Method and device for adjusting train operation diagram scale, electronic equipment and storage medium
阅读说明：本技术 列车运行图标尺的调整方法、装置、电子设备与存储介质 (Method and device for adjusting train operation diagram scale, electronic equipment and storage medium ) 是由 李金壑 徐硕 李俊松 程远瑶 赵青莉 张帅 郭俊垚 宋健健 于 2021-08-31 设计创作，主要内容包括：本发明提供一种列车运行图标尺的调整方法、装置、电子设备与存储介质,所述方法包括：确定待调整的列车运行图的标尺信息；基于最优标尺信息,对所述标尺信息进行调整,得到调整后的列车运行图；其中,所述最优标尺信息包括在多个历史标尺信息中确定出的最小乘客平均等车时间对应的第一标尺信息,所述多个历史标尺信息与所述标尺信息对应同一时间段。本发明提供的方法、装置、电子设备与存储介质,通过将乘客平均等车时间作为通用的评估标准,对不同的历史标尺方案进行评估,并基于由此确定出的最优标尺信息对列车运行图的标尺信息进行调整,从而提高了运行图标尺的调整效率和乘客的满意度,保障了列车的安全高效运行,降低了运营成本。(The invention provides a method and a device for adjusting a train working diagram scale, electronic equipment and a storage medium, wherein the method comprises the following steps: determining scale information of a train running chart to be adjusted; adjusting the scale information based on the optimal scale information to obtain an adjusted train operation diagram; the optimal scale information comprises 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. According to the method, the device, the electronic equipment and the storage medium provided by the invention, different historical scale schemes are evaluated by taking the average waiting time of passengers as a general evaluation standard, and the scale information of the train operation diagram is adjusted based on the determined optimal scale information, so that the adjustment efficiency of the scale of the operation diagram and the satisfaction degree of the passengers are improved, the safe and efficient operation of the train is ensured, and the operation cost is reduced.)
1. A method for adjusting a scale of a train working diagram is characterized by comprising the following steps:
determining scale information of a train running chart to be adjusted;
adjusting the scale information based on the optimal scale information to obtain an adjusted train operation diagram;
the optimal scale information comprises 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.
2. The method for adjusting the train operation diagram scale according to claim 1, wherein the optimal scale information further includes second scale information corresponding to a maximum train average passenger carrying rate, third scale information corresponding to a minimum train average operation interval, and fourth scale information corresponding to a minimum traffic jam breakdown time, which are determined from 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.
3. The method for adjusting the scale of the train operation chart according to claim 2, wherein the adjusting the scale information based on the optimal scale information further comprises:
inputting each piece of historical scale information, historical scale information of a previous time period of each piece of historical scale information and external environment information into a pre-constructed evaluation model to obtain an evaluation result corresponding to each piece of historical scale information; the evaluation result comprises results of a plurality of evaluation indexes, wherein the evaluation indexes comprise passenger average waiting time, train average passenger carrying rate, train average running interval and traffic jam relief time;
and determining the optimal scale information based on the evaluation result corresponding to each historical scale information.
4. The method for adjusting the scale of the train operation chart according to claim 3, wherein the adjusting the scale information based on the optimal scale information comprises:
determining the importance degree of each scale element in the scale information to each evaluation index based on the weight parameter of the evaluation model;
and adjusting the scale information based on the importance degree of each scale element to each evaluation index and the optimal scale information.
5. The method for adjusting the train operation diagram scale according to claim 3, wherein the evaluation model is obtained by training based on sample historical scale information of a plurality of time periods, sample evaluation results corresponding to the sample historical scale information, sample external environment information and a random gradient descent algorithm.
6. The method for adjusting the train operation diagram scale according to any one of claims 1 to 5, wherein the adjusting the scale information based on the optimal scale information to obtain the adjusted train operation diagram comprises:
determining an adjustment strategy of the scale information based on the optimal scale information;
and if the traffic jam relief time corresponding to the adjustment strategy is smaller than a preset relief time threshold, adjusting the scale information based on the adjustment strategy to obtain the adjusted train operation diagram.
7. An adjusting device for a train diagram scale, comprising:
the determining module is used for determining scale information of the train working diagram to be adjusted;
the adjusting module is used for adjusting the scale information based on the optimal scale information to obtain an adjusted train operation diagram;
the optimal scale information comprises first scale information which is determined from a plurality of historical scale information and corresponds to the minimum average waiting time of passengers, and the plurality of historical scale information and the scale information correspond to the same time period.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the processor when executing the program implements the steps of the method of adjusting a train diagram scale according to any one of claims 1 to 6.
9. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the method of adjusting a train diagram scale according to any one of claims 1 to 6.
10. A computer program product comprising a computer program, wherein the computer program when executed by a processor implements the steps of a method of adjusting a train diagram scale according to any one of claims 1 to 6.
Technical Field
The invention relates to the technical field of rail transit, in particular to a method and a device for adjusting a train working diagram ruler, electronic equipment and a storage medium.
Background
The train operation diagram is a diagram of the relation between the train operation time and the space by using a coordinate principle, and a two-dimensional coordinate system is used for describing the sequence of the train occupation interval. The scale of the map typically includes information that affects train spacing, such as train stop time, turn back time, train grade, etc. In the actual operation process, when unforeseen events such as line equipment failure or short-term passenger flow volume surge occur, the risk of regional communication blockage becomes high at a late time on a large scale, in addition, a large number of passengers are gathered at the platform due to unbalanced departure intervals, potential safety hazards are easily caused, and the satisfaction degree of the passengers is also reduced.
Currently, in order to reduce the adverse effect of an emergency, a dispatcher manually adjusts a scale of a running chart according to manual experience. However, the public transportation conditions of different cities and different lines are different, so that it is difficult to find a set of general evaluation standards to quantify the actual operation performance of different scales, and the existing manual adjustment method has the disadvantages of high implementation difficulty, low adjustment efficiency and high operation cost.
Disclosure of Invention
The invention provides a method and a device for adjusting a train operation diagram scale, electronic equipment and a storage medium, which are used for overcoming the defect of low adjustment efficiency of the operation diagram scale in the prior art and improving the adjustment efficiency of the operation diagram scale.
The invention provides a method for adjusting a scale of a train working diagram, which comprises the following steps:
determining scale information of a train running chart to be adjusted;
adjusting the scale information based on the optimal scale information to obtain an adjusted train operation diagram;
the optimal scale information comprises 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.
According to the method for adjusting the scale of the train operation diagram, provided by the invention, the optimal scale information further comprises second scale information corresponding to the maximum train average passenger carrying rate, third scale information corresponding to the minimum train average operation interval and fourth scale information corresponding to the minimum traffic jam break-out time, which are determined from 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.
According to the method for adjusting the scale of the train working diagram provided by the invention, the scale information is adjusted based on the optimal scale information, and the method also comprises the following steps:
inputting each piece of historical scale information, historical scale information of a previous time period of each piece of historical scale information and external environment information into a pre-constructed evaluation model to obtain an evaluation result corresponding to each piece of historical scale information; the evaluation result comprises results of a plurality of evaluation indexes, wherein the evaluation indexes comprise passenger average waiting time, train average passenger carrying rate, train average running interval and traffic jam relief time;
and determining the optimal scale information based on the evaluation result corresponding to each historical scale information.
According to the method for adjusting the scale of the train working diagram provided by the invention, the adjustment of the scale information based on the optimal scale information comprises the following steps:
determining the importance degree of each scale element in the scale information to each evaluation index based on the weight parameter of the evaluation model;
and adjusting the scale information based on the importance degree of each scale element to each evaluation index and the optimal scale information.
According to the method for adjusting the scale of the train working diagram, the evaluation model is obtained by training based on sample historical scale information of a plurality of time periods, sample evaluation results corresponding to the sample historical scale information, sample external environment information and a random gradient descent algorithm.
According to the method for adjusting the scale of the train working diagram provided by the invention, based on the optimal scale information, the scale information is adjusted to obtain the adjusted train working diagram, and the method comprises the following steps:
determining an adjustment strategy of the scale information based on the optimal scale information;
and if the traffic jam relief time corresponding to the adjustment strategy is smaller than a preset relief time threshold, adjusting the scale information based on the adjustment strategy to obtain the adjusted train operation diagram.
The invention also provides a device for adjusting the scale of the train working diagram, which comprises:
the determining module is used for determining scale information of the train working diagram to be adjusted;
the adjusting module is used for adjusting the scale information based on the optimal scale information to obtain an adjusted train operation diagram;
the optimal scale information comprises first scale information which is determined from a plurality of historical scale information and corresponds to the minimum average waiting time of passengers, and the plurality of historical scale information and the scale information correspond to the same time period.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of any one of the above methods for adjusting the scale of the train working diagram.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of adjusting a train diagram scale as described in any one of the above.
The present invention also provides a computer program product comprising a computer program, which when executed by a processor implements the steps of the method for adjusting a scale of a train diagram as described in any one of the above.
According to the method, the device, the electronic equipment and the storage medium for adjusting the train operation diagram scale, different historical scale schemes are evaluated by taking the average waiting time of passengers as a universal evaluation standard, and the scale information of the train operation diagram is adjusted based on the determined optimal scale information, so that the adjustment efficiency of the operation diagram scale and the satisfaction degree of the passengers are improved, the safe and efficient operation of a train is guaranteed, the operation cost is reduced, and the corresponding optimal scale information is respectively provided for subsequent adjustment reference for each time slot, so that the adjustment method can cope with the change of passenger flow in different time slots, and the operation efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for adjusting a scale of a train working diagram according to the present invention;
FIG. 2 is a schematic flow chart of an optimal scale information obtaining method provided by the present invention;
FIG. 3 is a schematic diagram of a network structure of an evaluation model provided by the present invention;
FIG. 4 is a second schematic flow chart of the method for adjusting the scale of the train working diagram according to the present invention;
FIG. 5 is a schematic structural diagram of an adjusting device for a scale of a train working diagram provided by the invention;
fig. 6 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a method for adjusting a scale of a train working diagram. Fig. 1 is a schematic flow chart of a method for adjusting a scale of a train diagram according to the present invention, and as shown in fig. 1, the method includes:
and step 110, determining scale information of the train working diagram to be adjusted.
Specifically, the train diagram to be adjusted, that is, the train diagram that needs to be adjusted according to the scale information, may be, for example, a train diagram when an emergency such as a line equipment failure or a sudden increase in short-term passenger flow occurs. The scale information of the train operation diagram may include information that affects train operation intervals, such as train stop time, train return time, train operation level, and the like, and may also include other scale information, for example, a time period corresponding to the scale information, and the embodiment of the present invention is not limited to this specifically.
Step 120, adjusting the scale information based on the optimal scale information to obtain an adjusted train operation diagram;
the optimal scale information comprises first scale information corresponding to the average waiting time of the minimum passengers determined in the plurality of historical scale information, and the plurality of historical scale information and the scale information correspond to the same time period.
Specifically, it is considered that in the prior art, usually, a dispatcher manually adjusts a scale of a running chart according to manual experience, however, public transportation conditions of different cities and different lines are different, and it is difficult to find a set of general evaluation standards to quantify actual operation performance of different scales.
Aiming at the problem, the embodiment of the invention considers the importance of the satisfaction degree of the passengers, takes the average waiting time of the passengers as an evaluation index to evaluate the actual operation performance of different scale information, and the field dispatcher can adopt the index to evaluate different scale adjustment schemes to realize efficient and ordered operation, thereby greatly reducing the workload of the dispatcher of the control center, reducing the organization and command difficulty of the field control personnel and simultaneously reducing the time cost of the passengers for traveling.
Considering that the operation performance of the staff gauge information is greatly influenced by the tidal change of the passenger flow (such as the peak of the morning and evening of a working day, the peak of a holiday, and the like), the embodiment of the invention divides the whole day into each time period, and then determines the corresponding optimal staff gauge information for each time period. On the basis, after the time period corresponding to the scale information to be adjusted is determined, the corresponding optimal scale information can be determined according to the time period, the scale information to be adjusted is adjusted according to the optimal scale information, and the adjusted train operation diagram is obtained, so that the processing capacity of a line operator on emergency and the satisfaction degree of passengers are improved, and the safe and efficient operation of the train is guaranteed.
Here, the division of the time period may be set according to the adjustment accuracy required by the user, for example, 30 minutes, 1 hour, or the like. The specific adjustment mode of the scale information may be to directly adjust the scale information to be adjusted to the optimal scale information, or may be to determine new scale information again by using the optimal scale information as a reference, and then adjust the scale information to be adjusted to the new scale information.
The optimal scale information can comprise first scale information, the determination mode of the first scale information can specifically be that a large amount of scale information, namely historical scale information, of a plurality of historical train operation diagrams corresponding to the scale information to be adjusted in the same time period is collected in advance, then average waiting time of passengers corresponding to the historical scale information is compared, and the historical scale information with the minimum average waiting time of the passengers is used as the first scale information. For example, the scale information to be adjusted corresponds to a time period of 8: 30-9: 00, then can be collected for half a year or within a month 8: 30-9: 00, the first scale information is the history scale information having the smallest average waiting time of the passengers among the history scale information. Here, the average waiting time of the passenger may be calculated according to a face recognition technology, or may be estimated by inputting historical scale information into the neural network model, which is not specifically limited in the embodiment of the present invention.
According to the method provided by the embodiment of the invention, the average waiting time of passengers is taken as a universal evaluation standard, different historical scale schemes are evaluated, and the scale information of the train operation diagram is adjusted based on the determined optimal scale information, so that the adjustment efficiency of the scale of the operation diagram and the satisfaction degree of the passengers are improved, the safe and efficient operation of the train is ensured, the operation cost is reduced, and in addition, the corresponding optimal scale information is respectively provided for the subsequent adjustment reference for each time period, so that the adjustment method can cope with the change of passenger flow in different time periods, and the operation efficiency is improved.
Based on any one of the above embodiments, the optimal scale information further includes second scale information corresponding to the maximum train average passenger carrying rate, third scale information corresponding to the minimum train average running interval, and fourth scale information corresponding to the minimum traffic jam relief time, which are determined from the plurality of historical scale information;
step 120 includes:
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.
Specifically, in order to provide a plurality of optimal scale information for a user to select according to the needs of the user, the embodiment of the present invention estimates actual operation performance of different scale information by using the average train occupancy, the average train running interval, and the traffic jam breakdown time as estimation indexes from the perspective of train operation and the ability to solve traffic jams, and determines the second scale information corresponding to the maximum average train occupancy, the third scale information corresponding to the minimum average train running interval, and the fourth scale information corresponding to the minimum traffic jam breakdown time from the plurality of historical scale information.
On the basis, a 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, determine a final adjustment strategy, and adjust the scale information to be adjusted according to the adjustment strategy, so that balance between improvement of riding satisfaction and energy-saving and efficient online operation can be achieved.
It should be noted that, after an emergency occurs, the risk of regional traffic congestion increases at a later time when the train is in a large scale, the average waiting time of passengers increases in a short time, and this trend can be changed by adjusting the scale information of the train operation diagram (for example, measures such as increasing the train operation level, shortening the train operation cycle, and increasing the standby trains). Therefore, the traffic jam relief time of the scale information can be measured by the total time of the process that the average waiting time of passengers in the station is changed from low to high and then is restored to a normal level.
Based on any of the above embodiments, step 120 further includes:
inputting the historical scale information, the historical scale information of the previous time period of the historical scale information and the external environment information into a pre-constructed evaluation model to obtain an evaluation result corresponding to the historical scale information; the evaluation result comprises results of a plurality of evaluation indexes, wherein the plurality of evaluation indexes comprise passenger average waiting time, train average passenger carrying rate, train average running interval and traffic jam relief time;
and determining the optimal scale information based on the evaluation result corresponding to each historical scale information.
Specifically, in order to improve the efficiency and accuracy of scale evaluation, an evaluation model may be constructed in advance, and then each piece of historical scale information, historical scale information of a previous time period of each piece of historical scale information, and external environment information are input into the evaluation model, so that an evaluation result corresponding to each piece of historical scale information can be obtained.
Here, the external environment information may include external information such as a passenger volume, a weather environment, and the like. The evaluation model may be a single neural network model or a combination of multiple neural network models, which is not specifically limited in this embodiment of the present invention. The evaluation result can comprise results corresponding to a plurality of evaluation indexes such as average passenger waiting time, average train passenger carrying rate, average train running interval and traffic jam relief time.
On the basis, the first scale information corresponding to the minimum average passenger waiting time, the second scale information corresponding to the maximum average train passenger carrying rate, the third scale information corresponding to the minimum average train running interval and the fourth scale information corresponding to the minimum traffic jam relief time can be determined according to the evaluation result corresponding to each historical scale information, so that the optimal scale information can be obtained and used for adjusting the scale information to be adjusted, and the economic benefit of a rail transit operation enterprise is improved.
It should be noted that the evaluation model may only evaluate one or some evaluation indexes, and specifically, the evaluation model may be implemented by setting different evaluation indexes corresponding to different output weights before inputting the scale information into the evaluation model.
Based on any of the above embodiments, fig. 2 is a schematic flow chart of the optimal scale information obtaining method provided by the present invention, as shown in fig. 2, different pieces of operation icon scale information are evaluated by taking the average waiting time of passengers as an example, data cleaning is performed on historical operation diagram data, abnormal data and redundant data are removed, data dimensionality is reduced, standardized standardization of data is completed to accelerate convergence of weight parameters, missing data is filled up by using the most similar data items, then the historical operation diagram data after data processing is divided into groups by taking 30 minutes as a unit, so that multiple sets of historical scale information in different time periods can be obtained, corresponding optimal scale information is determined according to each set of historical scale information, and finally, optimal scale information in each time period of the whole day can be obtained, thereby facilitating drawing work of subsequent drawing personnel.
For each group of historical scale information, a plurality of historical scale information in the same time period included in the group of data can be input into an evaluation model for evaluation, so that the first scale information with the minimum average waiting time of passengers in the group of historical scale information can be obtained, and the first scale information is used as the optimal scale information in the time period by taking the average waiting time of passengers as an adjustment strategy. In addition, if the average waiting time of the passengers of the optimal scale information still does not meet the user requirements, more historical scale information in the same time period can be acquired and input into the evaluation model, and therefore the optimal scale information can be continuously updated based on historical data analysis.
Based on any of the above embodiments, step 120 includes:
determining the importance degree of each scale element in the scale information to each evaluation index based on the weight parameter of the evaluation model;
and adjusting the scale information based on the importance degree of each scale element to each evaluation index and the optimal scale information.
Specifically, the scale information may include information of a plurality of scale elements such as train stop time, train turn-back time, train operation level, station average passenger density, scale time period, and the like. According to the weight parameters of different neurons in the pre-trained evaluation model, the importance degree of each scale element in the scale information to each evaluation index can be determined, then, all the scale elements can be sorted according to the importance degree of the evaluation index aiming at each evaluation index, and on the basis, the scale information to be adjusted can be adjusted according to the sorting result of each evaluation index and the optimal scale information corresponding to each evaluation index.
Here, the specific adjustment manner of the scale information may be to determine the scale elements to be adjusted according to the sorting result of one of the evaluation indexes, adjust the scale elements to be adjusted in the scale information according to the optimal scale information corresponding to the evaluation index, or determine the scale elements to be adjusted by comprehensively considering the sorting results of a plurality of evaluation indexes, and adjust the scale elements to be adjusted in the scale information by comprehensively referring to the optimal scale information corresponding to the plurality of evaluation indexes.
And (3) acquiring the weight parameters of the evaluation model based on data mining of historical operation diagram data, and sequencing the importance of different factors influencing each evaluation index, so that a scheme for scale information optimization and automatic strategy adjustment can be conveniently worked out.
Based on any of the above embodiments, the evaluation model is obtained by training based on the sample historical scale information of a plurality of time periods, the sample evaluation result corresponding to the sample historical scale information, the sample external environment information, and the random gradient descent algorithm.
Specifically, in order to evaluate and quantify the operation performance of the historical scale information, the evaluation model may be obtained by acquiring sample historical scale information of a plurality of time periods in advance, determining a sample evaluation result corresponding to the sample historical scale information, and then training the initial model according to the sample historical scale information, the corresponding sample evaluation result, and the sample external environment information. In addition, in order to improve the speed of model convergence and obtain an evaluation model with a good effect, the objective function of the initial model can be optimized by using a random gradient descent algorithm in the process of training the initial model, and finally the optimal evaluation model can be obtained.
It should be noted that, an evaluation model may be pre-trained for each time period of the whole day, where a plurality of time periods refer to a time period corresponding to the scale information to be adjusted and a previous time period of the time period, or a unified evaluation model may be pre-trained for all time periods of the whole day, where a plurality of time periods refer to all time periods of the whole day.
Based on any of the above embodiments, sample historical train operation diagram data can be collected in advance for data analysis, and from the perspective of train operation, key information such as arrival and departure time, interval operation time, passenger carrying rate, punctuality rate and the like of each train at all stations is recorded. In addition, the average waiting time of passengers in different time periods of each station and the time consumption ratio of the average passenger flow volume to the distance between peak time periods are collected and calculated through a face recognition technology. Therefore, the sample historical scale information of a plurality of time periods and the sample evaluation result corresponding to the sample historical scale information can be obtained and used for training the evaluation model.
FIG. 3 is a schematic diagram of a network structure of an evaluation model provided by the present invention, and as shown in FIG. 3, the evaluation model includes an input layer, a hidden layer and an output layer; the input layer vector comprises the current scale information of scale elements such as the stop time of a train, the turn-back time of the train, the running grade of the train, the average passenger density of the station, the scale time period and the like, the scale information in the previous time period and the external environment information; the activation function of the hidden layer is a Sigmoid function; the output layer is the evaluation indexes of the performance of the evaluation scales such as the average train running interval, the average train passenger carrying rate, the average waiting time of passengers, the traffic jam relief time, the number of the transferred trains and the like. The training procedure of the evaluation model is as follows:
assuming that the input to the neuron is a vectorThe weight vector is(the bias term is w0) If the activation function is a sigmoid function, then its output y:
the sigmoid function is defined as follows:
taking the sum of the squares of the errors of all output layer nodes of the evaluation model as an objective function, wherein each evaluation index corresponds to one output layer node:
wherein E isdError, t, of sample diRepresenting the true value, y, of the ith output layer nodeiRepresenting the output value of the ith output layer node.
Then, optimizing the objective function by using a random gradient descent algorithm, and fitting data by continuously optimizing model parameters:
wherein, wjiIs a weight parameter from the input layer node i to the output layer node j, and η is a constant of the learning rate.
Finally, a trained evaluation model can be obtained, and the importance degree of each scale element in the scale information to each evaluation index is determined according to the weight parameter of the evaluation model. In addition, since the scale information adopted in the previous time period is simultaneously considered by the model, the trained evaluation model can be used for predicting the traffic jam relief time of different scale information.
Based on any of the above embodiments, step 120 includes:
determining an adjustment strategy of the scale information based on the optimal scale information;
and if the traffic jam relief time corresponding to the adjustment strategy is smaller than a preset relief time threshold, adjusting the scale information based on the adjustment strategy to obtain an adjusted train operation diagram.
Specifically, in order to enable the adjusted train running diagram to have a strong capacity of solving the problem of congestion, after the optimal scale information is obtained, an adjustment strategy of the scale information can be determined according to the optimal scale information, whether the traffic congestion relief time corresponding to the adjustment strategy is smaller than a preset relief time threshold value or not is judged, if the traffic congestion relief time corresponding to the adjustment strategy is smaller than the relief time threshold value, the scale information can be adjusted according to the adjustment strategy to obtain the adjusted train running diagram, and if the traffic congestion relief time corresponding to the adjustment strategy is not smaller than the relief time threshold value, a new adjustment strategy needs to be determined again until the adjustment strategy meets the requirement of being smaller than the relief time threshold value.
Further, the adjustment strategy may be selected manually or determined automatically, the optimal scale information may be directly used as the adjustment strategy, or the optimal scale information may be used as a reference, and the adjustment strategy is determined by comprehensively considering factors such as the estimated traffic jam relief time after the scale information is adjusted, the size of the train number range affected by the adjustment, the number of the calls of the spare vehicles, and the like.
The traffic jam relief time corresponding to the adjustment strategy may 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 a subsequent recommended adjustment scheme.
Based on any of the above embodiments, fig. 4 is a second flow diagram of the method for adjusting a scale of a train operation diagram provided by the present invention, as shown in fig. 4, an adjustment strategy of information of a plurality of scales can be determined according to optimal scale information, then traffic jam relief time corresponding to different adjustment strategies is compared, the adjustment strategy with the shortest traffic jam relief time is taken as a current adjustment strategy, whether the current adjustment strategy meets an adjustment requirement smaller than a relief time threshold value is immediately judged, if yes, the scale information to be adjusted can be directly switched to new scale information of the current adjustment strategy, so that a problem of unforeseen passenger flow change can be solved, and if not, a new adjustment strategy needs to be determined again until the adjustment strategy meets the requirement smaller than the relief time threshold value.
The determination mode of the adjustment strategy can be set arbitrarily according to the requirements of the line operator, for example, the average running interval of the train is short as much as possible, or the number of the called vehicles is small as much as possible, or the trip experience of passengers is improved by keeping the train full load rate at a balanced and reasonable level on the basis of meeting the dispatching safety.
The following describes the adjustment device for the train diagram ruler provided by the present invention, and the adjustment device for the train diagram ruler described below and the adjustment method for the train diagram ruler described above can be referred to correspondingly.
Based on any one of the above embodiments, the embodiment of the invention provides a device for adjusting a scale of a train working diagram. Fig. 5 is a schematic structural diagram of an adjusting device for a train working diagram scale, which comprises:
a determining module 510, configured to determine scale information of a train diagram to be adjusted;
an adjusting module 520, configured to adjust the scale information based on the optimal scale information to obtain an adjusted train operation diagram;
the optimal scale information comprises first scale information which is determined from a plurality of historical scale information and corresponds to the minimum average waiting time of passengers, and the plurality of historical scale information and the scale information correspond to the same time period.
According to the device provided by the embodiment of the invention, the average waiting time of passengers is taken as a universal evaluation standard, different historical scale schemes are evaluated, and the scale information of the train running chart is adjusted based on the determined optimal scale information, so that the adjustment efficiency of the scale of the running chart and the satisfaction degree of the passengers are improved, the safe and efficient running of the train is ensured, the operation cost is reduced, and in addition, the corresponding optimal scale information is respectively provided for the subsequent adjustment reference for each time period, so that the adjustment method can cope with the change of passenger flow in different time periods, and the operation efficiency is improved.
Based on any one of the above embodiments, the optimal scale information further includes second scale information corresponding to the maximum train average passenger carrying rate, third scale information corresponding to the minimum train average running interval, and fourth scale information corresponding to the minimum traffic jam relief time, which are determined from the plurality of historical scale information;
the adjustment module 520 is configured to:
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.
Based on any embodiment, the apparatus further includes an optimal scale determination module, configured to:
inputting the historical scale information, the historical scale information of the previous time period of the historical scale information and the external environment information into a pre-constructed evaluation model to obtain an evaluation result corresponding to the historical scale information; the evaluation result comprises results of a plurality of evaluation indexes, wherein the plurality of evaluation indexes comprise passenger average waiting time, train average passenger carrying rate, train average running interval and traffic jam relief time;
and determining the optimal scale information based on the evaluation result corresponding to each historical scale information.
Based on any of the above embodiments, the adjusting module 520 is configured to:
determining the importance degree of each scale element in the scale information to each evaluation index based on the weight parameter of the evaluation model;
and adjusting the scale information based on the importance degree of each scale element to each evaluation index and the optimal scale information.
Based on any of the above embodiments, the evaluation model is obtained by training based on the sample historical scale information of a plurality of time periods, the sample evaluation result corresponding to the sample historical scale information, the sample external environment information, and the random gradient descent algorithm.
Based on any of the above embodiments, the adjusting module 520 is configured to:
determining an adjustment strategy of the scale information based on the optimal scale information;
and if the traffic jam relief time corresponding to the adjustment strategy is smaller than a preset relief time threshold, adjusting the scale information based on the adjustment strategy to obtain an adjusted train operation diagram.
Fig. 6 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 6: a processor (processor)610, a communication Interface (Communications Interface)620, a memory (memory)630 and a communication bus 640, wherein the processor 610, the communication Interface 620 and the memory 630 communicate with each other via the communication bus 640. The processor 610 may invoke logic instructions in the memory 630 to perform a method of adjusting a scale of a train trip diagram, the method comprising: determining scale information of a train running chart to be adjusted; adjusting the scale information based on the optimal scale information to obtain an adjusted train operation diagram; the optimal scale information comprises first scale information corresponding to the average waiting time of the minimum passengers determined in the plurality of historical scale information, and the plurality of historical scale information and the scale information correspond to the same time period.
In addition, the logic instructions in the memory 630 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention further provides a computer program product, the computer program product 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, a computer can execute the method for adjusting a scale of a train diagram provided by the above methods, and the method includes: determining scale information of a train running chart to be adjusted; adjusting the scale information based on the optimal scale information to obtain an adjusted train operation diagram; the optimal scale information comprises first scale information corresponding to the average waiting time of the minimum passengers determined in the plurality of historical scale information, and the plurality of historical scale information and the scale information correspond to the same time period.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program, which when executed by a processor, implements a method for adjusting a scale of a train diagram provided by the above methods, the method comprising: determining scale information of a train running chart to be adjusted; adjusting the scale information based on the optimal scale information to obtain an adjusted train operation diagram; the optimal scale information comprises first scale information corresponding to the average waiting time of the minimum passengers determined in the plurality of historical scale information, and the plurality of historical scale information and the scale information correspond to the same time period.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.