A method, system and terminal for analyzing congestion points of locomotive transport group

Abstract

The present invention relates to a method, system and terminal for analyzing the blocking point of a locomotive transport group, and relates to the field of railway traffic management, which includes obtaining the current time; determining the driving link from a preset link time database according to the current time; determining the start time from a preset link time database according to the driving link; calculating the difference between the current time and the start time, and defining it as the link duration; determining the driving blocking point analysis model from a preset driving blocking point analysis database according to the driving link; determining the blocking point probability from the driving blocking point analysis model according to the link duration; when the blocking point probability exceeds the preset blocking point occurrence interval, controlling the preset warning device according to the driving link to issue a driving blocking point warning. The present application has the effect of improving the efficiency of railway transportation and being able to timely predict the blocking points that appear.

Description

Technical Field

[0001] The present invention relates to the field of railway traffic management, and in particular to a method, system and terminal for analyzing congestion points of a locomotive transport group. Background Art

[0002] The locomotive transport group blockage refers to the location where the transportation efficiency is reduced or interrupted due to various reasons during the railway transportation process.

[0003] In the existing technology, the railway hub is a key node in my country's railway transportation network, and its operating efficiency directly affects the dispatching efficiency of the entire railway system. Taking Xuzhou North Hub as an example, the station faces many problems such as late attendance, congestion in maintenance operations, and obstacles in receiving trains. The traditional dispatching management based on manual experience can no longer meet the increasingly complex needs.

[0004] When there are many railway lines and the railway coverage is wide, it is not easy for managers to monitor the operation of all routes in real time, which leads to managers failing to discover traffic jams in time, thereby reducing the efficiency of railway transportation. Summary of the invention

[0005] In order to improve the efficiency of railway transportation and timely predict the occurrence of congestion points, the present invention provides a locomotive transportation group congestion point analysis method, system and terminal.

[0006] In a first aspect, the present invention provides a method for analyzing a congestion point of a locomotive transport group, which adopts the following technical solution:

[0007] A method for analyzing a blockage point of a locomotive transport group, comprising:

[0008] Get the current time;

[0009] Determine the driving link from the preset link time database according to the current time;

[0010] Determine the start time from a preset link time database according to the driving link;

[0011] Calculate the difference between the current time and the start time and define it as the session duration;

[0012] Determine a driving congestion point analysis model from a preset driving congestion point analysis database according to the driving link;

[0013] Determine the probability of a traffic jam from the traffic jam analysis model based on the duration of each link;

[0014] When the probability of a jam exceeds the preset jam occurrence interval, a jam warning is issued according to the preset warning device controlled by the driving link.

[0015] By adopting the above technical solution, the driving operation includes multiple links. By collecting the data of the driving operation and obtaining the logistic regression models of different links based on the data training, the probability of occurrence of blockages in different links is predicted through different logistic regression models, and then the blockages in each link of the driving operation are predicted in time, so as to dispatch trains in time to reduce the occurrence of blockages and improve the efficiency of railway transportation.

[0016] Optionally, a line congestion point analysis method is also included, and the line congestion point analysis method includes:

[0017] Get the line number of the line;

[0018] Determine train traffic data from a preset train traffic database according to the current time, the route number and the preset route prediction duration;

[0019] Determine the number of stops from a preset stop number matching model according to train traffic data;

[0020] When the number of intermediate stops exceeds the preset intermediate stop congestion interval, the preset warning device is controlled according to the line number to issue a line congestion warning.

[0021] By adopting the above technical solutions, time series prediction models are established for major lines. The model parameters are fitted using historical data to predict the number of stops on each line in the future. Once the preset threshold is exceeded, the line is judged to be blocked, and trains are dispatched in time to reduce the number of stops and improve the efficiency of railway transportation.

[0022] Optionally, a station congestion point analysis method is also included, and the station congestion point analysis method includes:

[0023] Get the station number of the station;

[0024] Determine the inbound train number from the preset inbound database according to the current time, station number and the preset station predicted duration;

[0025] Determine the outbound train number from the preset outbound database according to the current time, station number and the preset station predicted duration;

[0026] Calculate the sum of the number of inbound trains and the number of outbound trains, and define it as the dispatched train number;

[0027] When the dispatched train number falls into the preset dispatching congestion interval, the preset warning device is controlled according to the station number to issue a station dispatching congestion warning.

[0028] By adopting the above technical solution, a corresponding time series prediction model is established for the stations that are of key concern to the railway transportation hub. The number of trains entering and leaving the station is taken as the target variable. It is predicted whether there will be too many trains entering and leaving the station in the future, resulting in station scheduling congestion. Then, trains are dispatched in time to reduce the number of trains entering and leaving the station and improve the efficiency of railway transportation.

[0029] Optionally, the station congestion analysis method further includes:

[0030] Determine the entry vehicle number from the preset entry database according to the current time and station number;

[0031] Determine the entry dwell time and entry time from the preset entry database according to the entry vehicle number;

[0032] Calculate the sum of the entry time and the entry residence time, and define it as the exit time;

[0033] Determine the outbound train number from the preset outbound train number matching model according to the outbound time, the current time and the preset inbound forecast duration;

[0034] Determine the entry train number from the preset entry database according to the current time, station number and preset entry prediction time;

[0035] Calculate the difference between the number of trains entering the warehouse and the number of trains leaving the warehouse, and define it as the actual number of trains;

[0036] When the actual train number falls into the preset depot congestion interval, the preset warning device is controlled according to the station number to issue a depot congestion warning.

[0037] By adopting the above technical solution, for the key stations of the railway transportation hub, the number of trains entering the depot and leaving the depot is used as the target variable to predict the number of trains that will actually enter the depot in the future, so that when there are too many trains actually entering the depot, it will be determined as a congestion.

[0038] Optionally, the station congestion analysis method further includes:

[0039] Determine the demobilization vehicle number from the preset demobilization database according to the current time, the station number and the preset demobilization prediction duration;

[0040] Determine the number of people leaving work from a preset vehicle and personnel database according to the leaving vehicle number;

[0041] When the number of people leaving the station falls into the preset leaving congestion interval, the preset warning device is controlled according to the station number to issue a leaving congestion warning.

[0042] By adopting the above technical solution, the number of people leaving work in the future can be predicted for key stations in railway transportation hubs, so that when the number of people leaving work is too large, it can be determined as a congestion, and trains can be dispatched to ensure that people leave work in an orderly manner during peak hours.

[0043] Optionally, a personnel supply bottleneck analysis method is also included, and the personnel supply bottleneck analysis method includes:

[0044] Get the actual number of people assigned to a class;

[0045] According to the actual number of people assigned to a class, determine whether the actual number of people assigned to a class is lower than the historical average from the preset class assignment history judgment model;

[0046] When the actual number of people on duty is lower than the historical average, the preset warning device will be controlled to issue a congestion warning during the break time;

[0047] Determine the number of people scheduled for shifts from a preset scheduled shift database according to the current time;

[0048] Calculate the difference between the planned number of people on a shift and the actual number of people on a shift, and define it as the number of people on a shift difference;

[0049] Calculate the quotient of the difference in the number of people assigned to a class and the planned number of people assigned to a class, and define it as the shortage ratio;

[0050] When the shortage ratio falls into a preset planned supply congestion interval, the preset warning device is controlled to issue a planned supply congestion warning.

[0051] By adopting the above technical solution, historical actual shift data is collected and the average value of historical shifts is calculated. When the actual number of shift personnel is less than the average value of historical shift personnel, it means that there are more people taking a break at this time, that is, the rest time of the personnel is too concentrated. At this time, a rest time congestion warning is issued to remind managers to optimize the personnel allocation plan, collect historical planned shift data, and establish an optimization model to minimize the deviation between the planned shift data and the actual shift data. When the actual number of shifts is significantly lower than the planned number of shifts, it means that the average workload of the shift personnel has increased significantly. At this time, a planned supply congestion warning is issued to remind managers to optimize the personnel allocation plan.

[0052] Optionally, the personnel supply bottleneck analysis method further includes:

[0053] Get the employee's employee number;

[0054] Determine check-in time from the preset apartment management database based on employee number;

[0055] Calculate the difference between the current time and the check-in time and define it as the rest time;

[0056] When the rest time falls into the preset rest time congestion interval, the preset warning device is controlled according to the employee number to issue a rest time congestion warning.

[0057] By adopting the above technical solution, the actual check-in and check-out status of employee apartments can be obtained, so as to calculate the rest time of each employee, and when the employee's rest time is too short, the rest time bottleneck will be issued to remind managers to optimize the personnel deployment plan.

[0058] Optionally, a method for processing personnel supply bottlenecks is also included, and the method for processing personnel supply bottlenecks includes:

[0059] When the rest time falls into the preset rest time congestion interval, the check-in number and rest time of the person who is still in the apartment and has the earliest check-in time are determined from the preset apartment management database according to the current time;

[0060] Calculate the difference between the current time and the rest time and define it as the check-in duration;

[0061] When the check-in duration does not fall within the preset rest time congestion interval, the preset attendance management device is controlled according to the employee number to issue a rest notice, and the preset attendance management device is controlled according to the check-in number to issue an attendance notice.

[0062] By adopting the above technical solution, when the staff on duty do not have enough rest time, the employee who is located in the apartment and has checked in the earliest time can be found according to the check-in record of the apartment, so as to adjust the shift in time, thereby reducing the situation of insufficient rest for the staff.

[0063] In the second aspect, the present application provides a locomotive transport group congestion analysis system, which adopts the following technical solution:

[0064] A locomotive transport group congestion analysis system, comprising:

[0065] The acquisition module is used to obtain the current time, line number, station number, actual number of people on duty and employee number;

[0066] A memory for storing a program of a control method of any of the above-mentioned methods for analyzing congestion points of a locomotive transport group;

[0067] The program in the processor and the memory can be loaded and executed by the processor to implement the control method of any of the above-mentioned locomotive transport group congestion analysis methods.

[0068] In a third aspect, the present application provides a smart terminal, which adopts the following technical solution:

[0069] An intelligent terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute any of the above-mentioned locomotive transport group congestion analysis methods.

[0070] By adopting the above technical solution, the driving operation includes multiple links. By collecting the data of the driving operation and obtaining the driving congestion analysis models of different links according to the data training, the probability of congestion in different links is predicted through different driving congestion analysis models, and then the congestion in each link of the driving operation is predicted in time, so as to dispatch trains in time to reduce the occurrence of congestion and improve the efficiency of railway transportation.

[0071] In summary, the present application includes at least one of the following beneficial technical effects:

[0072] 1. The driving operation includes multiple links. By collecting the data of the driving operation and training the logistic regression models of different links based on the data, the probability of the occurrence of the blocking points in different links can be predicted through different logistic regression models, and then the blocking points in each link of the driving operation can be predicted in time, so as to dispatch the trains in time to reduce the occurrence of blocking points and improve the efficiency of railway transportation;

[0073] 2. By predicting congestion in both lines and stations through the time series prediction model, it is possible to more comprehensively diagnose the possible congestion problems in railway transportation hubs, integrate the line and station prediction results, form a comprehensive congestion warning, and provide strong support for dispatching decisions;

[0074] 3. Through the optimization modeling method, we analyze the two types of personnel supply bottlenecks: personnel supply and rest time. By combining the two dimensions of crew members and trains, we can achieve all-round bottleneck monitoring and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a flow chart of a method for analyzing the blocking points of a locomotive transport group;

[0076] Figure 2 It is a flow chart of the line congestion analysis method;

[0077] Figure 3 This is the process of station congestion analysis method Figure 1 ;

[0078] Figure 4 This is the process of station congestion analysis method Figure 2 ;

[0079] Figure 5 This is the process of station congestion analysis method Figure 3 ;

[0080] Figure 6 The process of analyzing the bottleneck of personnel supply Figure 1 ;

[0081] Figure 7 The process of analyzing the bottleneck of personnel supply Figure 2;

[0082] Figure 8 It is a flow chart of the method for handling bottlenecks in personnel supply. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the following Figure 1 -Attached Figure 8 It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0084] The embodiment of the present application discloses a method, system and terminal for analyzing the congestion of a locomotive transport group. In the present invention, various models are used for calculation, such as the logistic regression model is used for the starting congestion, the time series analysis is used for the receiving congestion, and the optimization modeling is used for the personnel supply congestion. It also combines the idea of ​​blending the two dimensions of crew members and trains, and can achieve all-round congestion monitoring and early warning.

[0085] Reference Figure 1 , a method for analyzing a blockage point of a locomotive transport group comprises the following steps:

[0086] Step 100: Get the current time.

[0087] The current time refers to the current moment, which can be obtained through a timer. The method for obtaining the current time is selected by the staff based on actual conditions and will not be elaborated here.

[0088] Step 101: Determine the driving phase from a preset phase time database according to the current time.

[0089] The link time database refers to a database that records the start and end time of each link of the driving operation. The driving operation generally includes multiple links such as attendance delay, attendance time, car reception time, maintenance time, depot time, station organization, wind test time and departure time. Among them, attendance delay refers to the difference between the actual attendance time and the planned attendance time; attendance time refers to the attendance alcohol test time to the attendance completion time; car reception time refers to the time from the attendance completion to the card insertion; maintenance time refers to the time from the card insertion to the first train movement; depot time refers to the time from the first train movement to pressing the exit key; station organization refers to the time from depot to proper coupling; wind test time refers to the time from proper coupling to the start of the first wind charging; departure time refers to the time from the completion of the braking test to the opening of the ground signal. Whenever a link is completed, the time will be recorded in the corresponding position in the link time database.

[0090] The driving link is the link in the current driving operation matched from the link time database according to the current time.

[0091] Step 102: Determine the start time from a preset link time database according to the driving link.

[0092] The start time is the start time of the driving link matched from the link time database, that is, the end time of the link before the driving link.

[0093] Step 103: Calculate the difference between the current time and the start time, and define it as the session duration.

[0094] The session duration refers to the length of time spent in the driving session.

[0095] Step 104: Determine a driving congestion point analysis model from a preset driving congestion point analysis database according to the driving link.

[0096] The driving congestion analysis database refers to a database that records different driving congestion analysis models, wherein the driving congestion analysis model refers to multiple different logistic regression models obtained through different sample training. The driving congestion analysis model includes attendance delay analysis model, attendance time analysis model, vehicle pickup time analysis model, maintenance time analysis model, warehouse delivery time analysis model, station organization analysis model, test wind time analysis model and departure time analysis model. Each driving congestion analysis model corresponds to a link of the driving operation one by one, and the driving congestion analysis model of the corresponding link can match the probability of congestion in each link according to the length of time passed in each link.

[0097] The driving congestion analysis model is a neural network model corresponding to the driving link determined from the driving congestion analysis database.

[0098] Step 105: Determine the jam probability from the driving jam analysis model according to the link duration.

[0099] The jam probability is the probability value of a jam occurring in the driving process matched by the driving jam analysis model.

[0100] Step 106: When the probability of a traffic jam exceeds a preset traffic jam occurrence interval, a preset warning device is controlled according to the driving link to issue a driving traffic jam warning.

[0101] The congestion point occurrence interval refers to the pre-set probability interval for the occurrence of congestion points. The congestion point occurrence interval is selected by the staff based on actual conditions and will not be elaborated here.

[0102] The warning device refers to a device used to remind railway traffic management personnel of the occurrence of a traffic jam. The warning device is selected by the staff according to the actual situation and will not be described in detail here. The probability of a traffic jam exceeding the traffic jam occurrence interval means that the probability of a traffic jam in the driving process exceeds the preset threshold, that is, a traffic jam is likely to occur at this time. At this time, the warning device sends a traffic jam warning corresponding to the driving process to the management personnel to remind the management personnel to dispatch trains in time to reduce the occurrence of traffic jams.

[0103] The traffic jam warning is a warning issued by a warning device. The form of the traffic jam warning is selected by the staff according to the actual situation and will not be elaborated here.

[0104] Reference Figure 2 , the line congestion analysis method includes the following steps:

[0105] Step 200: Get the line number of the line.

[0106] The line number refers to a pre-set number used to distinguish railway lines. Each line number corresponds to a railway line one-to-one. The line number can be automatically read through the railway traffic management system. The setting and acquisition methods of the line number are selected by the staff based on actual conditions and will not be elaborated here.

[0107] Step 201: Determine train traffic data from a preset train traffic database according to the current time, the route number and the preset route prediction duration.

[0108] The predicted route duration refers to the unit time length set in advance to predict congestion points on the route. The predicted route duration is selected by the staff based on actual conditions and will not be elaborated here.

[0109] The train passage database records the trains passing through each railway line and their passing times. The train passage data is the trains passing through the railway with the line number and their passing times within the time length of the line predicted length from the current time matched from the train passage database.

[0110] Step 202: Determine the number of stops from a preset stop number matching model according to train traffic data.

[0111] The stop number matching model refers to a time series prediction model obtained through sample training in advance. The stop number matching model can determine the number of stops that occur during the predicted length of time of the line from the current time based on train traffic data.

[0112] Step 203: When the number of intermediate stops exceeds the preset intermediate stop congestion interval, a preset warning device is controlled according to the line number to issue a line congestion warning.

[0113] The stop and congestion interval refers to the pre-set interval of the number of stops where congestion occurs. The stop and congestion interval is selected by the staff based on actual conditions and will not be elaborated here.

[0114] The number of stops exceeding the stop congestion interval means that the predicted number of stops exceeds the preset threshold, that is, there is a high probability of line congestion. At this time, the warning device sends a congestion warning corresponding to the line number to the management personnel to remind the management personnel to dispatch trains in time to reduce line congestion. The line congestion warning is a warning issued by the warning device. The form of the line congestion warning is selected by the staff according to the actual situation, which will not be elaborated here.

[0115] Reference Figure 3 ,The station congestion analysis method includes the following steps:

[0116] Step 300: Get the station number of the station.

[0117] The station number refers to a pre-set number used to distinguish stations. Each station number corresponds to a railway station one by one. The station number can be automatically read through the railway traffic management system. The setting and acquisition methods of the station number are selected by the staff based on actual conditions and will not be elaborated here.

[0118] Step 301: Determine the inbound train number from a preset inbound database according to the current time, station number and preset station predicted duration.

[0119] The predicted duration of a station refers to the unit time length set in advance for predicting congestion in the station. The predicted duration of the station is selected by the staff based on actual conditions and will not be elaborated here.

[0120] The inbound database records the trains entering each station and their entry times. The inbound train number is the total number of trains entering the station with the station number from the predicted time length of the station from the current time matched from the inbound database.

[0121] Step 302: Determine the outbound train number from a preset outbound database according to the current time, the station number and the preset station predicted duration.

[0122] The outbound database records the trains leaving each station and their departure times. The outbound train number is the total number of trains leaving the station with the station number from the predicted time length of the station from the current time matched from the outbound database.

[0123] Step 303: Calculate the sum of the inbound train number and the outbound train number, and define it as the dispatched train number.

[0124] The number of scheduled trains refers to the total number of trains entering and leaving the station during the time from the current time to the predicted time of the station.

[0125] Step 304: When the dispatched train number falls into the preset dispatching congestion interval, the preset warning device is controlled according to the station number to issue a station dispatching congestion warning.

[0126] The dispatching congestion interval refers to the pre-set dispatching train interval where congestion occurs. The dispatching congestion interval is selected by the staff based on actual conditions and will not be elaborated here.

[0127] The number of dispatches exceeding the dispatch congestion interval means that the number of trains that need to be dispatched per unit time exceeds the preset threshold, that is, there is a high probability of station dispatch congestion. At this time, the warning device sends a congestion warning corresponding to the station number to the management personnel to remind the management personnel to dispatch trains in time to reduce station congestion. The station dispatch congestion warning is a warning issued by the warning device. The form of the station dispatch congestion warning is selected by the staff according to the actual situation, which will not be elaborated here.

[0128] Reference Figure 4 , the station congestion analysis method also includes the following steps:

[0129] Step 305: Determine the entry vehicle number from a preset entry database according to the current time and station number.

[0130] The vehicle number refers to a pre-set number used to distinguish vehicles. Each vehicle number corresponds to a train one-to-one. The setting method of the vehicle number is selected by the staff based on actual conditions and will not be elaborated here.

[0131] The entry database records the vehicle numbers of trains entering the garage of each station, their entry times and the duration of their stay. The entry vehicle number is the vehicle number of the train staying in the garage of the station with the station number matched from the entry database.

[0132] Step 306: Determine the entry dwelling time and entry time from a preset entry database according to the entry vehicle number.

[0133] The depot stay time is the estimated length of time that the train with the depot vehicle number matched from the depot database stays in the depot. The depot time is the time when the train with the depot vehicle number matched from the depot database enters the depot.

[0134] Step 307: Calculate the sum of the entry time and the entry residence time, and define it as the exit time.

[0135] The departure time is the time when the train is expected to leave the depot.

[0136] Step 308: Determine the outbound train number from the preset outbound train number matching model according to the outbound time, the current time and the preset inbound prediction duration.

[0137] The predicted entry time refers to the unit time length set in advance to predict the congestion that will occur when a train enters the depot. The predicted entry time is selected by the staff based on actual conditions and will not be elaborated here.

[0138] The outbound train number matching model refers to a neural network model obtained through sample training in advance. The outbound train number matching model can match the total number of trains whose outbound times are between the current time and the predicted entry time. The outbound train number is the result obtained by matching the outbound train number matching model.

[0139] Step 309: Determine the entry train number from the preset entry database according to the current time, station number and preset entry prediction duration.

[0140] The number of trains entering the depot is the total number of trains entering the depot of the station with the station number from the current time to the predicted depot time, obtained by matching the depot database.

[0141] Step 310: Calculate the difference between the inbound train number and the outbound train number, and define it as the real train number.

[0142] The actual number of trains refers to the total number of trains actually added to the garage from the current time to the predicted time for entry into the garage.

[0143] Step 311: When the actual train number falls into the preset depot congestion interval, the preset warning device is controlled according to the station number to issue a depot congestion warning.

[0144] The depot congestion interval refers to the actual train interval where congestion occurs that is set in advance. The depot congestion interval is selected by the staff based on actual conditions and will not be elaborated here.

[0145] The actual number of trains falling into the dispatching congestion interval means that too many trains enter the depot within the predicted depot time. At this time, there is a high probability of depot congestion. At this time, the warning device sends a congestion warning corresponding to the station number to the management personnel to remind the management personnel to dispatch trains in time to reduce the depot congestion. The depot congestion warning is a warning issued by the warning device. The form of the depot congestion warning is selected by the staff according to the actual situation, which will not be elaborated here.

[0146] Reference Figure 5 , the station congestion analysis method also includes the following steps:

[0147] Step 312: Determine the demobilization vehicle number from a preset demobilization database according to the current time, the station number and the preset demobilization prediction duration.

[0148] The predicted demobilization time refers to the unit time length set in advance to predict the congestion point that will occur when the train is demobilized. The predicted demobilization time is selected by the staff based on the actual situation and will not be elaborated here.

[0149] The demobilization database records the vehicle numbers and demobilization times of demobilized trains in the garages of various stations. The demobilization vehicle number is the vehicle number of the train demobilized in the garage of the station number matched from the demobilization database during the period from the current time to the predicted demobilization time.

[0150] Step 313: Determine the number of people leaving work from a preset vehicle and personnel database according to the leaving vehicle number.

[0151] The vehicle personnel database records the names of the crew members on each train, and the number of retired personnel is the total number of crew members on the train with the retired vehicle number matched from the vehicle personnel database.

[0152] Step 314: When the number of people leaving work falls into the preset leaving work congestion interval, the preset warning device is controlled according to the station number to issue a leaving work congestion warning.

[0153] The de-staffing congestion interval refers to the pre-set interval of the number of de-staffing people where congestion occurs. The in-staffing congestion interval is selected by the staff based on actual conditions and will not be elaborated here.

[0154] If the number of people leaving work falls into the leaving work congestion interval, it means that too many people will leave work within the predicted leaving work time. At this time, there is a high probability of leaving work congestion. At this time, the warning device will issue a congestion warning corresponding to the station number to the management personnel to remind the management personnel to dispatch trains in time to reduce the leaving work congestion. The leaving work congestion warning is a warning issued by the warning device. The form of the leaving work congestion warning is selected by the staff according to the actual situation, which will not be elaborated here.

[0155] Reference Figure 6 ,The personnel supply bottleneck analysis method includes the following steps:

[0156] Step 400: Get the actual number of people assigned to a class.

[0157] The actual number of people assigned to a shift refers to the total number of people who are actually present. The actual number of people assigned to a shift can be obtained through the records in the attendance system. The method for obtaining the actual number of people assigned to a shift is chosen by the staff based on the actual situation and will not be elaborated here.

[0158] Step 401: judging whether the actual number of people assigned to a class is lower than the historical average value from a preset class assignment history judgment model according to the actual number of people assigned to a class.

[0159] The historical judgment model for class matching refers to a neural network model obtained through sample training in advance. The historical judgment model for class matching can match the historical average value of actual class matching according to the historical data of actual class matching, and compare the actual number of class matching with the historical average value to determine whether the actual number of class matching is lower than the historical average value.

[0160] Step 402: When the actual number of people on duty is lower than the historical average, the preset warning device is controlled to issue a break time congestion warning.

[0161] The actual number of people assigned to a shift is lower than the historical average, which means that the number of people currently on duty is small, that is, most of the people are resting. At this time, the rest time of the people is too concentrated, that is, the staff scheduling plan is unreasonable and not conducive to work arrangements. At this time, the warning device will issue a rest time blockage warning to remind managers to optimize the staff scheduling plan.

[0162] The time congestion warning is a warning issued by a warning device. The form of the time congestion warning is selected by the staff according to the actual situation and will not be elaborated here.

[0163] Step 403: Determine the number of people scheduled for shift assignment from a preset scheduled shift assignment database according to the current time.

[0164] The planned shift database records the daily workload and the number of shift members required to complete the workload. The planned shift member number is the number of shift members required to complete the workload at the current time matched from the planned shift database.

[0165] Step 404: Calculate the difference between the planned number of people assigned to a class and the actual number of people assigned to a class, and define it as the number of people assigned to a class difference.

[0166] The difference in the number of students in a class refers to the difference between the planned number of students in a class and the actual number of students in a class.

[0167] Step 405: Calculate the quotient of the difference in the number of people assigned to a class and the planned number of people assigned to a class, and define it as the shortage ratio.

[0168] The shortage ratio refers to the proportion of the shortage of class numbers in the planned class numbers.

[0169] Step 406: When the shortage ratio falls into a preset planned supply congestion interval, a preset warning device is controlled to issue a planned supply congestion warning.

[0170] The planned supply congestion interval refers to the pre-set interval of insufficient ratio of the number of people where congestion occurs. The planned supply congestion interval is selected by the staff based on the actual situation and will not be elaborated here.

[0171] The insufficient number of people ratio falling into the planned supply congestion interval means that the actual number of people on duty is significantly lower than the planned number of people on duty. At this time, the average workload of the staff who complete the current time's workload with the actual number of people on duty has increased significantly, which is likely to lead to an extension of the working hours of the staff at the current time. At this time, a warning is issued through the warning device to remind management personnel to reduce the workload or increase the number of people on duty to reduce the workload and increase the working hours of the staff. The planned supply congestion warning is a warning issued by the warning device. The form of the planned supply congestion warning is selected by the staff according to the actual situation, which will not be elaborated here.

[0172] Reference Figure 7 , the personnel supply bottleneck analysis method also includes the following steps:

[0173] Step 407: Get the employee number of the employee.

[0174] The employee number refers to a number set in advance to distinguish employees. The employee number is obtained when the employee leaves the apartment. The employee number can be obtained through the apartment's access control system. The setting and acquisition methods of the employee number are selected by the staff based on actual conditions and will not be elaborated here.

[0175] Step 408: Determine the check-in time from the preset apartment management database according to the employee number.

[0176] The apartment management database records the ID number of each employee and the time when he / she checked in and checked out of the apartment. The check-in time is the most recent check-in time of the employee whose ID is matched from the apartment management database.

[0177] Step 409: Calculate the difference between the current time and the check-in time, and define it as the rest time.

[0178] Rest duration refers to the length of time that the employee with the employee number rests in the apartment from the check-in time to the current time.

[0179] Step 410: When the rest time falls into the preset rest time congestion interval, the preset warning device is controlled according to the employee number to issue a rest time congestion warning.

[0180] The rest time congestion interval refers to the pre-set time interval where the rest time of personnel is insufficient. The rest time congestion interval is selected by the staff based on actual conditions and will not be elaborated here.

[0181] If the rest time falls into the rest time congestion interval, the rest time of the employee with the employee number is insufficient. At this time, a warning is issued through the warning device to remind the management personnel that there is fatigue, so that the management personnel can adjust the shift plan in time to compensate for fatigued personnel. Rest time congestion warning is a warning issued by the warning device. The form of rest time congestion warning is selected by the staff according to the actual situation, which will not be elaborated here.

[0182] Reference Figure 8 , the method for dealing with the bottleneck of personnel supply includes the following steps:

[0183] Step 500: When the rest time falls into the preset rest time congestion interval, the check-in number and rest time of the person who is still in the apartment and has the earliest check-in time are determined from the preset apartment management database according to the current time.

[0184] The check-in number is the employee number of the person who is currently staying in the apartment and has the earliest check-in time, which is matched from the apartment management database. The rest time is the time when the person with the check-in number matched from the apartment management database checks into the apartment.

[0185] Step 501: Calculate the difference between the current time and the rest time, and define it as the check-in duration.

[0186] The length of stay is the length of time the person with the stay number has rested in the apartment from the rest time to the current time.

[0187] Step 502: When the check-in duration does not fall within the preset rest time congestion interval, the preset attendance management device is controlled to issue a rest notice according to the employee number, and the preset attendance management device is controlled to issue an attendance notice according to the check-in number.

[0188] The attendance management device refers to a device used to notify personnel to attend work. The attendance management device generally uses a personal terminal bound to the employee number. The attendance management device is selected by the staff according to the actual situation and will not be described in detail here. If the check-in time does not fall into the rest time congestion interval, it means that the person with the check-in number has had a sufficient rest. At this time, the employee with the check-in number is notified through the attendance management device to continue resting, and the employee with the check-in number is notified to attend work to reduce the fatigue of the personnel, thereby improving the rationality of personnel scheduling.

[0189] Based on the same inventive concept, an embodiment of the present invention provides a locomotive transport group congestion analysis system, including:

[0190] The acquisition module is used to obtain the current time, line number, station number, actual number of people on duty and employee number;

[0191] A memory for storing a program of a control method of any of the above-mentioned methods for analyzing congestion points of a locomotive transport group;

[0192] The program in the processor and the memory can be loaded and executed by the processor to implement the control method of any of the above-mentioned locomotive transport group congestion analysis methods.

[0193] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0194] Based on the same inventive concept, an embodiment of the present invention provides an intelligent terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute any of the above-mentioned locomotive transport group congestion analysis methods.

[0195] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0196] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A method for analyzing congestion points of a locomotive transport group, characterized in that: include: Get the current time; Determine the driving link from the preset link time database according to the current time. The driving operation includes attendance delay, attendance time, vehicle pickup time, maintenance time, depot time, station organization, wind test time and departure time. The driving link is the link in the current driving operation matched from the link time database according to the current time; Determine the start time from a preset link time database according to the driving link; Calculate the difference between the current time and the start time and define it as the session duration; Determine a driving congestion analysis model from a preset driving congestion analysis database according to the driving link, wherein the driving congestion analysis model refers to a plurality of different logistic regression models obtained through training with different samples, each of the driving congestion analysis models corresponds to a link of the driving operation one by one, and the driving congestion analysis model of the corresponding link can match the congestion probability of the link according to the link duration of each link; When the probability of a jam exceeds the preset jam occurrence interval, a jam warning is issued according to the preset warning device controlled by the driving link.

2. A method for analyzing congestion points of a locomotive transport group according to claim 1, characterized in that: It also includes a line congestion point analysis method, which includes: Get the line number of the line; Determine train traffic data from a preset train traffic database according to the current time, the route number and the preset route prediction duration; Determine the number of stops from a preset stop number matching model according to train traffic data; When the number of intermediate stops exceeds the preset intermediate stop congestion interval, the preset warning device is controlled according to the line number to issue a line congestion warning.

3. A method for analyzing congestion points of locomotive transport groups according to claim 1, characterized in that: The station congestion point analysis method is also included, and the station congestion point analysis method includes: Get the station number of the station; Determine the inbound train number from the preset inbound database according to the current time, station number and the preset station predicted duration; Determine the outbound train number from the preset outbound database according to the current time, station number and the preset station predicted duration; Calculate the sum of the number of inbound trains and the number of outbound trains, and define it as the dispatched train number; When the dispatched train number falls into the preset dispatching congestion interval, the preset warning device is controlled according to the station number to issue a station dispatching congestion warning.

4. A method for analyzing congestion points of locomotive transport groups according to claim 3, characterized in that: The station congestion analysis method further includes: Determine the entry vehicle number from the preset entry database according to the current time and station number; Determine the entry dwell time and entry time from the preset entry database according to the entry vehicle number; Calculate the sum of the entry time and the entry residence time, and define it as the exit time; Determine the outbound train number from the preset outbound train number matching model according to the outbound time, current time and preset inbound forecast duration; Determine the entry train number from the preset entry database according to the current time, station number and preset entry prediction time; Calculate the difference between the number of trains entering the warehouse and the number of trains leaving the warehouse, and define it as the actual number of trains; When the actual train number falls into the preset depot congestion interval, the preset warning device is controlled according to the station number to issue a depot congestion warning.

5. A method for analyzing congestion points of locomotive transport groups according to claim 4, characterized in that: The station congestion analysis method further includes: Determine the demobilization vehicle number from the preset demobilization database according to the current time, the station number and the preset demobilization prediction duration; Determine the number of people leaving work from a preset vehicle and personnel database according to the leaving vehicle number; When the number of people leaving the station falls into the preset leaving congestion interval, the preset warning device is controlled according to the station number to issue a leaving congestion warning.

6. A method for analyzing congestion points of locomotive transport groups according to claim 1, characterized in that: It also includes a personnel supply bottleneck analysis method, which includes: Get the actual number of people assigned to a class; According to the actual number of people assigned to a class, determine whether the actual number of people assigned to a class is lower than the historical average from the preset class assignment history judgment model; When the actual number of people on duty is lower than the historical average, the preset warning device will be controlled to issue a congestion warning during the break time; Determine the number of people scheduled for shifts from a preset scheduled shift database according to the current time; Calculate the difference between the planned number of people on a shift and the actual number of people on a shift, and define it as the number of people on a shift difference; Calculate the quotient of the difference in the number of people assigned to a class and the planned number of people assigned to a class, and define it as the shortage ratio; When the shortage ratio falls into a preset planned supply congestion interval, the preset warning device is controlled to issue a planned supply congestion warning.

7. A method for analyzing congestion points of locomotive transport groups according to claim 6, characterized in that: The personnel supply bottleneck analysis method further includes: Get the employee number of the employee; Determine check-in time from the preset apartment management database based on employee number; Calculate the difference between the current time and the check-in time and define it as the rest time; When the rest time falls into the preset rest time congestion interval, the preset warning device is controlled according to the employee number to issue a rest time congestion warning.

8. A method for analyzing congestion points of locomotive transport groups according to claim 7, characterized in that: It also includes a method for processing personnel supply bottlenecks, and the method for processing personnel supply bottlenecks includes: When the rest time falls into the preset rest time congestion interval, the check-in number and rest time of the person who is still in the apartment and has the earliest check-in time are determined from the preset apartment management database according to the current time; Calculate the difference between the current time and the rest time and define it as the check-in duration; When the check-in duration does not fall within the preset rest time congestion interval, the preset attendance management device is controlled according to the employee number to issue a rest notice, and the preset attendance management device is controlled according to the check-in number to issue an attendance notice.

9. A locomotive transport group congestion analysis system, characterized in that: include: The acquisition module is used to obtain the current time, line number, station number and employee number; A memory, used to store a program of a method for analyzing a congestion point of a locomotive transport group according to any one of claims 1 to 8; The program in the processor and the memory can be loaded and executed by the processor to implement a locomotive transport group congestion analysis method as described in any one of claims 1 to 8.

10. An intelligent terminal, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program which can be loaded by the processor and executes a method for analyzing congestion points of a locomotive transport group as described in any one of claims 1 to 8.

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