Method and system for measuring and calculating highest gathered number of people in station based on card swiping data in station

Abstract

The invention discloses a method and a system for measuring and calculating the highest gathered number of passengers in a station based on pull-in card swiping data, and the method comprises the steps: carrying out the fitting based on the pull-in card swiping data of passengers of different trains, obtaining a logarithmic normal distribution function of the arrival time of the passengers of the different trains, and obtaining key parameters mu and sigma; training a decision tree model; outputting key parameters mu and sigma of each train of the station to be measured and calculated through the decision tree model, obtaining a logarithmic normal distribution function of arrival time of passengers of each train, and calculating an arrival distribution curve of the passengers of each train; and superposing the passenger arrival distribution curves of the trains to obtain a passenger arrival distribution curve of the whole station, wherein the highest point of the ordinate is the highest number of gathered passengers of the station. According to the method, various factors such as station environment attributes, train attributes and different train passenger arrival laws are comprehensively considered, the problems that an existing maximum gathered passenger number calculation method is simple in consideration factor and large in calculation result deviation are solved, the building scale of a passenger station building can be reasonably determined, and railway construction investment is saved.

Description

Technical Field

[0001] The present invention relates to the field of integrated transportation technology, and in particular to a method and system for calculating the maximum number of people gathered at a station based on station entry card swiping data. Background Art

[0002] The conventional railway maximum gathering number prediction method was formed in the 1960s or 1970s. This method has some problems. The calculation model of the traditional method cannot reflect the passenger gathering process of the station. The coefficients used in the calculation mostly follow the empirical values ​​set by the existing railway lines in the last century. The impact of the urban transportation system on the waiting time of passengers at the station is not considered, and it is difficult to meet the requirements of the actual work of railway station design. At present, many large and medium-sized cities have opened urban rail transit, which has brought great changes to the travel methods and habits of passengers. In addition, due to the great changes in the composition of railway passenger cars and passenger characteristics, the maximum gathering coefficient will change greatly, and the design specifications of conventional railways cannot meet the design needs of passenger dedicated lines. In view of the above problems of the conventional calculation method, it is particularly important to explore a new method for calculating the maximum gathering number of passengers at railway stations to reasonably determine the construction scale of passenger stations and save railway construction investment. Summary of the invention

[0003] The present application provides a method and system for calculating the maximum number of people gathered at a station based on card swiping data at the station, so as to solve the problems that the traditional calculation method has simple considerations, large deviations in the calculation results, and cannot meet the requirements of actual work in railway station design.

[0004] According to the first aspect, an embodiment provides a method for calculating the maximum number of people gathered at a station based on station card swiping data, the method comprising:

[0005] Collect passenger entry card swiping data, train attribute data and station attribute data, and perform data preprocessing;

[0006] Based on the passenger card swiping data of different trains, the least squares method is used to fit the logarithmic normal distribution function of the passenger arrival time of different trains, and the key parameters μ and σ of the logarithmic normal distribution function fitted for each train are obtained;

[0007] Based on the key parameters μ and σ obtained by fitting each train and the corresponding train attribute data and station attribute data, a training set and a test set are constructed, and the established decision tree model is trained and tested. The input of the decision tree model is the train attribute data and the station attribute data, and the model output is the key parameters μ and σ;

[0008] Input the train attribute data and station attribute data related to each train predicted by the station to be measured into the trained decision tree model, output the key parameters μ and σ of the logarithmic normal distribution function of the passenger arrival time of each train, and obtain the logarithmic normal distribution function of the passenger arrival time of each train. Combined with the predicted total number of passengers corresponding to each train at the corresponding station, calculate the passenger arrival distribution curve of each train;

[0009] The passenger arrival distribution curves of each train are superimposed to obtain the passenger arrival distribution curve of the entire station. The highest point of the ordinate of the passenger arrival distribution curve of the entire station is the highest number of people gathered in the station, and the corresponding abscissa is the time point when the highest number of people gathered occurs.

[0010] Furthermore, the passenger card swiping data includes the boarding station, passenger information, and the time of card swiping at the station; the train attribute data includes departure / passing, high-speed train / conventional speed, and departure time; the station attribute data includes the level of the city, city GDP, city population, and whether there is rail transit connection.

[0011] Further, data preprocessing includes:

[0012] Filter and clean the collected data, delete invalid data and retain valid data.

[0013] Furthermore, the training set and test set are constructed based on the key parameters μ and σ obtained by fitting each train and the corresponding train attribute data and station attribute data, including:

[0014] The fitting parameter sets μ and σ of each train and 90% of the corresponding train attributes and station attribute data are used as training data. The classification and regression tree algorithm in MATLAB software is used to train the decision tree model of parameters μ and σ, and the remaining 10% of the data is used to test the regression effect of the model.

[0015] Furthermore, the method further comprises:

[0016] According to the station's rule that ticket checking starts 20 minutes before train departure and stops 5 minutes before departure, it is approximately assumed that waiting passengers leave the waiting room and enter the platform at a uniform speed within 15 minutes after security check. The passenger dissipation pattern of each train is calculated based on the departure time and the number of passengers, and the passenger dissipation distribution curve of each train is obtained. The passenger dissipation distribution curve of each train is superimposed to obtain the dissipation distribution curve of the entire station. The maximum number of people gathered in the station is determined based on the passenger arrival distribution curve of the entire station and the highest point of the ordinate of the dissipation distribution curve.

[0017] According to the second aspect, in one embodiment, a system for calculating the maximum number of people gathered at a station based on station card swiping data is provided, the system comprising:

[0018] The data collection module is used to collect passenger entry card swiping data, train attribute data and station attribute data, and perform data preprocessing;

[0019] A fitting module is used to fit the log-normal distribution function of the passenger arrival time of different trains based on the passenger card swiping data of different trains by using the least squares method, and obtain the key parameters μ and σ of the log-normal distribution function fitted for each train;

[0020] A decision tree model training module is used to construct a training set and a test set based on the key parameters μ and σ obtained by fitting each train and the corresponding train attribute data and station attribute data, and to train and test the established decision tree model. The input of the decision tree model is the train attribute data and the station attribute data, and the model output is the key parameters μ and σ;

[0021] The maximum gathering number estimation module is used to input the train attribute data and station attribute data related to each train predicted by the station to be measured into the trained decision tree model, output the key parameters μ and σ of the logarithmic normal distribution function of the passenger arrival time of each train, and obtain the logarithmic normal distribution function of the passenger arrival time of each train, and calculate the passenger arrival distribution curve of each train in combination with the predicted total number of passengers corresponding to each train at the corresponding station;

[0022] The passenger arrival distribution curves of each train are superimposed to obtain the passenger arrival distribution curve of the entire station. The highest point of the ordinate of the passenger arrival distribution curve of the entire station is the highest number of people gathered in the station, and the corresponding abscissa is the time point when the highest number of people gathered occurs.

[0023] Furthermore, the decision tree model training module is specifically used for:

[0024] The fitting parameter sets μ and σ of each train and 90% of the corresponding train attributes and station attribute data are used as training data. The classification and regression tree algorithm in MATLAB software is used to train the decision tree model of parameters μ and σ, and the remaining 10% of the data is used to test the regression effect of the model.

[0025] Furthermore, the maximum number of people gathered is calculated by the module, which is specifically used for:

[0026] According to the station's rule that ticket checking starts 20 minutes before train departure and stops 5 minutes before departure, it is approximately assumed that waiting passengers leave the waiting room and enter the platform at a uniform speed within 15 minutes after security check. The passenger dissipation pattern of each train is calculated based on the departure time and the number of passengers, and the passenger dissipation distribution curve of each train is obtained. The passenger dissipation distribution curve of each train is superimposed to obtain the dissipation distribution curve of the entire station. The maximum number of people gathered in the station is determined based on the passenger arrival distribution curve of the entire station and the highest point of the ordinate of the dissipation distribution curve.

[0027] According to the third aspect, an embodiment provides an electronic device, the device comprising: a processor and a memory;

[0028] The memory is used to store one or more program instructions;

[0029] The processor is used to run one or more program instructions to execute the steps of a method for calculating the maximum number of people gathered at a station based on station card swiping data as described in any of the above items.

[0030] According to the fourth aspect, an embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a method for calculating the maximum number of people gathered at a station based on entry card swiping data as described in any of the above items are implemented.

[0031] The present application provides a method and system for calculating the maximum number of people gathered at a station based on card entry data, which collects passenger card entry data, train attribute data, and station attribute data, and performs data preprocessing; based on the passenger card entry data of different trains, the least squares method is used to fit the logarithmic normal distribution function of the passenger arrival time of different trains, and the key parameters μ and σ of the logarithmic normal distribution function fitted for each train are obtained; based on the key parameters μ and σ fitted for each train and the corresponding train attribute data and station attribute data, a training set and a test set are constructed, and the established decision tree model is trained and tested, wherein the input of the decision tree model is the train attribute data and the station attribute data. , the model output is the key parameters μ, σ; the train attribute data and station attribute data related to each train predicted by the station to be measured are input into the trained decision tree model, the key parameters μ, σ of the logarithmic normal distribution function of the passenger arrival time of each train are output, and the logarithmic normal distribution function of the passenger arrival time of each train is obtained, and the passenger arrival distribution curve of each train is calculated in combination with the predicted total number of passengers corresponding to each train of the station; the passenger arrival distribution curve of each train is superimposed to obtain the passenger arrival distribution curve of the entire station, and the highest point of the ordinate of the passenger arrival distribution curve of the entire station is the highest number of people gathered at the station, and the corresponding abscissa is the time point when the highest number of people gathered appears. In the calculation process of the present invention, the environmental attributes of the station, the train attributes, and the passenger arrival rules of different trains are effectively combined, the factors considered are more comprehensive, and the calculation results are more convincing. The calculation results can intuitively reflect the number of passengers gathered at different times of the day in the station, and intuitively depict the dynamic change process of the number of passengers gathered in the station within a day and night, which is more instructive for station design and operation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1A flow chart of a method for calculating the maximum number of people gathered at a station based on station card swiping data provided by an embodiment of the present invention;

[0033] Figure 2 A specific implementation flow chart of a method for calculating the maximum number of people gathered at a station based on station card swiping data provided by an embodiment of the present invention;

[0034] Figure 3 A diagram of the train passenger gathering and distribution process in a method for calculating the maximum number of people gathered at a station based on station card swiping data provided by an embodiment of the present invention;

[0035] Figure 4 A dynamic fitting diagram of the passenger gathering and distribution process of a station in a method for calculating the maximum number of people gathered at a station based on station card swiping data provided by an embodiment of the present invention;

[0036] Figure 5 A schematic diagram of the logical structure of a system for calculating the maximum number of people gathered at a station based on station card swiping data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present application better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present application are not shown or described in the specification, this is to avoid the core part of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0038] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0039] The first embodiment of the present invention provides a method for calculating the maximum number of people gathered at a station based on the data of card entry. Figure 1 and Figure 2 Provide detailed explanation.

[0040] like Figure 1 As shown, in step S100, passenger entry card swiping data, train attribute data and station attribute data are collected and data preprocessing is performed.

[0041] The above steps specifically include:

[0042] Collect passenger card swiping data at existing railway passenger stations (boarding station, passenger information, card swiping time, etc.), train attribute data (originating / passing, high-speed / conventional speed, departure time, etc.), and station-related attributes (city level, city GDP, city population, whether there is rail transit connection, etc.) to establish a basic database.

[0043] Then screen and clean the data, delete invalid data, retain valid data, and when conditions permit, expand the data volume as much as possible.

[0044] like Figure 1 As shown, in step S200, based on the card swiping data of passengers entering the station on different trains, the least squares method is used to fit the log-normal distribution function of the passenger arrival time of different trains, and the key parameters μ and σ of the log-normal distribution function fitted for each train are obtained.

[0045] Specifically, the passenger card swiping data of different trains are extracted from the database, and the logarithmic normal distribution function of the passenger arrival time of different trains is fitted and analyzed by the least squares method using MATLAB software in combination with the train attributes and station attributes, and the key parameters μ and σ of the fitting function of each train are stored.

[0046] lnX~N(μ,σ 2 )

[0047] Among them, X is the passenger's advance arrival time, which can reflect the arrival pattern of passengers on a specific train.

[0048] like Figure 1 As shown, in step S300, a training set and a test set are constructed based on the key parameters μ and σ obtained by fitting each train and the corresponding train attribute data and station attribute data, and the established decision tree model is trained and tested. The input of the decision tree model is the train attribute data and the station attribute data, and the model output is the key parameters μ and σ.

[0049] Specifically, a decision tree model is adopted, and the fitting parameter sets μ, σ of each train obtained by 90% of the fitting in step S200 and the corresponding train attributes and station attribute data are used as training data. The classification and regression tree algorithm in MATLAB software is used to train a decision tree model with parameters μ and σ, and the remaining 10% of the data in step S200 are used to test the regression effect of the model.

[0050] like Figure 1As shown, in step S400, the train attribute data and station attribute data related to each train at the station to be measured are input into the trained decision tree model, and the key parameters μ and σ of the log-normal distribution function of the passenger arrival time of each train are output, and the log-normal distribution function of the passenger arrival time of each train is obtained. Combined with the total number of passengers corresponding to each train at the station, the passenger arrival distribution curve of each train is calculated.

[0051] Specifically, with the help of the established decision tree model, if the station attributes and train attributes of any train are known, the key parameters μ and σ of the passenger arrival regularity distribution function of the train can be determined, and the log-normal distribution function of the arrival regularity of the train can be obtained.

[0052] [X 车站属性 , X 列车属性 ]→N(μ,σ 2 )

[0053] According to the passenger arrival distribution function of a certain train K, combined with the total number of passengers of each passenger train corresponding to the station (the total number here refers to the total number of passengers who take the Kth train through the station. The training set is obtained through the card swiping data of existing stations. This solution will mainly be used in station design in the future. The total number of passengers is the predicted value in the design process of the railway project to which it belongs), the distribution curve of the passengers arriving at the station of the Kth train (the curve of the number of people gathering over time) can be obtained, as shown in Figure 3 Similarly, the passenger arrival distribution curve of each passenger train at the station can be obtained.

[0054] The security check boarding time of each train can be used to determine the security check boarding rules and dissipation curves of each train. Generally, the station starts checking tickets 20 minutes before the train departs and stops checking tickets 5 minutes before departure. It can be approximately assumed that the waiting passengers leave the waiting room and enter the platform at a uniform speed within 15 minutes. Therefore, the passenger dissipation rule of the kth train can be calculated by the train departure time and the number of passengers, and the passenger dissipation process curve of the kth train (the curve of the number of people gathering over time) can be obtained, such as Figure 3 shown.

[0055] like Figure 1 As shown, in step S500, the passenger arrival distribution curves of each train are superimposed to obtain the passenger arrival distribution curve of the entire station, and the highest point of the vertical coordinate of the passenger arrival distribution curve of the entire station is the highest number of people gathered in the station, and the corresponding horizontal coordinate is the time point when the highest number of people gathered occurs.

[0056] Specifically, by using the station passenger arrival distribution curve and the station passenger dissipation distribution curve obtained in step S400, and by using the graphical method, the passenger arrival and dissipation curves of each train are superimposed to obtain the overall passenger arrival and dissipation distribution curve of the station, such as Figure 4As shown, the highest point is the maximum number of passengers gathered at the station.

[0057] Specifically, the passenger gathering process of a single train is plotted. According to the train departure schedule, the number of passengers, the log-normal distribution of arrivals, and the distribution curve of the train passengers, the passenger arrival distribution curve of the train can be obtained. The passenger gathering and distribution process of different trains can be accumulated to obtain the passenger gathering and distribution process of the station throughout the day. The highest point of the ordinate is the highest number of passengers gathered at the station on that day, and the corresponding abscissa is the time point when the highest number of passengers gathered occurs.

[0058] The formulaic calculation process for the maximum number of people gathered at a station is as follows:

[0059]

[0060] ——The number of people who entered the station by swiping their cards at time t on the kth train;

[0061] ——The number of people who checked their tickets and boarded the kth train at time t.

[0062] The embodiment of the present invention provides a method for calculating the maximum number of people gathered at a station based on card entry data, which collects passenger card entry data, train attribute data and station attribute data, and performs data preprocessing; based on the passenger card entry data of different trains, the least squares method is used to fit the logarithmic normal distribution function of the passenger arrival time of different trains, and the key parameters μ and σ of the logarithmic normal distribution function fitted for each train are obtained; based on the key parameters μ and σ fitted for each train and the corresponding train attribute data and station attribute data, a training set and a test set are constructed, and the established decision tree model is trained and tested, wherein the input of the decision tree model is the train attribute data and the station attribute data. , the model output is the key parameters μ, σ; the train attribute data and station attribute data related to each train predicted by the station to be measured are input into the trained decision tree model, the key parameters μ, σ of the logarithmic normal distribution function of the passenger arrival time of each train are output, and the logarithmic normal distribution function of the passenger arrival time of each train is obtained, and the passenger arrival distribution curve of each train is calculated in combination with the predicted total number of passengers corresponding to each train of the station; the passenger arrival distribution curve of each train is superimposed to obtain the passenger arrival distribution curve of the entire station, and the highest point of the ordinate of the passenger arrival distribution curve of the entire station is the highest number of people gathered at the station, and the corresponding abscissa is the time point when the highest number of people gathered appears. In the calculation process of the present invention, the environmental attributes of the station, the train attributes, and the passenger arrival rules of different trains are effectively combined, the factors considered are more comprehensive, and the calculation results are more convincing. The calculation results can intuitively reflect the number of passengers gathered at different times of the day in the station, and intuitively depict the dynamic change process of the number of passengers gathered in the station within a day and night, which is more instructive for station design and operation management.

[0063] Corresponding to the above-disclosed method for calculating the maximum number of people gathered at a station based on the card entry data, the embodiment of the present invention further discloses a system for calculating the maximum number of people gathered at a station based on the card entry data, such as Figure 5 As shown, it specifically includes:

[0064] The data collection module is used to collect passenger entry card swiping data, train attribute data and station attribute data, and perform data preprocessing;

[0065] A fitting module is used to fit the log-normal distribution function of the passenger arrival time of different trains based on the passenger card swiping data of different trains by using the least squares method, and obtain the key parameters μ and σ of the log-normal distribution function fitted for each train;

[0066] A decision tree model training module is used to construct a training set and a test set based on the key parameters μ and σ obtained by fitting each train and the corresponding train attribute data and station attribute data, and to train and test the established decision tree model. The input of the decision tree model is the train attribute data and the station attribute data, and the model output is the key parameters μ and σ;

[0067] The maximum gathering number estimation module is used to input the train attribute data and station attribute data related to each train of the station to be measured into the trained decision tree model, output the key parameters μ and σ of the logarithmic normal distribution function of the passenger arrival time of each train, and obtain the logarithmic normal distribution function of the passenger arrival time of each train, and calculate the passenger arrival distribution curve of each train in combination with the total number of passengers corresponding to each train at the corresponding station;

[0068] The passenger arrival distribution curves of each train are superimposed to obtain the passenger arrival distribution curve of the entire station. The highest point of the ordinate of the passenger arrival distribution curve of the entire station is the highest number of people gathered in the station, and the corresponding abscissa is the time point when the highest number of people gathered occurs.

[0069] Furthermore, the decision tree model training module is specifically used for:

[0070] The fitting parameter sets μ and σ of each train and 90% of the corresponding train attributes and station attribute data are used as training data. The classification and regression tree algorithm in MATLAB software is used to train the decision tree model of parameters μ and σ, and the remaining 10% of the data is used to test the regression effect of the model.

[0071] Furthermore, the maximum number of people gathered is calculated by the module, which is specifically used for:

[0072] According to the station's rule that ticket checking starts 20 minutes before train departure and stops 5 minutes before departure, it is approximately assumed that waiting passengers leave the waiting room and enter the platform at a uniform speed within 15 minutes after security check. The passenger dissipation pattern of each train is calculated based on the departure time and the number of passengers, and the passenger dissipation distribution curve of each train is obtained. The passenger dissipation distribution curve of each train is superimposed to obtain the dissipation distribution curve of the entire station. The maximum number of people gathered in the station is determined based on the passenger arrival distribution curve of the entire station and the highest point of the ordinate of the dissipation distribution curve.

[0073] It should be noted that for the detailed description of a system for calculating the maximum number of people gathered at a station based on card swiping data provided in an embodiment of the present invention, reference can be made to the relevant description of a method for calculating the maximum number of people gathered at a station based on card swiping data provided in an embodiment of the present application, which will not be repeated here.

[0074] In addition, an embodiment of the present invention also provides an electronic device, comprising: a processor and a memory; the memory is used to store one or more program instructions; the processor is used to run one or more program instructions to execute the steps of a method for calculating the maximum number of people gathered at a station based on entry card swiping data as described in any of the above items.

[0075] It should be noted that, for the detailed description of an electronic device provided in an embodiment of the present invention, reference can be made to the relevant description of a method for calculating the maximum number of people gathered at a station based on card swiping data provided in an embodiment of the present application, which will not be repeated here.

[0076] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a method for calculating the maximum number of people gathered at a station based on entry card swiping data as described in any of the above items are implemented.

[0077] It should be noted that, for the detailed description of a computer-readable storage medium provided in an embodiment of the present invention, reference can be made to the relevant description of a method for calculating the maximum number of people gathered at a station based on card swiping data provided in an embodiment of the present application, which will not be repeated here.

[0078] Those skilled in the art will appreciate that all or part of the functions of the various methods in the above-mentioned embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to implement the above-mentioned functions. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the above-mentioned functions can be implemented. In addition, when all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can also be stored in a storage medium such as a server, another computer, disk, optical disk, flash disk or mobile hard disk, and can be downloaded or copied and saved in the memory of the local device, or the system of the local device is updated, and when the program in the memory is executed by the processor, all or part of the functions in the above-mentioned embodiments can be implemented.

[0079] The above specific examples are used to illustrate the present invention, which is only used to help understand the present invention and is not intended to limit the present invention. For those skilled in the art, according to the concept of the present invention, some simple deductions, modifications or substitutions can be made.

Claims

1. A method for calculating the maximum number of people gathered at a station based on station card swiping data, characterized in that: The method comprises: Collect passenger entry card swiping data, train attribute data and station attribute data, and perform data preprocessing; Based on the passenger card swiping data of different trains, the least squares method is used to fit the logarithmic normal distribution function of the passenger arrival time of different trains, and the key parameters μ and σ of the logarithmic normal distribution function fitted for each train are obtained; Based on the key parameters μ and σ obtained by fitting each train and the corresponding train attribute data and station attribute data, a training set and a test set are constructed, and the established decision tree model is trained and tested. The input of the decision tree model is the train attribute data and the station attribute data, and the model output is the key parameters μ and σ; The train attribute data and station attribute data related to each train predicted by the station to be measured are input into the trained decision tree model, and the key parameters μ and σ of the logarithmic normal distribution function of the passenger arrival time of each train are output. The logarithmic normal distribution function of the passenger arrival time of each train is obtained, and the passenger arrival distribution curve of each train is calculated in combination with the predicted total number of passengers corresponding to each train at the corresponding station; The passenger arrival distribution curves of each train are superimposed to obtain the passenger arrival distribution curve of the entire station. The highest point of the ordinate of the passenger arrival distribution curve of the entire station is the highest number of people gathered in the station, and the corresponding abscissa is the time point when the highest number of people gathered occurs.

2. A method for calculating the maximum number of people gathered at a station based on station card swiping data as claimed in claim 1, characterized in that: The passenger card swiping data includes the boarding station, passenger information, and card swiping time; the train attribute data includes departure / passing, high-speed train / conventional speed, and departure time; the station attribute data includes the city level, city GDP, city population, and whether there is rail transit connection.

3. A method for calculating the maximum number of people gathered at a station based on station card swiping data as claimed in claim 1, characterized in that: Data preprocessing, including: Screen and clean the collected data, delete invalid data and retain valid data.

4. A method for calculating the maximum number of people gathered at a station based on station card swiping data as claimed in claim 1, characterized in that: The training set and test set are constructed based on the key parameters μ and σ obtained by fitting each train and the corresponding train attribute data and station attribute data, including: The fitting parameter sets μ and σ of each train and 90% of the corresponding train attributes and station attribute data are used as training data. The classification and regression tree algorithm in MATLAB software is used to train the decision tree model of parameters μ and σ, and the remaining 10% of the data is used to test the regression effect of the model.

5. A method for calculating the maximum number of people gathered at a station based on station card swiping data as claimed in claim 1, characterized in that: The method further comprises: According to the station's rule that ticket checking starts 20 minutes before train departure and stops 5 minutes before departure, it is approximately assumed that waiting passengers leave the waiting room and enter the platform at a uniform speed within 15 minutes after security check. The passenger dissipation pattern of each train is calculated based on the departure time and the number of passengers, and the passenger dissipation distribution curve of each train is obtained. The passenger dissipation distribution curve of each train is superimposed to obtain the dissipation distribution curve of the entire station. The maximum number of people gathered in the station is determined based on the passenger arrival distribution curve of the entire station and the highest point of the ordinate of the dissipation distribution curve.

6. A system for calculating the maximum number of people gathered at a station based on station card swiping data, characterized in that: The system comprises: The data collection module is used to collect passenger entry card swiping data, train attribute data and station attribute data, and perform data preprocessing; A fitting module is used to fit the log-normal distribution function of the passenger arrival time of different trains based on the passenger card swiping data of different trains by using the least squares method, and obtain the key parameters μ and σ of the log-normal distribution function fitted for each train; A decision tree model training module is used to construct a training set and a test set based on the key parameters μ and σ obtained by fitting each train and the corresponding train attribute data and station attribute data, and to train and test the established decision tree model. The input of the decision tree model is the train attribute data and the station attribute data, and the model output is the key parameters μ and σ; The maximum gathering number estimation module is used to input the train attribute data and station attribute data related to each train predicted by the station to be measured into the trained decision tree model, output the key parameters μ and σ of the logarithmic normal distribution function of the passenger arrival time of each train, and obtain the logarithmic normal distribution function of the passenger arrival time of each train, and calculate the passenger arrival distribution curve of each train in combination with the predicted total number of passengers corresponding to each train at the corresponding station; The passenger arrival distribution curves of each train are superimposed to obtain the passenger arrival distribution curve of the entire station. The highest point of the ordinate of the passenger arrival distribution curve of the entire station is the highest number of people gathered in the station, and the corresponding abscissa is the time point when the highest number of people gathered occurs.

7. A station maximum gathering number estimation system based on station card swiping data according to claim 6, characterized in that: The decision tree model training module is specifically used for: The fitting parameter sets μ and σ of each train and 90% of the corresponding train attributes and station attribute data are used as training data. The classification and regression tree algorithm in MATLAB software is used to train the decision tree model of parameters μ and σ, and the remaining 10% of the data is used to test the regression effect of the model.

8. The system for calculating the maximum number of people gathered at a station based on the card entry data according to claim 6, characterized in that: The maximum number of people gathered is calculated by the module, which is specifically used for: According to the station's rule that ticket checking starts 20 minutes before train departure and stops 5 minutes before departure, it is approximately assumed that waiting passengers leave the waiting room and enter the platform at a uniform speed within 15 minutes after security check. The passenger dissipation pattern of each train is calculated based on the departure time and the number of passengers, and the passenger dissipation distribution curve of each train is obtained. The passenger dissipation distribution curve of each train is superimposed to obtain the dissipation distribution curve of the entire station. The maximum number of people gathered in the station is determined based on the passenger arrival distribution curve of the entire station and the highest point of the ordinate of the dissipation distribution curve.

9. An electronic device, characterized in that: The device comprises: a processor and a memory; The memory is used to store one or more program instructions; The processor is used to run one or more program instructions to execute the steps of a method for calculating the maximum number of people gathered at a station based on entry card swiping data as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for calculating the maximum number of people gathered at a station based on entry card swiping data as described in any one of claims 1 to 5.

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