CN113628432A - Intelligent early warning system for subway people flow current limiting - Google Patents
Intelligent early warning system for subway people flow current limiting Download PDFInfo
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Abstract
The invention discloses an intelligent warning system for limiting subway passenger flow, which comprises a passenger flow analysis and warning unit, a warning management and control unit and a database, wherein the system acquires images of passenger flows inside and outside a subway station based on a monitoring facility and an AI autonomous face recognition technology, performs statistical arrangement through a carding statistical unit, takes the age distribution condition of the passenger flows inside the subway station as an important influence factor of a station content storage limit value under four different warning combination modes, actively adjusts warning parameters according to different conditions, analyzes the passenger flow change trend inside the subway station and the riding intention of the passenger flows outside the subway station, generates an alarm or the arrival moment of a passenger flow peak, provides data support for subsequent workers, can quickly and effectively reflect the passenger flow condition inside the subway station by a condition warning prompt model and timely informs the preset solution to the workers, the risk of intensive stream of people in the subway station is effectively reduced, and the daily trip of people is facilitated.
Description
Technical Field
The invention relates to an intelligent threshold system, in particular to an intelligent early warning system for subway people flow current limiting.
Background
Along with the advance of urbanization, population gathers to city center, leads to the increase of city population, and urban traffic pressure increases, and subway becomes the important vehicle of people's daily trip on duty because of its characteristics such as punctual, quick, convenient, bearing capacity is big.
However, under some special conditions, such as extreme weather and the occurrence of congestion and intensive personnel in the subway station during commuting peak, subway station managers cannot form effective early warning and prevention in advance by knowing the congestion condition of the subway station, although early warning test points are released in some big cities, the early warning test points are not perfect enough, active upgrade cannot be performed, and the modes of age hierarchy and early warning cannot be divided, so that accurate management and control are performed. Therefore, an intelligent subway people flow limiting early warning system is provided.
Disclosure of Invention
The invention aims to provide an intelligent early warning system for subway people flow current limiting.
The technical problem solved by the invention is as follows:
(1) how to solve the problem that subway station management cannot analyze the pedestrian flow condition and the change trend in advance so as to prepare in advance by acquiring the pedestrian flow image and analyzing and early warning;
(2) how to divide the early warning into four modes, after the modes are combined, the set parameters in each early warning mode are adaptively adjusted, so that the early warning alarm in each mode can have different solutions, and meanwhile, the standards for pedestrian flow current limiting in the subway are different according to different age distributions, thereby greatly reducing the potential safety hazards of children and old people;
(3) how to regularly carry out big data commonality analysis and establish the model through people flow analysis early warning unit to the bearing capacity and the risk of current subway station are judged in advance to quick discernment, and then make early warning and take precautions against in advance, improve reaction rate.
The invention can be realized by the following technical scheme: subway people flows intelligent early warning system of current-limiting includes: the system comprises a pedestrian flow acquisition unit, a data base and a data base, wherein the pedestrian flow acquisition unit is used for acquiring the image data of the pedestrian flow outside the subway station and the image data of the pedestrian flow inside the subway station and storing the image data of the pedestrian flow inside the subway station into the data base;
the combing and counting unit is used for extracting the image data of the pedestrian flow outside the station and the image data of the pedestrian flow inside the station from the database within set time to carry out combing and counting to obtain the binding data of the conversion factors and the age distribution ratio data inside the station, and meanwhile, carrying out counting to the pedestrian flow in each monitoring area divided inside the station;
and the people flow analysis early warning unit is used for dividing the early warning modes, correcting the accommodating limit value according to different age levels in the subway station, correcting and comparing the accommodating limit value with the actual accommodating amount, transmitting the generated signal or data to the early warning management and control unit for identification, and taking corresponding management and control measures, and periodically analyzing the early warning mode with the largest number of alarm times and generating a condition early warning prompt model.
The invention has further technical improvements that: the people flow collecting unit collects image data based on monitoring facilities outside and inside the subway station, and the image monitoring facilities inside the subway station have an AI autonomous face recognition function and analyze age levels of people in images.
The invention has further technical improvements that: the combing and counting unit is used for judging whether the advancing direction of the corresponding figure faces to the subway station or not according to the difference of time nodes and the change of the position of the corresponding figure by arranging and marking the people stream image sequence at the same point, so that the intended passenger is determined, proportional operation is carried out according to the number of people entering the station entrance area in the time period, and meanwhile weather, time and an operation result are bound to obtain conversion factor binding data.
The invention has further technical improvements that: the image data of the monitoring facilities in each monitoring area in the combing and counting unit are obtained at different time points, and the monitoring facilities in the waiting area simultaneously obtain the people stream image data of getting on and off the bus and count the corresponding number of people.
The invention has further technical improvements that: the people flow analysis early warning unit divides the early warning mode into a sunny mode, a rain and snow mode, a morning and evening peak mode and a holiday mode according to the difference between the weather and the time, and the weather mode and the time mode are combined in pairs to obtain four working early warning modes of the early warning system.
The invention has further technical improvements that: the passenger flow analysis early warning unit matches passenger conversion coefficients in a corresponding time period and a corresponding early warning mode according to weather and data in the conversion factor binding data, calculates a mean value of the conversion coefficients and compares the mean value with the passenger conversion coefficients in the current conversion factor binding data, so that the density of the riding intention in the outdoor passenger flow is determined, and data support is provided for early warning of a follow-up early warning management and control unit.
The invention has further technical improvements that: in the process of correcting the accommodating limit value, the calculated value of the correction coefficient is influenced by the preset age influence coefficient, the preset age influence coefficient corresponding to the age interval of 1-8 years and more than 65 years is the largest, and the closer to the middle age stage, the smaller the corresponding preset age influence coefficient is.
The invention has further technical improvements that: the condition early warning prompt model provides common conditions under specific early warning conditions, the model provides preset solutions, and when corresponding early warning alarms appear, the early warning management and control unit directly transmits the preset solutions to workers in a wireless communication mode to timely and effectively process the early warning alarms.
The invention has further technical improvements that: the number display device arranged on one side of the door of the subway train stops at a platform and displays the maximum number of passengers and the actual number of passengers, and the indication value of the number display device changes along with the change of the number of passengers in the corresponding subway carriage.
Compared with the prior art, the invention has the following beneficial effects:
the invention acquires images of people flow inside and outside the subway station based on monitoring facilities and AI autonomous face recognition technology, and performs statistical arrangement through a carding statistical unit, under four different early warning combination modes, the age distribution condition of the people stream in the subway station is taken as an important influence factor of the station content storage limit value, the early warning parameters are actively adjusted according to different conditions, meanwhile, the traffic change trend in the subway station and the riding intention of traffic outside the station are analyzed to generate an alarm or a traffic peak arrival time, data support is provided for the preparation of subsequent subway workers, and the generated condition early warning prompt model can quickly and effectively reflect the traffic condition in the subway station and timely inform the workers of a preset solution, so that the workers can be familiar with the early warning process in advance, a large amount of time is saved, and the risk of intensive traffic in the subway station is effectively reduced.
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In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a block diagram of the system of the present invention;
FIG. 2 is a schematic flow chart of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the embodiments, structures, features and effects according to the present invention will be given with reference to the accompanying drawings and preferred embodiments.
Referring to fig. 1-2, the intelligent subway people flow limiting early warning system includes a people flow collecting unit, a combing and counting unit, a people flow analyzing and early warning unit, an early warning control unit and a database;
this intelligent early warning system is based on 1km within range bus stop, sharing bicycle stop, private vehicle drop point and pavement high definition image monitoring facility around the subway station, and image monitoring facility in the subway station also carries out image acquisition in real time simultaneously, and image monitoring facility in the subway station possesses the autonomic facial recognition function of AI, and the image monitoring area in the subway station divide into four parts, and these four parts are respectively: the system comprises an entrance area, a security inspection area, a waiting area and an exit gate area, wherein a people stream acquisition unit acquires people stream image data outside a station and people stream image data inside the station by respectively utilizing a high-definition image monitoring facility outside the subway station and an image monitoring facility inside the subway station, and the people stream acquisition unit specifically comprises the following steps:
outside the subway station: acquiring three groups of people stream image data of each monitoring point location during each acquisition, respectively acquiring the three groups of people stream image data at three time nodes at preset time intervals, marking the three groups of people stream image data as people stream image sequences, and storing the people stream image sequences serving as off-station people stream image data into a database;
in the subway station: in four areas divided by an image monitoring area in a subway station, monitoring facilities in an entrance area and a security inspection area continuously acquire image data, a corresponding image is added with a timestamp and then is stored in a database, a waiting area starts to acquire the image data one minute before the next subway arrives, the image acquisition work is automatically stopped after a gate of the waiting area is closed, an exit gate area starts to acquire the image data thirty seconds after the subway stops at a current subway station platform and automatically stops after the next subway arrives for three minutes, and the image data acquired by the waiting area and the exit gate area is added with the timestamp when the subway enters the platform and then is stored in the database;
the combing statistic unit combs and counts the image data of the off-station people stream stored in the database every half hour, and combs and counts the image data of the in-station people stream three minutes before the subway arrives at the station every shift, wherein the specific combing statistic process is as follows:
step S1: extracting the image data of the off-station people stream from the database, wherein the extracted image data of the off-station people stream is acquired within the first half hour, the people stream image sequences are arranged according to the time sequence, three groups of people stream image data contained in each people stream image sequence are also arranged according to the time sequence, and the point positions of the acquired images are respectively marked as A, B, C and other English characters;
step S2: for the point A, comparing and identifying the people stream image data acquired by the three time nodes, matching each person in the three images, marking the same task by using the same mark, judging whether the advancing direction of the corresponding person faces to the subway station according to the difference of the time nodes and the change of the position of the corresponding person, if so, marking the corresponding person as an intention passenger, increasing a counter by one, and if not, not performing any processing;
step S3: the other point locations have the same carding statistical mode as the point location A in the step S2, so that the readings of the counter are accumulated to obtain the number of the intended passengers and a timestamp is added;
step S4: extracting in-station pedestrian flow image data from a database, wherein the in-station pedestrian flow image of the in-station entrance area in the first twenty-five minutes is obtained, the number of people entering the in-station entrance area in the time period is counted, the number of people and the number of the intended passengers are subjected to proportional calculation to obtain the passenger conversion coefficient corresponding to the timestamp, and meanwhile, the weather data corresponding to the timestamp are collected and are bound with the timestamp and the passenger conversion coefficient to obtain conversion factor binding data;
step S5: the number of people in an entrance area, a security inspection area and a waiting area corresponding to a subway line is counted respectively, the age data of each passenger in each area is judged through an AI autonomous face recognition function, and the age data is divided into the following stages: 1-8 years old, 9-18 years old, 19-50 years old, 50-65 years old and over 65 years old, counting the number of passengers in each area according to different age stages, and calculating the ratio of the number of passengers in different age stages to the total number of passengers in the area to obtain age distribution ratio data;
step S6: after a subway of a certain line arrives at a station, a monitoring facility in a waiting area acquires people stream image data of getting on and off the subway and counts corresponding people, a people number display is arranged on the door side of a subway carriage, and the people number display can display the maximum number of people bearing the subway and the actual number of people bearing the subway in a corresponding carriage section in real time.
The data transmission that the statistical unit will comb statistics carries out early warning analysis to the human stream analysis early warning unit, and the early warning analysis is taken into consideration the scope with conditions such as weather, time, passenger's age and is analyzed, specifically is:
step SS 1: the early warning mode is divided into a sunny mode, a rain and snow mode, a morning and evening peak mode and a holiday mode according to different weather and different time, and the weather mode and the time mode are combined in pairs;
step SS 2: acquiring conversion factor binding data from a combing and counting unit, determining that a current early warning mode is one of four mode combinations of a clear day mode and a morning and evening peak mode, a clear day mode and a holiday mode, a rain and snow mode and a morning and evening peak mode or a rain and snow mode and a holiday mode according to a time interval in which weather data and a timestamp are located, wherein set people flow limit values under different mode combinations are different, and correction can be performed according to age distribution ratio data;
step SS 3: screening out passenger conversion coefficients under the condition that mode combinations are the same within one month or in the past in the same period (such as legal long and false), calculating the mean value of the conversion coefficients, calculating the current passenger conversion coefficient and the mean value of the conversion coefficients, if the current obtained passenger conversion coefficient exceeds ten percent of the mean value of the conversion coefficients, judging that the intention density of taking the bus outside the current station is large, otherwise, judging that the intention density of taking the bus outside the current station is small;
step SS 4: according to people flow image data of a security inspection opening area, a waiting opening area and an exit gate area in a subway station, calculating throughput data of the subway station during the entering and exiting periods of the subway of a certain line and the actual contained people flow in the subway station, when the throughput data is a positive value, extracting a preset containing limit value of the subway station in the mode matched with the current mode combination from a database, and when the throughput is a negative value, not performing any processing;
step SS 5: calculating to obtain the total age distribution ratio in the whole subway station according to the number of people in the entrance area, the security inspection area, the waiting area corresponding to the subway line and the age distribution ratio data, substituting the accommodation limit value extracted in the step SS4 in the corresponding early warning mode and the total age distribution ratio into a calculation formula for correction operation, wherein the calculation formula is as follows:
correction tolerance = tolerance limit correction factor, wherein:
step SS 6: comparing the actual contained pedestrian volume with the corrected contained limit value, if the actual contained pedestrian volume exceeds the corrected contained limit value, generating a pedestrian volume density alarm signal, if the actual contained pedestrian volume does not exceed the corrected contained limit value, predicting the time reaching the corrected contained limit value according to the difference value of the actual contained pedestrian volume and the corrected contained limit value and throughput data, marking the time as early warning time data, and then sending the early warning time data to an early warning management and control unit for scheduling;
step SS 7: the method comprises the steps that after a number display arranged on one side of a door of a subway train stops at a platform, the maximum number of people and the actual number of people are displayed, wherein the maximum number of people is a set value matched with a current early warning mode, an overload warning signal is generated when the actual number of people exceeds the maximum number of people, no processing is carried out when the actual number of people is smaller than the maximum number of people, and the number value of the number display changes along with the change of the number of people in a corresponding subway carriage.
The early warning management and control unit receives signals or data transmitted from the people flow analysis early warning unit, and takes corresponding measures to manage and control after identification, specifically:
when the current density of the outside-station passenger car intention is judged to be high, if the actual accommodation capacity is directly subjected to ratio operation with the accommodation limit value matched with the corresponding early warning mode, if the ratio exceeds sixty percent, the passenger flow with the peak is judged to exist, U-shaped fences are arranged in advance, and workers are assigned to carry out order maintenance, and if the ratio does not exceed sixty percent, no treatment is carried out;
when a pedestrian flow density alarm signal is identified, controlling the pedestrian flow input of an entrance area and the passing speed of a security check area, and simultaneously sending a prompt short message to a mobile equipment terminal of an intended passenger within 3km of a corresponding subway station through base station communication to guide the intended passenger to change other traffic modes;
when early warning time data are received, passenger guidance is carried out on the station-entering node, the station-exiting node and the transfer node in advance, so that the time of passengers staying in the subway station is saved, and the actual accommodation capacity in the subway station is reduced;
and after the overload alarm signal is identified, the staff guides and plans the passenger proportion of each gate in the area of the waiting gate in advance.
Meanwhile, the people flow analysis early warning unit also periodically carries out big data analysis on the early warning mode with the most alarm times in a set time period, common searching is carried out on the subway line with the alarm and the weather condition at that time through the time point when the alarm appears, a condition early warning prompting model is generated, meanwhile, a subway station manager provides a preset solution for the early warning condition under the common condition, in the subsequent operation process of the subway station, when the system is compared through data and is matched with the common condition in the condition early warning prompting model, corresponding early warning prompting information is automatically generated to the early warning management and control unit, the early warning management and control unit directly transmits the preset solution to workers through a wireless communication mode, precautionary measures are made in advance, and the occurrence of early warning alarms is reduced.
Although the present invention has been described with reference to the preferred embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but is intended to cover various modifications, equivalents and alternatives falling within the spirit and scope of the invention.
Claims (9)
1. Subway people flows intelligent early warning system of current-limiting, its characterized in that includes:
the system comprises a pedestrian flow acquisition unit, a data base and a data base, wherein the pedestrian flow acquisition unit is used for acquiring the image data of the pedestrian flow outside the subway station and the image data of the pedestrian flow inside the subway station and storing the image data of the pedestrian flow inside the subway station into the data base;
the combing and counting unit is used for extracting the image data of the pedestrian flow outside the station and the image data of the pedestrian flow inside the station from the database within set time to carry out combing and counting to obtain the binding data of the conversion factors and the age distribution ratio data inside the station, and meanwhile, carrying out counting to the pedestrian flow in each monitoring area divided inside the station;
and the people flow analysis early warning unit is used for dividing the early warning modes, correcting the accommodating limit value according to different age levels in the subway station, correcting and comparing the accommodating limit value with the actual accommodating amount, transmitting the generated signal or data to the early warning management and control unit for identification, and taking corresponding management and control measures, and periodically analyzing the early warning mode with the largest number of alarm times and generating a condition early warning prompt model.
2. The intelligent warning system for limiting the flow of people in a subway according to claim 1, wherein the people flow collecting unit collects image data based on monitoring facilities outside the subway station and inside the subway station, and the image monitoring facilities inside the subway station have an AI autonomous face recognition function to analyze age levels of people in the images.
3. The intelligent subway people flow limiting early warning system according to claim 1, wherein the carding statistical unit is used for determining an intended passenger by arranging and marking people flow image sequences at the same point, judging whether the advancing direction of a corresponding person faces to the subway station according to the difference of time nodes and the change of the position of the corresponding person, performing proportional operation according to the number of people entering the entrance area in the time period, and binding weather, time and the operation result to obtain conversion factor binding data.
4. An intelligent subway people stream current limiting early warning system according to claim 3, wherein the time points of obtaining the image data of the monitoring facilities in each monitoring area in the combing and counting unit are different, and the monitoring facilities in the waiting area obtain the people stream image data of getting on and off the train at the same time and count the corresponding number of people.
5. The intelligent subway people flow limiting early warning system according to claim 1, wherein the people flow analyzing early warning unit divides the early warning modes into a sunny mode, a rainy and snowy mode, a rush hour mode and a holiday mode according to the difference between the weather and the time, and the weather mode and the time mode are combined in pairs to obtain four working early warning modes of the early warning system.
6. The intelligent subway people flow limiting early warning system according to claim 3, wherein the people flow analysis early warning unit matches the passenger conversion coefficients in the corresponding time period and the corresponding early warning mode according to the weather and data in the conversion factor binding data, calculates the mean value of the conversion coefficients and compares the mean value with the passenger conversion coefficients in the current conversion factor binding data, thereby determining the density of the riding intention in the outdoor people flow and providing data support for the early warning of the follow-up early warning management and control unit.
7. The intelligent warning system for limiting the current of people in the subway according to claim 1, wherein in the process of correcting the accommodation limit value, the calculated value of the correction coefficient is influenced by a preset age influence coefficient, the preset age influence coefficient corresponding to the age interval of 1-8 years and 65 years is the largest, and the closer to the middle age, the smaller the corresponding preset age influence coefficient is.
8. The intelligent subway people flow limiting early warning system according to claim 1, wherein the condition early warning prompt model provides common conditions under specific early warning conditions, and the model provides a preset solution, when a corresponding early warning alarm occurs, the early warning management and control unit directly transmits the preset solution to a worker in a wireless communication mode, and the early warning alarm is timely and effectively processed.
9. The intelligent subway people flow limiting early warning system according to claim 1, wherein a people number display is arranged on one side of a door of a subway train, after a subway stops at a platform, the maximum number of people and the actual number of people are displayed, and the number value of the people number display changes along with the change of the number of people in a corresponding subway carriage.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114283386A (en) * | 2022-01-28 | 2022-04-05 | 浙江传媒学院 | Analysis and adaptation intensive scene people stream real-time monitoring system based on big data |
| CN114510001A (en) * | 2022-02-18 | 2022-05-17 | 陈芳 | Factory management system and method |
| CN118096087A (en) * | 2024-04-25 | 2024-05-28 | 大连地铁运营有限公司 | Green cloud platform service system and method thereof |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140289003A1 (en) * | 2013-03-25 | 2014-09-25 | Amadeus S.A.S. | Methods and systems for detecting anomaly in passenger flow |
| CN108805347A (en) * | 2018-06-05 | 2018-11-13 | 北方工业大学 | Passenger flow pool-based method for estimating passenger flow of associated area outside subway station |
| CN109086919A (en) * | 2018-07-17 | 2018-12-25 | 新华三云计算技术有限公司 | A kind of sight spot route planning method, device, system and electronic equipment |
| CN109961164A (en) * | 2017-12-25 | 2019-07-02 | 比亚迪股份有限公司 | Passenger flow forecast method and device |
| CN110400462A (en) * | 2019-07-26 | 2019-11-01 | 中国安全生产科学研究院 | Track traffic for passenger flow monitoring and pre-alarming method and its system based on fuzzy theory |
| CN112434859A (en) * | 2020-11-26 | 2021-03-02 | 国电南瑞科技股份有限公司 | Rail transit underground station environment-friendly regulation method combined with passenger flow prediction technology |
-
2021
- 2021-10-13 CN CN202111193660.2A patent/CN113628432B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140289003A1 (en) * | 2013-03-25 | 2014-09-25 | Amadeus S.A.S. | Methods and systems for detecting anomaly in passenger flow |
| CN109961164A (en) * | 2017-12-25 | 2019-07-02 | 比亚迪股份有限公司 | Passenger flow forecast method and device |
| CN108805347A (en) * | 2018-06-05 | 2018-11-13 | 北方工业大学 | Passenger flow pool-based method for estimating passenger flow of associated area outside subway station |
| CN109086919A (en) * | 2018-07-17 | 2018-12-25 | 新华三云计算技术有限公司 | A kind of sight spot route planning method, device, system and electronic equipment |
| CN110400462A (en) * | 2019-07-26 | 2019-11-01 | 中国安全生产科学研究院 | Track traffic for passenger flow monitoring and pre-alarming method and its system based on fuzzy theory |
| CN112434859A (en) * | 2020-11-26 | 2021-03-02 | 国电南瑞科技股份有限公司 | Rail transit underground station environment-friendly regulation method combined with passenger flow prediction technology |
Non-Patent Citations (1)
| Title |
|---|
| 张清泉: "轨道交通客流预警***研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114283386A (en) * | 2022-01-28 | 2022-04-05 | 浙江传媒学院 | Analysis and adaptation intensive scene people stream real-time monitoring system based on big data |
| CN114510001A (en) * | 2022-02-18 | 2022-05-17 | 陈芳 | Factory management system and method |
| CN118096087A (en) * | 2024-04-25 | 2024-05-28 | 大连地铁运营有限公司 | Green cloud platform service system and method thereof |
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