AU2020101782A4 - Method and system for identifying and eliminating railway transport capacity bottleneck - Google Patents
Method and system for identifying and eliminating railway transport capacity bottleneck Download PDFInfo
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Abstract
The present invention provides a method for identifying and eliminating a railway transport
capacity bottleneck. It combines modeling with simulation, and uses big data to identify and
eliminate bottlenecks. A mathematical model is used to find a solution to direct the simulation
model optimization. Simulation tests make up for the shortcomings of an insufficiently refined
scheme obtained through the mathematical model. The method provided by the present invention
implements feedback and adjustment of the mathematical model and simulation during the
operation, to ensure the correctness of results and the feasibility of the elimination scheme,
providing a new idea and technology for utilizing the carrying capacity.
19
DRAWINGS
Obtain and preprocess basic data, and perform
analysis and identification on the preprocessed basic
data, to obtain bottleneck point parameters
Establish a mathematical model based on the
bottleneck point parameters, to obtain a set of
candidate transportation capacity operation schemes
Perform simulation calculation on the set of
candidate transportation capacity operation
schemes
Adjusttheset oT candidate transportation capacity
operation schemes based on a result of the
simulation calculation, to obtain an optimal operation
scheme
Compare the optimal operation scheme with actual
operation data
FIG. 1
Description
Obtain and preprocess basic data, and perform analysis and identification on the preprocessed basic data, to obtain bottleneck point parameters
Establish a mathematical model based on the bottleneck point parameters, to obtain a set of candidate transportation capacity operation schemes
Perform simulation calculation on the set of candidate transportation capacity operation schemes
Adjusttheset oT candidate transportation capacity operation schemes based on a result of the simulation calculation, to obtain an optimal operation scheme
Compare the optimal operation scheme with actual operation data
FIG. 1
This application claims priority from Chinese patent application 201910882261.3, filed 18 September 2019, the entire content of which is incorporated by reference. TECHNICAL FIELD The present invention relates to the field of railway transportation organization, and in particular, to a method and a system for identifying and eliminating a railway transport capacity bottleneck. BACKGROUND The utilization of the transport capacity of the railway is dynamic and instantaneous, and becomes invalid upon expiry. The improper use of the transport capacity directly leads to the "bottleneck" effect: a certain point or section of a route becomes a weak point or section in the railway network, and restricts the overall capacity and operation level of the railway, causing the failure to meet the passenger and cargo transportation demand at some stations. The transport capacity refers to the maximum transportation volume that is supported by the railway system by making full use of the existing technologies and devices under the given device and personnel conditions in a unit time. The railway transport capacity depends not only on the number of fixed devices and their configuration structure, but also on the space-time configuration of mobile devices and the mutual adaptation of the fixed devices and mobile devices. The carrying capacities calculated according to sections, stations, maintenance, and fixed devices such as the water supply and power supply devices may be different. The device with the weakest capacity limits the capacity of an entire district. This weakest capacity is the final carrying capacity of the district. Similarly, in the entire transportation system, a part with the weakest transport capacity always has a decisive restriction or so-called "bottleneck" effect on the transport capacity. On major railway lines, the utilization of the carrying capacity of the transportation restricted parts or "bottleneck" sections is usually the key to ensuring smooth transportation and affects the overall transportation. In these parts or sections, transportation must be carefully planned, organized, and balanced to minimize transportation fluctuations and maximize the carrying capacity while ensuring the transportation quality. Therefore, bottleneck identification and elimination is an important way to utilize and strengthen the railway transport capacity. However, the conventional bottleneck identification and elimination technologies have disadvantages: 1) In the prior art, a mathematical model or a simulation method is used alone to identify and eliminate bottlenecks. However, a pure
I mathematical model is difficult to express the constraints of the complex train organization process, and the scheme is not refined enough. A simulation model without the help of a mathematical model is not goal-oriented and is low in solution efficiency. 2) There lacks systematic research on bottleneck identification and elimination. In the prior art, an "elimination" scheme is proposed after a "bottleneck" is identified, but a new "bottleneck" that may be caused by the "elimination" scheme is rarely studied. SUMMARY Examples of the present invention provide a method and a system for identifying and eliminating a railway transport capacity bottleneck, to solve the problem that the prior-art bottleneck elimination scheme is difficult to reflect the real situation of the railway transportation and cannot obtain highly feasible and practical results. To achieve the above purpose, the present invention provides the following technical solutions. A method for identifying and eliminating a railway transport capacity bottleneck includes: obtaining and preprocessing basic data, and performing analysis and identification on the preprocessed basic data, to obtain bottleneck point parameters; establishing a mathematical model based on the bottleneck point parameters, to obtain a set of candidate carrying capacity operation schemes; performing simulation calculation on the set of candidate carrying capacity operation schemes; adjusting the set of candidate carrying capacity operation schemes based on a result of the simulation calculation, to obtain an optimal operation scheme; and comparing the optimal operation scheme with actual operations data, and obtaining a scheme for identifying and eliminating railway transport capacity bottlenecks based on a comparison result. Preferably, the obtaining and preprocessing basic data includes: calibrating missing values in the basic data; processing abnormal values in the basic data; and supplementing and converting the basic data, and importing the data into an application subsystem that is used to perform analysis and identification on the preprocessed basic data. Preferably, the application subsystem includes: a data layer, used to connect to the database; a logic layer, used to store and read the preprocessed basic data, and perform arithmetic processing on the preprocessed basic data; and an interaction layer, used to display a result of the arithmetic processing performed at the logic layer. Preferably, the performing, by the logic layer, arithmetic processing on the preprocessed basic data includes: calculating a theoretical section carrying capacity according to an average minimum interval method formula N = T/(+ iFrerf), where N is the section carrying capacity, T is section carrying valid time, I is an average minimum time interval, and frerf is average required buffer time; and comparing the actual number of trains carried in a section with the theoretical section carrying capacity, to obtain a capacity utilization rate of the section. Preferably, the method further includes: displaying and outputting, by the interaction layer, the capacity utilization rate of the section through a capacity utilization heat map. Preferably, the establishing a mathematical model based on the bottleneck point parameters, to obtain a set of candidate carrying capacity operation schemes includes: establishing a mathematical model Max z = p iEL EU k k by taking the maximization of the volume on a line as objective I, where z1 is an objective function; establishing a mathematical model Max z 2 = ag|EBET,kEK k - Nij I
+ a?]|EOET,kEK7 k- Ngj|Iby taking the minimization of changes to a train organization scheme as objective II, where z 2 is an objective function; and normalizing the objective functions z 1 and z 2 , to obtain a mathematical model Maxz 3
Aiz1 - A 2z 2 , where l and A2 are weight coefficients for the objectives I and II. Preferably, the performing simulation calculation on the set of candidate carrying capacity operation schemes includes the following sub-steps: initializing a simulation target status; running a simulation clock; when a next event moment becomes a current event moment, determining whether a simulation target real-time status meets a preset execution condition, and if yes, executing a simulation event corresponding to the simulation target real-time status; if no, repeating this sub step; and determining whether the simulation target real-time status meets an end simulation condition after the simulation event is executed, and if yes, outputting a simulation calculation result and ending the simulation calculation; if no, returning to the third sub-step. Preferably, the simulation target status includes: a train status, a line utilization status, and a locomotive status. In a second aspect, the present invention provides a system for identifying and eliminating a railway transport capacity bottleneck, including: a data processing and analysis subsystem, configured to obtain and preprocess basic data, to obtain bottleneck point parameters; a model analysis subsystem, configured to establish a mathematical model based on the bottleneck point parameters, to obtain a set of candidate carrying capacity operation schemes; and a simulation analysis subsystem, configured to perform simulation calculation on the set of candidate carrying capacity operation schemes; adjust the set of candidate carrying capacity operation schemes based on a result of the simulation calculation to obtain an optimal operation scheme; and compare the optimal operation scheme with actual operations data, and obtain a scheme for identifying and eliminating railway transport capacity bottlenecks based on a comparison result. Preferably, the data processing and analysis subsystem includes an application subsystem, and the data processing and analysis subsystem is further configured to: calibrate missing values in the basic data; process abnormal values in the basic data; and supplement and convert the basic data, and import the data into the application subsystem. According to the technical solutions provided by the foregoing examples of the present invention, the method and the system for identifying and eliminating a railway transport capacity bottleneck provided by the present invention combine modeling with simulation, and use a big data analysis technology to identify and eliminate bottlenecks. A mathematical model is used to find a solution to direct the simulation model optimization. Simulation tests make up for the shortcomings of an insufficiently refined scheme obtained through the mathematical model. The method provided by the present invention implements feedback and adjustment of the mathematical model and simulation during the operation, to ensure the correctness of results and the feasibility of the elimination scheme, providing a new idea and technology for utilizing the carrying capacity. The additional aspects and advantages of the present invention will be partially given in the following description, and become clear in the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF DRAWINGS To describe the technical solutions in the examples of the present invention more clearly, the following briefly introduces the accompanying drawings required for describing the examples. Apparently, the accompanying drawings in the following description show merely some examples of the present invention, and a person of ordinary skill in the art may still derive other drawings from these accompanying drawings without creative efforts.
FIG. 1 is a logical block diagram of a method for identifying and eliminating a railway transport capacity bottleneck according to the present invention. FIG. 2 is an implementation flowchart of a method for identifying and eliminating a railway transport capacity bottleneck according to the present invention. FIG. 3 is a logical block diagram for data analysis of a method for identifying and eliminating a railway transport capacity bottleneck according to the present invention. FIG. 4 is a first capacity utilization heat map for a method for identifying and eliminating a railway transport capacity bottleneck according to the present invention. FIG. 5 is a second capacity utilization heat map for a method for identifying and eliminating a railway transport capacity bottleneck according to the present invention. FIG. 6 is a principle diagram of train operation simulation for a method for identifying and eliminating a railway transport capacity bottleneck according to the present invention. FIG. 7 is a flowchart for a simulation operation example of a method for identifying and eliminating a railway transport capacity bottleneck according to the present invention. FIG. 8 is a main program flowchart for station operation simulation of a method for identifying and eliminating a railway transport capacity bottleneck according to the present invention. FIG. 9 is a program flowchart for passing-through train operation simulation of a method for identifying and eliminating a railway transport capacity bottleneck according to the present invention. FIG. 10 is a logical block diagram for a system for identifying and eliminating a railway transport capacity bottleneck according to the present invention. FIG. 11 is a flowchart for a system for identifying and eliminating a railway transport capacity bottleneck according to the present invention. FIG. 12 is a diagram showing a visual output method of a simulation analysis subsystem of a system for identifying and eliminating a railway transport capacity bottleneck according to the present invention. In the figure, 101. data processing and analysis subsystem; 1011. application subsystem; 102. model analysis subsystem; and 103. simulation analysis subsystem. DETAILED DESCRIPTION The examples of the present invention are described below in detail. Examples of the examples are shown in the accompanying drawings. The same or similar numerals represent the same or similar elements or elements having the same or similar functions throughout the specification. The examples described below with reference to the accompanying drawings are exemplary, and are only used to explain the present invention but should not be construed as a limitation to the present invention. Those skilled in the art can understand that, unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the word "including" used in the specification of the present invention refers to the presence of the described features, integers, steps, operations, elements and/or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It should be understood that when an element is "connected" or "coupled" to another element, it can be connected or coupled to the another element directly or through an intermediate element. In addition, "connected" or "coupled" used herein may include wireless connection or coupling. The term "and/or" used herein includes any unit and all combinations of one or more of the associated listed items. Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as those commonly understood by those of ordinary skill in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood as having meanings consistent with the meanings in the context of the prior art, and unless otherwise defined herein, they will not be explained in ideal or overly formal meanings. For ease of understanding of the examples of the present invention, several specific examples will be taken as examples for further explanation and description in conjunction with the accompanying drawings, and each example does not constitute a limitation to the examples of the present invention. As shown in FIG.1 and FIG. 2, a method for identifying and eliminating a railway transport capacity bottleneck according to the present invention includes the following steps: obtaining and preprocessing basic data, and performing analysis and identification on the preprocessed basic data, to obtain bottleneck point parameters; establishing a mathematical model based on the bottleneck point parameters, and solving the model to obtain a set of candidate carrying capacity operation schemes; performing simulation calculation on the set of candidate carrying capacity operation schemes, and outputting a result of the simulation calculation; adjusting the set of candidate carrying capacity operation schemes based on the result of the simulation calculation, to obtain an optimal operation scheme; and comparing the optimal operation scheme with actual operation data, and evaluating the advantages and disadvantages of the scheme based on a comparison result, to fully explore the transport capacity of the railway, and obtain a scheme for identifying and eliminating a railway transport capacity bottleneck, to optimize the capacity utilization. The method for identifying and eliminating a railway transport capacity bottleneck provided by the present invention combine modeling with simulation, and uses big data to identify and eliminate bottlenecks. A mathematical model is used to find a solution to direct the simulation model optimization. Simulation tests make up for the shortcomings of an insufficiently refined scheme obtained through the mathematical model. The method provided by the present invention implements feedback and adjustment of the mathematical model and simulation during the operation, to ensure the correctness of results and the feasibility of the elimination scheme, providing a new idea and technology for utilizing the carrying capacity. Further, in some preferred examples, the basic data is obtained from train dispatching command system (TDCS) data and station operation data. A TDCS includes an information processing technology, intelligent software, a network technology, and a modern computer technology. It is composed of a Railway Corporation's central TDCS system, the Railway Bureau's TDCS system, and a station system. The TDCS stores core transportation dispatching command data in a database (DB for short). In actual application, an application server and a train operation and dispatching subsystem of the TDCS are all connected to the database to receive train dispatching data in real time, format, process and reorganize the data, and store the data in the core database. Each query server and terminal can query current data and historical information by connecting to the database. It can be seen that the TDCS has accumulated a large amount of train operation data, which is authentic and reliable, and contains rich decision support information. Based on the TDCS data, capacity utilization can be analyzed accurately in real time, and the capacity utilization in special districts and periods can be reflected dynamically. The method of analyzing the capacity utilization based on the TDCS data helps scientifically grasp the utilization of line capacity, providing a basis for further planning, decision-making, and potential exploration. After the basic data is obtained, it is imported into the database for preprocessing. In these examples, the database may be an Oracle database. As shown in FIG. 2, preprocessing the basic data includes: calibrating missing values in the basic data; processing abnormal values in the basic data, where the processing process includes identification and screening; and supplementing and converting the basic data, and importing the data into an application subsystem that is used to perform analysis and identification on the preprocessed basic data. Further, as shown in FIG. 2 and FIG. 3, the application subsystem includes three layers: a data layer, used to connect to the database that preprocesses the basic data; a logic layer, used to store and read the preprocessed basic data, and perform arithmetic processing on the preprocessed basic data, where the storing, reading, and arithmetic processing can be performed in any order; and an interaction layer, used to display a result of the arithmetic processing performed at the logic layer. In the examples provided by the present invention, the analysis and identification on the preprocessed basic data includes analysis, calculation, and identification. In an application example, the performing, by the logic layer, arithmetic processing on the preprocessed basic data includes the following sub-steps: calculating a theoretical section carrying capacity according to an average minimum time interval method formula N = T/(I+ trerf), where N is the section carrying capacity, T is section carrying valid time, Iis an average minimum interval, and Frerf is average required buffer time; and comparing the actual number of trains carried in a section with the theoretical section carrying capacity, to obtain a capacity utilization rate of the section. The capacity utilization is an important indicator to evaluate locations of the railway passage bottlenecks. Through a large amount of data analysis, the interactive layer can generate and output a capacity utilization heat map of each section, and bottleneck point parameters such as the bottleneck location can be obtained by analyzing the heat map. Taking the Baotou-Shenmu railway line of CHN Energy as an example, the foregoing analysis framework is used to analyze the operation data of the line, to obtain the capacity utilization of each section, as shown in FIG. 4 and FIG. 5. It is easy to find the transportation-intensive sections from the figures. The required parameters and capacity bottlenecks are obtained based on data processing analysis and bottleneck identification technologies, laying the foundation for bottleneck elimination. Those skilled in the art should understand that the settings of the foregoing application subsystem are only examples. Other existing or future data analysis and calculation application types with the same function, if applicable to the examples of the present invention, should also be included in the protection scope of the present invention and referenced herein. In the examples provided by the present invention, obtaining the set of candidate carrying capacity operation schemes, as an important part of the technical framework system of the present invention, is essential to generate an initial feasible scheme, avoiding the lack of an optimization direction when only the simulation method is used. In the examples provided by the present invention, it is preferable to generate the set of candidate carrying capacity operation schemes by changing the weight ratio system of the multi-objective function and important capacity parameters. Through the analysis of the railway transportation organization model, the applicant found that, due to the big difference between the freight transport organization mode and the passenger transport organization mode, it is necessary to establish mathematical models separately based on their respective characteristics. However, both a freight transport model and a passenger transport model can be classified into the multi-objective programming model of network flows with constraints. In some preferred examples, a dual-objective model is adopted. A mathematical model Max z, = ( iEL AjEU Xjm k is established by taking maximization of the volume on a line as objective I (the freight transport volume is the freight tonnage, and the passenger transport volume is the passenger volume), where z 1 is an objective function, 9 is a standard load of trains on a heavy-haul railway, L is a set of loading stations, U is a set of unloading stations, K is a set of empty/heavy-load train types, 0 represents 5000-ton trains, 1 represents 10000-ton trains, T means dividing a day into time periods, 0 E T = {1,2,3,4}, xk represents the number of loaded-wagon trains of type k that run between i -> j during period 0, and mk represents the number of marshalling trains of the empty/loaded-wagon train type k, where k E K, mo = 1, and m, = 2. Because the cost of changing the current transportation organization mode on a line globally is relatively high, the present invention performs partial optimization on the current transportation organization mode, with the minimum changes to the actual scheme. Therefore, a mathematical model Min z2 = aij lEBET,kEK jk - Nil I|+ aqJET,kEK jk - Ng/ I is established by taking the maximum similarity with the normal train transportation scheme or the minimum change to the train organization scheme as objective II, where z 2 is an objective function. This scheme requires the minimum changes, which can be understood as the minimum transportation loss cost after the train scheme is changed. In this case, the cost coefficients aokand a'kneed to be modified in the unit of 10,000 tons, p is a standard load of trains on a heavy-haul railway, L is a set of loading stations, U is a set of unloading stations, K is a set of empty/loaded-wagon train types, 0 represents 5000-ton trains, 1 represents 10000-ton trains, T means dividing a day into time periods, 0 E T = {1,2,3,4}, xk represents the number of loaded-wagon trains of type k that run between i -j during period 0, y O;k represents the number of empty-wagon trains of type k that run between i jduring period 0, N Ok and N 1k represent the numbers of empty-wagon trains and loaded-wagon trains that run according to the original scheme i - , and mk represents the number of marshalling trains of the empty/loaded-wagon train type k, where k E K, mo = 1, and m, = 2. In this example, a solver used to solve the model is preferably a Lingo solver, but it cannot solve a multi-objective function. Therefore, objective functions z 1 and z 2 are normalized, and weight coefficients are used to transform the multi-objective functions into a mathematical model Max z 3 = 1 1z - 1 2 z 2 for obtaining single-objective functions, where A, and A2 are weighting coefficients of objectives I and II, and their specific values can be determined by considering the bias between different objectives according to the actual situation. The set of candidate carrying capacity operation schemes obtained through the foregoing mathematical model mainly meets the following three constraints: network stream balance constraint; maximum capacity constraint; and supply and demand balance constraint. Based on the established mathematical model, the set of candidate schemes can be generated by changing the weight coefficients A, and A2 of the objective functions and required capacity parameters C (station capacity constraint) and Nj (section capacity constraint). A final transportation scheme needs to be determined through simulation, feedback, and adjustment. The applicant found that the newly generated scheme is not refined enough, and only eliminates the static bottlenecks under the current capacity parameters. In reality, due to the complex and changing transportation organization process, bottleneck locations will dynamically change with the transportation organization scheme over time. Therefore, it is necessary to further consider the various complex constraints between stations through simulation tests, and make continuous iterative optimization, feedback and adjustment, to eliminate bottlenecks and obtain a train operation scheme that can better utilize the capacity of stations. The train operation process is to combine the path activities of mobile devices in fixed facilities such as the railway network, signal devices, and station routes in time-space domain and perform control and deduction based on the train operation plan. The simulation process is to identify the time and place of occurrence of related elements by tracking the train activity, and realize the dynamic description of train operation. Therefore, the basis of the train operation organization simulation must be train operation activities. Train operation is the cornerstone of train operation organization simulation and also the core of capacity utilization simulation. FIG. 6 shows the principles of the train operation process. Based on the above principles, the basic logic of simulation calculation is to import station information of a line, basic attribute information of a train, technical operation time of a station, and scheme data, and establish a data interface between external data and a simulation system to read and write data. Discrete event logic diagrams of station operations are constructed through abstraction, and secondary development is performed in programming languages (such as Java), to dynamically trace trains, print train information, select pathways, avoid conflicts, and output statistical data. In some preferred examples, the performing simulation calculation on the set of candidate channel capability operation schemes includes the following sub-steps: initializing a simulation target status; running a simulation clock, where the simulation clock can be preset; when a next event moment becomes a current event moment, determining whether a simulation target real-time status meets a preset execution condition, and if yes, executing a simulation event corresponding to the simulation target real-time status; if no, repeating this sub step; and determining whether the simulation target real-time status meets an end simulation condition after the simulation event is executed, and if yes, outputting a simulation calculation result and ending the simulation calculation; if no, returning to the third sub-step. In the specific implementation process, because the operation organization of trains between stations is a discrete event, a discrete event system can be used to define a railway channel simulation system. The discrete event system generally includes entities, attributes, events, activities, processes, and states. Further, taking discrete events as the starting point of a scheme feasibility simulation system, a calculation model for multi-train tracking operation is established, and then a simulation model is established by using an object-oriented programming language (such as Java) to model basic information such as a railway network and train operation attributes. Discrete events are abstracted into a computer system, and microscopic simulation is performed. An event-step method is used to promote the operation of the system, and a process interactive method is used for simulation policies. In an exemplary simulation operation implementation, as shown in FIG. 7, the following steps are included: issuing a start simulation command to the system, so that the system starts simulation calculation; setting a time ratio of the simulation clock and controlling the simulation clock to advance step by step according to the specified time ratio; initializing a real-time (current) status at the same time as the simulation starts, where in this example, the real-time status includes a real-time status of trains, a real-time operating status of lines, and a real-time status of locomotives; as the simulation clock advances, when a next event moment becomes a current event moment, the event becomes eligible for execution at the current time point; determining whether a simulation target real-time status meets a preset execution condition, that is, whether a next event occurs, where for example, when a station is performing a departure operation, when there is no available departure route, the train needs to wait for an idle route to trigger the departure operation; when the next event is triggered, a next state of the system needs to be updated to record the next states of the train and the locomotive and arrange a travel path for the train; after the event is executed, determining whether a simulation target real-time status meets the end simulation condition after the execution of the simulation event, where the end condition can be set to total simulation duration or satisfaction of specific indicators; and if the end condition is not met, performing again the step of determining whether the simulation target real-time status meets the preset execution condition. A further detailed example describes a process of simulating the train operation at a station in the computer system. FIG. 8 is a main program flowchart of the station operation simulation. The technical operation processes of trains at the station are abstracted as a logical relationship of discrete events that have both common and unique features. The operation process of a train passing through a station is used as an example, as shown in FIG. 9 (the following names are from the function names in the program code): Stepl: Determine whether there is an available track based on a track selection table or function (Selectfixedline) and an occupation status of each track at the station (linestate(lineid, presenttime)). Step2: Determine whether there is an available route based on a route selection table or function (Selectshortestpatharrivedroute) and each route of the station and its hostile route occupation status (routestate(routeid, present time)). If no, perform Step 3; if yes, perform Step 4. Step3: Determine whether all available tracks of the train event are traversed. If yes, delay the train arrival and proceed to a next train operation; if no, list traversed tracks as non-selectable tracks and return to Step 1. Step4: Determine a receiving route and a stop track of the train, calculate its departure time based on the stop time, change the route and track attributes to occupied, and keep the attributes until the estimated departure time of the train (after the operation is complete, an attribute of the train is automatically changed to already at station according to a judgment condition for the already-at-station attribute). The foregoing process is a simulation model implementation process. However, during the actual train operation process, the actual carrying capacity of the station and the section is related to the train operation organization during the time period, so it is unreasonable to set the station and section carrying capacity parameters to fixed values in the simulation test. It is necessary to adjust the test based on the simulation feedback to further consider the complex constraints of the station and section. Based on the capacity utilization between stations after the simulation test, the delay of trains after random interference, and various speed indicators, the section and station carrying capacity parameters are fed back and adjusted, and the model parameters are iteratively optimized, so that a generated scheme is more in line with realistic requirements, to balance and reasonably utilize the capacity of each station and each section by properly arranging the starting and ending points of trains, and the ratio and frequency of trains in different time periods. Those skilled in the art should understand that the simulation calculation and programming examples cited above are only to better illustrate the technical solutions of the examples of the present invention, rather than limiting the examples of the present invention. Any method for determining simulation based on the same bottleneck elimination principle falls in the scope of the examples of the present invention. The transportation scheme based on the railway carrying capacity utilization generated in this example is an adjustment scheme that aims to minimize the difference from the existing transportation scheme. The simulation system compares the newly generated transportation scheme with an actual working diagram of a certain day, increases or decreases the train running lines based on the existing transportation scheme, and performs simulation to find out the key factors causing the bottleneck and modify key trains related to the bottleneck in the working diagram. Under the existing framework, additional trains will be run according to demand, including planning of new operating lines and adjustment of existing operating lines. During the peak hours of transportation, the train working diagram should be adjusted to specify an adjustable degree between the ideal starting time and the starting time of a newly added train to make a line shortest, and embed a solution algorithm into the simulation model to plan a line for the newly added train while meeting constraints such as the station interval and occupancy uniqueness, thereby simulating the transportation scheme. The present invention provides a system for executing the foregoing method. As shown in FIG. 10 and FIG. 11, the system includes: a data processing and analysis subsystem 101, configured to obtain and preprocess basic data, and perform analysis and identification on the preprocessed basic data, to obtain bottleneck point parameters; a model analysis subsystem 102, connected to the data processing and analysis subsystem 101 and configured to establish a mathematical model based on the bottleneck point parameters, to obtain a set of candidate carrying capacity operation schemes; and a simulation analysis subsystem 103, connected to the data processing and analysis subsystem 101 and the model analysis subsystem 102 and configured to perform simulation calculation on the set of candidate carrying capacity operation schemes; adjust the set of candidate carrying capacity operation schemes based on a result of the simulation calculation to obtain an optimal operation scheme; compare the optimal operation scheme with actual operation data, and evaluate the advantages and disadvantages of the scheme based on a comparison result, to fully explore the transport capacity of the railway, and obtain a scheme for identifying and eliminating a railway transport capacity bottleneck, to optimize the capacity utilization. In this example, the simulation analysis subsystem 103 is further configured to visually output the working processes of the data processing analysis subsystem 101 and the model analysis subsystem 102, as well as the simulation analysis process and results. The visual output methods include data display, text tables, charts (such as curves and bar graphs), as shown in FIG. 12. Further, in some preferred examples, the data processing and analysis subsystem 101 includes an application subsystem 1011, and the data processing and analysis subsystem 101 is further configured to: calibrate missing values in the basic data; process abnormal values in the basic data; and supplement and convert the basic data, and import the data into the application subsystem 1011 that is configured to perform analysis and identification on the preprocessed basic data. Further, the application subsystem 1011 includes: a data layer, used to connect to the database; a logic layer, used to store and read the preprocessed basic data, and perform arithmetic processing on the preprocessed basic data; and an interaction layer, used to display a result of the arithmetic processing performed at the logic layer. Further, in some preferred examples, the model analysis subsystem 102 can: Ok establish a mathematical model Max z, =' p iEL ZjEU Xjk Mk by taking the maximization of the volume on a line as objective I, where z1 is an objective function; establish a mathematical model Max z 2 = aJEBET,kEK jk - Nijk+ ai $ET,kEK k_ j by taking the minimization of changes to a train organization scheme as objective II, where z 2 is an objective function; and normalize the objective functions z 1 and z 2 , to obtain a mathematical model Max z 3 = 11z
A 2z 2 , where A1 and A2 are weight coefficients for the objectives I and II. Further, in some preferred examples, the simulation analysis subsystem 103 can further: initialize a simulation target status; run a simulation clock; when a next event moment becomes a current event moment, determine whether a simulation target real-time status meets a preset execution condition, and if yes, execute a simulation event corresponding to the simulation target real-time status; if no, repeat this sub-step; and determine whether the simulation target real-time status meets an end simulation condition after the simulation event is executed, and if yes, output a simulation calculation result and end the simulation calculation; if no, return to the third sub-step. In summary, the method and the system for identifying and eliminating a railway transport capacity bottleneck according to the present invention have the following advantages: (1) The present invention not only can eliminate static bottlenecks, but also can obtain the dynamic change rules of the bottlenecks, ensuring the correctness of the results and the effectiveness of the elimination solution. (2) The parameters of the model for generating a set of candidate carrying capacity schemes are based on a large amount of historical operation data, and the final scheme better satisfies realistic requirements. (3) The scheme is refined, authentic, and reliable, achieves a high solution efficiency, and implements balanced utilization and coordinated optimization of capacities, providing a new idea and technology for leveraging the carrying capacity. (4) Data acquisition, analysis, identification, and processing, and simulation and output of bottleneck elimination schemes are integrated into one system, achieving higher data accuracy without intermediate processes. (5) The present invention can be applied to heavy-haul transportation channel enterprises such as coal transportation, or promoted to the main railway channel network, and provide a complete set of schemes for optimizing the utilization of the railway carrying capacity. The above merely describes specific examples of the present invention, but the protection scope of the present invention is not limited thereto. A person skilled in the art can easily conceive modifications or replacements within the technical scope of the present invention, and these modifications or replacements shall fall within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (5)
- What is claimed is: 1. A method for identifying and eliminating a railway transport capacity bottleneck, comprising: obtaining and preprocessing basic data, and performing analysis and identification on the preprocessed basic data, to obtain bottleneck point parameters; establishing a mathematical model based on the bottleneck point parameters, to obtain a set of candidate carrying capacity operation schemes; performing simulation calculation on the set of candidate carrying capacity operation schemes; adjusting the set of candidate carrying capacity operation schemes based on a result of the simulation calculation, to obtain an optimal operation scheme; and comparing the optimal operation scheme with actual operations data, and obtaining a scheme for identifying and eliminating railway transport capacity bottlenecks based on a comparison result.
- 2. The method according to claim 1, wherein the obtaining and preprocessing basic data comprises: calibrating missing values in the basic data; processing abnormal values in the basic data; and supplementing and converting the basic data, and importing the data into an application subsystem that is used to perform analysis and identification on the preprocessed basic data; wherein the application subsystem comprises: a data layer, used to connect to the database; a logic layer, used to store and read the preprocessed basic data, and perform arithmetic processing on the preprocessed basic data; and an interaction layer, used to display a result of the arithmetic processing performed at the logic layer; wherein the performing, by the logic layer, arithmetic processing on the preprocessed basic data comprises: calculating a theoretical section carrying capacity according to an average minimum interval method formula N = T/(I+ iFrerf), wherein N is the section carrying capacity, T is section carrying valid time, Iis an average minimum time interval, and frerf is average required buffer time; and comparing the actual number of trains carried in a section with the theoretical section carrying capacity, to obtain a capacity utilization rate of the section; wherein the method further comprises: displaying and outputting, by the interaction layer, the capacity utilization rate of the section through a capacity utilization heat map.
- 3. The method according to claim 1, wherein the establishing a mathematical model based on the bottleneck point parameters, to obtain a set of candidate carrying capacity operation schemes comprises: establishing a mathematical model Max z, = cp iEL jEU jkMk by taking the maximization of the volume on a line as objective I, wherein z1 is an objective function; establishing a mathematical model Max z2 = aJEET,kEK jk - NljkI+ ak|EBT,kK 7 k- Ntj|Iby taking the minimization of changes to a train organization scheme as objective II, wherein z 2 is an objective function; and normalizing the objective functions z 1 and z2 , to obtain a mathematical model Maxz 3z 1 1 - A 2z 2 , wherein A, and A2 are weight coefficients for the objectives I and II.
- 4. The method according to claim 1, wherein the performing simulation calculation on the set of candidate carrying capacity operation schemes comprises the following sub-steps: initializing a simulation target status; running a simulation clock; when a next event moment becomes a current event moment, determining whether a simulation target real-time status meets a preset execution condition, and if yes, executing a simulation event corresponding to the simulation target real-time status; if no, repeating this sub step; and determining whether the simulation target real-time status meets an end simulation condition after the simulation event is executed, and if yes, outputting a simulation calculation result and ending the simulation calculation; if no, returning to the third sub-step.
- 5. A system for identifying and eliminating a railway transport capacity bottleneck, comprising: a data processing and analysis subsystem, configured to obtain and preprocess basic data, to obtain bottleneck point parameters; a model analysis subsystem, configured to establish a mathematical model based on the bottleneck point parameters, to obtain a set of candidate carrying capacity operation schemes; and a simulation analysis subsystem, configured to perform simulation calculation on the set of candidate carrying capacity operation schemes; adjust the set of candidate carrying capacity operation schemes based on a result of the simulation calculation to obtain an optimal operation scheme; and compare the optimal operation scheme with actual operations data, and obtain a scheme for identifying and eliminating railway transport capacity bottlenecks based on a comparison result; wherein the data processing and analysis subsystem comprises an application subsystem, and the data processing and analysis subsystem is further configured to: calibrate missing values in the basic data; process abnormal values in the basic data; and supplement and convert the basic data, and import the data into the application subsystem.
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| CN110751453A (en) | 2020-02-04 |
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