Subway line intelligent scheduling optimization method and system based on real-time data analysis

Abstract

The invention relates to the technical field of subway line scheduling, and discloses a subway line intelligent scheduling optimization system based on real-time data analysis, which comprises a data acquisition unit, a data processing analysis unit, a scheduling decision execution unit, a user interface display unit and a data storage unit. According to the subway line intelligent scheduling optimization method and system based on real-time data analysis, a multi-source data fusion technology is introduced, and data from different systems are integrated to form a comprehensive data view, so that the system can master the operation condition of a subway line more comprehensively, powerful support is provided for scheduling decision making, and the scheduling efficiency is improved. By integrating advanced real-time data acquisition, processing and analysis technologies and combining an adaptive scheduling algorithm, multi-source data fusion and an intelligent prediction model, efficient and intelligent scheduling of the subway line is realized, the operation efficiency of the subway line is improved, the operation cost is reduced, and more comfortable and convenient travel experience is provided for passengers.

Description

Technical Field

[0001] The present invention relates to the technical field of subway line scheduling, and in particular to a subway line intelligent scheduling optimization method and system based on real-time data analysis. Background Art

[0002] As an important part of modern urban transportation, the operation efficiency and service quality of subways are directly related to the travel experience of citizens and the traffic conditions of the city. In recent years, with the development of technologies such as big data and artificial intelligence, real-time data analysis has been widely used in various fields. However, the traditional subway dispatching system mainly relies on fixed dispatching rules and predefined dispatching plans. This static dispatching method is unable to cope with complex and changing operating environments and demands. In the field of urban rail transit, intelligent dispatching optimization systems based on real-time data analysis have become a research hotspot.

[0003] At present, some subway dispatching systems have been proposed, which mainly rely on manual operation and fixed dispatching rules. They can usually only analyze data from a single source and lack the ability to fuse multi-source data, which makes the system unable to fully and accurately grasp the operation status of the subway line. In addition, the dispatching optimization algorithms of the existing technology are often too simple to cope with the complex and changing operating environment and needs, and cannot achieve true intelligent dispatching. Therefore, it is necessary to invent a subway line intelligent dispatching optimization method and system based on real-time data analysis to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a subway line intelligent scheduling optimization method and system based on real-time data analysis to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a subway line intelligent scheduling optimization system based on real-time data analysis, comprising a data acquisition unit, a data processing and analysis unit, a scheduling decision execution unit, a user interface display unit, and a data storage unit, wherein the data acquisition unit is signal-connected to the data processing and analysis unit, the data processing and analysis unit is signal-connected to the scheduling decision execution unit, the user interface display unit is signal-connected to the data acquisition unit, the data processing and analysis unit, and the scheduling decision execution unit, and the data storage unit is signal-connected to the data acquisition unit, the data processing and analysis unit, and the scheduling decision execution unit.

[0006] The data acquisition unit includes a train position acquisition module, a train speed acquisition module, a passenger flow statistics module, and an equipment status acquisition module.

[0007] The data processing and analysis unit includes a data preprocessing module, a data cleaning module, and a data integration module.

[0008] The scheduling decision execution unit includes an intelligent prediction model generation module, an intelligent scheduling decision module, and a control execution module.

[0009] The user interface display unit includes a real-time operation data display module, a scheduling scheme display module, a prediction result display module, and a scheduling information release module.

[0010] The data storage unit includes an original data storage module, a processed data storage module, a scheduling solution storage module, and a prediction result storage module.

[0011] Preferably, the train position acquisition module is used to collect real-time position data of the train.

[0012] The train speed collection module is used to collect real-time speed data of the train.

[0013] The passenger flow statistics module is used to collect data on the number of people entering each station.

[0014] The device status acquisition module is used to collect parameter data of the device operating status.

[0015] Preferably, the data preprocessing module is used to preprocess the collected data.

[0016] The data cleaning module is used to screen the preprocessed data and remove abnormal, useless and missing data.

[0017] The data integration module is used to centrally integrate the cleaned data.

[0018] Preferably, the intelligent prediction model generation module is used to generate an intelligent prediction model.

[0019] The intelligent scheduling decision module is used to generate intelligent scheduling decisions based on the processed data and the intelligent prediction model, including optimization and adjustment of departure intervals, operating speeds, and entry and exit time control.

[0020] The control execution module is used to convert the dispatching decision into specific control instructions and send them to various subsystems of the subway line for precise regulation.

[0021] Preferably, the real-time operation data display module is used to display the data collected during the train operation in real time.

[0022] The scheduling scheme display module is used to display the scheduling scheme calculated by the intelligent scheduling decision module on the screen.

[0023] The prediction result display module is used to release real-time dispatch information to passengers.

[0024] Preferably, the raw data storage module is used to store the raw data collected by the data collection unit into a database.

[0025] The processing data storage module is used to store the data processed and analyzed by the data processing and analysis unit into a database.

[0026] The scheduling solution storage module is used to store the scheduling solution results generated in the scheduling decision execution unit in the database.

[0027] The prediction result storage module is used to store the display data in the user interface display unit into the database.

[0028] Preferably, the data preprocessing module includes data screening, identification, classification and missing value processing, the data cleaning module includes duplicate data cleaning, incomplete data cleaning, data format cleaning and erroneous data cleaning, and the data integration module includes solving data matching, conversion and redundancy problems.

[0029] The intelligent scheduling optimization method of subway lines based on real-time data analysis includes the following steps: S1. First, the real-time position and speed information data of the train during its travel are collected through the train position acquisition module and the train speed acquisition module. At the same time, the passenger flow data of each station is collected through the passenger flow statistics module, and the operation data of the equipment is collected through the equipment status acquisition module. The collected information is uploaded to the database through the original data storage module. At the same time, the train information, passenger flow information and equipment status operation information are displayed in real time on the monitoring display screen through the real-time operation data display module.

[0030] S2. The raw data in the database are screened, identified, classified and missing value processed through the data preprocessing module. After the preprocessing is completed, the data is screened through the data cleaning module to remove duplicate, abnormal, useless and missing data. The cleaned data is then matched and converted through the data integration module, and the integrated data is uploaded to the database through the processing data storage module.

[0031] S3. Establish an intelligent prediction model through the intelligent prediction model generation module, then import the data in the database into the intelligent prediction model through the intelligent scheduling decision module, and give a decision plan through the intelligent prediction model to calculate the appropriate departure interval, running speed and entry and exit time, and display the calculation results on the monitoring display screen through the prediction result display module, and store the decision plan in the database through the prediction result storage module, and then convert the decision results into control commands through the scheduling execution module, and send them to various subsystems of the subway line for precise control, and display the results on the monitoring screen through the scheduling plan display module, and store the command plan in the data through the scheduling plan storage module, and push the scheduling information to passengers in real time through the scheduling information release module.

[0032] Compared with the prior art, the present invention provides a subway line intelligent scheduling optimization method and system based on real-time data analysis, which has the following beneficial effects: 1. The intelligent scheduling optimization method and system for subway lines based on real-time data analysis introduces multi-source data fusion technology to integrate and consolidate data from different systems to form a comprehensive data view, which enables the system to more comprehensively grasp the operating status of subway lines and provide strong support for scheduling decisions.

[0033] 2. The intelligent scheduling optimization method and system for subway lines based on real-time data analysis adopts an adaptive scheduling algorithm and an intelligent prediction model, which can dynamically adjust the scheduling strategy according to real-time data. This scheduling method driven by real-time data can more accurately respond to complex and changing operating environments and demands, and achieve more efficient scheduling.

[0034] 3. The intelligent dispatching optimization method and system for subway lines based on real-time data analysis proposes an intelligent coordination and linkage control mechanism, which enables the intelligent dispatching system of subway lines to seamlessly connect and work in coordination with other related systems.

[0035] 4. The intelligent scheduling optimization method and system for subway lines based on real-time data analysis integrates advanced real-time data collection, processing and analysis technologies, combines adaptive scheduling algorithms, multi-source data fusion and intelligent prediction models, realizes efficient and intelligent scheduling of subway lines, improves the operating efficiency of subway lines, reduces operating costs, and provides passengers with a more comfortable and convenient travel experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative labor: Figure 1 It is a schematic diagram of the system structure of the present invention; Figure 2 This is a schematic diagram of the structure of the data acquisition unit system of the present invention; Figure 3 This is a schematic diagram of the data processing and analysis unit system structure of the present invention; Figure 4 This is a schematic diagram of the system structure of the scheduling decision execution unit of the present invention; Figure 5 This is a schematic diagram of the structure of the user interface display unit system of the present invention; Figure 6 It is a schematic diagram of the structure of the data storage unit system of the present invention.

[0037] In the figure: 1. Data acquisition unit; 11. Train position acquisition module; 12. Train speed acquisition module; 13. Passenger flow statistics module; 14. Equipment status acquisition module; 2. Data processing and analysis unit; 21. Data preprocessing module; 22. Data cleaning module; 23. Data integration module; 3. Dispatch decision execution unit; 31. Intelligent prediction model generation module; 32. Intelligent dispatch decision module; 33. Control execution module; 4. User interface display unit; 41. Real-time operation data display module; 42. Dispatch plan display module; 43. Prediction result display module; 44. Dispatch information release module; 5. Data storage unit; 51. Original data storage module; 52. Processed data storage module; 53. Dispatch plan storage module; 54. Prediction result storage module. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0040] See also Figure 1-6The present invention provides a technical solution: a subway line intelligent scheduling optimization system based on real-time data analysis, including a data acquisition unit 1, a data processing and analysis unit 2, a scheduling decision execution unit 3, a user interface display unit 4, and a data storage unit 5. The data acquisition unit 1 is signal-connected to the data processing and analysis unit 2, the data processing and analysis unit 2 is signal-connected to the scheduling decision execution unit 3, the user interface display unit 4 is signal-connected to the data acquisition unit 1, the data processing and analysis unit 2, and the scheduling decision execution unit 3, and the data storage unit 5 is signal-connected to the data acquisition unit 1, the data processing and analysis unit 2, and the scheduling decision execution unit 3.

[0041] The data collection unit 1 includes a train position collection module 11 , a train speed collection module 12 , a passenger flow statistics module 13 , and an equipment status collection module 14 .

[0042] The data processing and analysis unit 2 includes a data preprocessing module 21 , a data cleaning module 22 , and a data integration module 23 .

[0043] The scheduling decision execution unit 3 includes an intelligent prediction model generation module 31 , an intelligent scheduling decision module 32 , and a control execution module 33 .

[0044] The user interface display unit 4 includes a real-time operation data display module 41 , a scheduling scheme display module 42 , a prediction result display module 43 , and a scheduling information release module 44 .

[0045] The data storage unit 5 includes an original data storage module 51 , a processed data storage module 52 , a scheduling solution storage module 53 , and a prediction result storage module 54 .

[0046] Furthermore, the train position acquisition module 11 is used to acquire real-time position data of the train.

[0047] The train speed collection module 12 is used to collect real-time speed data of the train.

[0048] The passenger flow statistics module 13 is used to collect data on the number of passengers entering each station.

[0049] The device status collection module 14 is used to collect parameter data of the device operating status.

[0050] Furthermore, the data preprocessing module 21 is used to preprocess the collected data.

[0051] The data cleaning module 22 is used to screen the pre-processed data and remove abnormal, useless and missing data.

[0052] The data integration module 23 is used to centrally integrate the cleaned data.

[0053] Furthermore, the intelligent prediction model generation module 31 is used to generate an intelligent prediction model.

[0054] The intelligent dispatch decision module 32 is used to generate intelligent dispatch decisions based on the processed data and the intelligent prediction model, including optimization and adjustment of departure intervals, operating speeds, and entry and exit time control.

[0055] The control execution module 33 is used to convert the dispatching decision into specific control instructions and send them to various subsystems of the subway line for precise regulation.

[0056] Furthermore, the real-time operation data display module 41 is used to display the data collected during the operation of the train in real time.

[0057] The scheduling scheme display module 42 is used to display the scheduling scheme calculated by the intelligent scheduling decision module 32 on the screen.

[0058] The prediction result display module 43 is used to release real-time dispatch information to passengers.

[0059] Furthermore, the original data storage module 51 is used to store the original data collected by the data collection unit 1 in a database.

[0060] The data processing and data storage module 52 is used to store the data processed and analyzed by the data processing and analysis unit 2 into a database.

[0061] The scheduling solution storage module 53 is used to store the scheduling solution result generated by the scheduling decision execution unit 3 in the database.

[0062] The prediction result storage module 54 is used to store the display data in the user interface display unit 4 into a database.

[0063] Furthermore, the data preprocessing module 21 includes data screening, identification, classification and missing value processing, the data cleaning module 22 includes duplicate data cleaning, incomplete data cleaning, data format cleaning and erroneous data cleaning, and the data integration module 23 includes solving data matching, conversion and redundancy problems.

[0064] The intelligent scheduling optimization method of subway lines based on real-time data analysis includes the following steps: S1. First, the real-time position and speed information data of the train during its travel are collected through the train position collection module 11 and the train speed collection module 12. At the same time, the passenger flow data of each station is collected through the passenger flow statistics module 13, and the operation data of the equipment is collected through the equipment status collection module 14. The collected information is uploaded to the database through the original data storage module 51. At the same time, the train information, passenger flow information and equipment status operation information are displayed in real time on the monitoring display screen through the real-time operation data display module 41.

[0065] S2. The raw data in the database are screened, identified, classified and missing value processed through the data preprocessing module 21. After the preprocessing is completed, the data is screened through the data cleaning module 22 to remove duplicate, abnormal, useless and missing data. The cleaned data is then matched and converted through the data integration module 23, and the integrated data is uploaded to the database through the processing data storage module 52.

[0066] S3. An intelligent prediction model is established through the intelligent prediction model generation module 31, and then the data in the database is imported into the intelligent prediction model through the intelligent scheduling decision module 32, and a decision plan is given through the intelligent prediction model to calculate the appropriate departure interval, running speed and entry and exit time, and the calculation result is displayed on the monitoring display screen through the prediction result display module 43, and the decision plan is stored in the database through the prediction result storage module 54, and then the decision result is converted into a control command through the scheduling execution module, and sent to each subsystem of the subway line for precise control, and at the same time, the result is displayed on the monitoring screen through the scheduling plan display module 42, and the command plan is stored in the data through the scheduling plan storage module 53, and the scheduling information is pushed to passengers in real time through the scheduling information release module 44.

[0067] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

Claims

1. A subway line intelligent dispatching optimization system based on real-time data analysis, comprising a data acquisition unit (1), a data processing and analysis unit (2), a dispatching decision execution unit (3), a user interface display unit (4), and a data storage unit (5), characterized in that: The data acquisition unit (1) is signal-connected to the data processing and analysis unit (2), the data processing and analysis unit (2) is signal-connected to the scheduling decision execution unit (3), the user interface display unit (4) is signal-connected to the data acquisition unit (1), the data processing and analysis unit (2) and the scheduling decision execution unit (3), and the data storage unit (5) is signal-connected to the data acquisition unit (1), the data processing and analysis unit (2) and the scheduling decision execution unit (3); The data collection unit (1) comprises a train position collection module (11), a train speed collection module (12), a passenger flow statistics module (13), and an equipment status collection module (14); The train position acquisition module (11) is used to acquire real-time position data of the train; The train speed collection module (12) is used to collect real-time speed data of the train; The passenger flow statistics module (13) is used to collect data on the number of people entering each station; The equipment status acquisition module (14) is used to acquire parameter data of the equipment operating status; The data processing and analysis unit (2) comprises a data preprocessing module (21), a data cleaning module (22), and a data integration module (23); The data preprocessing module (21) is used to preprocess the collected data; The data cleaning module (22) is used to screen the pre-processed data and remove abnormal, useless and missing data; The data integration module (23) is used to centrally integrate the cleaned data; The scheduling decision execution unit (3) comprises an intelligent prediction model generation module (31), an intelligent scheduling decision module (32), and a control execution module (33); The intelligent prediction model generation module (31) is used to generate an intelligent prediction model; The intelligent dispatch decision module (32) is used to generate intelligent dispatch decisions based on the processed data and the intelligent prediction model, including optimization and adjustment of departure intervals, running speeds, and entry and exit time control; The control execution module (33) is used to convert the dispatching decision into specific control instructions and send them to various subsystems of the subway line for precise control; The user interface display unit (4) comprises a real-time operation data display module (41), a scheduling scheme display module (42), a prediction result display module (43), and a scheduling information release module (44); The real-time operation data display module (41) is used to display the data collected during the operation of the train in real time; The scheduling scheme display module (42) is used to display the scheduling scheme calculated by the intelligent scheduling decision module (32) on the screen; The prediction result display module (43) is used to release real-time dispatch information to passengers; The data storage unit (5) comprises an original data storage module (51), a processed data storage module (52), a scheduling scheme storage module (53), and a prediction result storage module (54); The raw data storage module (51) is used to store the raw data collected by the data collection unit (1) into a database; The processing and data storage module (52) is used to store the data processed and analyzed by the data processing and analysis unit (2) into a database; A scheduling solution storage module (53) is used to store the scheduling solution result generated by the scheduling decision execution unit (3) in a database; The prediction result storage module (54) is used to store the display data in the user interface display unit (4) into a database.

2. The subway line intelligent dispatching optimization system based on real-time data analysis according to claim 1 is characterized in that: The data preprocessing module (21) includes data screening, identification, classification and missing value processing, the data cleaning module (22) includes duplicate data cleaning, incomplete data cleaning, data format cleaning and erroneous data cleaning, and the data integration module (23) includes solving data matching, conversion and redundancy problems.

3. The intelligent scheduling optimization method for subway lines based on real-time data analysis is characterized by: The following steps are involved: S1. First, the train position acquisition module (11) and the train speed acquisition module (12) are used to acquire the real-time position and speed information data of the train during its travel. Meanwhile, the passenger flow statistics module (13) is used to acquire the passenger flow data of each station entering and leaving the station. Furthermore, the equipment status acquisition module (14) is used to acquire the operation data of the equipment. The acquired information is uploaded to the database through the original data storage module (51). Meanwhile, the train information, passenger flow information and equipment status operation information are displayed in real time on the monitoring display screen through the real-time operation data display module (41); S2, screening, identifying, classifying and processing missing values ​​of the raw data in the database through the data preprocessing module (21), after the preprocessing is completed, screening the data through the data cleaning module (22), removing duplicate, abnormal, useless and missing data, then matching and converting the cleaned data through the data integration module (23), and uploading the integrated data to the database through the processing data storage module (52); S3. An intelligent prediction model is established through an intelligent prediction model generation module (31), and then the data in the database is imported into the intelligent prediction model through an intelligent scheduling decision module (32), and a decision plan is given through the intelligent prediction model to calculate the appropriate departure interval, running speed and entry and exit time, and the calculation result is displayed on the monitoring display screen through the prediction result display module (43), and the decision plan is stored in the database through the prediction result storage module (54), and then the decision result is converted into a control command through the scheduling execution module, and sent to each subsystem of the subway line for precise control, and at the same time, the result is displayed on the monitoring screen through the scheduling plan display module (42), and the command plan is stored in the data through the scheduling plan storage module (53), and the scheduling information is pushed to passengers in real time through the scheduling information release module (44).

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