Method and device for optimizing transportation efficiency of heavy haul railway station

Abstract

The invention provides a heavy haul railway station transportation efficiency optimization method and device, and belongs to the technical field of railway heavy haul transportation. The method comprises the steps of obtaining various operation index data of multiple months, performing normalization processing on the operation index data, and generating multiple evaluation data sets; calculating the weight of each evaluation index by means of an entropy weight method, and obtaining a transportation efficiency evaluation score of each month by using a good and inferior solution distance method; the months with the scores lower than a preset value are determined as to-be-optimized months, and the indexes with the weights larger than the preset value are set as to-be-optimized indexes; and finally, matching corresponding measures for the to-be-optimized indexes of the to-be-optimized months according to the constructed transportation efficiency optimization measure table, so that a user can carry out transportation optimization work. According to the invention, comprehensive evaluation and continuous optimization of the transportation efficiency are realized by integrating the evaluation method and the precise optimization measure, and the operation management efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of heavy-haul railway transportation, and particularly relates to a method for optimizing the transportation efficiency of a heavy-haul railway station and a device for optimizing the transportation efficiency of a heavy-haul railway station. Background Art

[0002] At present, the overall transportation capacity of heavy-haul railways in China has ranked first in the world. Among them, the total mileage of railways with a traction weight exceeding 8,000 tons has exceeded 5,000 kilometers, and a new heavy-haul railway layout has been formed. Under this situation, the Shuohuang Railway Company of the National Energy Group has achieved world-leading results in key indicators such as train operation organization, application of heavy-haul trains, and transportation efficiency. As a key means to improve railway freight efficiency and reduce costs, heavy-haul railway transportation is increasingly demonstrating its important value.

[0003] Heavy-haul railways are characterized by large total train weight, large axle load, and high train operation density. Railway technical stations are key nodes in railway coal transportation. As an important train marshalling station for the railway transportation of the National Energy Group, the transportation efficiency and operation quality of Shenchi South Station not only affect the timely supply of energy and raw materials, but also have a profound impact on the regional economy and the national logistics network. Therefore, it is urgent to carry out research on the operation capacity and optimization strategies of heavy-haul railway technical stations.

[0004] Currently, the research on the transportation capacity and optimization of heavy-haul railway technical stations of large integrated energy enterprises at home and abroad mainly includes the following methods: (1) Design a comprehensive track utilization optimization plan. Input data such as different train operation scenarios and track conditions into it, and simulate and verify the plan; (2) First, divide the railway operation time in detail to form time slices, and then construct an undirected graph model for the allocation and utilization of arrival and departure tracks according to the occupied and idle states of arrival and departure tracks in different time slices. On this basis, use the simulated annealing algorithm to perform iterative calculations on this model to continuously optimize the allocation plan of arrival and departure tracks in each time slice; (3) Select the queuing theory calculation method, and then determine parameters such as its input process (such as the time interval distribution of train arrivals) and service mechanism (such as the time for train disintegration and marshalling) for each operation system of the station, and establish a corresponding queuing model.

[0005] However, the existing research methods for the transport capacity and its optimization of heavy-haul railway technical stations have the following problems: (1) They mainly focus on the optimization of local links, such as the optimization scheme of track utilization, the model of arrival and departure track allocation and utilization, the queuing theory calculation method, etc. They only focus on a single aspect and ignore the mutual connection between operation indicators and links; (2) They fail to fully consider the mutual influence between different operation indicators, resulting in weak pertinence of optimization measures; (3) When simulating and calculating, the utilization of data is limited and the information behind the data is not fully explored; (4) The operation environment of heavy-haul railway stations is complex and changeable, and the models and algorithms in the existing technologies are difficult to adapt to this change, resulting in unstable optimization effects. Summary of the Invention

[0006] The purpose of the embodiments of the present invention is to provide a method and device for optimizing the transport efficiency of heavy-haul railway stations to solve the above problems.

[0007] To achieve the above purpose, the embodiments of the present invention provide a method for optimizing the transport efficiency of heavy-haul railway stations, including: Obtain the transport efficiency evaluation schemes of multiple months of heavy-haul railway stations; wherein, the transport efficiency evaluation scheme includes operation index data of multiple categories; After normalizing the operation index data of multiple categories in multiple months, determine the operation index data of the same category in multiple months as the evaluation data of the same category, and obtain multiple evaluation data sets; Using each evaluation data set as an evaluation index, determine the entropy weight method weight of each evaluation index through the entropy weight method; Using the technique for order preference by similarity to ideal solution (TOPSIS) method, calculate the entropy weight method weight of each evaluation index and the evaluation data set corresponding to each evaluation index to obtain the transport efficiency evaluation scores of multiple months; Determine the months with transport efficiency evaluation scores less than the preset evaluation score as the months to be optimized, and determine the evaluation indexes with entropy weight method weights greater than the preset weight as the evaluation indexes to be optimized; Based on the evaluation indexes to be optimized in the months to be optimized, match them with the pre-constructed transport efficiency optimization measure table to obtain the optimization measures for the months to be optimized, so that users can optimize the transport of heavy-haul railway stations according to the optimization measures for the months to be optimized; wherein, the pre-constructed transport efficiency optimization measures are used to represent the mapping relationship between the evaluation indexes to be optimized and the corresponding optimization measures.

[0008] Optionally, determining the entropy weight method weight of each evaluation index through the entropy weight method includes: Calculate the proportion of each operation index data under each evaluation index; Calculate the information entropy of each evaluation index based on the proportion of each operation index data under each evaluation index; Calculate the entropy weight method weight of each evaluation index based on the information entropy of each evaluation index.

[0009] Optionally, the calculation rule for the weight of each operation index data under each evaluation index is as follows: ; where represents the weight of the j-th operation index data under the i-th evaluation index, represents the j-th operation index data under the i-th evaluation index, represents a total of operation index data, that is .

[0010] Optionally, the calculation rule for the information entropy of each evaluation index is as follows: ; where represents the information entropy of the i-th evaluation index, represents the weight of the j-th operation index data under the i-th evaluation index, represents a total of operation index data, that is .

[0011] Optionally, the calculation rule for the weight of the entropy weight method of each evaluation index is as follows: ; where represents the weight of the entropy weight method of the i-th evaluation index, represents the information entropy of the i-th evaluation index, represents a total of m evaluation indexes, that is .

[0012] Optionally, using the TOPSIS method, calculate the weight of the entropy weight method of each evaluation index and the evaluation data set corresponding to each evaluation index to obtain the transportation efficiency evaluation scores for multiple months, including: Take the maximum value in the evaluation data set corresponding to each evaluation index as the positive ideal solution of each evaluation index; Take the minimum value in the evaluation data set corresponding to each evaluation index as the negative ideal solution of each evaluation index; Based on the positive ideal solution of each evaluation index, the negative ideal solution of each evaluation index, the weight of the entropy weight method of each evaluation index, and the evaluation data set corresponding to each evaluation index, obtain the weighted distance from the transportation efficiency evaluation plan of each month to the positive ideal solution of each evaluation index and the weighted distance to the negative ideal solution; Based on the weighted distance from the transportation efficiency evaluation plan of each month to the positive ideal solution of each evaluation index and the weighted distance to the negative ideal solution, obtain the transportation efficiency evaluation score of each month.

[0013] Optionally, the calculation rule for the weighted distance from the transportation efficiency evaluation plan of each month to the positive ideal solution of each evaluation index is as follows: ; where represents the weighted distance from the transportation efficiency evaluation scheme of the z-th month to the positive ideal solution of each evaluation index, represents the entropy weight method weight of the i-th evaluation index, represents the positive ideal solution of the i-th evaluation index, represents the operation index data of the z-th month under the i-th evaluation index, represents a total of M months, that is .

[0014] Optionally, the calculation rule for the weighted distance from the transportation efficiency evaluation scheme of each month to the negative ideal solution of each evaluation index is: ; where represents the weighted distance from the transportation efficiency evaluation scheme of the z-th month to the negative ideal solution of each evaluation index, represents the entropy weight method weight of the i-th evaluation index, represents the negative ideal solution of the i-th evaluation index, represents the operation index data of the z-th month under the i-th evaluation index, represents a total of M months, that is .

[0015] Optionally, the calculation rule for the transportation efficiency evaluation score of each month is: ; where represents the transportation efficiency evaluation score of the z-th month, represents the weighted distance from the transportation efficiency evaluation scheme of the z-th month to the positive ideal solution of each evaluation index, represents the weighted distance from the transportation efficiency evaluation scheme of the z-th month to the negative ideal solution of each evaluation index.

[0016] In the second aspect of the embodiments of the present invention, the present invention further provides an optimization device for the transportation efficiency of a heavy-haul railway station, including: A data acquisition module, configured to acquire transportation efficiency evaluation schemes of multiple months of a heavy-haul railway station; wherein, the transportation efficiency evaluation scheme includes various types of operation index data; A data processing module, configured to normalize various types of operation index data of multiple months, and determine the operation index data of the same type in multiple months as the same type of evaluation data to obtain multiple evaluation data sets; A weight calculation module, configured to use each evaluation data set as an evaluation index, and determine the entropy weight method weight of each evaluation index by the entropy weight method; A scoring calculation module, which is used to calculate the evaluation scores of the transportation efficiency for multiple months by using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method with respect to the entropy weight method weights of each evaluation index and the evaluation data sets corresponding to each evaluation index; An optimization determination module, which is used to determine the months with transportation efficiency evaluation scores less than the preset evaluation score as the months to be optimized, and determine the evaluation indexes with entropy weight method weights greater than the preset weight as the evaluation indexes to be optimized; An optimization execution module, which is used to match the evaluation indexes to be optimized in the months to be optimized with a pre-constructed transportation efficiency optimization measure table to obtain the optimization measures for the months to be optimized, so that the user can optimize the transportation of the heavy-haul railway station according to the optimization measures for the months to be optimized; wherein, the pre-constructed transportation efficiency optimization measures are used to represent the mapping relationship between the evaluation indexes to be optimized and the corresponding optimization measures.

[0017] Advantages of the present invention: (1) Comprehensive evaluation and comprehensive optimization: By obtaining the operation index data of multiple categories for multiple months and conducting comprehensive and all-round analysis using the entropy weight method and the TOPSIS method, the months and evaluation indexes to be optimized can be accurately found, realizing the comprehensive improvement of transportation efficiency.

[0018] (2) Precise optimization measures: By pre-constructing a transportation efficiency optimization measure table and matching the evaluation indexes to be optimized in the months to be optimized with it, more targeted and operable optimization measures can be generated.

[0019] (3) Full utilization of data: Normalize and deeply analyze the operation index data of multiple categories for multiple months, extract valuable information, and provide strong support for the evaluation and optimization of transportation efficiency.

[0020] (4) Adapt to complex operation environments: By continuously collecting and analyzing operation data, the operation status and change trends of the station can be grasped in real time, and the evaluation indexes and optimization measures can be adjusted in a timely manner to adapt to complex and changeable operation environments.

[0021] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the specification. Together with the following specific implementation, they are used to explain the embodiments of the present invention, but do not constitute a limitation to the embodiments of the present invention. In the drawings: Figure 1 is a flowchart of the heavy-haul railway station transportation efficiency optimization method provided by the embodiment of the present invention; Figure 2 is a structural schematic diagram of the heavy-haul railway station transportation efficiency optimization device provided by the embodiment of the present invention. Detailed implementation manners

[0023] The following will describe in detail the specific implementation manners of the embodiments of the present invention with reference to the accompanying drawings. It should be understood that the specific implementation manners described herein are only for the purpose of illustrating and explaining the embodiments of the present invention, and are not used to limit the embodiments of the present invention.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments, and are not intended to limit this application.

[0025] In the description of the embodiments of this application, technical terms such as "first" and "second" are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity, specific order or primary-secondary relationship of the indicated technical features. In the description of the embodiments of this application, "a plurality of" means more than two, unless otherwise specifically defined.

[0026] Embodiment 1 Please refer to Figure 1 , Figure 1 which is a schematic flowchart of the method for optimizing the transportation efficiency of a heavy-haul railway station provided by the embodiments of the present invention. The method includes the following steps: S100, obtaining the transportation efficiency evaluation schemes of a heavy-haul railway station for multiple months; wherein, the transportation efficiency evaluation scheme includes various types of operation index data; It should be noted that the present invention is a scheme for optimizing the transportation efficiency of a heavy-haul railway station.

[0027] The following gives an example of a heavy-haul railway station: Taking Shenchi South Station as an example, it is located in Shenchi County, Xinzhou City, Shanxi Province, and is connected to the Shenshuo Line, Zhunchi Line, and Shuohuang Line. The daily coal transportation volume is nearly one million tons. It is a key node leading to the three major seaports of Huanghua Port, Tianjin Port, and Longkou Port. It is the largest ten-thousand-ton and twenty-thousand-ton marshalling station on the Shuohuang Railway, undertaking important tasks such as the access, evacuation, and marshalling of empty and loaded cars on the Baoshen Railway and the Xinshuo Railway. The operation organization has a fast rhythm and heavy tasks. It is connected to the largest coal discharging port in China (Huanghua Port). Its unique hub position makes this station play a crucial role in the transfer of goods between regions and is of great significance in the coal railway logistics network and energy supply guarantee in China.

[0028] Shenchi South Station has two yards and 42 receiving and departure lines. Yard I, as the down-line yard, has 18 tracks, each with an effective length of over 2,800 meters. It can not only meet the requirements for receiving and dispatching 20,000-ton trains but also handle various technical operations. To better organize and manage the operation of trains, Yard I is divided into 6 groups of track bundles, each consisting of 3 tracks and interconnected by 3 crossover switches, enabling flexible locomotive changeover and car body disintegration operations for 10,000-ton trains. This unique design also divides the tracks into four track sections: Track 1, Track 2, Track 3, and Track 4. Among them, Track 1 in Yard I is the main line of the Shuohuang Line, Track 15 in Yard I leads to the direction of Shenmu North on the Shenshuo Line, and Track 16 in Yard I is connected to the direction of Waixigou on the Zhunchi Line.

[0029] Yard II, as the up-line yard, has 24 receiving and departure lines. Except for the effective length of the receiving and departure line at Track 1 in Yard II being 1,273 meters and the special situations of Tracks 22, 23, and 24, the effective lengths of the remaining receiving and departure lines all exceed 2,800 meters, fully meeting the strict requirements for handling 20,000-ton heavy-haul train operations. Track 2 in Yard II is the main line of the Shenshuo Line; Track 3 in Yard II has a dual function, being able to connect to the direction of Suning North on the Shuohuang Line and lead to the direction of Shenmu North on the Shenshuo Line; and Track 4 in Yard II is the main line in the direction of Waixigou on the Zhunchi Line. This complex and precise track design ensures the efficient operation and powerful sorting capacity of Shenchi South Station.

[0030] In addition, every 3 receiving and departure lines from Track 4 to Track 21 in Yard II are divided into a group of track bundles, with a total of 6 groups of track bundles. The 3 receiving and departure lines in each group of track bundles are connected by 3 crossover switches and divided into 4 sections, in the same way as the track bundle division in Yard I. Tracks 22, 23, and 24 in Yard II are functionally divided into inter-yard communication lines, which can be used for the exchange of vehicle flows between Yard I and Yard II to stay. At the same time, Track 22 is connected to the depot for temporary repairs and can detain and repair faulty vehicles.

[0031] Specifically, the transportation efficiency of a heavy-haul railway station will surely be reflected by the operation index data of the heavy-haul railway station. That is, when the transportation efficiency of the heavy-haul railway station changes, there will surely be numerical changes in the operation index data of the heavy-haul railway station. Based on this, when evaluating the transportation efficiency of a heavy-haul railway station, the operation index data collected from the heavy-haul railway station is preferably data that can characterize the transportation efficiency of the heavy-haul railway station.

[0032] In one embodiment, various types of operation index data include: average monthly transfer time, track occupancy time, number of skylight days, average monthly number of 20,000-ton train departures, average monthly traffic volume, coal freight turnover volume, average weather impact index, utilization rate of new technologies for improving efficiency, and on-time rate of coal special line cargo loading.

[0033] The average monthly transfer time refers to the average time that goods or passengers experience from arriving at a transportation hub (such as a railway station, airport, port, etc.) to leaving the hub within a one-month time range.

[0034] Track occupancy time refers to the average length of time a train or vehicle occupies a railway track (track).

[0035] Window days are time periods reserved on the train timetable when no trains are scheduled to run in order for the railway department to inspect and maintain lines, signals, power supply and other equipment. They are usually calculated in days.

[0036] The monthly average number of 20,000-ton trains refers to the average number of trains with a load capacity of 20,000 tons running each month.

[0037] The average monthly freight volume refers to the total amount of goods or the total number of passengers transported by rail in a month.

[0038] Coal freight turnover refers to the product of coal freight volume and transportation distance within a certain period (such as month or year).

[0039] The average weather impact index is a comprehensive indicator that measures the impact of weather factors on transportation and other related businesses.

[0040] The utilization rate of new technologies that improve efficiency refers to the proportion of new technologies that have been adopted in transportation or related businesses that can improve work efficiency among all related technologies.

[0041] The on-time rate of coal-dedicated line cargo loading refers to the proportion of cargo loaded on time according to the planned time on the coal-dedicated transportation line.

[0042] For ease of understanding, the following exemplary operation indicator data of various categories of heavy-duty railway stations in multiple months are given as shown in Table 1: Table 1 Operation index data of heavy-haul railway stations

[0043] From the data in Table 1, we can see that the average monthly transit time is basically between 2.7 and 2.8, of which the average monthly transit time from May to October is 2.8, and the rest of the months is 2.7. In terms of the number of window days, December to April of the following year is significantly lower than May to November, which shows that coal transportation is relatively busy during this period, which is consistent with the fact that coal consumption is relatively high for centralized heating in the north during this period. Regarding the average number of 20,000-ton trains per month, the average monthly transportation volume, and the average weather impact index, the data from May to October are generally lower than those of other months, mainly because the consumption of coal for centralized heating in winter is relatively high.

[0044] S200, after normalizing the operation indicator data of multiple categories in multiple months, determine the operation indicator data of the same category in multiple months as the same category of evaluation data, and obtain multiple evaluation data sets; In addition, according to the numerical meanings of the operation index data of heavy-haul railway stations, the operation index data are divided into two categories: positive indexes and negative indexes. After normalization, the positive indexes are marked as and the negative indexes are marked as after normalization.

[0045] For the sake of easy understanding, the data in Table 1 are normalized below to obtain the operation index data of multiple categories for multiple months of heavy-haul railway stations after normalization, as shown in Table 2 below: Table 2 Operation Index Data of Heavy-Haul Railway Stations after Normalization

[0046] It can be understood that to evaluate the transportation efficiency of heavy-haul railway stations, it is necessary to collect the operation index data of multiple categories for multiple months of heavy-haul railway stations, then perform denormalization on the collected operation index data of multiple categories for multiple months of heavy-haul railway stations, and then determine that the operation index data of the same type for multiple months after normalization are of the same type of evaluation data set to obtain multiple evaluation data sets.

[0047] S300, taking each evaluation data set as an evaluation index, determines the entropy weight method weights of each evaluation index through the entropy weight method; It should be noted that to quantitatively evaluate the transportation efficiency of heavy-haul railway stations, it is necessary to ensure the consistency of the entire evaluation system, that is, to design an evaluation index system that can be widely applied, and then propose a corresponding quantitative evaluation method for this system, so as to facilitate the popularization of the entire scheme and serve all heavy-haul railway stations. Based on this, the present invention needs to construct a corresponding evaluation index system for the transportation efficiency of heavy-haul railway stations. For the sake of easy understanding, the evaluation index system for the transportation efficiency of heavy-haul railway stations is exemplarily given below, as shown in Table 3: Table 3 Evaluation Index System for the Transportation Efficiency of Heavy-Haul Railway Stations

[0048] It should be noted that the evaluation index system for the transportation efficiency of heavy-haul railway stations is a multi-level evaluation system. The multi-level evaluation system includes target indicators, criterion indicators, and evaluation indicators; each target indicator includes multiple criterion indicators; each criterion indicator includes multiple evaluation indicators. Within its own evaluation layer, it is to be evaluated as an evaluation item, but this evaluation item will also serve as an evaluation factor for the upper level. For example: when the target indicator is used as the evaluation item, the criterion indicator serves as the evaluation factor for the target indicator; when the criterion indicator is used as the evaluation item, the evaluation indicator serves as the evaluation factor for the criterion indicator. As shown in Table 3, optionally, the target layer of the evaluation index system for the transportation efficiency of heavy-haul railway stations is operation efficiency, which includes multiple criterion indicators such as the average monthly transfer time, track occupancy time, and number of skylight days. And the transportation capacity includes: multiple evaluation indicators such as the monthly average number of 20,000-ton departure trains, monthly average traffic volume, and coal freight turnover volume. The content included in other criterion indicators can be referred to Table 3 and will not be elaborated here. In this embodiment, actually only the evaluation indicators are used, and Table 3 is only for facilitating the understanding of the structure of the evaluation index system for the transportation efficiency of heavy-haul railway stations.

[0049] In one embodiment, step S300 specifically includes: S310, calculating the proportion of each operation index data under each evaluation index; Specifically, the calculation rule for the proportion of each operation index data under each evaluation index is: ; where represents the proportion of the j-th operation index data under the i-th evaluation index, represents the j-th operation index data under the i-th evaluation index, represents there are operation index data in total, that is .

[0050] S320, calculating the information entropy of each evaluation index based on the proportion of each operation index data under each evaluation index; Specifically, the calculation rule for the information entropy of each evaluation index is: ; where represents the information entropy of the i-th evaluation index, represents the proportion of the j-th operation index data under the i-th evaluation index, represents there are operation index data in total, that is .

[0051] S330, calculating the entropy weight method weight of each evaluation index based on the information entropy of each evaluation index.

[0052] Specifically, the calculation rule for the entropy weight method weight of each evaluation index is: ; where It represents the entropy weight method weight of the i-th evaluation index. It represents the information entropy of the i-th evaluation index. It represents that there are m evaluation indexes, that is .

[0053] For the sake of easy understanding, the information entropy and weight values of each evaluation index are calculated based on the data in Table 2 as shown in Table 4 below: Table 4 Information Entropy and Weight Values of Each Evaluation Index

[0054] S400, using the TOPSIS method, calculate the entropy weight method weights of each evaluation index and the evaluation data sets corresponding to each evaluation index to obtain the transportation efficiency evaluation scores for multiple months; In one embodiment, step S400 specifically includes: S410, taking the maximum value in the evaluation data set corresponding to each evaluation index as the positive ideal solution of each evaluation index; Specifically, the positive ideal solution is defined as: ; where represents the positive ideal solution.

[0055] S420, taking the minimum value in the evaluation data set corresponding to each evaluation index as the negative ideal solution of each evaluation index; Specifically, the positive ideal solution is defined as: ; where represents the positive ideal solution.

[0056] S430, based on the positive ideal solution of each evaluation index, the negative ideal solution of each evaluation index, the entropy weight method weight of each evaluation index, and the evaluation data set corresponding to each evaluation index, obtain the weighted distance from the transportation efficiency evaluation plan of each month to the positive ideal solution of each evaluation index and the weighted distance to the negative ideal solution; Specifically, the calculation rule for the weighted distance from the transportation efficiency evaluation plan of each month to the positive ideal solution of each evaluation index is: ; where represents the weighted distance from the transportation efficiency evaluation plan of the z-th month to the positive ideal solution of each evaluation index, represents the entropy weight method weight of the i-th evaluation index, represents the positive ideal solution of the i-th evaluation index, represents the operation index data of the z-th month under the i-th evaluation index, represents that there are M months in total, that is .

[0057] Specifically, the calculation rule for the weighted distance from the transportation efficiency evaluation plan of each month to the negative ideal solution of each evaluation index is: ; among which, represents the weighted distance from the transportation efficiency evaluation plan for the z-th month to the negative ideal solution of each evaluation index, represents the entropy weight method weight of the i-th evaluation index, represents the negative ideal solution of the i-th evaluation index, represents the operation index data of the z-th month under the i-th evaluation index, represents that there are M months in total, that is, .

[0058] S440. Based on the weighted distances from the transportation efficiency evaluation plans for each month to the positive ideal solution and the negative ideal solution of each evaluation index, the transportation efficiency evaluation scores for each month are obtained.

[0059] In one embodiment, the calculation rule for the transportation efficiency evaluation scores for each month is: ; among which, represents the transportation efficiency evaluation score for the z-th month, represents the weighted distance from the transportation efficiency evaluation plan for the z-th month to the positive ideal solution of each evaluation index, represents the weighted distance from the transportation efficiency evaluation plan for the z-th month to the negative ideal solution of each evaluation index.

[0060] For the sake of easy understanding, the transportation efficiency evaluation scores for each month are calculated based on the data in Table 4 as shown in Table 5 below: Table 5 Transportation Efficiency Evaluation Scores for Each Month

[0061] Regarding the evaluation indexes of the transportation efficiency of heavy-haul railway stations, the weights of the three indexes of the average monthly transfer time, the number of skylight days, and the average weather impact index are relatively large, which are 0.3252, 0.1397, and 0.1025 respectively. Among them, the first two indexes are related to transportation efficiency, and the last index is related to meteorological factors. It can be seen that improving the average monthly transfer time of Shenchi South Station, reducing the impact of skylights, and doing a good job in dealing with severe weather such as strong winds and freezing in Shenchi South Station can effectively improve the transportation efficiency of this station.

[0062] S500. Determine the months with transportation efficiency evaluation scores less than the preset evaluation score as the months to be optimized, and determine the evaluation indexes with entropy weight method weights greater than the preset weight as the evaluation indexes to be optimized; For example: Assume that the preset evaluation score is 0.1 and the preset weight is 0.08. According to the data in Table 5, the months less than 0.1 include May, June, July, September, and October. According to the data in Table 4, the evaluation indexes greater than 0.08 include track occupancy time, number of skylight days, average weather impact index, and technological innovation.

[0063] S600 matches the evaluation indicators to be optimized for the month to be optimized with the pre-constructed transportation efficiency optimization measure table to obtain the optimization measures for the month to be optimized, so that users can optimize the transportation of heavy-haul railway stations according to the optimization measures for the month to be optimized; among them, the pre-constructed transportation efficiency optimization measures are used to represent the mapping relationship between the evaluation indicators to be optimized and the corresponding optimization measures.

[0064] It should be noted that the evaluation indicators to be optimized are determined according to their weight values. Therefore, the evaluation indicators to be optimized for each month are the same, and the optimization measures are also the same.

[0065] Table 6 Pre-constructed Transportation Efficiency Optimization Measure Table

[0066] For example: After calculation, May and June are the months to be optimized, and the evaluation indicators to be optimized include track occupancy time, number of skylight days, average weather impact index, and technological innovation. Therefore, the optimization measures for May and June are to reasonably arrange train dispatching, scientifically plan maintenance tasks, establish a weather warning mechanism, make preparations in advance for bad weather, and increase R & D investment, introduce new technologies and equipment, and improve the intelligent level of operation.

[0067] Specifically, the optimization measures for the evaluation indicator of the average number of 20,000-ton train departures per month can be to strictly execute the daily transportation plan issued every day and arrange the locomotive operation plan in advance in combination with the transportation volume. Baoshen and Xinshuo Railways increase the formation of 10,000-ton trains to reach the scheduled departure ratio of 10,000-ton trains, and reduce the operation time of transfer trains occupying the arrival and departure tracks of the station. Increase the organization of long-haul trains in the Xinshuo direction, release the capacity of the arrival and departure tracks in Station Yard II, and improve the transfer efficiency of the station.

[0068] Specifically, the optimization measures for the evaluation indicator of the average number of 20,000-ton train departures per month can also be to actively connect with the two upstream railway companies, understand the types and ratios of each train class in combination with the skylight opening situation, and maximize the organization of 16,000-ton and 20,000-ton train departures. According to the train stage plan, arrange the train receiving tracks and train arrival and departure plans in advance, and relatively fix the use of arrival and departure tracks. Make full use of the gap between the arrival of trains in the Daxin direction to handle the pick-up and delivery operations of the coal special line.

[0069] Specifically, the optimization measures for the evaluation indicator of the average weather impact index can be to strengthen meteorological warnings according to the special geographical attributes of the station, take measures in advance to reduce the stay time of vehicles in the station. Conduct hierarchical warnings on the dwell time of loaded vehicles to prevent large-point vehicles. Keep a close eye on the implementation of safety measures such as anti-rail breakage and anti-freezing heave, and conduct key control on links such as fault repair, switch switching, ice-breaking and snow-sweeping, and line inspection to ensure the safe, efficient and unobstructed operation of the integrated throat.

[0070] Specifically, the optimization measures for the evaluation index of skylight days can be that the upstream coal resources show wide fluctuations in transportation volume as the market fluctuates. The upstream coal resource acquisition capacity should be increased, the long-term mechanism for cooperation with external coal suppliers should be continuously optimized, and new coal sources should be actively expanded to maintain the stability and sufficiency of upstream external coal resources. At the same time, skylight maintenance should be reasonably arranged according to the level of loading volume, so as to reduce the impact of fluctuations in coal transportation volume on the operating efficiency of Shenchi South Station.

[0071] Specifically, the optimization measures for the evaluation index of technological innovation can be to increase the application and promotion of advanced technologies such as heavy-load railway transportation and mobile blocking, and promote the operation of 30,000-ton heavy-load trains. Important station transfer hubs should accelerate the transformation of railway transportation equipment such as high-power electric locomotives, new energy shunting locomotives, and wireless ECP system train braking systems. Relying on scientific and technological achievements, the train transfer efficiency and safety control level of Shenchi South Station will be continuously improved, and the group's integrated coal transportation level and energy security capabilities will be further improved.

[0072] Specifically, the optimization measures for the evaluation indicator of average monthly transportation volume can be to continue to develop bulk cargo, especially long-distance large-volume reverse transportation customers, and explore the full process of forward and reverse transportation by road, port and shipping. On the premise of ensuring the smooth forward coal transportation, increase the organization of non-coal transportation, add to the railway transportation volume, increase the volume and create benefits while maintaining the efficient and stable operation of the station.

[0073] Beneficial effects of the present invention: (1) Comprehensive evaluation and integrated optimization: By obtaining multiple categories of operational indicator data for multiple months and using the entropy weight method and the distance method between good and bad solutions to conduct a comprehensive and integrated analysis, we can accurately identify the months and evaluation indicators to be optimized, thereby achieving a comprehensive improvement in transportation efficiency.

[0074] (2) Accurate optimization measures: By pre-constructing a transport efficiency optimization measures table and matching it with the evaluation indicators to be optimized in the month to be optimized, more targeted and operational optimization measures can be generated.

[0075] (3) Full use of data: Normalize and deeply analyze various types of operational indicator data from multiple months to extract valuable information, providing strong support for the evaluation and optimization of transportation efficiency.

[0076] (4) Adapting to complex operating environments: By continuously collecting and analyzing operating data, the station operation status and changing trends can be understood in real time, and evaluation indicators and optimization measures can be adjusted in a timely manner to adapt to the complex and changing operating environment.

[0077] Embodiment 2 Based on the same inventive concept, Figure 2 As shown, the embodiment of the present invention further provides a heavy-load railway station transportation efficiency optimization device 200, comprising: The data acquisition module 210 is used to acquire the transport efficiency evaluation scheme of the heavy-duty railway station for multiple months; wherein the transport efficiency evaluation scheme includes multiple categories of operation index data; The data processing module 220 is used to normalize the operation indicator data of multiple categories in multiple months, determine the operation indicator data of the same category in multiple months as the same category of evaluation data, and obtain multiple evaluation data sets; A weight calculation module 230 is used to use each evaluation data set as an evaluation indicator and determine the entropy weight method weight of each evaluation indicator through an entropy weight method; The scoring calculation module 240 is used to calculate the entropy weight of each evaluation index and the evaluation data set corresponding to each evaluation index by using the superior and inferior solution distance method to obtain the transportation efficiency evaluation scores of multiple months; The optimization determination module 250 is used to determine the month whose transportation efficiency evaluation score is less than the preset evaluation score as the month to be optimized, and determine the evaluation index whose entropy weight method weight is greater than the preset weight as the evaluation index to be optimized; The optimization execution module 260 is used to match the evaluation index to be optimized of the month to be optimized with the pre-constructed transport efficiency optimization measures table to obtain the optimization measures of the month to be optimized, so that the user can optimize the transportation of the heavy-load railway station according to the optimization measures of the month to be optimized; wherein the pre-constructed transport efficiency optimization measures are used to characterize the mapping relationship between the evaluation index to be optimized and the corresponding optimization measures.

[0078] It should be understood that the device corresponds to the above-mentioned heavy-load railway station transportation efficiency optimization method embodiment, and can execute the various steps involved in the above-mentioned method embodiment. The specific functions of the device can be found in the above description. To avoid repetition, the detailed description is appropriately omitted here. The device includes at least one software function module that can be stored in a memory in the form of software or firmware or solidified in the operating system (OS) of the device.

[0079] Embodiment 3 Based on the same inventive concept, an embodiment of the present invention also provides an electronic device, including: a processor and a memory, the memory storing machine-readable instructions executable by the processor, and the machine-readable instructions, when executed by the processor, execute the above-mentioned heavy-load railway station transportation efficiency optimization method.

[0080] In a typical configuration, an electronic device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0081] The memory may include non-permanent memory in the form of computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0082] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0083] Embodiment 4 Based on the same inventive concept, an embodiment of the present invention also provides a computer-readable storage medium storing computer instructions, which when run on a computer, cause the computer to execute the above-mentioned optimized method for the transportation efficiency of heavy-haul railway stations.

[0084] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0085] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate for implementation in the processFigure 1 one or more processes and / or blocks Figure 1 a device for the functions specified in one or more blocks

[0086] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device that implements the functions in the process Figure 1 one or more processes and / or blocks Figure 1 specified in one or more blocks

[0087] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the process Figure 1 one or more processes and / or blocks Figure 1 specified in one or more blocks

[0088] In addition, it should be noted that, among the various specific technical features described in the above specific embodiments, they can be combined in any appropriate manner without conflict. To avoid unnecessary repetition, the embodiments of the present invention do not separately describe various possible combination methods

[0089] In addition, in each embodiment of the embodiments of the present application, each functional module can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part

[0090] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, commodity or device including the element

[0091] The above are only the embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application

Claims

1. A method for optimizing the transportation efficiency of a heavy-load railway station, characterized in that: include: Obtaining a transport efficiency evaluation plan for a heavy-duty railway station for multiple months; wherein the transport efficiency evaluation plan includes multiple categories of operational indicator data; After normalizing the operation indicator data of multiple categories in multiple months, the operation indicator data of the same category in multiple months are determined to be the same category of evaluation data, and multiple evaluation data sets are obtained; Taking each evaluation data set as the evaluation index, the entropy weight method weight of each evaluation index is determined by the entropy weight method; The entropy weight method weights of each evaluation index and the evaluation data set corresponding to each evaluation index are calculated using the superior and inferior solution distance method to obtain the transportation efficiency evaluation scores for multiple months. Determine the months whose transport efficiency evaluation scores are less than the preset evaluation scores as the months to be optimized, and determine the evaluation indicators whose entropy weight method weights are greater than the preset weights as the evaluation indicators to be optimized; Based on matching the evaluation indicators to be optimized in the month to be optimized with the pre-constructed transport efficiency optimization measures table, the optimization measures for the month to be optimized are obtained, so that the user can optimize the transportation of heavy-load railway stations according to the optimization measures for the month to be optimized; wherein, the pre-constructed transport efficiency optimization measures are used to characterize the mapping relationship between the evaluation indicators to be optimized and the corresponding optimization measures.

2. The method for optimizing the transportation efficiency of a heavy-load railway station according to claim 1, characterized in that: The entropy weight method is used to determine the entropy weight of each evaluation index, including: Calculate the proportion of each operating indicator data under each evaluation indicator; The information entropy of each evaluation indicator is calculated based on the proportion of each operating indicator data under each evaluation indicator; The entropy weight method weight of each evaluation indicator is calculated based on the information entropy of each evaluation indicator.

3. The method for optimizing the transportation efficiency of a heavy-load railway station according to claim 2, characterized in that: The calculation rules for the proportion of each operating indicator data under each evaluation indicator are as follows: ;in, It represents the proportion of the jth operation indicator data under the i-th evaluation indicator, represents the jth operating indicator data under the i-th evaluation indicator, Indicates shared Operational indicator data, namely .

4. The method for optimizing the transportation efficiency of a heavy-load railway station according to claim 2, characterized in that: The calculation rules of information entropy of each evaluation index are as follows: ;in, represents the information entropy of the i-th evaluation index, It represents the proportion of the jth operation indicator data under the i-th evaluation indicator, Indicates shared Operational indicator data, namely .

5. The method for optimizing the transportation efficiency of a heavy-load railway station according to claim 2, characterized in that: The calculation rules of the entropy weight method weights of each evaluation index are as follows: ;in, represents the entropy weight of the i-th evaluation index, represents the information entropy of the i-th evaluation index, It means there are m evaluation indicators, namely .

6. The method for optimizing the transportation efficiency of a heavy-haul railway station according to claim 1, characterized in that: Using the superior and inferior solution distance method, the entropy weight method weights of each evaluation index and the evaluation data set corresponding to each evaluation index are calculated to obtain the transportation efficiency evaluation scores for multiple months, including: The maximum value in the evaluation data set corresponding to each evaluation index is taken as the positive ideal solution of each evaluation index; The minimum value in the evaluation data set corresponding to each evaluation index is taken as the negative ideal solution of each evaluation index; Based on the positive ideal solution of each evaluation index, the negative ideal solution of each evaluation index, the entropy weight method weight of each evaluation index and the evaluation data set corresponding to each evaluation index, the weighted distance from the transportation efficiency evaluation scheme of each month to the positive ideal solution and the weighted distance from the negative ideal solution of each evaluation index are obtained; Based on the weighted distance of the transportation efficiency evaluation scheme of each month to the positive ideal solution and the weighted distance of the negative ideal solution of each evaluation index, the transportation efficiency evaluation score of each month is obtained.

7. The method for optimizing the transportation efficiency of a heavy-haul railway station according to claim 6, characterized in that: The calculation rule of the weighted distance from the transportation efficiency evaluation scheme of each month to the positive ideal solution of each evaluation index is: ;in, represents the weighted distance from the transport efficiency evaluation scheme of the zth month to the positive ideal solution of each evaluation index, represents the entropy weight of the i-th evaluation index, represents the positive ideal solution of the i-th evaluation index, represents the operating indicator data of the zth month under the i-th evaluation indicator, It means there are M months in total, that is .

8. The method for optimizing the transportation efficiency of a heavy-haul railway station according to claim 6, characterized in that: The calculation rule of the weighted distance from the transportation efficiency evaluation scheme of each month to the negative ideal solution of each evaluation index is: ;in, represents the weighted distance from the transport efficiency evaluation scheme of the zth month to the negative ideal solution of each evaluation index, represents the entropy weight of the i-th evaluation index, represents the negative ideal solution of the i-th evaluation index, represents the operating indicator data of the zth month under the i-th evaluation indicator, It means there are M months in total, that is .

9. The method for optimizing the transportation efficiency of a heavy-load railway station according to claim 6, characterized in that: The calculation rules for the transportation efficiency evaluation score of each month are as follows: ;in, represents the transport performance evaluation score of the zth month, represents the weighted distance from the transport efficiency evaluation scheme of the zth month to the positive ideal solution of each evaluation index, Represents the weighted distance from the transportation efficiency evaluation scheme for the zth month to the negative ideal solution of each evaluation index.

10. A heavy-load railway station transportation efficiency optimization device, characterized in that: include: A data acquisition module is used to acquire a transport efficiency evaluation scheme of a heavy-duty railway station for multiple months; wherein the transport efficiency evaluation scheme includes multiple categories of operation indicator data; A data processing module is used to normalize the operation indicator data of multiple categories in multiple months, determine the operation indicator data of the same category in multiple months as the same category of evaluation data, and obtain multiple evaluation data sets; A weight calculation module is used to use each evaluation data set as an evaluation indicator and determine the entropy weight method weight of each evaluation indicator through the entropy weight method; The scoring calculation module is used to calculate the entropy weight of each evaluation index and the evaluation data set corresponding to each evaluation index by using the superior and inferior solution distance method to obtain the transportation efficiency evaluation scores for multiple months; An optimization determination module is used to determine the month whose transportation efficiency evaluation score is less than the preset evaluation score as the month to be optimized, and to determine the evaluation index whose entropy weight method weight is greater than the preset weight as the evaluation index to be optimized; The optimization execution module is used to match the evaluation indicators to be optimized in the month to be optimized with the pre-constructed transportation efficiency optimization measures table to obtain the optimization measures for the month to be optimized, so that the user can optimize the transportation of heavy-load railway stations according to the optimization measures for the month to be optimized; wherein the pre-constructed transportation efficiency optimization measures are used to characterize the mapping relationship between the evaluation indicators to be optimized and the corresponding optimization measures.

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