Train operation diagram scale adjustment method, device, electronic device and storage medium

Abstract

The present invention provides a method, device, electronic device and storage medium for adjusting the scale of a train operation diagram, the method comprising: determining the scale information of a train operation diagram to be adjusted; adjusting the scale information based on optimal scale information to obtain an adjusted train operation diagram; wherein the optimal scale information includes first scale information corresponding to the minimum average passenger waiting time determined in multiple historical scale information, and the multiple historical scale information and the scale information correspond to the same time period. The method, device, electronic device and storage medium provided by the present invention evaluate different historical scale schemes by taking the average passenger waiting time as a universal evaluation standard, and adjust the scale information of the train operation diagram based on the optimal scale information determined thereby, thereby improving the adjustment efficiency of the operation diagram scale and passenger satisfaction, ensuring the safe and efficient operation of the train, and reducing operating costs.

Description

Technical Field

[0001] The present invention relates to the field of rail transit technology, and in particular to a method, device, electronic equipment and storage medium for adjusting a train operation diagram scale. Background Art

[0002] The train operation diagram is a diagram that uses the coordinate principle to illustrate the relationship between train operation time and space, and uses a two-dimensional coordinate system to describe the order of train occupation intervals. The scale of the operation diagram usually includes information that affects the train interval, such as train stop time, turnaround time, and train operation level. In actual operation, when unforeseen events such as line equipment failure or short-term passenger flow surge occur, trains are delayed on a large scale and the risk of regional traffic jams increases. In addition, uneven departure intervals will also cause a large number of passengers to gather on the platform, which is likely to cause safety hazards and reduce passenger satisfaction.

[0003] At present, in order to reduce the adverse effects of emergencies, dispatchers usually manually adjust the scale of the operation diagram based on manual experience. However, the public transportation conditions in different cities and different routes are different, and it is difficult to find a set of universal evaluation standards to quantify the actual operation performance of different scales. The existing manual adjustment method is difficult to implement, has low adjustment efficiency, and high operating costs. Summary of the invention

[0004] The present invention provides a method, device, electronic device and storage medium for adjusting a train operation diagram scale, so as to solve the defect of low adjustment efficiency of the operation diagram scale in the prior art and improve the adjustment efficiency of the operation diagram scale.

[0005] The present invention provides a method for adjusting a train operation diagram scale, comprising:

[0006] Determine the scale information of the train operation diagram to be adjusted;

[0007] Based on the optimal scale information, the scale information is adjusted to obtain an adjusted train operation diagram;

[0008] The optimal scale information includes first scale information corresponding to the minimum average passenger waiting time determined in a plurality of historical scale information, and the plurality of historical scale information and the scale information correspond to the same time period.

[0009] According to a method for adjusting a train operation diagram scale provided by the present invention, the optimal scale information further includes second scale information corresponding to the maximum average train passenger load factor determined in the plurality of historical scale information, third scale information corresponding to the minimum average train operation interval, and fourth scale information corresponding to the minimum traffic congestion relief time;

[0010] The adjusting the scale information based on the optimal scale information includes:

[0011] The scale information is adjusted based on at least one of the first scale information, the second scale information, the third scale information, and the fourth scale information.

[0012] According to a method for adjusting a train operation diagram scale provided by the present invention, the scale information is adjusted based on the optimal scale information, and the method further includes:

[0013] Input each historical scale information, the historical scale information of the previous time period of each historical scale information, and the external environment information into a pre-constructed evaluation model to obtain the evaluation results corresponding to each historical scale information; the evaluation results include the results of multiple evaluation indicators, and the multiple evaluation indicators include the average waiting time of passengers, the average passenger load factor of trains, the average train running interval, and the traffic congestion relief time;

[0014] The optimal scale information is determined based on the evaluation results corresponding to the various historical scale information.

[0015] According to a method for adjusting a train operation diagram scale provided by the present invention, adjusting the scale information based on the optimal scale information includes:

[0016] Determining the importance of each scale element in the scale information to each evaluation indicator based on the weight parameters of the evaluation model;

[0017] The scale information is adjusted based on the importance of each scale element to each evaluation index and the optimal scale information.

[0018] According to a method for adjusting a train operation diagram scale provided by the present invention, the evaluation model is obtained based on sample historical scale information of multiple time periods, sample evaluation results corresponding to the sample historical scale information, sample external environment information and stochastic gradient descent algorithm training.

[0019] According to a method for adjusting the scale of a train operation diagram provided by the present invention, the scale information is adjusted based on the optimal scale information to obtain an adjusted train operation diagram, including:

[0020] Based on the optimal scale information, determining an adjustment strategy for the scale information;

[0021] If the traffic congestion relief time corresponding to the adjustment strategy is less than a preset relief time threshold, the scale information is adjusted based on the adjustment strategy to obtain the adjusted train operation diagram.

[0022] The present invention also provides a device for adjusting a train operation diagram scale, comprising:

[0023] A determination module, used to determine the scale information of the train operation diagram to be adjusted;

[0024] An adjustment module, used for adjusting the scale information based on the optimal scale information to obtain an adjusted train operation diagram;

[0025] The optimal scale information includes first scale information corresponding to the minimum average waiting time of passengers determined from a plurality of historical scale information, and the plurality of historical scale information and the scale information correspond to the same time period.

[0026] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for adjusting the train operation diagram scale as described in any one of the above are implemented.

[0027] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for adjusting the train operation diagram scale as described in any of the above are implemented.

[0028] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the method for adjusting the train operation diagram scale as described in any one of the above are implemented.

[0029] The train operation diagram scale adjustment method, device, electronic device and storage medium provided by the present invention evaluate different historical scale schemes by taking the average waiting time of passengers as a universal evaluation standard, and adjust the scale information of the train operation diagram based on the optimal scale information determined thereby, thereby improving the adjustment efficiency of the operation diagram scale and the satisfaction of passengers, ensuring the safe and efficient operation of the train, reducing the operating cost, and providing corresponding optimal scale information for each time period for subsequent adjustment reference, so that the adjustment method can cope with the changes in passenger flow in different time periods and improve the operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 This is one of the flow charts of the method for adjusting the train operation diagram scale provided by the present invention;

[0032] Figure 2 It is a flow chart of the method for obtaining the optimal scale information provided by the present invention;

[0033] Figure 3 is a schematic diagram of the network structure of the evaluation model provided by the present invention;

[0034] Figure 4 This is the second flow chart of the method for adjusting the train operation diagram scale provided by the present invention;

[0035] Figure 5 It is a structural schematic diagram of a device for adjusting a train operation diagram scale provided by the present invention;

[0036] Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] An embodiment of the present invention provides a method for adjusting a train operation diagram scale. Figure 1 It is one of the flow charts of the method for adjusting the train operation diagram scale provided by the present invention, such as Figure 1 As shown, the method includes:

[0039] Step 110, determining the scale information of the train operation diagram to be adjusted.

[0040] Specifically, the train operation diagram to be adjusted is a train operation diagram that needs to adjust the scale information, for example, it can be a train operation diagram when an emergency occurs, such as a line equipment failure, a short-term passenger flow surge, etc. The scale information of the train operation diagram may include information that affects the train operation interval, such as the train stop time, the train return time, and the train operation level, and may also include other scale information, such as the time period corresponding to the scale information, etc., which is not specifically limited in the embodiment of the present invention.

[0041] Step 120, adjusting the scale information based on the optimal scale information to obtain an adjusted train operation diagram;

[0042] The optimal scale information includes first scale information corresponding to the minimum average passenger waiting time determined in multiple historical scale information, and the multiple historical scale information and the scale information correspond to the same time period.

[0043] Specifically, considering that the existing technology usually requires dispatchers to manually adjust the scale of the operation diagram based on manual experience, however, the public transportation conditions in different cities and on different lines are different, and it is difficult to find a set of universal evaluation standards to quantify the actual operating performance of different scales. The manual adjustment method of the existing technology is difficult to implement, has low adjustment efficiency and high operating costs.

[0044] To address this issue, the embodiment of the present invention takes into account the importance of passenger satisfaction and uses the average waiting time of passengers as an evaluation indicator to evaluate the actual operational performance of different scale information. On-site dispatchers can use this indicator to evaluate different scale adjustment plans to achieve efficient and orderly operations, thereby greatly reducing the workload of control center dispatchers, reducing the difficulty of organization and command of on-site control personnel, and reducing the time cost of passenger travel.

[0045] Considering that the operational performance of the scale information is greatly affected by the tidal changes of passenger flow (such as morning and evening peaks on weekdays, holiday peaks, etc.), the embodiment of the present invention divides the whole day into various time periods, and then determines the corresponding optimal scale information for each time period. On this basis, after determining the time period corresponding to the scale information to be adjusted, the corresponding optimal scale information can be determined according to the time period, and then the scale information to be adjusted is adjusted according to the optimal scale information to obtain the adjusted train operation diagram, thereby improving the line operator's ability to handle emergencies and passenger satisfaction, and ensuring the safe and efficient operation of the train.

[0046] Here, the division of the time period can be set according to the adjustment accuracy required by the user, such as 30 minutes, 1 hour, etc. The specific adjustment method of the scale information can be to directly adjust the scale information to be adjusted to the optimal scale information, or to re-determine the new scale information with the optimal scale information as a reference, and then adjust the scale information to be adjusted to the new scale information, which is not specifically limited in the embodiment of the present invention.

[0047] The optimal scale information may include first scale information, and the first scale information may be determined by pre-collecting a large amount of scale information of multiple historical train operation diagrams corresponding to the same time period as the scale information to be adjusted, i.e., historical scale information, and then comparing the average waiting time of passengers corresponding to these historical scale information, and taking the historical scale information with the smallest average waiting time of passengers as the first scale information. For example, if the time period corresponding to the scale information to be adjusted is 8:30-9:00, then historical scale information from 8:30-9:00 within half a year or a month may be collected, and the historical scale information with the smallest average waiting time of passengers among these historical scale information may be taken as the first scale information. Here, the average waiting time of passengers may be calculated based on face recognition technology, or may be estimated by inputting the historical scale information into a neural network model, and the embodiments of the present invention do not specifically limit this.

[0048] The method provided by the embodiment of the present invention evaluates different historical scale schemes by taking the average waiting time of passengers as a universal evaluation standard, and adjusts the scale information of the train operation diagram based on the optimal scale information determined thereby, thereby improving the adjustment efficiency of the operation diagram scale and the satisfaction of passengers, ensuring the safe and efficient operation of the train, and reducing the operating cost. In addition, corresponding optimal scale information is provided for each time period for subsequent adjustment reference, so that the adjustment method can cope with the changes in passenger flow in different time periods and improve the operating efficiency.

[0049] Based on any of the above embodiments, the optimal scale information further includes second scale information corresponding to the maximum average train passenger load factor determined in the plurality of historical scale information, third scale information corresponding to the minimum average train running interval, and fourth scale information corresponding to the minimum traffic congestion relief time;

[0050] Step 120 includes:

[0051] The scale information is adjusted based on at least one of the first scale information, the second scale information, the third scale information, and the fourth scale information.

[0052] Specifically, in order to provide multiple optimal scale information for users to choose according to their own needs, the embodiment of the present invention takes the average train passenger load, the average train running interval and the traffic congestion relief time as evaluation indicators to evaluate the actual operation performance of different scale information from the perspectives of train operation and the ability to solve traffic congestion. The second scale information corresponding to the maximum average train passenger load, the third scale information corresponding to the minimum average train running interval and the fourth scale information corresponding to the minimum traffic congestion relief time are determined from multiple historical scale information.

[0053] On this basis, the user can refer to at least one of the first scale information, the second scale information, the third scale information and the fourth scale information included in the optimal scale information to determine the final adjustment strategy, and adjust the scale information to be adjusted according to the adjustment strategy, so as to achieve a balance between improving passenger satisfaction and energy-saving and efficient online operation.

[0054] It should be noted that when an emergency occurs, trains are delayed on a large scale, the risk of regional traffic jams increases, and the average waiting time of passengers in a short period of time increases. This trend can be changed by adjusting the scale information of the train operation diagram (such as improving the train operation level, shortening the train operation cycle, adding spare trains, etc.). Therefore, the total time of the average waiting time of passengers in the station from low to high and then back to normal can be used to measure the traffic jam relief time of the scale information.

[0055] Based on any of the above embodiments, before step 120, the following steps may be further included:

[0056] Input each historical scale information, the historical scale information of the previous time period of each historical scale information, and the external environment information into a pre-built evaluation model to obtain the evaluation results corresponding to each historical scale information; the evaluation results include the results of multiple evaluation indicators, and the multiple evaluation indicators include the average waiting time of passengers, the average passenger load factor of trains, the average train running interval, and the traffic congestion relief time;

[0057] Based on the evaluation results corresponding to each historical scale information, the optimal scale information is determined.

[0058] Specifically, in order to improve the efficiency and accuracy of scale evaluation, an evaluation model can be constructed in advance, and then the historical scale information, the historical scale information of the previous time period of each historical scale information, and the external environment information are input into the evaluation model to obtain the evaluation results corresponding to each historical scale information.

[0059] Here, the external environment information may include external information such as passenger flow and weather environment. The evaluation model may be a single neural network model or a combination of multiple neural network models, which is not specifically limited in the embodiment of the present invention. The evaluation results may include the results corresponding to multiple evaluation indicators such as the average waiting time of passengers, the average passenger load factor of trains, the average running interval of trains, and the time for traffic congestion relief.

[0060] On this basis, according to the evaluation results corresponding to each historical scale information, the first scale information corresponding to the minimum average passenger waiting time, the second scale information corresponding to the maximum average train passenger load factor, the third scale information corresponding to the minimum average train running interval and the fourth scale information corresponding to the minimum traffic congestion relief time can be determined. In this way, the optimal scale information can be obtained and used to adjust the scale information to be adjusted, thereby improving the economic benefits of rail transit operating companies.

[0061] It should be noted that the evaluation model can only evaluate one or several evaluation indicators, which can be achieved by setting different evaluation indicators to correspond to different output weights before inputting the scale information into the evaluation model.

[0062] Based on any of the above embodiments, Figure 2 is a flow chart of the optimal scale information acquisition method provided by the present invention, such as Figure 2 As shown in the figure, taking the average waiting time of passengers as an example to evaluate different timetable scale information, firstly, the historical timetable data is cleaned, abnormal data and redundant data are eliminated, the data dimension is reduced, the data is standardized to accelerate the convergence of weight parameters, and the most similar data items are used to fill the missing data. Then, the historical timetable data after data processing is divided into groups in units of 30 minutes, thereby obtaining multiple groups of historical scale information of different time periods, and then the corresponding optimal scale information is determined according to each group of historical scale information, and finally the optimal scale information for each time period of the day can be obtained, which is convenient for the subsequent drawing work of map editors.

[0063] For each set of historical scale information, multiple historical scale information of the same time period included in the set of data can be input into the evaluation model for evaluation, thereby obtaining the first scale information with the smallest average waiting time for passengers in the set of historical scale information, and using the first scale information as the optimal scale information for the time period with the average waiting time for passengers as the adjustment strategy. In addition, if the average waiting time for passengers in the optimal scale information still does not meet user needs, more historical scale information of the same time period can be obtained and input into the evaluation model, so that the optimal scale information can be continuously updated based on historical data analysis.

[0064] Based on any of the above embodiments, step 120 includes:

[0065] Based on the weight parameters of the evaluation model, determine the importance of each scale element in the scale information to each evaluation indicator;

[0066] The scale information is adjusted based on the importance of each scale element to each evaluation indicator and the optimal scale information.

[0067] Specifically, the scale information may include information on multiple scale elements such as train stop time, train turnaround time, train operation level, average passenger density at the station, scale time period, etc. According to the weight parameters of different neurons in the pre-trained evaluation model, the importance of each scale element in the scale information to each evaluation indicator can be determined. Then, for each evaluation indicator, all scale elements can be sorted according to the importance of the evaluation indicator. On this basis, the scale information to be adjusted can be adjusted according to the sorting results of each evaluation indicator and the optimal scale information corresponding to each evaluation indicator.

[0068] Here, the specific method of adjusting the scale information can be to determine the scale element that needs to be adjusted based on the sorting result of one of the evaluation indicators, and then adjust the scale element that needs to be adjusted in the scale information based on the optimal scale information corresponding to the evaluation indicator. It can also be to comprehensively consider the sorting results of multiple evaluation indicators to determine the scale element that needs to be adjusted, and then comprehensively refer to the optimal scale information corresponding to multiple evaluation indicators to adjust the scale element that needs to be adjusted in the scale information. The embodiment of the present invention does not make specific limitations on this.

[0069] Based on data mining of historical operation chart data, the weight parameters of the evaluation model are obtained, which can rank the importance of different factors affecting each evaluation indicator, making it easier to formulate plans for scale information optimization and automatic adjustment strategies.

[0070] Based on any of the above embodiments, the evaluation model is obtained based on sample historical scale information of multiple time periods, sample evaluation results corresponding to the sample historical scale information, sample external environment information, and stochastic gradient descent algorithm training.

[0071] Specifically, in order to achieve quantitative evaluation of the operational performance of historical scale information, the evaluation model can be obtained by pre-collecting sample historical scale information of multiple time periods, and determining the sample evaluation results corresponding to these sample historical scale information, and then training the initial model based on these sample historical scale information, the corresponding sample evaluation results and the sample external environment information. In addition, in order to increase the speed of model convergence and obtain a better evaluation model, the objective function of the initial model can be optimized using the stochastic gradient descent algorithm during the training of the initial model, and finally the optimal evaluation model can be obtained.

[0072] It should be noted that an evaluation model can be pre-trained for each time period of the day. In this case, multiple time periods refer to the time period corresponding to the scale information to be adjusted, and the previous time period of the time period. A unified evaluation model can also be pre-trained for all time periods of the day. In this case, multiple time periods refer to all time periods of the day. The embodiments of the present invention do not make specific limitations on this.

[0073] Based on any of the above embodiments, sample historical train operation diagram data can be collected in advance for data analysis. From the perspective of train operation, key information such as the arrival and departure time, interval operation time, passenger load factor, and punctuality rate of each train at all stations can be recorded. In addition, the average waiting time of passengers at each station in different time periods and the time ratio of interval travel between peak and off-peak passenger flow periods are collected and calculated through face recognition technology. In this way, sample historical scale information of multiple time periods and sample evaluation results corresponding to the sample historical scale information can be obtained for training the evaluation model.

[0074] Figure 3 is a schematic diagram of the network structure of the evaluation model provided by the present invention, such as Figure 3 As shown in the figure, the evaluation model includes an input layer, a hidden layer, and an output layer; the input layer vector includes the current scale information of scale elements such as train stop time, train turnaround time, train operation level, station average passenger density, scale time period, scale information in the previous time period, and external environment information; the activation function of the hidden layer is the Sigmoid function; the output layer is the evaluation indicators of the evaluation scale performance such as the average train operation interval, the average train passenger load factor, the average passenger waiting time, the traffic jam relief time, and the number of trains called. The training process of the evaluation model is as follows:

[0075] Assume that the input of a neuron is the vector The weight vector is (The bias term is w 0 ), the activation function is the sigmoid function, then its output y is:

[0076]

[0077] The sigmoid function is defined as follows:

[0078]

[0079] The sum of squared errors of all output layer nodes of the evaluation model is taken as the objective function, and each evaluation indicator corresponds to an output layer node:

[0080]

[0081] Among them, E d represents the error of sample d, t i Represents the true value of the i-th output layer node, y i Represents the output value of the i-th output layer node.

[0082] Then the stochastic gradient descent algorithm is used to optimize the objective function and fit the data by continuously optimizing the model parameters:

[0083]

[0084] Among them, w ji is the weight parameter from input layer node i to output layer node j, and η is a constant of learning rate.

[0085] Finally, the trained evaluation model can be obtained, and the importance of each scale element in the scale information to each evaluation indicator can be determined according to the weight parameters of the evaluation model. In addition, since the model also takes into account the scale information used in the previous time period, the trained evaluation model can be used to predict the traffic congestion relief time of different scale information.

[0086] Based on any of the above embodiments, step 120 includes:

[0087] Based on the optimal scale information, determining the adjustment strategy of the scale information;

[0088] If the traffic congestion relief time corresponding to the adjustment strategy is less than the preset relief time threshold, the scale information is adjusted based on the adjustment strategy to obtain an adjusted train operation diagram.

[0089] Specifically, in order to make the adjusted train schedule have a stronger ability to solve congestion problems, after obtaining the optimal scale information, the adjustment strategy of the scale information can be determined according to the optimal scale information, and it can be judged whether the traffic congestion relief time corresponding to the adjustment strategy is less than the preset relief time threshold. If it is less than the relief time threshold, the scale information can be adjusted according to the adjustment strategy to obtain the adjusted train schedule. Otherwise, it is necessary to re-determine a new adjustment strategy until the adjustment strategy meets the requirement of being less than the relief time threshold.

[0090] Furthermore, the adjustment strategy can be manually selected or automatically determined. The optimal scale information can be directly used as the adjustment strategy, or the optimal scale information can be used as a reference. At the same time, the adjustment strategy is determined by comprehensively considering factors such as the expected length of time for traffic congestion relief after the scale information is adjusted, the range of vehicles affected by the adjustment, and the number of spare vehicles to be called up. The embodiments of the present invention do not make specific limitations on this.

[0091] The traffic congestion relief time corresponding to the adjustment strategy can be obtained by inputting the adjustment strategy into the evaluation model. After the scale information is adjusted based on the adjustment strategy, the actual operation performance obtained after the adjustment can be recorded as a reference for subsequent recommended adjustment plans.

[0092] Based on any of the above embodiments, Figure 4 FIG. 2 is a flow chart of the method for adjusting the train operation diagram scale provided by the present invention. Figure 4As shown, adjustment strategies for multiple scale information can be determined based on the optimal scale information, and then the traffic congestion relief times corresponding to different adjustment strategies are compared, and the adjustment strategy with the shortest traffic congestion relief time is used as the current adjustment strategy. Then, it is determined whether the current adjustment strategy meets the adjustment requirement of being less than the relief time threshold. If so, the scale information to be adjusted can be directly switched to the new scale information of the current adjustment strategy, thereby solving the problem of unforeseen passenger flow changes. If not, a new adjustment strategy needs to be re-determined until the adjustment strategy meets the requirement of being less than the relief time threshold.

[0093] The adjustment strategy can be determined arbitrarily according to the needs of the line operator. For example, it can be to try to meet the requirement of a shorter average train running interval, or to try to meet the requirement of fewer vehicles to be called up, or to keep the train load factor at a balanced and reasonable level on the basis of meeting the dispatching safety to improve the travel experience of passengers.

[0094] The following is a description of the adjustment device for the train operation icon scale provided by the present invention. The adjustment device for the train operation icon scale described below and the adjustment method for the train operation icon scale described above can be referred to each other.

[0095] Based on any of the above embodiments, an embodiment of the present invention provides a device for adjusting a train operation diagram scale. Figure 5 1 is a schematic diagram of the structure of a device for adjusting a train operation diagram scale provided by the present invention, the device comprising:

[0096] A determination module 510 is used to determine the scale information of the train operation diagram to be adjusted;

[0097] An adjustment module 520, configured to adjust the scale information based on the optimal scale information to obtain an adjusted train operation diagram;

[0098] The optimal scale information includes first scale information corresponding to the minimum average waiting time of passengers determined from multiple historical scale information, and the multiple historical scale information and the scale information correspond to the same time period.

[0099] The device provided by the embodiment of the present invention evaluates different historical scale schemes by taking the average waiting time of passengers as a universal evaluation standard, and adjusts the scale information of the train operation diagram based on the optimal scale information determined thereby, thereby improving the adjustment efficiency of the operation diagram scale and the satisfaction of passengers, ensuring the safe and efficient operation of the train, and reducing the operating cost. In addition, corresponding optimal scale information is provided for each time period for subsequent adjustment reference, so that the adjustment method can cope with the changes in passenger flow in different time periods and improve the operating efficiency.

[0100] Based on any of the above embodiments, the optimal scale information further includes second scale information corresponding to the maximum average train passenger load factor determined in the plurality of historical scale information, third scale information corresponding to the minimum average train running interval, and fourth scale information corresponding to the minimum traffic congestion relief time;

[0101] The adjustment module 520 is used to:

[0102] The scale information is adjusted based on at least one of the first scale information, the second scale information, the third scale information, and the fourth scale information.

[0103] Based on any of the above embodiments, the device further includes an optimal scale determination module, which is used to:

[0104] Input each historical scale information, the historical scale information of the previous time period of each historical scale information, and the external environment information into a pre-built evaluation model to obtain the evaluation results corresponding to each historical scale information; the evaluation results include the results of multiple evaluation indicators, and the multiple evaluation indicators include the average waiting time of passengers, the average passenger load factor of trains, the average train running interval, and the traffic congestion relief time;

[0105] Based on the evaluation results corresponding to each historical scale information, the optimal scale information is determined.

[0106] Based on any of the above embodiments, the adjustment module 520 is used to:

[0107] Based on the weight parameters of the evaluation model, determine the importance of each scale element in the scale information to each evaluation indicator;

[0108] The scale information is adjusted based on the importance of each scale element to each evaluation indicator and the optimal scale information.

[0109] Based on any of the above embodiments, the evaluation model is obtained based on sample historical scale information of multiple time periods, sample evaluation results corresponding to the sample historical scale information, sample external environment information, and stochastic gradient descent algorithm training.

[0110] Based on any of the above embodiments, the adjustment module 520 is used to:

[0111] Based on the optimal scale information, determining the adjustment strategy of the scale information;

[0112] If the traffic congestion relief time corresponding to the adjustment strategy is less than the preset relief time threshold, the scale information is adjusted based on the adjustment strategy to obtain an adjusted train operation diagram.

[0113] Figure 6 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 6As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630 and a communication bus 640, wherein the processor 610, the communication interface 620 and the memory 630 communicate with each other through the communication bus 640. The processor 610 may call the logic instructions in the memory 630 to execute a method for adjusting the scale of a train operation diagram, the method comprising: determining scale information of a train operation diagram to be adjusted; adjusting the scale information based on optimal scale information to obtain an adjusted train operation diagram; wherein the optimal scale information includes first scale information corresponding to the minimum average waiting time of passengers determined in a plurality of historical scale information, and the plurality of historical scale information and the scale information correspond to the same time period.

[0114] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0115] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the train timetable scale adjustment method provided by the above methods, the method including: determining the scale information of the train timetable to be adjusted; adjusting the scale information based on the optimal scale information to obtain an adjusted train timetable; wherein the optimal scale information includes the first scale information corresponding to the minimum average passenger waiting time determined in multiple historical scale information, and the multiple historical scale information and the scale information correspond to the same time period.

[0116] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the train timetable scale adjustment method provided by the above-mentioned methods, the method comprising: determining the scale information of the train timetable to be adjusted; adjusting the scale information based on the optimal scale information to obtain an adjusted train timetable; wherein the optimal scale information includes first scale information corresponding to the minimum average passenger waiting time determined from multiple historical scale information, and the multiple historical scale information and the scale information correspond to the same time period.

[0117] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0118] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for adjusting a train operation diagram scale, characterized in that: include: Determine the scale information of the train operation diagram to be adjusted; Based on the optimal scale information, the scale information is adjusted to obtain an adjusted train operation diagram; The optimal scale information includes first scale information corresponding to the minimum average passenger waiting time determined in a plurality of historical scale information, and the plurality of historical scale information and the scale information correspond to the same time period; The adjusting the scale information based on the optimal scale information also includes: Input each historical scale information, the historical scale information of the previous time period of each historical scale information, and the external environment information into a pre-built evaluation model to obtain the evaluation results corresponding to each historical scale information; determine the optimal scale information based on the evaluation results corresponding to each historical scale information; The optimal scale information also includes second scale information corresponding to the maximum average train passenger load factor, third scale information corresponding to the minimum average train running interval, and fourth scale information corresponding to the minimum traffic congestion relief time determined in the plurality of historical scale information; The adjusting the scale information based on the optimal scale information includes: Adjusting the scale information based on at least one of the first scale information, the second scale information, the third scale information, and the fourth scale information; The evaluation result includes the results of multiple evaluation indicators, and the multiple evaluation indicators include the average waiting time of passengers, the average passenger load factor of trains, the average running interval of trains and the traffic congestion relief time; The adjusting the scale information based on the optimal scale information includes: Determining the importance of each scale element in the scale information to each evaluation indicator based on the weight parameters of the evaluation model; The scale information is adjusted based on the importance of each scale element to each evaluation index and the optimal scale information.

2. The method for adjusting the train operation diagram scale according to claim 1, characterized in that: The evaluation model is obtained based on sample historical scale information of multiple time periods, sample evaluation results corresponding to the sample historical scale information, sample external environment information, and stochastic gradient descent algorithm training.

3. The method for adjusting the train operation diagram scale according to any one of claims 1 to 2, characterized in that: Based on the optimal scale information, the scale information is adjusted to obtain an adjusted train operation diagram, including: Based on the optimal scale information, determining an adjustment strategy for the scale information; If the traffic congestion relief time corresponding to the adjustment strategy is less than a preset relief time threshold, the scale information is adjusted based on the adjustment strategy to obtain the adjusted train operation diagram.

4. A train operation diagram scale adjustment device, characterized in that: include: A determination module, used to determine the scale information of the train operation diagram to be adjusted; An adjustment module, used for adjusting the scale information based on the optimal scale information to obtain an adjusted train operation diagram; The optimal scale information includes the first scale information corresponding to the minimum average waiting time of passengers determined in multiple historical scale information, and the multiple historical scale information and the scale information correspond to the same time period; The device also includes an optimal scale determination module, which is used to input each historical scale information, the historical scale information of the previous time period of each historical scale information, and the external environment information into a pre-built evaluation model to obtain the evaluation results corresponding to each historical scale information; based on the evaluation results corresponding to each historical scale information, determine the optimal scale information; The optimal scale information also includes second scale information corresponding to the maximum average train passenger load factor, third scale information corresponding to the minimum average train running interval, and fourth scale information corresponding to the minimum traffic congestion relief time determined in the plurality of historical scale information; The adjusting module adjusts the scale information based on the optimal scale information, including: Adjusting the scale information based on at least one of the first scale information, the second scale information, the third scale information, and the fourth scale information; The evaluation result includes the results of multiple evaluation indicators, and the multiple evaluation indicators include the average waiting time of passengers, the average passenger load factor of trains, the average running interval of trains and the traffic congestion relief time; The adjusting module adjusts the scale information based on the optimal scale information, including: Determining the importance of each scale element in the scale information to each evaluation indicator based on the weight parameters of the evaluation model; The scale information is adjusted based on the importance of each scale element to each evaluation index and the optimal scale information.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for adjusting the train operation diagram scale as described in any one of claims 1 to 3 are implemented.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for adjusting the train operation diagram scale as described in any one of claims 1 to 3 are implemented.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for adjusting the train operation diagram scale as described in any one of claims 1 to 3 are implemented.

Images