CN112465213B - Auxiliary device and method of subway passenger information service system - Google Patents
Auxiliary device and method of subway passenger information service system Download PDFInfo
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Abstract
The invention discloses an auxiliary device and a method of a subway passenger information service system, wherein the device comprises: each weighing data acquisition unit is correspondingly arranged in a corresponding carriage of the train; each weighing data acquisition unit is used for acquiring load weight information in a corresponding carriage in real time; and transmitting the load weight information to a comfort degree calculating device through an automatic train monitoring system, analyzing and processing the received total train running information to obtain real-time passenger distribution data of each carriage of the current train, wherein the real-time passenger distribution data is used as basic data, and a congestion degree predicted value of each carriage after the current train arrives at a station and gets off passengers is calculated by combining historical data counted in advance by getting on and off passengers at the current time point of the current platform and a trend prediction algorithm, and is transmitted to a passenger information service system for displaying. The invention improves the comfort level and satisfaction degree of passengers taking the subway.
Description
Technical Field
The invention relates to the technical field of new media, in particular to an auxiliary device and method of a subway passenger information service system.
Background
At present, a passenger information service system (hereinafter referred to as a PIS system) is an important component in the whole subway operation system, and a multimedia comprehensive information system which takes a computer system as a core and enables passengers to timely and accurately know train operation information and public media information through an information display screen of a subway platform is adopted by relying on a multimedia network technology. The video information display system provides the train time information about the coming train and the direction of the train terminal station to passengers in a multimedia playing mode. The method provides convenience for passengers waiting at the platform to confirm the train shifts required to be taken and the expected taking time.
However, at present, passengers can only obtain basic operation information such as estimated arrival time of trains and terminal stations from display terminals of the platform. For the distribution situation of the passengers in the train carriages which are about to enter the station, the passengers waiting at the platform can hardly know the situation, so that the passengers waiting at certain platform doors can gather and wait the train blindly, and finally serious safety accidents such as the conflict between the passenger flow on and off the train door, the crowding and trampling, the limb conflict and the like can occur, which is also one of the fundamental factors of delayed operation caused by low efficiency of the subway getting on and off the station in the peak period.
Disclosure of Invention
The invention aims to provide an auxiliary device and method of a subway passenger information service system, which can be used for enabling a waiting passenger to obtain the congestion degree condition of each carriage of a current train after the passenger is taken off, so that the waiting passenger can orderly and uniformly queue and wait on a platform, and the comfort degree and satisfaction degree of the passenger taking a subway are improved.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
an assistance apparatus of a subway passenger information service system, comprising: each weighing data acquisition unit is correspondingly arranged in a corresponding carriage of the train; each weighing data acquisition unit is used for acquiring load weight information in a corresponding carriage in real time.
The temperature data acquisition units are correspondingly arranged in corresponding carriages of the train; each temperature data acquisition unit is used for acquiring the temperature information in the corresponding carriage in real time.
The train control management system is respectively connected with all the weighing data acquisition units and all the temperature data acquisition units; and the train control management system is used for receiving and forwarding all the load weight information and the temperature information.
And the vehicle-mounted control management subsystem is connected with the train control management system and is used for periodically summarizing all the load weight information and all the temperature information.
And the train automatic monitoring system is connected with the vehicle-mounted control management subsystem and is used for receiving the summarized load weight information and the temperature information and summarizing key information of the train to obtain total train operation information.
And the comfort degree calculating device is connected with the automatic train monitoring system and is used for analyzing and processing the received total train running information to obtain real-time passenger distribution data of each carriage of the current train, wherein the real-time passenger distribution data is used as basic data, and a congestion degree predicted value of each carriage after the current train arrives at the station and gets off passengers is calculated by combining historical data counted in advance by getting on and off passengers at the current time point of the current platform and a trend prediction algorithm.
And the passenger information service system is connected with the comfort degree calculation device and is used for outputting the key information of the train and the congestion degree predicted value of each carriage of the train to display terminals on each platform according to the platform number and the operation direction.
Preferably, the key information of the train includes: train arrival time information, train basic information and train forecast operation information.
Preferably, the method further comprises the following steps: and the communication front-end processor is used for receiving the train running total information sent by the automatic train monitoring system and forwarding the train running total information to the comfort level calculating device.
Preferably, the comfort level calculation device comprises: the system comprises a data prediction calculation module and a comfort degree calculation module;
the comfort degree calculation module is used for analyzing the received train operation total information according to the content of a communication protocol to obtain the load weight information of each carriage of all the trains at present, writing the load weight information of each carriage of all the trains into a database and informing the data prediction calculation module to update data;
the data prediction calculation module is used for calling a machine learning interface firstly after receiving a data updating event, revising a prediction error and optimizing each parameter in a prediction algorithm; then calculating a weighing predicted value of each carriage of the current train after the next platform stops and the passenger is taken off according to the latest loading weight information of each carriage of the train, the platform where the train is to stop and the current system time in the database, and returning the weighing predicted value to the comfort level calculation module;
and the comfort degree calculation module is also used for converting the weighing predicted value into the congestion degree predicted value of each carriage of the train according to the received weighing predicted value and the rated full load value of the corresponding carriage of the current train.
In another aspect, the present invention further provides an assistance method for a subway passenger information service system, including:
and acquiring a data packet and judging the message type of the data packet.
If the message type of the data packet is train compartment weighing information, analyzing the load weight information of each compartment of all the current trains according to the content of the communication protocol, and writing the load weight information of each compartment of all the trains into a database.
Calling a machine learning interface, correcting a prediction error, and optimizing each parameter in a prediction algorithm; and then calculating the predicted weighing value of each carriage of the current train after the next platform stops and the passenger gets off according to the latest loading weight information of each carriage of the train, the platform where the train is to stop and the current system time in the database.
And according to the rated full load value of each compartment of the current train, converting the weighing predicted value of each compartment of all the trains in the current period into a congestion degree predicted value of each compartment of the current train, comparing the congestion degree predicted value with the congestion degree predicted value of each compartment of the current train in the previous period, and if the congestion degree predicted value changes, sending the congestion degree predicted value of each compartment of the current train in the current period to a passenger information service system and displaying the congestion degree predicted value.
Preferably, the method further comprises the following steps: and if the message type of the data packet is the station entering information of the platform train, analyzing the data packet according to the content of the communication protocol to obtain the train group number of each platform which is about to enter the station in the current period, comparing the train group number of the train which is about to enter the station with the station entering information of the platform in the previous period, and if the train group number of the platform is changed, organizing a station train entering information change message to send to a passenger information service system and displaying the message.
Preferably, the method further comprises the following steps: and if the message type of the data packet is train compartment temperature information, analyzing the data packet according to the communication protocol content to obtain the temperature information of each compartment of all trains in the current period, comparing the temperature information with the temperature information of each compartment of the corresponding train in the previous period, and if the temperature information changes, organizing a compartment temperature information change message of the corresponding train to send to the passenger information service system and display the temperature information change message.
Preferably, the prediction algorithm is a pre-trained neural network algorithm.
Preferably, the passenger getting-on and getting-off weighing change value delta y of the train compartment corresponding to the current platform door and stopping in nearly one month is calculated by adopting the following formula:
Δy＝λ 1 x 1 +λ 2 x 2 +λ 3 x 3 +λ 4 x 4
in the formula, x 1 Representing a train stop time factor, x 2 Indicating the planned interval of train operation on that day, x 3 Representing the weather factor of the day, x 4 Representing the holding factors of large-scale activities around the subway station; lambda [ alpha ] 1 、λ 2 、λ 3 And λ 4 Respectively correspondingly representing the stop time factor x of the train 1 The planned operation interval factor x of the train on the same day 2 Weather factor x of the day 3 And the large-scale event around the subway station 4 Weight value of (a), and 1 +λ 2 +λ 3 +λ 4 1; solving the weight change value delta y of the passengers getting on and off the train compartment corresponding to the current platform door and stopping in nearly one month to obtain each weight value lambda 1 、λ 2 、λ 3 And λ 4 A linear optimal solution, which constitutes the training set;
and training the prediction algorithm by adopting the training set.
Preferably, the load weight information in the corresponding train car in the current period is used as a test set, and the test set is used as the input of the prediction algorithm, so as to obtain the predicted weighing value of each train car of the current train after stopping and getting off passengers at the next platform.
The invention has at least one of the following advantages:
1. the invention displays the comfort level measurement information such as the predicted congestion degree and the temperature of each carriage of the inbound train on the display terminal of the passenger information service system in real time in an intuitive mode, so that passengers can select the waiting position in advance according to the passenger distribution condition in the train, thereby avoiding the congestion of getting on and off the train and improving the efficiency of getting on and off the passengers.
2. The invention combines the real-time data acquisition of the train with the historical data statistics, utilizes the machine learning and passenger flow prediction technology, continuously improves the trend prediction algorithm of the passenger flow on and off the train, more accurately presents the carriage crowding degree, greatly improves the accuracy of the information release of the crowding degree, and improves the operation safety and the travel satisfaction of passengers.
Drawings
Fig. 1 is a main block diagram of an auxiliary device of a subway passenger information service system according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating an auxiliary method of a subway passenger information service system according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating a prediction algorithm in an assistance method of a subway passenger information service system according to an embodiment of the present invention.
Detailed Description
The following describes the assisting apparatus and method of a subway passenger information service system in detail with reference to the accompanying drawings and the detailed description. The advantages and features of the present invention will become more apparent from the following description. It should be noted that the drawings are in a very simplified form and are all drawn to a non-precise scale for the purpose of convenience and clarity only to aid in the description of the embodiments of the invention. To make the objects, features and advantages of the present invention comprehensible, reference is made to the accompanying drawings. It should be understood that the structures, ratios, sizes, etc. shown in the drawings and attached to the description are only for understanding and reading the disclosure of the present disclosure, and are not for limiting the scope of the present disclosure, so they do not have the essential meaning in the art, and any modifications of the structures, changes of the ratio relationships, or adjustments of the sizes, should fall within the scope of the present disclosure without affecting the efficacy and the achievable purpose of the present disclosure.
As shown in fig. 1, the auxiliary device of the subway passenger information service system provided by the present embodiment comprises: the system comprises a plurality of weighing data acquisition units (carriage load acquisition units) 101, wherein each weighing data acquisition unit 101 is correspondingly arranged in a corresponding carriage of the train; each weighing data acquisition unit 101 is used for acquiring load weight information in a corresponding carriage in real time. The number of cars provided for each train is 6 or 8.
The system comprises a plurality of temperature data acquisition units 102, wherein each temperature data acquisition unit 102 is correspondingly arranged in a corresponding compartment of the train; each of the temperature data acquisition units 102 is configured to acquire temperature information in a corresponding compartment in real time.
Each weighing data acquisition unit 101 and each temperature data acquisition unit 102 periodically output the acquired data to the train control management system 201 through a vehicle bus.
A Train Control Management System (TCMS)201 connected to all the weighing data acquisition units 101 and all the temperature data acquisition units 102, respectively; the train control management system 201 is configured to receive and forward all of the load weight information and the temperature information.
And an on-board control management subsystem (on-board CC subsystem) 202 connected to the train control management system 201, where the on-board control management subsystem 202 is configured to periodically aggregate all the load weight information and the temperature information. That is, the vehicle CC subsystem 202 lands the collected data (all the load weight information and the temperature information) of the whole train via the train-ground wireless communication network, and the collected data is finally sent to the ATS system 203 by the ground equipment.
And the train automatic monitoring system (ATS system) 203 is connected with the vehicle-mounted control management subsystem 202, and the train automatic monitoring system 203 is configured to receive the summarized load weight information and the summarized temperature information, and summarize key information of a train to obtain train operation total information.
And the comfort degree calculating device 300 is connected with the automatic train monitoring system 203, and the comfort degree calculating device 300 is used for analyzing and processing the received train running total information to obtain real-time passenger distribution data of each carriage of the current train, wherein the real-time passenger distribution data is used as basic data, and a congestion degree predicted value of each carriage after the current train arrives at the station and gets off passengers is calculated by combining historical data counted in advance by passengers getting on and off at the current time point of the current platform and a trend prediction algorithm.
And a passenger information service system (PIS system) 400 connected to the comfort level calculation device 300, wherein the passenger information service system 400 is configured to output the key information of the train and the predicted congestion level of each car of the train to a display terminal (platform PIS display terminal) 401 at each platform according to the platform number and the operation direction for display.
The key information of the train comprises: train arrival time information, train basic information and train forecast operation information.
This embodiment still includes: a communication Front End Processor (FEP)204, configured to receive train operation total information sent by the train automatic monitoring system 203 and forward the train operation total information to the comfort level calculating device 300. That is, the communication front-end processor in the machine room acquires the above-mentioned train operation total information from the ATS system 203 and sends the information to the train comfort level calculating device 300 in an interface communication manner together with the train arrival information of all the stations for unified analysis and processing.
The comfort level calculation device 300 includes: the device comprises a data prediction calculation module and a comfort degree calculation module.
The comfort degree calculation module is used for analyzing the received train operation total information according to the communication protocol content to obtain the load weight information of each carriage of all the trains at present, writing the load weight information of each carriage of all the trains into a database, and informing the data prediction calculation module to update data.
The data prediction calculation module is used for calling a machine learning interface, revising a prediction error and optimizing each parameter in a prediction algorithm after receiving a data updating event; and then calculating a weighing predicted value of each carriage of the current train after the next platform stops and passengers get off according to the latest loading weight information of each carriage of the train, the platform where the train is about to stop and the current system time in the database, and returning the weighing predicted value to the comfort level calculation module.
And the comfort degree calculation module is also used for converting the weighing predicted value into the congestion degree predicted value of each carriage of the train according to the received weighing predicted value and the rated full load value of the corresponding carriage of the current train.
The PIS server in the PIS system 400 analyzes train entrance information of each platform, a weighing predicted value of the train and a temperature real-time value (temperature information) in the carriage according to the received data packet, and synchronizes data (train entrance information of each platform, the weighing predicted value of the train and the temperature real-time value in the carriage) to the PIS terminal 401 in the PIS system 400 of each platform according to platform static configuration for displaying.
As shown in fig. 2 and 3, the present embodiment also provides an assistance method of a subway passenger information service system, including:
and acquiring a data packet (total train operation information) and judging the message type of the data packet.
And if the message type of the data packet is train compartment weighing information, analyzing the load weight information of each compartment of all the current trains according to the content of the communication protocol, and writing the load weight information of each compartment of all the trains into a database.
Calling a machine learning interface, correcting a prediction error, and optimizing each parameter in a prediction algorithm; and then calculating the predicted weighing value of each carriage of the current train after the next platform stops and the passenger gets off according to the latest loading weight information of each carriage of the train, the platform where the train is to stop and the current system time in the database.
And according to the rated full load value of each compartment of the current train, converting the weighing predicted value of each compartment of all the trains in the current period into a congestion degree predicted value of each compartment of the current train, comparing the congestion degree predicted value with the congestion degree predicted value of each compartment of the current train in the previous period, and if the congestion degree predicted value changes, sending the congestion degree predicted value of each compartment of the current train in the current period to a passenger information service system and displaying the congestion degree predicted value.
Further comprising: if the message type of the data packet is the station train entering information of the platform train, analyzing the data packet according to the content of the communication protocol to obtain the train group number (4 trains at most) of the train entering the station at each platform in the current period, comparing the train group number to be entered with the train entering information of the platform in the previous period, and if the train entering information of the platform train changes, organizing a station train entering information change message to transmit to a passenger information service system and displaying the change message.
Further comprising: and if the message type of the data packet is train compartment temperature information, analyzing the data packet according to the content of the communication protocol to obtain the temperature information of each compartment of all trains in the current period, comparing the temperature information with the temperature information of each compartment of the corresponding train in the previous period, and if the temperature information of each compartment of the corresponding train in the previous period changes, organizing a compartment temperature information change message of the corresponding train, sending the compartment temperature information change message to a passenger information service system and displaying the compartment temperature information change message.
The prediction algorithm is a pre-trained neural network algorithm.
In this embodiment, the method further includes: before the data packet is obtained, static configuration data such as a centralized station, a platform and the like are loaded, object creation and initialization are completed, and basic relation data are provided for data communication encapsulation analysis and prediction algorithm calculation. After receiving the data packet from the communication front-end processor 204 of the ATS system 203, the corresponding message type ID in the data packet is compared to determine which kind of communication information is.
In this embodiment, the method further includes: and when the communication thread receives the exit event notification, destroying all the objects, releasing the memory space and exiting the process. I.e. the auxiliary process described above is ended.
As shown in fig. 3, the training and calculation of the prediction algorithm includes the following processes: the data prediction calculation module obtains weighing data y before each train compartment enters the station and weighing data y' after the train leaves the station corresponding to the platform within one month from a historical database (historical data samples are stored in the historical database), and then the weighing change of passengers on the train compartment corresponding to the platform door is changed into the following steps:
Δy＝y'-y (1)
because there are many factors affecting passengers getting on and off a carriage, the factors are defined as variable data x, and multiplied by the eigenvalue weight lambda to obtain a formula:
Δy＝λ 1 x 1 +λ 2 x 2 +...+λ n x n +c (2)
wherein c is a constant, and lambda belongs to [ lambda ] 1 ,λ 2 ,···,λ n ]，x∈[x 1 ,x 2 ,···,x n ]。
For example, the distance between the platform door and the escalator entrance is not changed, and four factors are temporarily selected as the prediction variables, namely the train stop time factor x 1 The planned operation interval factor x of the train on the same day 2 Day weather factor x 3 Factor x for holding large-scale events around subway station 4 I.e. the formula can be simplified as:
Δy＝λ 1 x 1 +λ 2 x 2 +λ 3 x 3 +λ 4 x 4 (3)
in the formula, λ 1 、λ 2 、λ 3 And λ 4 Respectively correspondingly representing the stop time factor x of the train 1 The planned operation interval factor x of the train on the same day 2 Weather factor x of the same day 3 And the large-scale event around the subway station 4 Weight value of (a), and 1 +λ 2 +λ 3 +λ 4 ＝1。
substituting the weighing change data delta y of the last month and the variable data x into a formula, solving the formula (3) to obtain the weight (weighted value) lambda of each characteristic value 1 、λ 2 、λ 3 And λ 4 The linear optimal solution constitutes a training set for machine learning (training set for predictive algorithms).
The data prediction calculation module reads weighing data (load weight information) z of each carriage before the train enters the station and the current prediction variable value (namely x) 1 ～x 4 ) And substituting the formula to obtain the weighing predicted value of each compartment after the train is out of the station:
z'＝z+Δz＝z+λ 1 x 1 +λ 2 x 2 +λ 3 x 3 +λ 4 x 4 (4)
in the formula, z' represents a weighing predicted value of the carriage after the door of the train is closed, and delta z represents a weighing change value of the carriage before and after passengers get on or off the platform.
And training the prediction algorithm by adopting the training set.
And taking the load weight information in the corresponding train compartment in the current period as a test set, and taking the test set as the input of the prediction algorithm, thereby obtaining the weighing prediction value of each compartment of the current train after the next platform stops and passengers get off.
This embodiment has at least one of the following advantages:
1. according to the embodiment, comfort measurement information such as the predicted congestion degree and the temperature of each carriage of the inbound train is displayed on the display terminal of the passenger information service system in real time in an intuitive mode, so that passengers can select the waiting position according to the passenger distribution condition in the train in advance, the boarding and disembarking congestion is avoided, and the boarding and disembarking efficiency is improved.
2. According to the method, real-time data collection and historical data statistics of the train are combined, a trend prediction algorithm of train passenger flow on and off is continuously improved by utilizing machine learning and passenger flow prediction technologies, the carriage congestion degree is more accurately presented, the accuracy of congestion degree information issuing is greatly improved, and the operation safety and the passenger travel satisfaction degree are improved.
It is to be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It should be noted that the apparatuses and methods disclosed in the embodiments herein may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments herein. In this regard, each block in the flowchart or block diagrams may represent a module, program or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments herein may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be limited only by the attached claims.
Claims (6)
1. An assistance method of a subway passenger information service system, comprising:
acquiring a data packet and judging the message type of the data packet;
if the message type of the data packet is train compartment weighing information, analyzing the load weight information of each compartment of all the current trains according to the content of a communication protocol, and writing the load weight information of each compartment of all the trains into a database;
calling a machine learning interface, correcting a prediction error, and optimizing each parameter in a prediction algorithm; then calculating a weighing predicted value of each carriage of the current train after the next platform stops and passengers get off according to the latest loading weight information of each carriage of the train, the platform where the train is to stop and the current system time in the database;
converting the weighing predicted value of each carriage of all the trains in the current period into a congestion degree predicted value of each carriage of the current train according to the rated full load value of each carriage of the current train, comparing the predicted value with the congestion degree predicted value of each carriage of the current train in the previous period, and if the predicted value changes, sending the congestion degree predicted value of each carriage of the current train in the current period to a passenger information service system and displaying the predicted value; further comprising: if the message type of the data packet is station train entering information, analyzing the data packet according to the content of a communication protocol to obtain the train group number of each station which is about to enter the station at the current period, comparing the train group number of the station which is about to enter the station with the train entering information of the station at the previous period, and if the train entering information changes, organizing a station train entering information change message to send to a passenger information service system and displaying the message; further comprising: if the message type of the data packet is train compartment temperature information, analyzing the data packet according to the communication protocol content to obtain the temperature information of each compartment of all trains in the current period, comparing the temperature information with the temperature information of each compartment of the corresponding train in the previous period, and if the temperature information changes, organizing a compartment temperature information change message of the corresponding train to send to a passenger information service system and display the temperature information change message;
the prediction algorithm is a pre-trained neural network algorithm;
the change value delta y of the weighing of passengers getting on and off the train compartment corresponding to the current platform door and parked in nearly one month is calculated by adopting the following formula:
Δy＝λ 1 x 1 +λ 2 x 2 +λ 3 x 3 +λ 4 x 4
in the formula, x 1 Representing a train stop time factor, x 2 Indicating the planned interval of train operation on that day, x 3 Representing the weather factor of the day, x 4 Representing the holding factors of large-scale activities around the subway station; lambda 1 、λ 2 、λ 3 And λ 4 Respectively corresponding to the factors x representing the stop time of the train 1 The planned operation interval factor x of the train on the same day 2 Weather factor x of the same day 3 And the large-scale event around the subway station 4 Is a weighted value of, and λ 1 +λ 2 +λ 3 +λ 4 ＝1；
Solving the weight change value delta y of passengers getting on and off the train compartment corresponding to the current platform door and parked in nearly one month to obtain each weight value lambda 1 、λ 2 、λ 3 And λ 4 Forming a training set by a linear optimal solution;
and training the prediction algorithm by adopting the training set.
2. An assistant method of a subway passenger information service system as claimed in claim 1, wherein the load weight information of the corresponding train car in the current period is used as a test set, said test set is used as the input of said prediction algorithm, thereby obtaining the predicted weighing value of each car of said current train after the next platform stops.
3. An assist apparatus of a subway passenger information service system for implementing an assist method of the subway passenger information service system as claimed in claim 1 or 2, comprising:
each weighing data acquisition unit is correspondingly arranged in a corresponding carriage of the train; each weighing data acquisition unit is used for acquiring load weight information in a corresponding carriage in real time;
the system comprises a plurality of temperature data acquisition units, a plurality of control units and a plurality of control units, wherein each temperature data acquisition unit is correspondingly arranged in a corresponding compartment of a train; each temperature data acquisition unit is used for acquiring temperature information in a corresponding carriage in real time;
the train control management system is respectively connected with all the weighing data acquisition units and all the temperature data acquisition units; the train control management system is used for receiving and forwarding all the load weight information and the temperature information;
the vehicle-mounted control management subsystem is connected with the train control management system and is used for periodically summarizing all the load weight information and the temperature information;
the train automatic monitoring system is connected with the vehicle-mounted control management subsystem and is used for receiving the summarized load weight information and the summarized temperature information and summarizing key information of a train to obtain total train operation information;
the comfort degree calculating device is connected with the automatic train monitoring system and is used for analyzing and processing the received total train running information to obtain real-time passenger distribution data of each compartment of the current train, wherein the real-time passenger distribution data is used as basic data, and a congestion degree predicted value of each compartment after the current train arrives at a station and gets off passengers is calculated by combining historical data counted in advance by getting on and off passengers at the current time point of the current station and a trend prediction algorithm;
and the passenger information service system is connected with the comfort degree calculation device and is used for outputting the key information of the train and the congestion degree predicted value of each carriage of the train to display terminals on each platform according to the platform number and the operation direction.
4. The assistant apparatus of a subway passenger information service system as claimed in claim 3, wherein said key information of train comprises: train arrival time information, train basic information and train forecast operation information.
5. The assistant apparatus of a subway passenger information service system as claimed in claim 4, further comprising: and the communication front-end processor is used for receiving the train running total information sent by the automatic train monitoring system and forwarding the train running total information to the comfort level calculating device.
6. The assistant apparatus of a subway passenger information service system as claimed in claim 5, wherein said comfort level calculating means comprises: the system comprises a data prediction calculation module and a comfort degree calculation module;
the comfort degree calculation module is used for analyzing the received train operation total information according to the content of a communication protocol to obtain the load weight information of each carriage of all the trains at present, writing the load weight information of each carriage of all the trains into a database and informing the data prediction calculation module to update data;
the data prediction calculation module is used for calling a machine learning interface firstly after receiving a data updating event, revising a prediction error and optimizing each parameter in a prediction algorithm; then calculating a weighing predicted value of each carriage of the current train after the next platform stops and the passenger is taken off according to the latest loading weight information of each carriage of the train, the platform where the train is to stop and the current system time in the database, and returning the weighing predicted value to the comfort level calculation module;
and the comfort degree calculation module is also used for converting the weighing predicted value into the congestion degree predicted value of each carriage of the train according to the received weighing predicted value and the rated full load value of the corresponding carriage of the current train.
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| CN113572805A (en) * | 2021-05-17 | 2021-10-29 | 卡斯柯信号有限公司 | Subway rail transit passenger flow monitoring system and method based on weighing data |
| CN113810537A (en) * | 2021-09-10 | 2021-12-17 | 重庆思骑科技有限公司 | Method and system for checking mobile phone information without taking off hand in riding process |
| CN117035696B (en) * | 2023-10-09 | 2024-02-06 | 天津致新轨道交通运营有限公司 | Intelligent passenger transport management method for subway passenger transport service |
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