the accompanying drawings. It should be noted that the above application scenarios are only shown to facilitate understanding of the spirit and principle of the present application, and the implementation of the present application is not limited in this respect. It should be noted that the following method can be executed by the above terminal device or server, or by the terminal device and the server together. Here, it is specifically shown by taking the execution of the terminal device or the server as an example.

[0043] Figure 2 This is a flowchart of a method for adjusting urban rail transit capacity provided in one embodiment of the present application, see Figure 2As shown in FIG. 1 , a schematic diagram of the urban rail transit capacity adjustment process provided by an embodiment of the present application is provided. The method can be executed by a computer device, which can be Figure 1 The terminal device or server shown in the figure, the specific implementation process of the method includes the following steps 201 to 204:

[0044] Step 201: Determine the passenger retention factor corresponding to the target platform, and determine the linear relationship between the passenger retention factors.

[0045] Step 202: Input the passenger retention factor corresponding to the target platform and the linear relationship between the passenger retention factors into a pre-built station scene simulation model to obtain a station passenger flow simulation scene under the current capacity configuration mode. The station passenger flow simulation scene at least includes the real-time movement status of the virtual role model corresponding to each individual passenger.

[0046] Step 203: Calculate the passenger retention parameters of the target platform under the current capacity configuration mode based on the real-time motion status of the virtual role model corresponding to each individual passenger in the station passenger flow simulation scene.

[0047] Step 204: If the passenger retention parameter of the target platform is greater than the preset passenger retention threshold, the current capacity configuration mode is adjusted to obtain the target capacity configuration mode; wherein the target capacity configuration mode corresponds to the passenger retention parameter of the target platform being no greater than the preset passenger retention threshold.

[0048] In the technical solutions provided by some embodiments of the present disclosure, on the one hand, the method analyzes the factors that affect the retention of passengers at the target platform and the linear relationship between the factors, and uses them as the parameters of the model input to construct the real-time motion state of the virtual role model corresponding to each passenger individual, so as to intuitively simulate the real passenger flow situation during the station operation process, and then calculate the number and number of passengers stranded in the current operation simulation scenario, so as to judge whether the current passenger organization configuration has reached the standard of capacity and volume matching, and then adjust the current capacity configuration mode in a timely manner from the overall perspective, and overcome the technical problem that the decision-making separation between station personnel and trains in the existing technical solutions makes it difficult to accurately determine the configuration mode. On the other hand, if the number of passenger strandings is too many, it is necessary to change the parameter settings to adjust the passenger organization and deployment mode, and then obtain the optimal deployment plan for passenger flow evacuation passenger organization under different scenarios. This method can be combined with the actual operation scenario of the station to provide a quantifiable decision basis for dispatching personnel to carry out passenger organization and deployment, and avoid the technical problems of long time and low accuracy caused by manual decision-making.

[0049] The following will be combined with specific embodiments to Figure 2 The specific implementation methods of each step in the embodiment shown are described in detail:

[0050] In step 201, the passenger retention factor corresponding to the target platform is determined, and the linear relationship between the passenger retention factors is determined.

[0051] The target platform is any platform within the operating range of urban rail transit, for example, it may be the current platform where the train arrives; the passenger retention factor refers to the factors that affect the retention of passengers at the target platform.

[0052] Regarding the step of determining the passenger retention factor corresponding to the target platform in step 201, the process of determining the passenger retention factor corresponding to the target platform will be described below in conjunction with a specific embodiment:

[0053] In an optional embodiment of the present disclosure, the passenger retention factor corresponding to the target platform includes one or more of a first influencing factor within the target platform and a second influencing factor for the train.

[0054] The first influencing factor includes one or more of the number of waiting passengers when the train arrives at the target platform and the number of arriving passengers during the train's stop at the target platform.

[0055] Among them, the second influencing factor includes one or more of the rated passenger capacity of the train, the actual passenger capacity when the train stops at the target platform, the number of passengers getting off the train, the train stop time, whether the train skips a stop at the target platform, and whether the train clears passengers at the target platform.

[0056] For example, the reasons that affect passengers' stay at the target platform mainly depend on: the number of waiting passengers on the target platform in the direction of the train's travel when the train arrives at the station, the number of arriving passengers who arrive at the platform during the train's stop at the platform, and the train's own attributes. The above influencing factors can be summarized into two categories: the first influencing factor within the station and the second influencing factor for the train.

[0057] A. For the first impact factor in the above target station:

[0058] Exemplarily, the first influencing factor mainly includes the number of waiting passengers on the target platform when the train arrives at the target platform, and the number of passengers arriving at the target platform while the train stops at the target platform.

[0059] In an optional embodiment of the present disclosure, the number of waiting passengers on the target platform includes: the initial number of passengers arriving at the target platform at the start of the simulation, and one or more of the target number of passengers arriving at the target platform during the train running interval.

[0060] The simulation start time refers to the start time of the station simulation model operation, and the initial number of passengers refers to the number of existing passengers on the target platform at the start time of the station simulation model operation. The initial number of passengers can be a preset fixed value or a non-preset fixed value, and the non-preset fixed value can be, for example, a variable value.

[0061] Among them, the initial number of passengers and / or the target number of passengers is determined by the movement time of each passenger in the station and the number of passengers whose destination is the target platform, which is included in the station.

[0062] Exemplarily, the initial number of passengers at the target platform and the target number of passengers arriving at the target platform during the train travel interval are affected by one or more of the walking efficiency of passengers in the station and the number of passengers whose destination is the target platform during the time period.

[0063] The following will be combined with specific embodiments to illustrate the above-mentioned method for determining the movement time of each passenger in the station and the method for determining the number of passengers whose destination is the target platform.

[0064] In an optional embodiment of the present disclosure, the movement time of each passenger in the target platform is determined based on at least one of the following methods:

[0065] 1) The time it takes for each passenger to pass through each fixed equipment in the station;

[0066] 2) The time it takes for each passenger to pass through obstacles in the station, which is determined based on the placement of obstacles;

[0067] 3) The time it takes for each passenger to pass through the toll gate equipment;

[0068] 4) The travel time of each passenger in case of a preset emergency in the station;

[0069] 5) The passage time of each passenger at the target platform when the platform screen door closes;

[0070] 6) The travel time of each passenger during his / her movement to the target platform.

[0071] Regarding the above 1) the time it takes for each passenger to pass through each fixed device in the station: the factors affecting the passenger's movement time or walking efficiency in the target platform may include the time it takes for each passenger to pass through each fixed device in the station. The fixed devices in the station may be, for example, stairs, escalators, security inspection machines, and other equipment.

[0072] Regarding the above 2) the time it takes for each passenger to pass through obstacles in the station: the time it takes for each passenger to pass through the station is determined based on the placement of the obstacles.

[0073] The obstacles are guide obstacles set by station staff to reduce the number of passengers on the platform within a certain period of time. It should be understood that the longer the length of the obstacle is and the more turns it has, the longer the passenger's passage time will be.

[0074] Regarding the above 3) the time it takes for each passenger to pass through the toll gate equipment: whether the toll gate equipment can be used normally, the use direction of the toll gate equipment (such as the opening and closing direction of the gate) will affect the passengers' passage choices, and thus affect the time it takes for each passenger to pass through the toll gate equipment.

[0075] Regarding the above 4) the travel time of each passenger when a preset emergency occurs in the station: In the event of an emergency, the station's solution to the preset emergency will affect the passenger's walking efficiency, and thus affect the movement time of each passenger in the target platform. For example, when a small-scale fire occurs in the station, the fire area will be isolated, causing passengers to take a detour, which increases the movement time of passengers in the target platform.

[0076] Regarding the above 5) the time it takes for each passenger to close the platform screen door at the target platform: the opening and closing efficiency of the platform screen door will affect the speed at which passengers get on and off the train, and thus affect the number of passengers getting on the train. Accordingly, the fewer the number of passengers getting on the train, the more passengers will be stranded at the target platform.

[0077] Regarding the above 6) each passenger's own travel time during the movement to the target platform: Since the objective and subjective factors of the passengers will affect the passengers' own travel time during the movement to the target platform, the following will explain the own travel time in combination with various factors.

[0078] Among them, for subjective factors, passengers have a herd mentality: the degree to which a single passenger relies on the walking route chosen by the majority of passengers. For example, when an elevator is congested, some passengers may choose to take the stairs, while others may continue to wait for the elevator.

[0079] Each passenger has a corresponding walking habit: passengers make choices based on their own walking habits according to the internal layout of the station. For example, when a passenger passes through a toll gate, will he or she give priority to the toll gate closest to the security check machine? Or the passenger's boarding speed, for example, when the train door within the passenger's sight opens, the passenger can choose whether to speed up to enter the train or wait for the next train.

[0080] From the perspective of objective factors, for example, different walking speeds caused by the passengers themselves, which in turn lead to different exercise times. Among them, the passenger's own factors can be the passenger's gender, size, age, whether or not they carry luggage, etc. In terms of distance, the more luggage a passenger carries, the slower the walking speed will be.

[0081] In an optional embodiment of the present disclosure, the number of passengers whose destination is the target platform is determined based on at least one of the following methods:

[0082] 1) Based on the station being a transfer station, determine the number of passengers arriving at the target platform.

[0083] In this implementation, when the station is a transfer station, passengers may get off other lines at the station during the time period and reach the target platform through the transfer passage, thereby determining the number of passengers arriving at the target platform.

[0084] 2) Based on the station flow limit, determine the number of passengers arriving at the target platform.

[0085] In this embodiment, it is necessary to determine the flow limit status of the station, and then determine the number of passengers arriving at the target platform. That is, the flow limit of the station is usually divided into three conventional flow limit modes: flow limit outside the station, flow limit outside the paid area, and flow limit inside the paid area. Each of the above flow limit modes will affect the number of people limited to flow. For example, when a preset number of people pass through in this time period, passengers are prohibited from passing in the next time period.

[0086] 3) Determine the number of passengers arriving at the target platform based on the open or closed status of the station entrance and exit.

[0087] In this embodiment, the number of passengers arriving at the target platform can also be determined based on the open or closed state of the station entrance and exit. That is, when a certain entrance of the station is closed, it will affect the number of passengers entering the station at the entrance, and further affect the number of passengers arriving at the target platform.

[0088] 4) Based on the passenger flow entering the station during the simulation time period, determine the number of passengers arriving at the target platform.

[0089] The simulation time period is the time period between the start time of the simulation and the end time of the simulation.

[0090] In this embodiment, the number of passengers arriving at the target platform can also be determined based on the passenger flow during the simulation period. That is, during the simulation period, there will be continuous passenger flow entering the station through various entrances and exits and then arriving at the destination (i.e., the target platform).

[0091] It should be noted that the number of passengers arriving at the target platform may be the number of passengers determined by the above single or multiple methods.

[0092] In the above embodiment, by comprehensively counting the first influencing factors that affect the passengers' retention at the target platform, the calculation accuracy of the passenger retention factor can be improved, thereby improving the accuracy of the model analysis to obtain the optimal capacity allocation plan.

[0093] B. For the second influencing factor for trains mentioned above:

[0094] In an optional embodiment of the present disclosure, the second influencing factor includes one or more of the rated passenger capacity of the train, the actual passenger capacity when the train stops at the target platform, the number of passengers getting off the train, the train stop time, whether the train skips a stop at the target platform, and whether the train clears passengers at the target platform.

[0095] Among them, the second influencing factor is the impact of the train itself on passenger detention.

[0096] Exemplarily, the second influencing factor includes one or more of the rated passenger capacity of the train, the actual passenger capacity when the train stops at the target platform, the number of passengers getting off the train, the train stop time, whether the train skips a stop at the target platform, and whether the train clears passengers at the target platform.

[0097] The rated passenger capacity of the above train indicates the upper limit of the number of passengers that the train can carry. It is understandable that the size of the train carriages determines the upper limit of the number of passengers that the train can carry. The smaller the train carriages and the fewer the carriages, the smaller the rated passenger capacity.

[0098] The actual number of passengers on board and the number of passengers getting off the train when the train stops at the target platform determine the maximum number of passengers that can get on the train during the train stop. That is, the more passengers on board and the fewer passengers getting off the train, the smaller the maximum number of passengers that can enter the train. In addition, the length of the train stop time affects the number of passengers getting on and off under the same conditions. That is, the longer the stop time, the more people can get on the train without exceeding the train's rated passenger capacity.

[0099] Whether the train skips at the target platform indicates that when the train skips at the target platform, that is, the train passes directly without stopping, no passengers will get on or off; conversely, if the train does not skip at the target platform, passengers can get on and off normally without exceeding the train's rated passenger capacity. Whether the train clears passengers at the target platform indicates that when the train clears passengers, passengers will only get off after the train arrives at the target platform; conversely, if the train does not clear passengers at the target platform, passengers can get on and off normally without exceeding the train's rated passenger capacity.

[0100] Continuing to refer to step 201, for the step of determining the linear relationship between the passenger retention factors in step 201, the factors affecting passenger retention are usually multidimensional. Figure 3 The embodiment shown in the figure exemplifies the steps of determining the linear relationship between the passenger retention factors. Figure 3 As shown, the method comprises steps 301 to 304:

[0101] Step 301: Obtain the distribution ratio of each passenger retention factor, and obtain the initial number of passengers arriving at the target platform at the start time of the simulation.

[0102] Step 302: Determine whether the initial number of passengers is a preset fixed value.

[0103] If the initial number of passengers is a preset fixed value, step 303 is executed to determine the linear relationship between the passenger retention factors based on formula (1):

[0104]

[0105] In the linear equation system shown in formula (1), A 11 To A mn is the distribution ratio of each passenger retention factor, x 1 to x n is the retention factor of each passenger, Y 1 To Y m is the number of passengers stranded corresponding to each passenger stranding factor, k is the initial number of passengers, and k is a preset fixed value;

[0106] On the contrary, if the initial number of passengers is not a preset fixed value, step 304 is executed to determine the linear relationship between the passenger retention factors based on formula (2):

[0107]

[0108] In the linear equation system shown in formula (2), A 11 To A mn is the distribution ratio between each passenger retention factor, x 1 to x n is the retention factor of each passenger, Y 1 To Y m is the number of passengers stranded corresponding to each passenger stranding factor, k 1 to k m is the initial number of passengers corresponding to each passenger retention factor, and k 1 to k m The above non-preset fixed values ​​refer to values ​​that change, rather than fixed values.

[0109] The above embodiment sets multiple passenger retention factors and the linear relationship formed between each passenger retention factor, which can simulate the passenger retention situation under different station scenarios, improve the adaptability range of the method provided by the embodiment of the present disclosure, and thus improve the reliability.

[0110] In step 202, the passenger retention factor corresponding to the target platform and the linear relationship between the passenger retention factors are input into a pre-constructed station scene simulation model to obtain a station passenger flow simulation scene under the current capacity configuration mode. The station passenger flow simulation scene at least includes the real-time movement status of the virtual character model corresponding to each individual passenger.

[0111] Among them, the station scene simulation model can be used to obtain the station passenger flow simulation scene under the current capacity configuration mode. The station passenger flow simulation scene not only includes the station passenger flow and the passenger retention status of the target platform under the current capacity configuration mode, but also includes the real-time movement status of each passenger's corresponding virtual role model.

[0112] Furthermore, before inputting the passenger retention factor corresponding to the target platform and the linear relationship between the passenger retention factors into the pre-built station scene simulation model, it is also necessary to pre-build the station scene simulation model. Figure 4 The process of constructing a station scene simulation model is exemplified. The construction of the station scene simulation model can be achieved by superimposing multiple models such as agent modeling, physical model, human-computer interaction modeling, and time modeling. It is understandable that the construction of the station scene simulation model can also be achieved based on a single or partial modeling method, and the embodiments of the present disclosure do not impose any special restrictions on this.

[0113] Figure 4 A flow chart of a method for constructing a station scene simulation model provided in one embodiment of the present application; Figure 4 As shown, the method comprises steps 401 to 404:

[0114] 1)Agent modeling:

[0115] Step 401: Generate a virtual character model for each passenger in the station, and perform action proxy on the virtual character model based on the passenger retention factor corresponding to the target platform to obtain a first action proxy for the virtual character model.

[0116] For example, for each passenger in the station, each Agent in the established model corresponds to a passenger in the station, that is, for each passenger generated in the station, a virtual character model will be generated through an Agent for action proxy. That is, the first action proxy for the virtual character model includes a series of factors such as the purpose of movement in the station (such as passengers entering the station to take a train, getting off the train, getting off the train to transfer), the walking mode in the station (such as the walking speed / travel time of passengers), and the actions in the station (such as entering the station through equipment, waiting, selecting different sections, getting on and off the train).

[0117] 2) Physical modeling:

[0118] Step 402: Generate a physical device model for the device set in the station, and perform action proxy on each physical device model in the device set to obtain a second action proxy for the physical device model.

[0119] Among them, the equipment set in the station includes the fixed equipment shown in the above embodiments, such as stairs, escalators, security inspection machines, etc., and also includes dynamic equipment, such as toll gates, switch doors, elevator doors, etc.

[0120] In this embodiment, various equipment entities existing in the station are simulated by physical modeling, and a second action agent is added to the corresponding dynamic equipment. For example, action simulation is performed for elevator equipment (such as start and stop, availability, one-way or two-way passage, door opening and closing), etc.

[0121] 3) Human-computer interaction modeling:

[0122] Step 403: Based on the second action agent, the first action agent of the virtual character model is empowered with thinking to obtain a third action agent of the virtual character model.

[0123] Exemplarily, the physical equipment in the station will affect the passenger's travel route and time in the station. Then, the first action agent of the passenger in the simulation scene can be empowered by human-computer interaction modeling in this embodiment. That is, data is allocated to the virtual role model. This is because in the process of building the model, the Agent has no thinking except for its inherent attributes when it is created. It is necessary to allocate data to it, so as to empower the Agent so that it has certain thinking ability and the possibility of taking actions in the station. Secondly, by allocating the actions of the physical model, the decision made by the Agent (i.e., the first action agent of the virtual role model) when passing through the virtual device is affected, thereby realizing scenario simulation with different results in different situations.

[0124] 4) Temporal Modeling:

[0125] Step 404: construct a station scene simulation model based on the first action agent, the second action agent, and the third action agent in continuous time.

[0126] For example, the significance of the station scene simulation model is that the simulation parameters are switched through different operation instructions within a period of time, so as to obtain various simulation results to provide auxiliary decision-making for the operation and production of urban rail transit. Therefore, the discrete event model does not meet the conditions for use, that is, the collection within a certain period will not obtain guiding results. Therefore, the continuous time method is used for simulation, for example, the frame rate of each frame is 0.2s, that is, five simulations within 1 second.

[0127] In an optional embodiment of the present disclosure, the station scene simulation model constructed above can be a four-layer architecture model based on the Model-View-ViewModel (MVVM) framework, and the four-layer architecture includes a presentation layer, an object layer, a business layer, and a data layer.

[0128] Among them, Model-View-View-Model (MVVM) helps to clearly separate the business and presentation logic of the application from the user interface. Maintaining a clear separation between application logic and UI helps solve many development problems and makes the application easier to test, maintain, and evolve.

[0129] It is understandable that the station scene simulation model can also be implemented using other frameworks, and the embodiments of the present disclosure do not impose any special limitations on this.

[0130] In this embodiment, by constructing a simulation model by superimposing multiple models such as agent modeling, physical model, human-computer interaction modeling, and time modeling, a station scene simulation model that is infinitely close to the actual scene can be obtained, which is convenient for subsequent accurate simulation of the dynamic flow of individual passengers based on the station scene simulation model, and simulation of the station operation passenger flow, thereby improving accuracy.

[0131] Furthermore, after the station scene simulation model is constructed, the passenger retention factor corresponding to the target platform and the linear relationship between the passenger retention factors can be input into the pre-constructed station scene simulation model, that is, the data import is completed, and the model parameters are configured to calculate the platform passenger retention situation under different parameter scenarios.

[0132] In an optional embodiment of the present disclosure, in response to a configuration operation on a target parameter, the target parameter value corresponding to the configuration operation is used as a model parameter of a station scene simulation model, so that the station scene simulation model generates a station passenger flow simulation scene under the current capacity configuration mode according to the target parameter value.

[0133] The target parameters include one or more of train form parameters, passenger parameters, passenger flow parameters, equipment parameters in the station, and capacity configuration mode parameters. Multiple sub-parameters can also be set under each of the above target parameters to simulate different station operation scenarios based on different parameters.

[0134] In this embodiment, the target parameters can be opened to the operation management personnel so that they can flexibly configure them based on the open target parameters to simulate various complex station operation scenarios, thereby exploring the optimal solution of the matrix equation group shown in the above formula (1) and formula (2), and providing comprehensive auxiliary decision support for dispatching personnel.

[0135] In this regard, based on the station scene simulation model constructed above, the various equipment in the station can be physically simulated, and the real real-time dynamic flow of individual passengers can be simulated based on the various equipment in the station, thereby achieving the effect of simulating the passenger flow situation of station operations.

[0136] In step 203, based on the real-time motion status of the virtual role model corresponding to each individual passenger in the station passenger flow simulation scene, the passenger retention parameters of the target platform under the current capacity configuration mode are calculated.

[0137] The passenger detention parameter includes a combination of one or more of the number of stranded passengers and the number of stranded passengers. Based on the real-time movement status of each individual passenger, it is possible to obtain in real time whether the individual passenger can board the train.

[0138] For example, based on the real-time movement status of the virtual character model corresponding to each individual passenger in the station passenger flow simulation scene, parameters such as the number of stranded passengers and the number of stranded passengers at the target platform under the current capacity configuration mode can be calculated.

[0139] It should be noted that when calculating the passenger detention parameters, the following conditions must be met to comply with the operational logic in the actual scenario:

[0140] 1) If n trains pass through the same platform, and the same passenger is detained after all n trains depart, the detention situation must be continuous rather than discrete. That is, the passenger may be detained after the departure of the first train, the second train, the third train... the n-3rd departure, and the passenger gets on the train without being detained after the n-2th arrival; or the passenger is detained after the departure of the second train, the third train, the fourth train... the n-2th departure, and the passenger gets on the train without being detained after the n-1st arrival, etc. There will be no situation where the passenger is detained after the departure of the first train, the second train, the third train, the fourth train... the n-2th departure, and the passenger gets on the train without being detained after the n-1st arrival.

[0141] 2) Once a passenger gets on a train at the same platform, he or she will not appear again during the train's route cycle.

[0142] 3) Each passenger has a unique ID.

[0143] When executing step 203, in an optional embodiment of the present disclosure, when the passenger detention parameter includes at least the number of passenger detention times and the number of stranded passengers, step 203 can determine the number of train departures at the target platform; for any target number of train departures N among the train departure times, based on the real-time motion status of the virtual character model corresponding to each individual passenger in the station passenger flow simulation scene, calculate the set of the number of stranded passengers stranded on the target platform corresponding to each passenger detention number after the train departs for the Nth time, and based on the number of stranded passengers in the set of stranded passengers corresponding to each passenger detention number, obtain the passenger detention parameter of the target platform.

[0144] Among them, each passenger included in the stranded passenger number set corresponds to unique identification information;

[0145] For example, the remaining passengers (i.e., stranded passengers) on the platform at each departure are identified as a set, labeled P. After the first train departs, the stranded passengers on the platform are P(1), after the second train departs, the stranded passengers on the platform are P(2), and so on. After the Nth train departs, the stranded passengers on the platform are P(n). Each passenger has a unique identifier in the set. Using the reverse method, reverse statistics are performed after each train departs. The number of stranded passengers generated after the Nth train departs is first counted. The calculation process is as follows:

[0146] After the Nth train departs:

[0147] The number of passengers stranded for the first time is: P(n)-P(n)∩P(n-1)

[0148] The number of passengers stranded for the second time is: P(n)∩P(n-1)

[0149] The set of passengers that generate the third stranded number of passengers is: P(n)∩P(n-1)∩P(n-2)

[0150] …

[0151] The set of the number of stranded passengers generated by the passengers for the Nth time is: P(n)∩P(n-1)∩P(n-2)∩……∩P(2)∩P(1)

[0152] Correspondingly, as follows:

[0153] After the departure of train No. N-1:

[0154] The number of passengers stranded for the first time is: P(n-1)-P(n-1)∩P(n-2)

[0155] The number of passengers stranded for the second time is: P(n-1)∩P(n-2)

[0156] The set of passengers that generate the third stranded number of passengers is: P(n-1)∩P(n-2)∩P(n-3)

[0157] …

[0158] The set of passengers that have their N-1th stay is: P(n-1)∩P(n-2)∩P(n-3)∩……∩P(2)∩P(1)

[0159] Based on the above embodiment, by counting the number of identifiers in the passenger detention number set corresponding to each passenger detention number after each train departure, the number of stranded passengers can be obtained, thereby obtaining the passenger detention parameters of the target platform.

[0160] In step 204, if the passenger retention parameter of the target platform is greater than the preset passenger retention threshold, the current capacity configuration mode is adjusted to obtain the target capacity configuration mode; wherein the target capacity configuration mode corresponds to the passenger retention parameter of the target platform not greater than the preset passenger retention threshold.

[0161] After calculating the number of passenger detentions and the number of stranded passengers at the target platform under the current capacity configuration mode in step 203, the parameters are compared with the preset passenger detention threshold to determine whether the current capacity configuration mode meets the capacity and volume requirements of the current urban rail transit. If so, there is no need to adjust the current capacity configuration mode and it can continue to be used; otherwise, if not, the current capacity configuration mode needs to be adjusted so that the adjusted target capacity configuration mode meets the capacity and volume requirements of the current urban rail transit, thereby reducing the situation where passengers are stranded multiple times and the number of stranded passengers is large.

[0162] For example, when adjusting the current capacity configuration mode, the passenger transportation organization and deployment plan can be optimized by adjusting the input parameters, and the simulation results can be observed to see whether the passenger detention phenomenon is solved. Through the above embodiment, the optimal deployment plan for passenger evacuation organization under the condition of multiple detention of platform passengers in different operation scenarios can be obtained through multiple model simulations. Combined with the actual operation scenario of the station, a quantifiable decision-making basis is provided for dispatchers to organize and deploy passenger transportation.

[0163] In order to facilitate observation by operators, the train departure time can be recorded to form a time domain, and this can be used as the horizontal axis (i.e., the X-coordinate), and the number of stranded people or the number of stranded times can be used as the vertical axis (i.e., the Y-coordinate) to draw a curve to dynamically display the number of stranded times or the number of stranded passengers going up / down at the station.

[0164] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0165] In order to implement the above urban rail transit capacity adjustment method, please refer to Figure 5 An embodiment of the present application provides an urban rail transit capacity adjustment device, and the urban rail transit capacity adjustment device 500 may include: a determination module 501, a data input module 502, a parameter calculation module 503 and a mode adjustment module 504.

[0166] The determination module 501 is configured to determine the passenger retention factor corresponding to the target platform and determine the linear relationship between the passenger retention factors;

[0167] The data input module 502 is configured to input the passenger retention factor corresponding to the target platform and the linear relationship between the passenger retention factors into the pre-built station scene simulation model to obtain the station passenger flow simulation scene under the current capacity configuration mode, and the station passenger flow simulation scene at least includes the real-time movement status of the virtual role model corresponding to each individual passenger;

[0168] The parameter calculation module 503 is configured to calculate the passenger retention parameters of the target platform under the current capacity configuration mode based on the real-time motion state of each passenger corresponding to the virtual role model in the station passenger flow simulation scene;

[0169] The mode adjustment module 504 is configured to execute if the passenger retention parameter of the target platform is greater than the preset passenger retention threshold, then adjust the current capacity configuration mode to obtain the target capacity configuration mode; wherein the target capacity configuration mode corresponds to the passenger retention parameter of the target platform not greater than the preset passenger retention threshold.

[0170] In an optional embodiment of the present disclosure, the passenger retention factor corresponding to the target platform includes one or more of a first influencing factor within the target platform and a second influencing factor for the train; wherein the first influencing factor includes one or more of the number of waiting passengers when the train arrives at the target platform, and the number of arriving passengers during the train's stop at the target platform; the second influencing factor includes one or more of the rated passenger capacity of the train, the actual number of passengers when the train stops at the target platform, the number of people getting off, the train's stop time, whether the train skips a stop at the target platform, and whether the train clears passengers at the target platform.

[0171] In an optional embodiment of the present disclosure, the number of waiting passengers includes the initial number of passengers arriving at the target platform at the start of the simulation, and one or more of the target number of passengers arriving at the target platform during the train travel interval; the initial number of passengers and / or the target number of passengers are determined by the movement time of each passenger in the station, and the number of passengers whose destination is the target platform, and the station includes the target platform.

[0172] In an optional embodiment of the present disclosure, the determination module is used to determine the movement time of each passenger in the target platform based on at least one of the following methods:

[0173] The time it takes for each passenger to pass through each fixed equipment in the station;

[0174] The time it takes for each passenger to pass through obstacles in the station is determined based on the placement of obstacles;

[0175] The time it takes for each passenger to pass through the toll gate equipment;

[0176] The travel time of each passenger in case of a preset emergency in the station;

[0177] The passage time of each passenger at the target platform when the platform screen door closes;

[0178] The travel time of each passenger during his / her movement at the target platform.

[0179] In an optional embodiment of the present disclosure, the determination module is used to determine the number of passengers whose destination is the target platform based on at least one of the following methods:

[0180] Based on the station being a transfer station, determine the number of passengers arriving at the target platform;

[0181] Determine the number of passengers arriving at the target platform based on the station flow limit;

[0182] Determine the number of passengers arriving at the target platform based on the open or closed status of the station entrances and exits;

[0183] Based on the passenger flow entering the station during the simulation time period, the number of passengers arriving at the target platform is determined.

[0184] In an optional embodiment of the present disclosure, the determination module is used to obtain the distribution ratio of each passenger retention factor and to obtain the initial number of passengers arriving at the target platform at the start time of the simulation;

[0185] If the initial number of passengers is a preset fixed value, the linear relationship between the passenger retention factors is determined based on the following formula (1):

[0186]

[0187] Among them, A 11 To A mn is the distribution ratio of each passenger retention factor, x 1 to x n is the retention factor of each passenger, Y 1 To Y m is the number of passengers stranded corresponding to each passenger stranding factor, k is the initial number of passengers, and k is a preset fixed value;

[0188] Alternatively, if the initial number of passengers is not a preset fixed value, the linear relationship between the passenger retention factors is determined based on the following formula (2):

[0189]

[0190] Among them, A 11 To A mn is the distribution ratio between each passenger retention factor, x 1 to x n is the retention factor of each passenger, Y 1 To Y m is the number of passengers stranded corresponding to each passenger stranding factor, k 1 to k m is the initial number of passengers corresponding to each passenger retention factor, and k 1 to k m Different or partially identical.

[0191] In an optional embodiment of the present disclosure, the device may also include a model building module, which is used to generate a virtual character model for each passenger in the station, and perform action proxy on the virtual character model based on the passenger retention factor corresponding to the target platform to obtain a first action agent for the virtual character model; generate a physical device model for the equipment set in the station, and perform action proxy on each physical device model in the equipment set to obtain a second action agent for the physical device model; empower the first action agent of the virtual character model with thinking based on the second action agent to obtain a third action agent of the virtual character model; and build a station scene simulation model based on the first action agent, the second action agent, and the third action agent in continuous time.

[0192] In an optional embodiment of the present disclosure, the station scene simulation model is a four-layer architecture model based on the Model-View-ViewModel (MVVM) framework, and the four-layer architecture includes a presentation layer, an object layer, a business layer, and a data layer.

[0193] In an optional embodiment of the present disclosure, the device may also include a parameter configuration module, which is used to, in response to a configuration operation on a target parameter, use the target parameter value corresponding to the configuration operation as a model parameter of a station scene simulation model, so that the station scene simulation model generates a station passenger flow simulation scene under a current capacity configuration mode according to the target parameter value; wherein the target parameters include one or more of train form parameters, passenger parameters, passenger flow parameters, equipment parameters in the station, and capacity configuration mode parameters.

[0194] In an optional embodiment of the present disclosure, the passenger detention parameter includes at least the number of passenger detention times and the number of stranded passengers; the parameter calculation module 503 is used to determine the number of train departures at the target platform; for any target number of train departures N among the train departure times, based on the real-time motion status of the virtual character model corresponding to each individual passenger in the station passenger flow simulation scene, the number of stranded passengers set corresponding to each passenger detention number on the target platform after the train departs for the Nth time is calculated, and each individual passenger included in the passenger stranded number set corresponds to unique identification information; based on the number of stranded passengers in the passenger stranded number set corresponding to each passenger detention number, the passenger detention parameter of the target platform is obtained.

[0195] For the specific limitations of the above-mentioned urban rail transit capacity adjustment device, please refer to the limitations of the urban rail transit capacity adjustment method above, which will not be repeated here. Each module in the above-mentioned urban rail transit capacity adjustment device can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0196] In one embodiment, a computer device is provided, the internal structure diagram of which can be as follows: Figure 6 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for adjusting the capacity of urban rail transit as described above is implemented. It includes: a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, any step in the method for adjusting the capacity of urban rail transit as described above is implemented.

[0197] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any step in the above-mentioned urban rail transit capacity adjustment method can be implemented.

[0198] 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 adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0199] 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 embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 generate 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.

[0200] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0201] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0202] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0203] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and 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 also intended to include these modifications and variations.

Claims

1. A method for adjusting the capacity of urban rail transit, characterized in that: include: Determining a passenger retention factor corresponding to a target platform, and determining a linear relationship between the passenger retention factors; Inputting the passenger retention factor corresponding to the target platform and the linear relationship between the passenger retention factors into a pre-built station scene simulation model to obtain a station passenger flow simulation scene under the current capacity configuration mode, wherein the station passenger flow simulation scene at least includes the real-time motion status of the virtual role model corresponding to each individual passenger; Calculate the passenger retention parameter of the target platform under the current capacity configuration mode based on the real-time motion state of the virtual role model corresponding to each individual passenger in the station passenger flow simulation scene; If the passenger detention parameter of the target platform is greater than the preset passenger detention threshold, the current capacity configuration mode is adjusted to obtain the target capacity configuration mode; wherein the passenger detention parameter of the target platform corresponding to the target capacity configuration mode is not greater than the preset passenger detention threshold.

2. The method according to claim 1, characterized in that The passenger retention factor corresponding to the target platform includes one or more of a first influencing factor in the target platform and a second influencing factor for the train; Among them, the first influencing factor includes one or more of the number of waiting passengers when the train arrives at the target platform, and the number of passengers arriving during the train's stop at the target platform; the second influencing factor includes one or more of the rated passenger capacity of the train, the actual number of passengers on board when the train stops at the target platform, the number of people getting off, the train's stop time, whether the train skips a stop at the target platform, and whether the train clears passengers at the target platform.

3. The method according to claim 2, characterized in that The number of waiting passengers includes the initial number of passengers arriving at the target platform at the start of the simulation, and one or more of the target number of passengers arriving at the target platform during the train travel interval; the initial number of passengers and / or the target number of passengers are determined by the movement time of each passenger in the station, and the number of passengers whose destination is the target platform, and the station contains the target platform.

4. The method according to claim 3, characterized in that The movement time of each passenger in the target platform is determined based on at least one of the following methods: The time it takes for each passenger to pass through each fixed equipment in the station; The time it takes for each passenger to pass through obstacles in the station, and the time it takes for each passenger to pass through the station is determined based on the arrangement of the obstacles; The time it takes for each passenger to pass through the toll gate equipment; The travel time of each passenger in case of a preset emergency in the station; The passage time of each passenger at the target platform when the platform platform platform screen door is closed; The travel time of each passenger during the movement in the target platform.

5. The method according to claim 3, characterized in that: The number of passengers whose destination is the target platform is determined based on at least one of the following methods: Determining the number of passengers arriving at the target platform based on the station being a transfer station; Determine the number of passengers arriving at the target platform based on the station flow restriction condition; Determine the number of passengers arriving at the target platform based on the open or closed status of the station entrance and exit; The number of passengers arriving at the target platform is determined based on the passenger flow entering the station during the simulation time period.

6. The method according to claim 1, characterized in that Determining the linear relationship between the passenger retention factors includes: Obtaining the distribution ratio of each passenger retention factor and obtaining the initial number of passengers arriving at the target platform at the start time of the simulation; If the initial number of passengers is a preset fixed value, the linear relationship between the passenger retention factors is determined based on the following formula (1): Among them, A 11 To A mn is the distribution ratio of each passenger retention factor, x1 to x n is the retention factor of each passenger, Y1 to Y m is the number of passengers stranded corresponding to each passenger stranding factor, k is the initial number of passengers, and k is the preset fixed value; Alternatively, if the initial number of passengers is not a preset fixed value, the linear relationship between the passenger retention factors is determined based on the following formula (2): Among them, A 11 To A mn is the distribution ratio between each passenger retention factor, x1 to x n is the retention factor of each passenger, Y1 to Y m is the number of passengers stranded corresponding to each passenger stranding factor, k1 to k m is the initial number of passengers corresponding to each passenger retention factor, and k1 to k m Different or partially identical.

7. The method according to claim 1, characterized in that Before inputting the passenger retention factor corresponding to the target platform and the linear relationship between the passenger retention factors into the pre-built station scene simulation model, the method further includes: For each passenger in the station, a virtual character model is generated, and an action proxy is performed on the virtual character model based on the passenger retention factor corresponding to the target platform to obtain a first action proxy for the virtual character model; Generate a physical device model for the device set in the station, and perform action proxy on each physical device model in the device set to obtain a second action proxy for the physical device model; Based on the second action agent, the first action agent of the virtual character model is empowered with thinking to obtain a third action agent of the virtual character model; The station scene simulation model is constructed based on the first action agent, the second action agent, and the third action agent in continuous time.

8. An urban rail transit capacity adjustment device, characterized in that: include: A determination module is configured to determine the passenger retention factor corresponding to the target platform and determine the linear relationship between the passenger retention factors; A data input module is configured to input the passenger retention factor corresponding to the target platform and the linear relationship between the passenger retention factors into a pre-built station scene simulation model to obtain a station passenger flow simulation scene under a current capacity configuration mode, wherein the station passenger flow simulation scene at least includes the real-time motion state of the virtual role model corresponding to each individual passenger; A parameter calculation module is configured to calculate the passenger retention parameter of the target platform under the current capacity configuration mode based on the real-time motion state of the virtual role model corresponding to each individual passenger in the station passenger flow simulation scene; The mode adjustment module is configured to execute if the passenger retention parameter of the target platform is greater than a preset passenger retention threshold, then adjust the current capacity configuration mode to obtain a target capacity configuration mode; wherein the target capacity configuration mode corresponds to the passenger retention parameter of the target platform being no greater than the preset passenger retention threshold.

9. A computer device comprising: It comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the urban rail transit capacity adjustment method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the urban rail transit capacity adjustment method described in any one of claims 1 to 7 are implemented.

Images