Subway passenger information perception method, device and system

Abstract

The present application discloses a method, device, and system for sensing subway passenger information, which relates to the field of digital information processing technology. The method includes the following steps: obtaining passenger behavior data within a preset historical time period; calculating the degree of stability of passenger changes and the degree of diversion of travel routes at any subway station based on the passenger behavior data; obtaining real-time passenger travel data at the current subway station when the degree of stability of passenger changes meets preset conditions; calculating the difference between the degree of diversion in real time and the degree of diversion of travel routes in history based on the real-time passenger travel data; and adjusting the service management information of the subway station based on the difference. The present application achieves the technical effect of adaptively adjusting the service management information of subway stations.

Description

Technical Field

[0001] The present application relates to the field of digital information processing technology, and in particular to a method, device and system for sensing subway passenger information. Background Art

[0002] Currently, when managing subway crews, it is usually necessary to manage the number of staff in different traffic sections based on the perceived passenger information. In related technologies, corresponding staff are usually arranged in different time periods based on preset plans, or appropriate operating vehicles are planned. However, since passengers' daily travel plans and passenger routes may change, managing subway crews only through preset personnel scheduling will lead to congestion at a certain subway station. Summary of the Invention

[0003] The main purpose of this application is to provide a method, device and system for perceiving subway passenger information, aiming to solve the technical problem in related technologies that subway crew members are managed only through preset personnel scheduling, which may lead to congestion at a certain subway station.

[0004] To achieve the above objectives, the present invention provides a method for sensing subway passenger information, comprising:

[0005] Obtain passenger behavior data within a preset historical time period;

[0006] Based on passenger behavior data, calculate the stability of passenger changes and the degree of bus route diversion at any subway station;

[0007] When the stability of passenger changes meets the preset conditions, obtain the real-time passenger travel data of the current subway station;

[0008] Based on the real-time passenger data, the difference between the real-time diversion degree and the historical diversion degree of the passenger route is calculated;

[0009] Based on the difference, the crew management information of the subway station is adjusted.

[0010] In one possible implementation of the present application, based on passenger behavior data, calculating the stability of passenger changes and the degree of bus route diversion at any subway station includes:

[0011] Based on the passenger behavior data, determine the set of movement paths, boarding time, and number of boarding times corresponding to each passenger within a preset statistical period, where the preset time period includes multiple preset statistical periods;

[0012] Based on the set of movement paths, the time of boarding and the number of rides, the riding stability of any passenger is calculated;

[0013] Determine the number of passengers at subway stations and calculate the degree of route diversion at any subway station based on the number of passengers;

[0014] Based on the riding stability and the number of passengers, the passenger change stability of any subway station is calculated.

[0015] In a possible implementation of the present application, the degree of bus route diversion at any subway station is calculated based on the number of passengers, including:

[0016] Based on the number of passengers, determine the number of passengers entering and leaving the current subway station and the number of lines entering the station;

[0017] Determine the number of periods for the preset statistical period;

[0018] Calculate the degree of passenger line diversion for each line at any subway station based on the number of entries and exits, the number of line entries, and the number of cycles.

[0019] In a possible implementation of the present application, based on real-time passenger travel data, calculating the difference between the real-time diversion degree and the historical travel route diversion degree includes:

[0020] For any route, the real-time diversion degree of the current route is calculated based on the real-time passenger data;

[0021] Based on the real-time diversion degree and the diversion degree of the bus routes in the historical operation process, the difference between the historical operation and the real-time operation of each line is calculated.

[0022] In a possible implementation of the present application, adjusting the crew management information of a subway station based on the difference includes:

[0023] Sort the lines in order of the difference to obtain the line data sequence;

[0024] Based on the difference, the number of on-duty personnel to be adjusted corresponding to each line in the line data sequence is calculated;

[0025] Based on the number of on-duty personnel to be adjusted, the service management information of the subway station will be adjusted.

[0026] In a possible implementation of the present application, calculating the number of on-duty personnel to be adjusted corresponding to each route in the route data sequence based on the difference degree includes:

[0027] determining a first difference degree that is a negative value and a second difference degree that is a positive value among the difference degrees;

[0028] Calculating first means corresponding to each first difference and second means corresponding to each second difference respectively;

[0029] Based on the preset number of on-duty personnel, the first mean and the second mean, the number of on-duty personnel to be adjusted corresponding to each line in the line data sequence is calculated.

[0030] In a possible implementation of the present application, adjusting the crew management information of a subway station based on the number of on-duty personnel to be adjusted includes:

[0031] Determining the sorting position of each line in the line data sequence;

[0032] When it is determined that the number of on-duty personnel needs to be reduced on a route, the number of personnel reductions corresponding to each route is calculated based on the sorting position and the number of routes that need to reduce personnel;

[0033] The personnel of the line will be reduced based on the number of personnel reductions.

[0034] In a possible implementation of the present application, after reducing the number of personnel on the line according to the number of personnel reduction, the method further includes:

[0035] When it is determined that the number of on-duty personnel needs to be increased on a route, the number of additional personnel corresponding to each route is calculated based on the sorting position and the number of routes that need to increase personnel;

[0036] The personnel of the line shall be increased according to the number of additional personnel.

[0037] The present application also provides a subway passenger information perception device, which is a physical node device. The subway passenger information perception device includes: a memory, a processor, and a program of a subway passenger information perception method stored in the memory and runnable on the processor. When the program of the subway passenger information perception method is executed by the processor, the steps of the above-mentioned subway passenger information perception method can be implemented.

[0038] The present application also provides a subway passenger information perception system, which includes:

[0039] A first acquisition module, the first acquisition module is used to obtain passenger behavior data within a historical preset time period;

[0040] A first calculation module is used to calculate the stability of passenger changes and the degree of bus route diversion at any subway station based on passenger behavior data;

[0041] The second acquisition module is used to obtain the real-time passenger travel data of the current subway station when the stability of passenger changes meets the preset conditions;

[0042] A second calculation module is used to calculate the difference between the real-time diversion degree and the historical running diversion degree of the bus route based on the real-time passenger riding data;

[0043] The adjustment module is used to adjust the crew management information of the subway station based on the difference.

[0044] The present application provides a subway passenger information perception method, device and system. Compared with the related art in which subway service is managed only by preset personnel scheduling, which may lead to congestion at a certain subway station, the present application obtains passenger behavior data within a historical time period, calculates the stability of passenger changes and the degree of diversion of travel routes at any subway station based on the obtained passenger behavior data, and when the stability of passenger changes meets the preset conditions, calculates the difference between the real-time diversion degree of the current subway station and the historical diversion degree of travel routes, so as to determine whether the current real-time passenger riding situation matches the degree of diversion of travel routes within the historical time period, so that the service management information of the subway station can be adaptively adjusted to avoid congestion caused by unreasonable station service personnel arrangements. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flowchart of the first embodiment of the subway passenger information perception method of the present application;

[0046] Figure 2 Schematic diagram of the entry and exit gate involved in the subway passenger information perception method of this application;

[0047] Figure 3 This is a flow chart of a second embodiment of the subway passenger information perception method of the present application;

[0048] Figure 4 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present application. DETAILED DESCRIPTION

[0049] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0050] The present application provides a method for sensing subway passenger information. In the first embodiment of the present application, the method for sensing subway passenger information is described as follows: Figure 1 , methods include:

[0051] Step S10, obtaining passenger behavior data within a preset historical time period;

[0052] Step S20, based on the passenger behavior data, calculating the passenger change stability and the degree of bus route diversion at any subway station;

[0053] Step S30: When the passenger change stability meets the preset conditions, obtain the real-time passenger travel data of the current subway station;

[0054] Step S40, based on the real-time passenger boarding data, calculating the difference between the real-time diversion degree and the historical running route diversion degree;

[0055] Step S50: adjusting the subway station's crew management information based on the difference.

[0056] This embodiment aims to adaptively adjust the crew management information of subway stations to avoid congestion caused by unreasonable station crew arrangements.

[0057] The specific steps are as follows:

[0058] Step S10: Obtain passenger behavior data within a preset historical time period.

[0059] As an example, the subway passenger information perception method can be applied to a subway passenger information perception device, which belongs to a subway passenger information perception system, and the subway passenger information perception system belongs to a subway passenger information perception equipment.

[0060] As an example, passenger behavior data includes the passenger's entry and exit time, the passenger's movement path, the passenger's location information, and the subway line taken.

[0061] As an example, the preset time period can be 1 month, 2 months, etc., and there is no specific limitation. When A Zi collects passenger behavior data, the time span covered by the historical time period is usually longer. Generally speaking, the subway trains taken by users on different days of the week may be the same. When the time span of the historical time period is long, the travel patterns of passengers can be obtained, thereby determining how to adjust the subway service information.

[0062] As an example, the preset time period includes multiple preset statistical periods. One preset statistical period may be one week, and the behavior data of each passenger is collected once every preset statistical period.

[0063] As an example, passenger behavior data can be obtained by:

[0064] 1. Ticketing system: At the entry and exit gates (such as Figure 2 Ticketing systems such as automatic ticket gates are equipped with sensors to record basic data such as passengers' entry and exit times. When passengers swipe their cards or scan codes, the sensors detect signal changes, record the passenger's ID information and entry and exit times, generate a timestamp, and upload it to the central server in real time.

[0065] 2. Video surveillance: High-definition cameras are deployed at key station locations (such as gates and concourses) to capture real-time information about passenger entry and exit, as well as movement within the station. The cameras capture images at 120 frames per second. Object detection algorithms are then used to identify passengers' specific locations as they enter and exit the station. The resulting movement paths for each passenger across different areas are then generated (for example, a path might be: "entrance, Line 4, toward Yueyang Tower").

[0066] 3. On-board equipment: Infrared sensors are installed on the platform corresponding to each carriage to detect the presence of passengers through human body thermal radiation signals and to count the number of people on the platform.

[0067] Step S20: Calculate the degree of passenger change stability and bus route diversion at any subway station based on passenger behavior data.

[0068] As an example, the passenger change stability represents the change stability of the number of passengers on the same day within different preset statistical periods corresponding to each subway station. For example, for the current subway station, the passenger change stability is obtained by calculating the movement trajectory and entry and exit time on the same day in each statistical period (for example, Tuesday of the first week and Tuesday of the second week). The more similar the movement trajectory and entry and exit time of the passengers on the same day of each week are, the greater the passenger change stability. By calculating the change stability of each passenger, the passenger change stability corresponding to the subway station is obtained.

[0069] As an example, the degree of diversification of bus routes indicates the degree of diversification of the routes that passengers take, as well as the number of passengers on each subway line. The greater the degree of diversification of the bus route, the more passengers pass through the route.

[0070] Wherein, step S20 also includes steps S21 to S24:

[0071] Step S21, based on the passenger behavior data, determine the movement path set, boarding time and number of boarding times corresponding to each passenger within a preset statistical period, and the preset time period includes multiple preset statistical periods.

[0072] As an example, passenger behavior data includes each passenger's movement path set, boarding time, and number of rides. The movement path set is a set of passenger's movement trajectories inside the subway station, the boarding time is the time point when entering the gate, and the number of rides is the number of times the passenger takes the train in a certain day (the number of entries and exits can be obtained through the ticketing system).

[0073] As an example, when the preset time period is one month and the preset statistical period is one week, the preset time period includes four preset statistical periods, and so on.

[0074] Step S22: Calculate the riding stability of any passenger based on the set of movement paths, the time of boarding the bus and the number of boarding times.

[0075] As an example, for the sth passenger on the jth day in each preset statistical period at the i-th station, the more similar the trajectory and entry and exit time of the sth passenger on the jth day in each statistical period, the higher the change stability of the sth passenger on the jth day, and the higher the riding stability. The mathematical formula is:

[0076]

[0077] Where, It represents the riding stability of the sth passenger at the i-th station on the j-th day in history. represents the number of rides of the sth passenger at the i-th station on the j-th day in history, represents the Jaccard correlation coefficient, represents the Euclidean distance, represents the normalization function, represents the set of moving paths of the sth passenger during the kth ride at the ith station on the jth day within the preset statistical period, represents the set of moving paths of the sth passenger during the rth ride at the i-th station on the j-th day within another preset statistical period, It represents the timestamp of the i-th station during the k-th ride of the s-th passenger on the j-th day in history. It represents the timestamp of the entry of the sth passenger into the i-th station during the r-th ride on the j-th day within another preset statistical period. Used to prevent the denominator from being zero.

[0078] Specifically, The larger the value of , the more closely the moving paths of the sth passenger at the ith station match the kth ride and the rth ride on the jth day within different preset statistical periods. The smaller the value of , the more consistent the entry time of the sth passenger at the i-th station during the k-th ride and the r-th ride on the j-th day within different preset statistical periods.

[0079] Step S23: Determine the number of passengers at the subway station, and calculate the degree of bus route diversion at any subway station based on the number of passengers.

[0080] As an example, the number of passengers can be the number of passengers entering and exiting the station gate, as well as the number of people on each platform. Based on the number of people entering and exiting the station and the number of people on the platform, the number of passengers on each route can be determined, and the degree of route diversion in the historical time period can be obtained.

[0081] The step S23 of calculating the degree of bus route diversion at any subway station based on the number of passengers includes:

[0082] Based on the number of passengers, determine the number of passengers entering and exiting the current subway station and the number of lines entering the station.

[0083] As an example, the number of entries and exits indicates the total number of passengers riding in a subway station, and the number of line entries indicates the number of passengers passing through the entrance of each line.

[0084] As an example, in the process of counting the number of passengers, with half an hour as a time series period, the number of passengers at each statistical point and the passenger trajectory are counted every half hour (for example, the number of passengers passing through the entry and exit gates and the number of people on each platform are counted every half hour).

[0085] Determine the number of periods in the preset statistical period.

[0086] As an example, the number of cycles can be 2, 3, 4, etc., without specific limitation.

[0087] Calculate the degree of passenger line diversion for each line at any subway station based on the number of entries and exits, the number of line entries, and the number of cycles.

[0088] As an example, for a subway station, each line has a corresponding bus route diversion degree. It is necessary to first calculate the bus route diversion degree of each line, and finally integrate it to obtain the bus route diversion degree of the current subway station. Determine the degree of passenger diversion to different lines in the pth time sequence period during the historical operation of the i-th station on the jth day. Specifically: for the q-th line of the i-th station in the p-th time sequence period, the bus route diversion degree for:

[0089]

[0090] Where, represents the historical diversion degree of the qth line of the i-th station in the p-th time series period, It represents the number of entries and exits counted by the entry and exit gate at the i-th station in the p-th time sequence period of the m-th preset statistical period; represents the number of line entrances that pass through the entrance of the qth line at the i-th station within the p-th time sequence period in the m-th preset statistical period (the number of passengers is obtained by identifying the video frame of the entrance of the qth line through the YoLo algorithm). The larger the value of , the more passengers pass through the qth route, and M represents the number of cycles in the preset statistical cycle.

[0091] Similarly, the degree of passenger line diversion for each line in the subway station in the pth time sequence period can be calculated.

[0092] Step S24: Based on the riding stability and the number of passengers, the passenger change stability of any subway station is calculated.

[0093] As an example, during daily operations, there are people who frequently ride the subway (regular passengers) and people who only ride occasionally (novice passengers). Since some novel passengers enter the station by purchasing temporary tickets instead of scanning a QR code with their mobile phones, the passenger ID obtained is a temporary ID. Therefore, the initial temporary passenger level of the i-th station on the j-th day (a certain day of the week, such as Monday) is:

[0094]

[0095] Where, represents the initial visitor level of the i-th station on the j-th day in history, Indicates the number of temporary IDs obtained by the i-th site on the j-th day in history, Indicates the total number of passenger IDs obtained by the i-th station on the j-th day in history.

[0096] As an example, when calculating the passenger change stability of any subway station, both regular passengers and new passengers need to be taken into account, so as to obtain a more accurate passenger change stability.

[0097] As an example, ride stability The larger the value of , the higher the stability of the change of the sth passenger at the i-th station on the j-th day in history. Therefore, the calculation method of the stability of the change of passengers at the i-th station on the j-th day in history can be:

[0098]

[0099] Where, Indicates the stability of passenger changes at the i-th station on the j-th day in history; It represents the total number of passenger IDs / passengers counted at the i-th station on the j-th day in history; represents the number of rides of the sth passenger at the i-th station on the j-th day in history, Indicates the number of statistical cycles, It represents the riding stability of the sth passenger at the i-th station on the j-th day in history, e is a natural constant, It represents the initial visitor level of the i-th station on the j-th day in history.

[0100] It represents the initial visitor level of the i-th station on the j-th day in history. The smaller the value of , the fewer temporary passengers there are at the i-th station on the j-th day in history. Therefore, the stability of the change of each passenger at the i-th station on the j-th day in history is more worthy of reference.

[0101] As an example, the more times the s-th passenger rides at the i-th station on the j-th day in history, and the higher the corresponding change stability, the more worthy of reference is the change stability of the s-th passenger at the i-th station on the j-th day in history. Therefore The larger the value of , the higher the stability of the change in passengers at the i-th station on the j-th day in history.

[0102] Step S30: When the passenger change stability meets the preset conditions, the real-time passenger travel data of the current subway station is obtained.

[0103] As an example, when the degree of stability of passenger changes is small, it means that the stability of passenger changes at the subway station is poor, and the changes in the behavior and number of passengers at the current subway station on the jth day in the historical time period are unstable.

[0104] As an example, when the passenger change stability is less than a certain threshold (for example, 0.5), it means that the passenger change stability of the subway station meets the preset conditions, that is, the passenger change stability value of the current station on the jth day in history is too small, and it is necessary to monitor the passenger flow on the jth day in real time to adjust the station's service management.

[0105] Then, according to a preset time period (which can be half an hour), the real-time passenger travel data of each subway station is counted. The real-time passenger travel data includes the number of passengers and passenger trajectories.

[0106] Step S40: Based on the real-time passenger boarding data, the difference between the real-time diversion degree and the historical diversion degree of the boarding route is calculated.

[0107] As an example, based on real-time passenger travel data, the real-time diversion degree can be calculated according to the number of passengers entering and exiting the subway station and the number of passengers entering the station corresponding to each line. Then, the difference between the real-time diversion degree and the diversion degree of the travel line in the historical operation process can be calculated, where the calculation method of the travel line diversion degree is the same as the real-time diversion degree.

[0108] Step S50: adjusting the service management information of the subway station based on the difference.

[0109] As an example, when the real-time diversion degree of the line during real-time operation is more similar to the diversion degree of the line during historical operation, the fewer on-duty personnel need to be adjusted. Conversely, the subway station needs to increase or reduce staff.

[0110] As an example, the service management information can be for scheduling routes or scheduling on-duty personnel. When the route needs to be scheduled, the route can be adjusted by detecting the number of passengers on a certain route at the subway station. For example, when there are many passengers on Line B at Station A, the number of stops at stations with fewer people or no passengers can be reduced, so as to arrive at Station A as quickly as possible. When the on-duty personnel needs to be adjusted, the number of on-duty personnel to be increased / decreased can be calculated based on the real-time diversion degree, thereby realizing real-time adjustment of the service management information.

[0111] The present application provides a method for perceiving subway passenger information. Compared with the related art in which subway service is managed only through preset personnel scheduling, which may lead to crowding at a certain subway station, the present application obtains passenger behavior data within a historical time period, calculates the stability of passenger changes and the degree of diversion of travel routes at any subway station through the obtained passenger behavior data, and when the stability of passenger changes meets the preset conditions, calculates the difference between the real-time diversion degree of the current subway station and the historical diversion degree of travel routes, so as to determine whether the current real-time passenger riding situation matches the degree of diversion of travel routes within the historical time period, thereby adaptively adjusting the service management information of the subway station to avoid congestion caused by unreasonable station service personnel arrangements.

[0112] Further, refer to Figure 3 Based on the first embodiment of the present application, another embodiment of the present application is provided. In this embodiment, step S40 of calculating the difference between the real-time diversion degree and the historical diversion degree of the riding route based on the real-time passenger riding data includes:

[0113] Step S41: For any line, the real-time diversion degree of the current line is calculated based on the real-time passenger travel data.

[0114] As an example, for any route, the real-time number of passengers entering and exiting the station and the number of passengers entering the station corresponding to the current route can be obtained based on the real-time passenger travel data, thereby calculating the real-time diversion degree of the current route. The real-time diversion degree is calculated in the same way as the diversion degree of the travel route.

[0115] Step S42 : Based on the real-time diversion degree and the route diversion degree in the historical operation process, the difference between the historical operation and the real-time operation of each route is calculated.

[0116] As an example, the difference between the historical operation and the real-time operation of each line can be obtained by subtracting the real-time diversion degree of each line from the diversion degree of the boarding line in the historical operation process.

[0117] As an example, take the qth line as an example, the difference between the passenger diversion situation in the real-time operation process of the line at the i-th station on the j-th day in the p-th time series cycle and the historical operation process is The mathematical formula is:

[0118]

[0119] Where, It represents the difference between the passenger diversion of the qth line in the real-time operation process of the jth day of the pth time series period at the i-th station and the historical operation process, represents the real-time diversion degree of the qth line at the i-th station in the p-th timing cycle, It represents the degree of passenger route diversion during the historical operation of the qth route at the i-th station in the p-th time series period.

[0120] Similarly, the passenger diversion situation of each line in the pth time series period during the real-time operation of the i-th station on the jth day and the similarity with the historical operation process can be calculated.

[0121] The step S50 of adjusting the subway station's crew management information based on the difference includes:

[0122] Sort the lines in order of the difference to obtain a line data sequence.

[0123] As an example, all lines are sorted in order of difference to obtain a line data sequence.

[0124] Based on the difference, the number of on-duty personnel to be adjusted corresponding to each line in the line data sequence is calculated.

[0125] As an example, where the difference A positive value indicates that the passenger flow on route q during the real-time operation of the p-th time series cycle at the i-th station on the j-th day is greater than that during the historical operation. Therefore, more personnel should be assigned to route q. Conversely, a negative value indicates that fewer personnel should be assigned to route q. When the difference is 0, the historical scheduling method can be used normally and no adjustment is required.

[0126] Among them, based on the difference, the number of on-duty personnel to be adjusted corresponding to each line in the line data sequence is calculated, including:

[0127] A first difference degree having a negative value and a second difference degree having a positive value among the difference degrees are determined.

[0128] As an example, the first difference is a difference of a negative value in each line, and the second difference is a difference of a positive value in each line.

[0129] The first mean corresponding to each first difference and the second mean corresponding to each second difference are calculated respectively.

[0130] As an example, the first mean is the mean of the sum of the negative differences between the passenger diversion conditions of all routes and the historical operation processes, and the second mean is the mean of the sum of the positive differences between the passenger diversion conditions of all routes and the historical operation processes.

[0131] Based on the preset number of on-duty personnel, the first mean and the second mean, the number of on-duty personnel to be adjusted corresponding to each line in the line data sequence is calculated.

[0132] As an example, the preset number of on-duty personnel is a preset value for adjusting the on-duty personnel, which may be a pre-arranged number of personnel dispatches. The calculation method of the number of on-duty personnel to be adjusted E may be:

[0133]

[0134] Where, Indicates that the number of on-duty personnel needs to be adjusted. It represents the mean of the sum of the positive differences between the passenger diversion conditions of all lines in the real-time operation process and the historical operation process in the p-th time series period of the i-th station on the j-th day, which is also the second mean. It represents the absolute value of the sum of the negative differences between the passenger diversion conditions of all lines in the real-time operation process of the p-th time series period of the i-th station on the j-th day and the historical operation process, and represents the absolute value of the first mean. Indicates the preset number of on-duty personnel.

[0135] As an example, The larger the value of , the greater the passenger diversion of some lines during the real-time operation of the p-th time sequence cycle on the j-th day of the i-th station is compared with the historical results, and the greater the need to adjust the on-duty personnel of these lines.

[0136] Based on the number of on-duty personnel to be adjusted, the service management information of the subway station will be adjusted.

[0137] As an example, after calculating the number of on-duty personnel to be adjusted, it is also necessary to determine the number of personnel who need to be reduced and the number of personnel who need to be increased on the line, and adjust the on-duty personnel based on these numbers of personnel.

[0138] The steps for adjusting the crew management information of a subway station based on the number of on-duty personnel to be adjusted include:

[0139] Determine the sorting position of each line in the line data sequence.

[0140] As an example, the lines in the line data sequence are arranged in order of size, and each line corresponds to a sorting position, for example, sequence 1, sequence 2, sequence 3, sequence 4, and so on.

[0141] When it is determined that the number of on-duty personnel on a route needs to be reduced, the number of personnel reductions corresponding to each route is calculated based on the sorting position and the number of routes that need to reduce the number of personnel.

[0142] As an example, the later the line that needs to reduce staff is in the line data sequence, the higher the number of staff reductions it has. The number of staff reductions corresponding to each line that needs to reduce staff is .in Indicates the number of lines that need to reduce the number of people. Indicates the ranking position of the g-th line that needs to reduce the number of people among all G lines that need to reduce the number of people.

[0143] For example, if the number of lines that need to reduce the number of people is 5 and the current ranking position of the line that needs to reduce the number of people is 3, then the number of people to be reduced is E× .

[0144] The personnel of the line will be reduced based on the number of personnel reductions.

[0145] As an example, after the number of personnel reductions is calculated, the personnel reduction process is performed on the previous personnel of each line according to the number of personnel reductions required.

[0146] Among them, after reducing the number of personnel on the line, it also includes:

[0147] When it is determined that the number of on-duty personnel needs to be increased on a route, the number of additional personnel corresponding to each route is calculated based on the sorting position and the number of routes that need to increase the number of personnel.

[0148] As an example, the lines that need to increase the number of people are increased. The earlier the line that needs to increase the number of people is in the line data sequence, the more people are increased. The number of people increased for each line that needs to increase the number of people is .in Indicates the number of lines that need to increase the number of people. Indicates the ranking position of the u-th line that needs to increase the number of people among all U lines that need to increase the number of people.

[0149] The personnel of the line shall be increased according to the number of additional personnel.

[0150] As an example, after the number of additional personnel is calculated, the previous personnel of each line are increased according to the number of additional personnel required.

[0151] In this embodiment, the number of personnel that need to be adjusted for each line is calculated by the difference in the degree of diversion between the real-time operation and the historical operation process, thereby avoiding improper management or congestion caused by too many passengers and too few on-duty personnel on different lines during the daily operation of the subway station.

[0152] The present application also provides a subway passenger information perception system, which includes:

[0153] A first acquisition module, the first acquisition module is used to obtain passenger behavior data within a historical preset time period;

[0154] A first calculation module is used to calculate the stability of passenger changes and the degree of bus route diversion at any subway station based on passenger behavior data;

[0155] The second acquisition module is used to obtain the real-time passenger travel data of the current subway station when the stability of passenger changes meets the preset conditions;

[0156] A second calculation module is used to calculate the difference between the real-time diversion degree and the historical running diversion degree of the bus route based on the real-time passenger riding data;

[0157] The adjustment module is used to adjust the crew management information of the subway station based on the difference.

[0158] Reference Figure 4 , Figure 4 It is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present application.

[0159] like Figure 4 As shown, the subway passenger information sensing device may include: a processor 1001 , a memory 1005 , and a communication bus 1002 . The communication bus 1002 is used to implement connection and communication between the processor 1001 and the memory 1005 .

[0160] Optionally, the subway passenger information sensing device may also include a user interface, a network interface, a camera, an RF (Radio Frequency) circuit, a sensor, a WiFi module, and the like. The user interface may include a display and an input submodule such as a keyboard. The optional user interface may also include a standard wired interface or a wireless interface. The network interface may include a standard wired interface or a wireless interface (such as a WiFi interface).

[0161] Those skilled in the art will understand that Figure 4 The structure of the subway passenger information sensing device shown in the figure does not constitute a limitation on the subway passenger information sensing device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0162] like Figure 4 As shown, memory 1005, a storage medium, may include an operating system, a network communication module, and a subway passenger information perception program. The operating system manages and controls the hardware and software resources of the subway passenger information perception device, supporting the operation of the subway passenger information perception program and other software and / or programs. The network communication module facilitates communication between components within memory 1005, as well as with other hardware and software in the subway passenger information perception system.

[0163] exist Figure 4 In the subway passenger information sensing device shown, the processor 1001 is used to execute the subway passenger information sensing program stored in the memory 1005 to implement the steps of any of the above subway passenger information sensing methods.

[0164] The specific implementation of the subway passenger information sensing device of the present application is basically the same as the embodiments of the above-mentioned subway passenger information sensing method, and will not be repeated here.

[0165] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0166] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0167] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0168] The above are only preferred embodiments of the present application and do not limit the scope of application of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application description and drawings, or directly or indirectly applied in other related technical fields, are also included in the scope of protection of the present application.

[0169] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0170] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for sensing subway passenger information, characterized in that: The method comprises: Obtain passenger behavior data within a preset historical time period; Based on the passenger behavior data, calculate the stability of passenger changes and the degree of bus route diversion at any subway station; The calculation of the passenger change stability and bus route diversion degree of any subway station based on the passenger behavior data includes: Based on the passenger behavior data, determining a set of movement paths, a boarding time, and a number of boarding times corresponding to each passenger's ride within a preset statistical period, wherein the preset time period includes a plurality of preset statistical periods; Calculating the riding stability of any passenger based on the set of movement paths, the boarding arrival time, and the number of boarding times; Determine the number of passengers at the subway station, and calculate the degree of route diversion at any subway station based on the number of passengers; Based on the riding stability and the number of passengers, calculate the passenger change stability of any subway station; When the passenger change stability meets the preset conditions, real-time passenger travel data of the current subway station is obtained; Based on the real-time passenger boarding data, calculating the difference between the real-time diversion degree and the historical diversion degree of the boarding route; The calculating, based on the real-time passenger boarding data, the difference between the real-time diversion degree and the historical diversion degree of the boarding route includes: For any route, the real-time diversion degree of the current route is calculated based on the real-time passenger travel data; Based on the real-time diversion degree and the bus route diversion degree in the historical operation process, the difference between the historical operation and the real-time operation of each route is calculated; Based on the difference, adjusting the crew management information of the subway station; The adjusting the crew management information of the subway station based on the difference includes: sorting the lines according to the order of the difference to obtain a line data sequence; Based on the difference, the number of on-duty personnel to be adjusted corresponding to each route in the route data sequence is calculated; Based on the number of on-duty personnel to be adjusted, the crew management information of the subway station is adjusted.

2. The subway passenger information perception method according to claim 1, characterized in that: The calculating of the degree of bus route diversion at any subway station based on the number of passengers includes: Based on the number of passengers, determine the number of passengers entering and leaving the current subway station and the number of passengers entering the station on the line; Determining the number of periods of the preset statistical period; Based on the number of entries and exits, the number of entries into the station by the line, and the number of cycles, the degree of passenger line diversion for each line at any subway station is calculated.

3. The subway passenger information perception method according to claim 1, characterized in that: The calculating, based on the difference, the number of on-duty personnel to be adjusted corresponding to each route in the route data sequence includes: determining a first difference degree that is a negative value among the differences and a second difference degree that is a positive value among the differences; respectively calculating a first mean corresponding to each of the first differences and a second mean corresponding to each of the second differences; Based on the preset number of on-duty personnel, the first mean and the second mean, the number of on-duty personnel to be adjusted corresponding to each line in the line data sequence is calculated.

4. The subway passenger information perception method according to claim 1, characterized in that: The adjusting of the crew management information of the subway station based on the number of on-duty personnel to be adjusted includes: Determining a sorting position of each of the lines in the line data sequence; When it is determined that the number of on-duty personnel on the line needs to be reduced, the number of personnel reductions corresponding to each line is calculated based on the sorting position and the number of lines that need to reduce personnel; The personnel of the line are reduced according to the number of reduced personnel.

5. The subway passenger information perception method according to claim 4, characterized in that: After reducing the number of personnel on the line according to the number of personnel reduction, the method further includes: When it is determined that the line needs to increase the number of on-duty personnel, the number of additional personnel corresponding to each line is calculated based on the sorting position and the number of lines that need to increase the number of personnel; The personnel of the line are increased by the increased number of personnel.

6. A subway passenger information sensing device, characterized in that: The device includes: a memory, a processor, and a subway passenger information perception program stored in the memory and capable of running on the processor, wherein the subway passenger information perception program is configured to implement the steps of the subway passenger information perception method according to any one of claims 1 to 5.

7. A subway passenger information perception system, the subway passenger information perception system being used to implement the steps of the subway passenger information perception method according to any one of claims 1 to 5, characterized in that: The subway passenger information perception system includes: a first acquisition module, configured to acquire passenger behavior data within a preset historical time period; A first calculation module, the first calculation module is used to calculate the stability of passenger changes and the degree of bus route diversion at any subway station based on the passenger behavior data; A second acquisition module, the second acquisition module is used to obtain real-time passenger travel data of the current subway station when the passenger change stability meets a preset condition; a second calculation module, configured to calculate, based on the real-time passenger boarding data, a difference between a real-time diversion degree and a historical diversion degree of the boarding route; An adjustment module is used to adjust the service management information of the subway station based on the difference.

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