CN116894055B - Railway line capacity utilization rate calculation method based on big data batch flow integration - Google Patents
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Abstract
The application discloses a method for calculating the capacity utilization rate of a railway line based on large data batch flow integration and a system for executing the method, which combine the advantages of batch processing and flow processing and realize the adjustment of the transportation environment based on the capacity utilization rate of a transportation line. The method and the system in the specification do not only adopt a single data processing mode aiming at the multi-hierarchy, complexity and variability of the railway transportation environment, but also graft different data processing modes through complex interface design, and adopt a batch processing mode to process data when the method and the system are suitable for batch processing in combination with the actual situation of the railway transportation environment. When the method is not suitable for batch processing, a mixed processing mode is adopted, and the utilization rate of the transportation line capacity obtained when the batch processing is performed historically is taken as one aspect of investigation, so that the result of the batch processing historically can be reflected on the evaluation of the current real-time transportation environment to a certain extent.
Description
Technical Field
The application relates to the technical field of big data processing, in particular to a method for calculating the capacity utilization rate of a railway line based on big data batch flow integration.
Background
The railway transportation in China is rapidly developed, and the aspects of line laying, traffic quantity and the like are improved. On one hand, the requirements of people on railway transportation are met to a certain extent; on the other hand, the complexity of the transportation environment is increased, and certain difficulty is brought to the management of railway transportation. The difficulty can be embodied as follows: the amount of data that needs to be processed is large. In the related technical field, batch processing can be adopted for big data, stream processing can also be adopted, and the data processing effects of the two are thousands of times. However, the national operators are wide, the railway distribution is very wide, the railway distribution conditions of different areas are different, and in addition, the management modes of stations and transportation lines of different areas are different, so that batch processing or stream processing is difficult to uniformly execute.
Therefore, how to reasonably process big data in combination with the railway transportation scene becomes a problem to be solved.
Disclosure of Invention
The embodiment of the application provides a railway line capacity utilization rate calculation method based on large data batch flow integration, so as to at least partially solve the technical problems.
The embodiment of the application adopts the following technical scheme:
In a first aspect, an embodiment of the present application provides a method for calculating a capacity utilization of a railway line based on a mass flow of data, the method being performed by a system for calculating a capacity utilization of a railway line based on a mass flow of data, the method comprising:
acquiring environmental data of a transportation environment in a historical time period of a designated time from a preset data classification management model; wherein the transportation environment comprises: a transportation line and a station between two adjacent transportation lines; the environmental data includes: the number of unidirectional traffic of the transportation line, the number of bidirectional traffic of the transportation line, the transportation line passenger transport density, the transportation line freight transport density, the longest time interval of adjacent trains of the transportation line, the shortest time interval of adjacent trains of the transportation line, the station departure history passenger seat rate and the station maximum history passenger flow;
determining a decision coefficient based on the environmental data; wherein the decision coefficient is inversely related to the ratio of the number of pairs of bidirectional traffic of the transportation line to the number of unidirectional traffic of the transportation line, inversely related to the ratio of the freight density of the transportation line to the passenger density of the transportation line, positively related to the product of the longest time interval between adjacent trains of the transportation line and the maximum value of the number of unidirectional traffic of the transportation line, and positively related to the shortest time interval between adjacent trains of the transportation line;
If the decision coefficient is not greater than a preset decision threshold, determining that a batch processing mode is adopted to determine the capacity utilization rate of the first transportation line based on the environmental data; wherein the first transportation line capacity utilization is used to characterize a degree of use of the transportation environment compared to a full load condition; the first transportation line capacity utilization rate is positively correlated with the decision coefficient and negatively correlated with the comprehensive relative occupancy of the transportation environment; the comprehensive relative occupancy is positively correlated with the maximum historical passenger flow volume of the station and is positively correlated with the last duration in the hybrid processing mode; transmitting the first transportation line capacity utilization rate to the site, so that the site manages the transportation environment to which the site belongs in future time based on the first transportation line capacity utilization rate;
if the decision coefficient is greater than the decision threshold, determining that the hybrid processing mode is in; taking the first transportation line capacity utilization rate obtained when the batch processing mode is last time as a historical utilization rate; determining a second transportation line capacity utilization based on the historical utilization and the environmental data; the second transportation line capacity utilization rate is positively correlated with the historical utilization rate, is positively correlated with the ratio of the departure real-time passenger seat rate of the station to the station departure historical passenger seat rate, and is positively correlated with the ratio of the departure real-time passenger seat rate to the real-time passenger flow rate of the station; and sending the second transportation line capacity utilization rate to the site, so that the site manages the transportation environment of the site at a future moment based on the second transportation line capacity utilization rate.
In an alternative embodiment of the present specification, the method further comprises:
and if the number of the trains running on the transportation line is monitored to be larger than a preset number threshold, acquiring the environment data from the data classification management model.
In an alternative embodiment of the present specification, the method further comprises:
the decision threshold is inversely related to the average axle weight of the train traveling on the transportation line as indicated by the environmental data.
In an alternative embodiment of the present specification, the method further comprises:
the decision threshold is inversely related to a reference value; the reference value is positively correlated with the product of the maximum traveling speed of the train traveling on the transportation route and the duration of the maximum traveling speed indicated by the environmental data, and is positively correlated with the ratio of the number of other trains traveling in the same direction as the train to the length of the transportation route when the train is at the maximum traveling speed.
In an alternative embodiment of the present specification, the method further comprises:
the specified duration and the last time are in the the duration of the hybrid processing mode duration is positively correlated.
In an alternative embodiment of the present specification, the method further comprises:
The transportation environment is a passenger-cargo mixed transportation environment.
In a second aspect, embodiments of the present application further provide a railroad line capacity utilization calculation system based on large data batch integration, the system comprising:
a data acquisition module configured to: acquiring environmental data of a transportation environment in a historical time period of a designated time from a preset data classification management model; wherein the transportation environment comprises: a transportation line and a station between two adjacent transportation lines; the environmental data includes: the number of unidirectional traffic of the transportation line, the number of bidirectional traffic of the transportation line, the transportation line passenger transport density, the transportation line freight transport density, the longest time interval of adjacent trains of the transportation line, the shortest time interval of adjacent trains of the transportation line, the station departure history passenger seat rate and the station maximum history passenger flow;
the decision coefficient determining module is configured to: determining a decision coefficient based on the environmental data; wherein the decision coefficient is inversely related to the ratio of the number of pairs of bidirectional traffic of the transportation line to the number of unidirectional traffic of the transportation line, inversely related to the ratio of the freight density of the transportation line to the passenger density of the transportation line, positively related to the product of the longest time interval between adjacent trains of the transportation line and the maximum value of the number of unidirectional traffic of the transportation line, and positively related to the shortest time interval between adjacent trains of the transportation line;
A first haul line capacity utilization determination module configured to: if the decision coefficient is not greater than a preset decision threshold, determining that a batch processing mode is adopted to determine the capacity utilization rate of the first transportation line based on the environmental data; wherein the first transportation line capacity utilization is used to characterize a degree of use of the transportation environment compared to a full load condition; the first transportation line capacity utilization rate is positively correlated with the decision coefficient and negatively correlated with the comprehensive relative occupancy of the transportation environment; the comprehensive relative occupancy is positively correlated with the maximum historical passenger flow volume of the station and is positively correlated with the last duration in the hybrid processing mode; transmitting the first transportation line capacity utilization rate to the site, so that the site manages the transportation environment to which the site belongs in future time based on the first transportation line capacity utilization rate;
a second haul line capacity utilization determination module configured to: if the decision coefficient is greater than the decision threshold, determining that the hybrid processing mode is in; taking the first transportation line capacity utilization rate obtained when the batch processing mode is last time as a historical utilization rate; determining a second transportation line capacity utilization based on the historical utilization and the environmental data; the second transportation line capacity utilization rate is positively correlated with the historical utilization rate, is positively correlated with the ratio of the departure real-time passenger seat rate of the station to the station departure historical passenger seat rate, and is positively correlated with the ratio of the departure real-time passenger seat rate to the real-time passenger flow rate of the station; and sending the second transportation line capacity utilization rate to the site, so that the site manages the transportation environment of the site at a future moment based on the second transportation line capacity utilization rate.
In a third aspect, an embodiment of the present application further provides an electronic device, including:
a processor; and
a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method steps of the first aspect.
In a fourth aspect, embodiments of the present application also provide a computer-readable storage medium storing one or more programs, which when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method steps of the first aspect.
The above at least one technical scheme adopted by the embodiment of the application can achieve the following beneficial effects:
the application provides a railway line capacity utilization rate calculation method based on big data batch flow integration and a system for executing the method, which combine the advantages of batch processing and flow processing and realize transportation environment adjustment based on transportation line capacity utilization rate. The method and the system in the specification do not only adopt a single data processing mode aiming at the multi-hierarchy, complexity and variability of the railway transportation environment, but also graft different data processing modes through complex interface design, and adopt a batch processing mode to process data when the method and the system are suitable for batch processing in combination with the actual situation of the railway transportation environment. When the method is not suitable for batch processing, a brand new data processing mode, namely a hybrid processing mode, is adopted, and the utilization rate of the transportation line capacity (namely the first transportation line capacity utilization rate) obtained when the batch processing is historically performed is taken as one aspect of investigation, so that the result of the batch processing in history can be reflected on the evaluation of the current real-time transportation environment to a certain extent, complicated interface design is not needed, incompatibility between the batch processing and the stream processing is not needed to be considered, and the method is beneficial to improving the management efficiency of the transportation environment.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a schematic process diagram of a method for calculating the capacity utilization rate of a railway line based on mass flow integration of large data according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
The application will be described in further detail below with reference to the drawings by means of specific embodiments. Wherein like elements in different embodiments are numbered alike in association. In the following embodiments, numerous specific details are set forth in order to provide a better understanding of the present application. However, one skilled in the art will readily recognize that some of the features may be omitted, or replaced by other elements, materials, or methods in different situations. In some instances, related operations of the present application have not been shown or described in the specification in order to avoid obscuring the core portions of the present application, and may be unnecessary to persons skilled in the art from a detailed description of the related operations, which may be presented in the description and general knowledge of one skilled in the art.
Furthermore, the described features, operations, or characteristics of the description may be combined in any suitable manner in various embodiments. Also, various steps or acts in the method descriptions may be interchanged or modified in a manner apparent to those of ordinary skill in the art. Thus, the various orders in the description and drawings are for clarity of description of only certain embodiments, and are not meant to be required orders unless otherwise indicated.
The numbering of the components itself, e.g. "first", "second", etc., is used herein merely to distinguish between the described objects and does not have any sequential or technical meaning. The term "coupled" as used herein includes both direct and indirect coupling (coupling), unless otherwise indicated.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
Batch, also known as Batch script. Batch processing is the bringing together of a certain amount of data to form a batch of data, and then processing the data in this batch. Both Spark and flank support batch processing, where Spark uses a batch model, i.e. a batch of data is read into memory once, then processed, and after the processing is completed, the result is written to disk. The flank also supports batch processing, but uses a batch processing mode based on stream processing, i.e., a batch of data is divided into a plurality of data streams to be processed, so that more efficient memory management and lower delay can be realized.
Streaming is a way to stream data, i.e. data enters the system one by one, and is then processed in real time. Both Spark and Flink support stream processing. In stream processing, the Flink uses an event driven model, i.e., real-time processing for each input event. Spark uses a micro-batch based model, i.e., the data stream is divided into small batches for processing. The real-time performance of a micro-batch based model is slightly inferior to that of an event-driven based model because a certain amount of data needs to be cached to start the process.
In the narrow sense of railway transportation environment, different stations respectively carry out more decentralized management on the transportation environment to which the stations belong; in a broad sense, the transportation environments to which different sites belong are managed more identically in a more macroscopic dimension. This allows for some isolation of the data processing content, and the manner in which the data is processed, between the different levels. If the transportation environment is managed only from a relatively macroscopic level, individual situations of the transportation environment to which the site belongs may be ignored, and thus the site may be out of management. If the transportation environment is managed only from a relatively microscopic level, the transportation environments to which different sites belong may be connected with each other, and management on a macroscopic level may be insufficient.
In addition, in the technical development history, batch processing and stream processing are respectively designed according to different use environments and different data processing demands, the difficulty of complete fusion of the two technologies is high, even if the fusion of the two technologies is realized to a certain extent, the actual demands can not be met in the data processing effect, and particularly, when the application scenes of complex transportation environments, obvious data hierarchy, crossed data and large data variable are emphasized in railway transportation, the advantages of batch processing and stream processing can be embodied in the data processing effect, so that the problem to be solved is solved urgently.
In an alternative embodiment of the present specification, the transportation environment in the present specification is a passenger-cargo mixed transportation environment.
The method in this specification is performed by a railroad capacity utilization calculation based on mass flow integration of large data. As shown in fig. 1, the method for calculating the capacity utilization rate of the railway line based on the large data batch flow integration in the specification comprises the following steps:
s100: and acquiring environmental data of the transportation environment in a historical time period of a designated time from the current moment from a preset data classification management model.
The method in this specification aims at: analyzing the transportation environment, and guiding the site to manage the transportation environment based on the analysis result.
The transportation environment in this specification includes a transportation route and a site between two adjacent transportation routes. The end point of the transport line is the site. For a certain site, the transport environment formed by each transport line taking the site as a starting point or taking the site as an ending point and the site together is the transport environment to which the site belongs. That is, the transportation environment in the present specification is defined for a certain site, and a transportation line included in a certain transportation environment may be one or more than one transportation line. For different sites, there may be a certain overlap between the transportation environments to which each site belongs, and analysis needs to be performed separately for the transportation environments to which each site belongs.
The data classification management model of the present specification is used for classifying and storing environmental data. Specifically, the classification mode is that, for each line, data corresponding to the line is acquired respectively, and a corresponding relationship between the line and the site is recorded. In addition, data corresponding to the site is also acquired. Therefore, when the transportation environment analysis is performed on a certain site, the data corresponding to the site can be obtained only by querying the site, and the data corresponding to each line included in the transportation environment to which the site belongs can also be queried. It can be seen that the data classification management model in the present specification not only has a storage function, but also has a data query function. Correspondingly, each site and the transportation line upload the acquired data to the data classification management model.
The environmental data in this specification includes transportation route data and station data. Wherein the transportation route data comprises: the number of unidirectional traffic on the transportation route (i.e., the total number of trains traveling on the transportation route), the number of bidirectional traffic on the transportation route (i.e., the number of pairs of trains traveling simultaneously on both sides of the transportation route at a certain time in a specified period), the transportation route passenger density (i.e., the number distribution of passenger trains (the trains in this specification include not only passenger trains but also freight trains) within a specified period), the transportation route freight density, the transportation route adjacent train longest time interval, and the transportation route adjacent train shortest time interval. The site data includes: the historical passenger seat rate of departure of the station (normally, even if the train is full, the station can bear people stream pressure), the passenger seat rate can be used for representing station resource idle conditions caused by the fact that the train is not full), and the maximum historical passenger flow rate of the station (different times of taking the train by passengers, different time periods when different passengers stay in the station, and different numbers of passengers in the station at different moments).
It can be seen that the environmental data in this specification are all historical data, but not all data generated historically, but environmental data of the transportation environment in a historical period of time of a specified duration from the current time. Therefore, on one hand, the screening of historical data can be realized, and the amount of data to be processed is reduced; on the other hand, the occupation condition of the transportation resources provided for the transportation environment in a certain historical time period which is closer to the current moment can be examined. The availability of resources for rail transportation varies, for example, a train 1 is driven on a certain transportation route, even if the train 1 is driven away from an a position on the transportation route, the a position is still in an unavailable state because the train 1 is positioned downstream of the a position and is still closer to the a position, the a position is in an idle state for other trains 1 to drive, but the a position is still in an unavailable state for ensuring the safety of the train 1 and avoiding rear-end collision. By the method of the present description, such a more concealed unusable state can be perceived. In addition, the method is similar to the method for the site, but the resource occupation condition of the site has the characteristics of the method.
S102: determining a decision coefficient based on the environmental data.
Decision coefficients in this specification are used to characterize: the historical transportation environment within the specified duration causes hysteresis in the availability status of the resource due to the occupancy of the resource, resulting in a degree of tension in the availability of the resource provided by the transportation environment at the current time. The higher the tension, the higher the degree to which the resource is not available, even though the resource is in an idle state.
In particular, the decision factor is inversely related to the ratio of the number of pairs of bidirectional traffic on the transportation line to the number of unidirectional traffic on the transportation line (the higher the ratio is, the lower the occupancy of unidirectional transportation line, whereas for a stop, the vehicles have a tendency to take hold-up passengers in time, which is beneficial for releasing the stop resources), inversely related to the ratio of the freight density of the transportation line to the passenger density of the transportation line (the schedulability of the freight is higher compared to the passenger traffic, for example, if a freight train cannot timely take the right of use of the line, the freight train can be suspended on a bypass track, waiting for the release of the line resources, but the timeliness must be satisfied, the hold-up hazard is greater, and the management pressure of the passenger to the stop is greater, the pressure of the freight to the stop is smaller), positively related to the product of the longest time interval adjacent to the transportation line and the maximum number of unidirectional traffic on the transportation line (indicating that the train is unevenly distributed in the transportation environment in time or geographically), and positively related to the shortest time interval adjacent train to the transportation line.
S104: and if the decision coefficient is not greater than a preset decision threshold, judging that a batch processing mode is adopted, and determining the first transportation line capacity utilization rate based on the environment data.
The batch processing mode is to process the environmental data in a batch processing mode. The decision coefficient is not greater than a preset decision threshold, which indicates that the resource occupation caused by the historical use of the transportation environment is continued to a lesser extent at the current moment, and the current transportation environment is not very stressed. The batch processing is only carried out on historical data, on one hand, real-time data is not required to be acquired, and the real-time data is not required to be processed; on the other hand, the batch processing has higher processing efficiency on big data, and is beneficial to improving the data processing efficiency.
In an alternative embodiment of the present description, the decision threshold is an empirical value. In another alternative embodiment of the present specification, the decision threshold is inversely related to an average axle weight of a train traveling on the transportation line represented by the environmental data. The higher the axle weight of the train, the greater the probability of an event indicating a high risk of occurrence in the event of emergency braking, the decision threshold should be lowered for safety reasons. Furthermore, in other alternative embodiments, the decision threshold is inversely related to a reference value that is positively related to the product of the maximum travel speed of the train traveling on the transportation route and the duration that it is at the maximum travel speed, and is positively related to the ratio of the number of other trains traveling in the same direction as the train to the length of the transportation route when the train is at the maximum travel speed.
The first transportation line capacity utilization in this specification is used to characterize the extent of use of the transportation environment compared to a full load condition. The "full load state" herein is a "full load" based on the occupancy of resources provided by the transport environment by the historically transported situation. For example, the theoretical maximum value of the resource provided by the transportation environment is 100, the occupation of the current resource by the historical transportation situation is 20, and the "full load state" at this time is 80.
Wherein the first haul line capacity utilization is positively correlated with the decision coefficient and negatively correlated with the integrated relative occupancy of the haul environment. The integrated relative occupancy is positively correlated to the maximum historical traffic for the site and to the last duration in the hybrid processing mode.
The data that is utilized before the first haul line capacity utilization is determined are primarily haul line data, that is, the process of determining decision coefficients primarily takes into account haul line conditions. It will be appreciated that most transportation lines are laid in relatively cool areas, on the one hand, such areas can provide sufficient space for line laying and, on the other hand, also can avoid the disturbance of train travel to residents in the urban area. This makes it difficult to solve the problems of failure, accident, natural disaster, etc. in the transportation line. The site is closer to the urban area, the transportation mode except the railway is more convenient, and the difficulty in treating the problem is lower. Therefore, the situation of the transportation route is prioritized. The decision coefficient is determined according to the condition of the transportation line, so that the difference of the conditions between the transportation line and the nodes can be effectively distinguished, and the problem classification identification is realized.
However, the situation of the station is not ignored, and when the decision coefficient is low, the occupation delay effect of the resource provided by the transportation line is low, and the possibility of the problem caused by the transportation line is low, the comprehensive relative occupation rate is introduced when the capacity utilization rate of the first transportation line is calculated based on the decision coefficient. The comprehensive relative occupancy rate is obtained based on the maximum historical passenger flow of the station (because the freight occupies less resources of the station and has separate loading and unloading space, the passenger flow is one of the main determining factors for guaranteeing the transportation safety), and the resource occupation delay degree of the station can be reflected. Furthermore, the integrated relative occupancy is positively correlated to the last time the hybrid processing mode was in duration. The method in the present specification starts the hybrid processing mode, which indicates that the transportation environment has resources occupation besides the delay of resources occupation, which is a situation that the resources are more tense. The longer the duration of time in the hybrid processing mode, the longer the problem of resource occupation delay caused by occupied resources will continue in the future, and attention should be paid to the implementation. The comprehensive relative occupancy rate can realize the beneficial effects brought by stream processing to a certain extent in a batch processing mode.
Thereafter, the first haul line capacity utilization rate is transmitted to the site such that the site manages the transportation environment to which the site belongs at a future time based on the first haul line capacity utilization rate.
Because of the certain hysteresis of resource release in the railway transportation environment, even the first transportation line capacity utilization rate obtained based on the historical data can not only represent the historical condition, but also represent the real-time condition at the current moment and the predicted condition at the future moment.
The first transportation line capacity utilization rate expresses the occupation condition of transportation resources provided by the site and the transportation environment to which the site belongs, and the higher the first transportation line capacity utilization rate is, the fewer resources are in an available state and the resources are released in a short time. After the station obtains the capacity utilization rate of the first transportation line, the station or the transportation line can be redistributed by combining the self regulation and control capacity of the station so as to realize management. The specific management mode can be controlled by the site itself. For example, to add station duty personnel, to arrange the station square to be part of a waiting room, to control the speed of the partial trains traveling on certain sections of certain transportation lines to avoid high risk events, etc.
S106: if the decision coefficient is larger than the decision threshold, determining that the first transportation line capacity utilization rate is in the mixed processing mode, and taking the first transportation line capacity utilization rate obtained when the first transportation line capacity utilization rate is in the batch processing mode last time as a historical utilization rate; a second haul line capacity utilization is determined based on the historical utilization and the environmental data.
Since the data processing is performed in the batch processing mode when the availability of the transportation environment resources is high, the adjustment to the hybrid processing mode means the switching of the data processing mode, and the influence caused by the switching and the resource occupation lag caused by the historical utilization rate to the current transportation environment and the transportation environment at the future time should also be considered.
The second transportation line capacity utilization rate in the specification is positively correlated with the historical utilization rate, positively correlated with the ratio of the station departure real-time passenger seat rate and the station departure historical passenger seat rate, and positively correlated with the ratio of the station departure real-time passenger seat rate and the station real-time passenger flow rate.
The historical utilization rate is obtained based on a strategy coefficient, and the strategy coefficient considers the condition of the transportation line, so that the second transportation line capacity utilization rate can represent the condition of the transportation line. In addition, the determination process of the capacity utilization rate of the second transportation route also considers the data of the station, and when the capacity of releasing transportation resources is insufficient for link transportation pressure, the problem of the first burst of the station is more likely due to the higher mobility of passengers, and the seat should consider the situation of the station.
It should be noted that, the hybrid processing mode in the present specification is essentially a stream processing combined with a historical utilization rate, and specifically, the pending utilization rate is obtained based on a ratio of the station departure real-time passenger seat rate to the station departure historical passenger seat rate, and a ratio of the station departure real-time passenger seat rate to the station real-time passenger flow rate. And when the real-time passenger flow of the station is not greater than a preset flow threshold (experience value), weighting the utilization rate to be determined by adopting the historical utilization rate to obtain the capacity utilization rate of the second transportation line. And when the real-time passenger flow of the station is larger than a preset flow threshold, directly taking the undetermined utilization rate as the capacity utilization rate of the second transportation line.
The application provides a railway line capacity utilization rate calculation method based on big data batch flow integration and a system for executing the method, which combine the advantages of batch processing and flow processing and realize transportation environment adjustment based on transportation line capacity utilization rate. The method and the system in the specification do not only adopt a single data processing mode aiming at the multi-hierarchy, complexity and variability of the railway transportation environment, but also graft different data processing modes through complex interface design, and adopt a batch processing mode to process data when the method and the system are suitable for batch processing in combination with the actual situation of the railway transportation environment. When the method is not suitable for batch processing, a brand new data processing mode, namely a hybrid processing mode, is adopted, and the utilization rate of the transportation line capacity (namely the first transportation line capacity utilization rate) obtained when the batch processing is historically performed is taken as one aspect of investigation, so that the result of the batch processing in history can be reflected on the evaluation of the current real-time transportation environment to a certain extent, complicated interface design is not needed, incompatibility between the batch processing and the stream processing is not needed to be considered, and the method is beneficial to improving the management efficiency of the transportation environment.
In an alternative embodiment of the description, the environmental data is obtained from the data sort management model if it is monitored that the number of trains traveling on the transportation route is greater than a preset number threshold (empirical value).
From the above analysis, it is found that the disaster occurring on the transportation route is often difficult to rescue, and if the situation of the route can be determined sensitively, the method in the present specification is determined from the transportation route.
In a further alternative embodiment of the present specification, the specified duration is positively correlated with a last duration in the hybrid processing mode. The mixed processing mode is a measure adopted under the condition of larger resource release pressure, and the longer the duration of the mixed processing mode is, the longer the pressure is, so that the greater pressure is brought to the release of the subsequent transportation environment resources, the designated duration is increased, and the serious delay is prevented from being ignored.
Further, the present specification also provides a large data batch flow integration based railway line capacity utilization computing system, the system comprising:
a data acquisition module configured to: acquiring environmental data of a transportation environment in a historical time period of a designated time from a preset data classification management model; wherein the transportation environment comprises: a transportation line and a station between two adjacent transportation lines; the environmental data includes: the number of unidirectional traffic of the transportation line, the number of bidirectional traffic of the transportation line, the transportation line passenger transport density, the transportation line freight transport density, the longest time interval of adjacent trains of the transportation line, the shortest time interval of adjacent trains of the transportation line, the station departure history passenger seat rate and the station maximum history passenger flow;
The decision coefficient determining module is configured to: determining a decision coefficient based on the environmental data; wherein the decision coefficient is inversely related to the ratio of the number of pairs of bidirectional traffic of the transportation line to the number of unidirectional traffic of the transportation line, inversely related to the ratio of the freight density of the transportation line to the passenger density of the transportation line, positively related to the product of the longest time interval between adjacent trains of the transportation line and the maximum value of the number of unidirectional traffic of the transportation line, and positively related to the shortest time interval between adjacent trains of the transportation line;
a first haul line capacity utilization determination module configured to: if the decision coefficient is not greater than a preset decision threshold, determining that a batch processing mode is adopted to determine the capacity utilization rate of the first transportation line based on the environmental data; wherein the first transportation line capacity utilization is used to characterize a degree of use of the transportation environment compared to a full load condition; the first transportation line capacity utilization rate is positively correlated with the decision coefficient and negatively correlated with the comprehensive relative occupancy of the transportation environment; the comprehensive relative occupancy is positively correlated with the maximum historical passenger flow volume of the station and is positively correlated with the last duration in the hybrid processing mode; transmitting the first transportation line capacity utilization rate to the site, so that the site manages the transportation environment to which the site belongs in future time based on the first transportation line capacity utilization rate;
A second haul line capacity utilization determination module configured to: if the decision coefficient is greater than the decision threshold, determining that the hybrid processing mode is in; taking the first transportation line capacity utilization rate obtained when the batch processing mode is last time as a historical utilization rate; determining a second transportation line capacity utilization based on the historical utilization and the environmental data; the second transportation line capacity utilization rate is positively correlated with the historical utilization rate, is positively correlated with the ratio of the departure real-time passenger seat rate of the station to the station departure historical passenger seat rate, and is positively correlated with the ratio of the departure real-time passenger seat rate to the real-time passenger flow rate of the station; and sending the second transportation line capacity utilization rate to the site, so that the site manages the transportation environment of the site at a future moment based on the second transportation line capacity utilization rate.
The system can execute the method in any of the foregoing embodiments and achieve the same or similar technical effects, and will not be described herein.
Fig. 2 is a schematic structural view of an electronic device according to an embodiment of the present application. Referring to fig. 2, at the hardware level, the electronic device includes a processor, and optionally an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, network interface, and memory may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 2, but not only one bus or type of bus.
And the memory is used for storing programs. In particular, the program may include program code including computer-operating instructions. The memory may include memory and non-volatile storage and provide instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the railway line capacity utilization rate calculation device based on large data batch flow integration on a logic level. And the processor is used for executing the program stored in the memory and particularly executing any railway line capacity utilization rate calculation method based on large data batch flow integration.
The method for calculating the capacity utilization rate of the railway line based on the large data batch flow integration disclosed in the embodiment shown in the figure 1 of the application can be applied to a processor or can be realized by the processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
The electronic device may also execute the method for calculating the capacity utilization rate of the railway line based on the large data batch integration in fig. 1, and implement the functions of the embodiment shown in fig. 1, which is not described herein.
The embodiments of the present application also provide a computer readable storage medium storing one or more programs, the one or more programs including instructions, which when executed by an electronic device comprising a plurality of application programs, perform any of the aforementioned method for large data batch integration based rail line capacity utilization calculation.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (9)
1. A method for calculating the capacity utilization rate of a railway line based on big data batch integration, wherein the method is executed by a railway line capacity utilization rate calculation system based on big data batch integration, and the method comprises the following steps:
acquiring environmental data of a transportation environment in a historical time period of a designated time from a preset data classification management model; wherein the transportation environment comprises: a transportation line and a station between two adjacent transportation lines; the environmental data includes: the number of unidirectional traffic of the transportation line, the number of bidirectional traffic of the transportation line, the transportation line passenger transport density, the transportation line freight transport density, the longest time interval of adjacent trains of the transportation line, the shortest time interval of adjacent trains of the transportation line, the station departure history passenger seat rate and the station maximum history passenger flow;
Determining a decision coefficient based on the environmental data; wherein the decision coefficient is inversely related to the ratio of the number of pairs of bidirectional traffic of the transportation line to the number of unidirectional traffic of the transportation line, inversely related to the ratio of the freight density of the transportation line to the passenger density of the transportation line, positively related to the product of the longest time interval between adjacent trains of the transportation line and the maximum value of the number of unidirectional traffic of the transportation line, and positively related to the shortest time interval between adjacent trains of the transportation line;
if the decision coefficient is not greater than a preset decision threshold, determining that a batch processing mode is adopted to determine the capacity utilization rate of the first transportation line based on the environmental data; wherein the first transportation line capacity utilization is used to characterize a degree of use of the transportation environment compared to a full load condition; the first transportation line capacity utilization rate is positively correlated with the decision coefficient and negatively correlated with the comprehensive relative occupancy of the transportation environment; the comprehensive relative occupancy is positively correlated with the maximum historical passenger flow volume of the station and is positively correlated with the last duration in the hybrid processing mode; transmitting the first transportation line capacity utilization rate to the site, so that the site manages the transportation environment to which the site belongs in future time based on the first transportation line capacity utilization rate;
If the decision coefficient is greater than the decision threshold, determining that the hybrid processing mode is in; taking the first transportation line capacity utilization rate obtained when the batch processing mode is last time as a historical utilization rate; determining a second transportation line capacity utilization based on the historical utilization and the environmental data; the second transportation line capacity utilization rate is positively correlated with the historical utilization rate, is positively correlated with the ratio of the departure real-time passenger seat rate of the station to the station departure historical passenger seat rate, and is positively correlated with the ratio of the departure real-time passenger seat rate to the real-time passenger flow rate of the station; and sending the second transportation line capacity utilization rate to the site, so that the site manages the transportation environment of the site at a future moment based on the second transportation line capacity utilization rate.
2. The method of claim 1, wherein the method further comprises:
and if the number of the trains running on the transportation line is monitored to be larger than a preset number threshold, acquiring the environment data from the data classification management model.
3. The method of claim 1, wherein the method further comprises:
The decision threshold is inversely related to the average axle weight of the train traveling on the transportation line as indicated by the environmental data.
4. The method of claim 1, wherein the method further comprises:
the decision threshold is inversely related to a reference value; the reference value is positively correlated with the product of the maximum traveling speed of the train traveling on the transportation route and the duration of the maximum traveling speed indicated by the environmental data, and is positively correlated with the ratio of the number of other trains traveling in the same direction as the train to the length of the transportation route when the train is at the maximum traveling speed.
5. The method of claim 1, wherein the method further comprises:
the specified duration is positively correlated with the last duration in the hybrid processing mode.
6. The method of claim 1, wherein the method further comprises:
the transportation environment is a passenger-cargo mixed transportation environment.
7. A large data batch integration-based railway line capacity utilization rate computing system, comprising:
a data acquisition module configured to: acquiring environmental data of a transportation environment in a historical time period of a designated time from a preset data classification management model; wherein the transportation environment comprises: a transportation line and a station between two adjacent transportation lines; the environmental data includes: the number of unidirectional traffic of the transportation line, the number of bidirectional traffic of the transportation line, the transportation line passenger transport density, the transportation line freight transport density, the longest time interval of adjacent trains of the transportation line, the shortest time interval of adjacent trains of the transportation line, the station departure history passenger seat rate and the station maximum history passenger flow;
The decision coefficient determining module is configured to: determining a decision coefficient based on the environmental data; wherein the decision coefficient is inversely related to the ratio of the number of pairs of bidirectional traffic of the transportation line to the number of unidirectional traffic of the transportation line, inversely related to the ratio of the freight density of the transportation line to the passenger density of the transportation line, positively related to the product of the longest time interval between adjacent trains of the transportation line and the maximum value of the number of unidirectional traffic of the transportation line, and positively related to the shortest time interval between adjacent trains of the transportation line;
a first haul line capacity utilization determination module configured to: if the decision coefficient is not greater than a preset decision threshold, determining that a batch processing mode is adopted to determine the capacity utilization rate of the first transportation line based on the environmental data; wherein the first transportation line capacity utilization is used to characterize a degree of use of the transportation environment compared to a full load condition; the first transportation line capacity utilization rate is positively correlated with the decision coefficient and negatively correlated with the comprehensive relative occupancy of the transportation environment; the comprehensive relative occupancy is positively correlated with the maximum historical passenger flow volume of the station and is positively correlated with the last duration in the hybrid processing mode; transmitting the first transportation line capacity utilization rate to the site, so that the site manages the transportation environment to which the site belongs in future time based on the first transportation line capacity utilization rate;
A second haul line capacity utilization determination module configured to: if the decision coefficient is greater than the decision threshold, determining that the hybrid processing mode is in; taking the first transportation line capacity utilization rate obtained when the batch processing mode is last time as a historical utilization rate; determining a second transportation line capacity utilization based on the historical utilization and the environmental data; the second transportation line capacity utilization rate is positively correlated with the historical utilization rate, is positively correlated with the ratio of the departure real-time passenger seat rate of the station to the station departure historical passenger seat rate, and is positively correlated with the ratio of the departure real-time passenger seat rate to the real-time passenger flow rate of the station; and sending the second transportation line capacity utilization rate to the site, so that the site manages the transportation environment of the site at a future moment based on the second transportation line capacity utilization rate.
8. An electronic device, comprising:
a processor; and
a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method of any of claims 1 to 6.
9. A computer readable storage medium storing one or more programs, which when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method of any of claims 1-6.
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