CN117172513B - Comprehensive transportation hub capacity scheduling system based on big data - Google Patents
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Abstract
The invention relates to the technical field of traffic capacity scheduling, in particular to a comprehensive traffic hub capacity scheduling system based on big data, which comprises a passenger flow situation awareness module for acquiring passenger flow density in each monitoring area, a capacity calculation module for calculating available capacity values of the monitoring areas, a data analysis module for acquiring historical passenger flow data to analyze passenger flow changes in the monitoring areas, and a capacity scheduling module, wherein the capacity scheduling module is used for adjusting operation time intervals of corresponding capacities to corresponding values or adjusting the number of trains to corresponding values when the available capacity values are lower than standard available capacity values, determining whether to call the capacities of the monitoring areas with the available capacity values meeting the standard according to the change amount of the passenger flow density in unit time, and calculating scheduling consumption parameters to determine the called monitoring areas if the available capacity values are lower than the standard available capacity values. The invention improves the operation efficiency of the transportation junction.
Description
Technical Field
The invention relates to the technical field of traffic capacity scheduling, in particular to a comprehensive traffic hub traffic capacity scheduling system based on big data.
Background
Public transportation such as buses and subways is a main mode of urban passenger evacuation, passenger flow of traffic stations is greatly increased in large cities and tourist cities, especially holidays, and monitoring of passenger flow density and timely scheduling of urban traffic capacity are important guarantees for urban passenger evacuation.
Chinese patent publication No.: CN111582691B discloses a traffic capacity matching method of passenger transportation hub multi-traffic mode based on double-layer planning, comprising: building a specific expression of a generalized cost function of each traffic mode of the passenger transportation hub, and building a double-layer planning model matched with the traffic capacity of the passenger transportation hub in multiple traffic modes, wherein the double-layer planning model comprises an upper-layer sub-model and a lower-layer sub-model, the upper-layer sub-model adjusts the departure interval of buses and rail traffic lines and the serviceable quantity of taxis in unit time, and the lower-layer sub-model provides a passenger flow distribution result for the upper-layer sub-model; and solving the double-layer planning model based on a genetic algorithm and an MSA algorithm, and outputting an individual with the largest fitness function value as a solving result of the double-layer planning model. The invention can accurately match the transport capacity of rail transit, bus and taxis according to the passenger demand of the passenger transport hub, and improve the transport energy utilization rate of each traffic mode of the passenger transport hub.
However, the capacity scheduling method in the prior art is not based on available capacity, so that the operation efficiency of the transportation junction is low, and the passenger flow evacuation speed is low.
Disclosure of Invention
Therefore, the invention provides a comprehensive transportation hub capacity scheduling system based on big data, which is used for solving the problems of lower operation efficiency and lower passenger flow evacuation speed of transportation hubs in the prior art.
In order to achieve the above object, the present invention provides a comprehensive transportation hub capacity scheduling system based on big data, comprising:
the area dividing module is used for dividing the transportation junction into a plurality of monitoring areas;
the passenger flow situation sensing module is connected with the region dividing module and used for acquiring passenger flow density in each monitoring region in an infrared imaging mode;
the transport capacity calculation module is connected with the passenger flow situation sensing module and used for calculating the available transport capacity value of any monitoring area when the passenger flow density of the monitoring area is larger than a preset passenger flow density standard;
the data analysis module is connected with the transport capacity scheduling module and used for acquiring historical passenger flow data so as to analyze passenger flow change in the monitoring area;
and the capacity scheduling module is respectively connected with the capacity calculation module and the passenger flow situation awareness module and is used for adjusting the running time interval of the corresponding capacity to the corresponding value or adjusting the number of trains to the corresponding value when the available capacity value is lower than the standard available capacity value, determining whether to call the capacity of the monitoring area with the available capacity value meeting the standard according to the change of the passenger flow density in unit time, and if so, calculating the scheduling consumption parameters to determine the called monitoring area.
Further, the passenger flow situation awareness module includes:
the thermal imaging unit is used for carrying out thermal imaging on the passenger flow of the monitoring area and calculating a color difference value between the thermal imaging and a preset thermal imaging image;
and the passenger flow density analysis unit is connected with the thermal imaging unit and is used for calculating the passenger flow density of the monitoring area when the color difference value is greater than or equal to a preset color difference value standard.
Further, the capacity calculation module includes:
the transport capacity calculation unit is used for calculating the available transport capacity value of any monitoring area when the passenger flow density of the monitoring area is larger than a preset passenger flow density standard;
and the capacity early warning unit is connected with the capacity calculation unit and is used for sending out capacity early warning when the available capacity value is lower than the standard available capacity value.
Further, the capacity scheduling module calculates the passenger flow density difference value under the first preset condition, and carries out secondary judgment on whether the passenger flow density of the monitoring area accords with the standard according to the passenger flow density difference value,
or, adopting a corresponding capacity scheduling strategy to evacuate passenger flow;
the first preset condition is that the passenger flow density of any monitoring area is larger than a preset passenger flow density standard;
the passenger flow density difference value is the difference value between the passenger flow density and a preset passenger flow density standard;
the passenger flow density of the monitoring area meets the standard, and whether the passenger flow density meets the standard is judged secondarily, wherein the passenger flow density difference is smaller than a first preset passenger flow density difference;
and adopting a corresponding transport capacity scheduling strategy to meet the condition that the passenger flow density difference value is larger than or equal to a first preset passenger flow density difference value.
Further, the data analysis module acquires historical passenger flow data of the monitoring area, calculates a passenger flow average value of a time period corresponding to the historical year according to the current time characteristic, draws a passenger flow increase curve based on the passenger flow average value, calculates the average curvature of the increase curve, and calculates theoretical passenger flow density of the current time period according to the average curvature and the passenger flow average value of the time period corresponding to the previous year so as to secondarily judge whether the passenger flow density of the monitoring area meets the standard.
Further, the data analysis module compares the theoretical passenger flow density of the current period with the passenger flow density of the monitoring area, if the theoretical passenger flow density of the current period is greater than or equal to the passenger flow density of the monitoring area, the capacity scheduling module judges that the passenger flow density of the monitoring area meets the standard, and if the theoretical passenger flow density of the current period is less than the passenger flow density of the monitoring area, the capacity scheduling module judges that the passenger flow density of the monitoring area does not meet the standard, and adopts a corresponding capacity scheduling strategy to evacuate the passenger flow.
Further, the capacity scheduling module is further provided with a second preset passenger flow density difference value, the first preset passenger flow density difference value is smaller than the second preset passenger flow density difference value, and the capacity scheduling module calculates a capacity difference value between an available capacity value and a standard available capacity value and adjusts an operation time interval of corresponding capacity to a corresponding value according to the capacity difference value under the condition that the passenger flow density difference value is smaller than or equal to the second preset passenger flow density difference value;
the capacity scheduling module is provided with a plurality of adjustment modes aiming at the operation time interval according to the capacity difference value, and the adjustment modes are different in adjustment amplitude of the operation time interval.
Further, when the passenger flow density difference value is larger than a second preset passenger flow density difference value, the transport capacity scheduling module is provided with a plurality of adjustment modes aiming at the number of trains according to the density secondary difference value, and the adjustment modes are different in adjustment amplitude of the number of trains;
the density secondary difference is the difference between the passenger flow density difference and the first preset passenger flow density difference.
Further, the capacity scheduling module acquires the variation of the passenger flow density in unit time after capacity scheduling is completed, and if the variation is smaller than a preset variation standard, the capacity scheduling module judges that the capacity of a monitoring area with the available capacity value meeting the standard is called.
Further, the capacity scheduling module calculates a scheduling consumption parameter F of a monitoring area with any available capacity value meeting the standard according to the following formula, and sets the scheduling consumption parameter F
，
Wherein L is the distance between any monitoring area and the monitoring area with the capacity early warning, L0 is the preset standard distance, alpha is the distance weight coefficient, Y is the available capacity value of the corresponding monitoring area, Y0 is the standard available capacity value, and beta is the capacity weight coefficient;
and the capacity scheduling module performs descending order arrangement on the calculated scheduling consumption parameters, and schedules the capacity of the monitoring area corresponding to the minimum value of the scheduling consumption parameters to the monitoring area with capacity early warning.
Compared with the prior art, the method has the beneficial effects that the passenger flow density of each monitoring area is monitored in real time, when the passenger flow density reaches the warning standard, the available capacity value of the monitoring area is calculated, if the capacity value is lower than the standard available capacity value, the capacity of each interval area is insufficient to bear the current passenger flow, the capacity scheduling module adopts a corresponding capacity scheduling strategy comprising adjusting the operation time interval of the corresponding capacity and adjusting the number of trains, after the capacity scheduling is finished, the capacity scheduling module acquires the variation of the passenger flow density in unit time, if the variation is smaller than the preset variation standard, the capacity scheduling module invokes the capacity of the monitoring area with the available capacity value conforming to the standard to carry out capacity support on the monitoring area with capacity warning, and through the technical scheme, the capacity scheduling linkage of each interval area is increased, the operation efficiency of a traffic junction is improved, and the passenger flow evacuation speed is improved.
Further, the passenger flow density analysis unit of the invention can trigger the action of calculating the passenger flow density of the monitoring area instead of real-time passenger flow density calculation when the color difference value of the thermal imaging image and the preset thermal imaging image is larger than or equal to the preset color difference value standard, thereby reducing the operation times and further improving the reaction speed of the system.
Further, the invention sets the preset passenger flow density, wherein the preset passenger flow density is a passenger flow parameter set according to the carrying capacity and the transport capacity of the transportation junction, when the actual passenger flow density reaches the preset passenger flow density, the passenger flow of the transportation junction reaches the warning standard, and at the moment, the transport capacity calculating unit calculates the available transport capacity value, provides a decision basis for the transport capacity scheduling strategy, so that the transportation junction can timely cope with large passenger flow, timely evacuate passengers, and the operation efficiency of the transportation junction is improved.
Further, the invention obtains the variation trend of the passenger flow through analyzing the historical data, and judges whether the passenger flow density of the monitored area meets the standard or not secondarily by calculating the theoretical passenger flow density of the current period, thereby improving the accuracy of judgment.
Further, when the number of train shifts is regulated, different regulating coefficients are set, and gradient regulation is carried out on the number of train shifts, so that the regulated train shifts can be matched with passenger flows to be evacuated, the running cost caused by excessive increase of the number of train shifts is avoided, and meanwhile, the passenger flow evacuation too slow caused by insufficient number of train shifts is avoided.
Further, the scheduling consumption parameter F is calculated, the scheduling consumption parameter F is a characteristic parameter of the consumption required by the scheduling between the monitoring areas, when the distance between other monitoring areas and the monitoring area with the capacity early warning is farther and the available capacity value of the other monitoring areas is smaller, the scheduling consumption parameter F is bigger, otherwise, when the distance between the other monitoring areas and the monitoring area with the capacity early warning is bigger and the available capacity value of the other monitoring areas is bigger, the scheduling consumption parameter F is smaller, and the monitoring area with the lowest consumption is selected to support the monitoring area with the capacity early warning through the calculation of the scheduling consumption parameter.
Drawings
FIG. 1 is a block diagram of a comprehensive transportation hub capacity scheduling system based on big data according to an embodiment of the present invention;
FIG. 2 is a block diagram of a traffic situation awareness module according to an embodiment of the present invention;
FIG. 3 is a block diagram illustrating an exemplary capacity calculation module according to an embodiment of the present invention;
FIG. 4 is a block diagram of a further architecture of a comprehensive transportation hub capacity scheduling system based on big data according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1 and 4, the comprehensive transportation hub capacity scheduling system based on big data according to the present invention includes:
the area dividing module is used for dividing the transportation junction into a plurality of monitoring areas;
the passenger flow situation sensing module is connected with the region dividing module and used for acquiring passenger flow density in each monitoring region in an infrared imaging mode;
the transport capacity calculation module is connected with the passenger flow situation sensing module and used for calculating the available transport capacity value of any monitoring area when the passenger flow density of the monitoring area is larger than a preset passenger flow density standard;
the data analysis module is connected with the transport capacity scheduling module and used for acquiring historical passenger flow data so as to analyze passenger flow change in the monitoring area;
and the capacity scheduling module is respectively connected with the capacity calculation module and the passenger flow situation awareness module and is used for adjusting the running time interval of the corresponding capacity to the corresponding value or adjusting the number of trains to the corresponding value when the available capacity value is lower than the standard available capacity value, determining whether to call the capacity of the monitoring area with the available capacity value meeting the standard according to the change of the passenger flow density in unit time, and if so, calculating the scheduling consumption parameters to determine the called monitoring area.
In this embodiment, the transportation hub includes transportation stations such as passenger stations, bus stations, subway stations, etc., and the transportation hub is divided into a plurality of monitoring areas, and when the monitoring areas are divided, the transportation hub can be divided according to administrative monitoring areas, or can be divided according to the passenger flow of the transportation stations, or each transportation station is divided into a single monitoring area.
According to the method, the passenger flow density of each monitoring area is monitored in real time, when the passenger flow density reaches the warning standard, the available capacity value of the monitoring area is calculated, if the capacity value is lower than the standard available capacity value, the fact that the capacity at the moment is insufficient to bear the current passenger flow is indicated, the capacity scheduling module adopts a corresponding capacity scheduling strategy comprising adjusting the operation time interval of the corresponding capacity and adjusting the number of trains, after capacity scheduling is completed, the capacity scheduling module obtains the variation of the passenger flow density in unit time, and if the variation is smaller than the preset variation standard, the capacity scheduling module calls the capacity of the available capacity value of the monitoring area conforming to the standard to carry out capacity support on the monitoring area with capacity early warning.
Referring to fig. 2, which is a block diagram of a traffic situation awareness module according to an embodiment of the present invention, the traffic situation awareness module includes:
the thermal imaging unit is used for carrying out thermal imaging on the passenger flow of the monitoring area and calculating a color difference value between the thermal imaging and a preset thermal imaging image;
and the passenger flow density analysis unit is connected with the thermal imaging unit and is used for calculating the passenger flow density of the monitoring area when the color difference value is greater than or equal to a preset color difference value standard.
When the passenger flow density of the monitoring area is calculated, the passenger flow density can be calculated according to one or more of a passenger flow statistical camera, a low-altitude panoramic personnel camera and an intelligent personnel density network camera which are arranged in the monitoring area.
The principle of thermal imaging is that human body (temperature) is subjected to thermal infrared radiation, colors after thermal imaging of different passenger flow densities are different, and the thermal imaging image is preset in the embodiment, specifically, the thermal imaging image with the preset color is set as a reference to judge whether the passenger flow density of a monitoring area is calculated or not.
According to the passenger flow density analysis unit, the passenger flow density of the monitoring area is calculated only when the color difference value of the thermal imaging image and the preset thermal imaging image is larger than or equal to the preset color difference value standard, and the passenger flow density is calculated in real time instead of real-time, so that the operation times are reduced, and the reaction speed of the system is improved.
Referring to fig. 3, which is a block diagram illustrating a capacity calculation module according to an embodiment of the present invention, the capacity calculation module includes:
the transport capacity calculation unit is used for calculating the available transport capacity value of any monitoring area when the passenger flow density of the monitoring area is larger than a preset passenger flow density standard;
and the capacity early warning unit is connected with the capacity calculation unit and is used for sending out capacity early warning when the available capacity value is lower than the standard available capacity value.
The present invention is not repeated, and the patent document CN107403254B is a traffic scheduling method based on traffic node passenger flow prediction, CN109858670B is a real-time early warning method for large passenger flow of rail transit station, CN110415518B is a monitoring early warning method, device, equipment and medium for passenger flow and traffic capacity, and the above patent document discloses a method for acquiring traffic capacity, so that those skilled in the art can understand that the available traffic capacity value is the difference between the current traffic capacity and the passenger flow volume to be evacuated, and in some specific embodiments, the available traffic capacity value can also be obtained by calculating the difference between the passenger flow change speed and the traffic carrying speed in unit time.
The invention sets the preset passenger flow density, wherein the preset passenger flow density is a passenger flow parameter set according to the carrying capacity and the transportation capacity of the transportation junction, when the actual passenger flow density reaches the preset passenger flow density, the passenger flow of the transportation junction reaches the warning standard, and at the moment, the transportation capacity calculating unit calculates the available transportation capacity value, provides decision basis for the transportation capacity scheduling strategy, so that the transportation junction can timely cope with large passenger flow, timely evacuate passengers, and improves the operation efficiency of the transportation junction.
Specifically, the capacity scheduling module calculates the passenger flow density difference value under a first preset condition, and judges whether the passenger flow density of the monitoring area meets the standard or not for the second time according to the passenger flow density difference value,
or, adopting a corresponding capacity scheduling strategy to evacuate passenger flow;
the first preset condition is that the passenger flow density of any monitoring area is larger than a preset passenger flow density standard;
the passenger flow density difference value is the difference value between the passenger flow density and a preset passenger flow density standard;
the passenger flow density of the monitoring area meets the standard, and whether the passenger flow density meets the standard is judged secondarily, wherein the passenger flow density difference is smaller than a first preset passenger flow density difference;
and adopting a corresponding transport capacity scheduling strategy to meet the condition that the passenger flow density difference value is larger than or equal to a first preset passenger flow density difference value.
Specifically, the data analysis module acquires historical passenger flow data of a monitoring area, calculates a passenger flow average value of a time period corresponding to the historical year according to the current time characteristic, draws a passenger flow increase curve based on the passenger flow average value, calculates the average curvature of the increase curve, and calculates the theoretical passenger flow density of the current time period according to the average curvature and the passenger flow average value of the time period corresponding to the previous year so as to secondarily judge whether the passenger flow density of the monitoring area meets the standard.
According to the method, the historical data are analyzed to obtain the variation trend of the passenger flow, and the theoretical passenger flow density in the current period is calculated to judge whether the passenger flow density in the monitored area meets the standard or not for the second time, so that the judgment accuracy is improved.
As will be appreciated by those skilled in the art, the time characteristics are divided according to the working days, the weekends and the holidays, for example, when the current time is the holiday, the average value of the passenger flows of the holidays of each year is calculated respectively, the theoretical passenger flow of the current period is the product of the average value of the passenger flows of the corresponding period of the previous year and the average curvature, and the theoretical passenger flow density of the current period=the theoretical passenger flow of the current period/(the time period of the unit area).
Specifically, the data analysis module compares the theoretical passenger flow density of the current period with the passenger flow density of the monitoring area, if the theoretical passenger flow density of the current period is greater than or equal to the passenger flow density of the monitoring area, the capacity scheduling module judges that the passenger flow density of the monitoring area meets the standard, and if the theoretical passenger flow density of the current period is less than the passenger flow density of the monitoring area, the capacity scheduling module judges that the passenger flow density of the monitoring area does not meet the standard, and adopts a corresponding capacity scheduling strategy to evacuate the passenger flow.
Specifically, the capacity scheduling module is further provided with a second preset passenger flow density difference value, the first preset passenger flow density difference value is smaller than the second preset passenger flow density difference value, and the capacity scheduling module calculates a capacity difference value between an available capacity value and a standard available capacity value and adjusts the operation time interval of the corresponding capacity to a corresponding value according to the capacity difference value under the condition that the passenger flow density difference value is smaller than or equal to the second preset passenger flow density difference value;
the capacity scheduling module is provided with a plurality of adjustment modes aiming at the operation time interval according to the capacity difference value, and the adjustment modes are different in adjustment amplitude of the operation time interval.
The capacity scheduling module is provided with a first preset capacity difference value and a second preset capacity difference value, wherein the first preset capacity difference value is smaller than the second preset capacity difference value,
if the capacity difference value is smaller than a first preset capacity difference value, the capacity scheduling module selects a first preset time interval adjustment coefficient alpha 1 to adjust the operation time interval, and the adjusted operation time interval = operation time interval x alpha 1 is set;
if the capacity difference value is greater than or equal to the first preset capacity difference value and less than the second preset capacity difference value, the capacity scheduling module selects a second preset time interval adjustment coefficient alpha 2 to adjust the operation time interval, and the adjusted operation time interval = operation time interval x alpha 2 is set;
if the capacity difference value is greater than or equal to the second preset capacity difference value, the capacity scheduling module selects a third preset time interval adjustment coefficient alpha 3 to adjust the operation time interval, and the adjusted operation time interval = operation time interval x alpha 3 is set;
this embodiment sets 0.7 < α3 < α2 < α1 < 1, preferably α1=0.9, α2=0.85, and α3=0.8.
It should be understood by those skilled in the art that in the present embodiment, when the operation time interval is adjusted, the operation time intervals of the buses and the subways are adjusted, and the capacity calculation unit calculates the available capacity values of the buses and the subways, respectively, when calculating the available capacity values of the monitoring areas.
Specifically, when the passenger flow density difference value is larger than a second preset passenger flow density difference value, the transport capacity scheduling module is provided with a plurality of adjustment modes aiming at the number of trains according to the density secondary difference value, and the adjustment modes are different in adjustment amplitude of the number of trains;
the density secondary difference is the difference between the passenger flow density difference and the first preset passenger flow density difference.
The capacity scheduling module is provided with a first preset density secondary difference value and a second preset density secondary difference value, wherein the first preset density secondary difference value is smaller than the second preset density secondary difference value,
if the density secondary difference value is smaller than a first preset density secondary difference value, the transport capacity scheduling module selects a first preset shift quantity adjusting coefficient e1 to adjust the train shift quantity, and the adjusted train shift quantity=train shift quantity×e1 is set;
if the density secondary difference value is greater than or equal to the first preset density secondary difference value and less than the second preset density secondary difference value, the capacity scheduling module selects a second preset shift number adjusting coefficient e2 to adjust the train shift number, and the adjusted train shift number=train shift number×e2 is set;
if the density secondary difference value is greater than or equal to a second preset density secondary difference value, the capacity scheduling module selects a third preset shift quantity adjusting coefficient e3 to adjust the train shift quantity, and the adjusted train shift quantity=train shift quantity×e3 is set;
in this embodiment, 1 < e2 < e3 < 1.1, preferably e1=1.03, e2=1.06, and e3=1.09 are set.
And if the calculated number of train shifts after adjustment is not a positive integer, the value of the number of train shifts after adjustment is a minimum positive integer larger than the calculated number of train shifts after adjustment.
As can be appreciated by those skilled in the art, the train shifts are shifts of buses and subways, the number of shifts of buses and subways is the running number of buses and subways, different adjusting coefficients are set when the number of train shifts is adjusted, and gradient adjustment is performed on the number of train shifts, so that the adjusted train shifts can be matched with passenger flows to be evacuated, the running cost of excessively increasing the number of train shifts is avoided, and meanwhile, the passenger flow evacuation too slow caused by insufficient number of train shifts is avoided.
Specifically, the capacity scheduling module acquires the variation of the passenger flow density in unit time after capacity scheduling is completed, and if the variation is smaller than a preset variation standard, the capacity scheduling module judges and invokes the capacity of a monitoring area with the available capacity value meeting the standard.
Specifically, the capacity scheduling module calculates a scheduling consumption parameter F of a monitoring area with any available capacity value meeting a standard according to the following formula, and sets the scheduling consumption parameter F
，
Wherein L is the distance between any monitoring area and the monitoring area with the capacity early warning, L0 is the preset standard distance, alpha is the distance weight coefficient, Y is the available capacity value of the corresponding monitoring area, Y0 is the standard available capacity value, and beta is the capacity weight coefficient;
and the capacity scheduling module performs descending order arrangement on the calculated scheduling consumption parameters, and schedules the capacity of the monitoring area corresponding to the minimum value of the scheduling consumption parameters to the monitoring area with capacity early warning.
In the embodiment of the invention, alpha is more than 0.3 and less than 0.5, beta is more than 0.5 and less than 0.7, preferably alpha=0.45, and beta=0.55.
According to the method, the dispatching consumption parameter F is calculated, the dispatching consumption parameter F is a characteristic parameter of the consumption required by dispatching among the monitoring areas, when the distance between other monitoring areas and the monitoring area with the capacity early warning is farther and the available capacity value of the other monitoring areas is smaller, the dispatching consumption parameter F is smaller and the monitoring area with the capacity early warning is selected to support the monitoring area with the capacity early warning by calculating the dispatching consumption parameter, and by the technical scheme, comprehensive dispatching of the capacities of different monitoring areas is realized and the operation efficiency is improved.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A comprehensive transportation hub capacity scheduling system based on big data, comprising:
the area dividing module is used for dividing the transportation junction into a plurality of monitoring areas;
the passenger flow situation sensing module is connected with the area dividing module and used for acquiring passenger flow density in each monitoring area in an infrared imaging mode;
the transport capacity calculation module is connected with the passenger flow situation sensing module and used for calculating the available transport capacity value of any monitoring area when the passenger flow density of the monitoring area is larger than a preset passenger flow density standard;
the data analysis module is connected with the transport capacity scheduling module and used for acquiring historical passenger flow data so as to analyze the passenger flow change in the monitoring area;
the capacity scheduling module is respectively connected with the capacity calculation module and the passenger flow situation awareness module and is used for adjusting the running time interval of the corresponding capacity to the corresponding value or adjusting the number of trains to the corresponding value when the available capacity value is lower than the standard available capacity value, determining whether to call the capacity of the monitoring area with the available capacity value meeting the standard according to the change of the passenger flow density in unit time, and if yes, calculating scheduling consumption parameters to determine the called monitoring area;
the capacity scheduling module calculates the passenger flow density difference value under a first preset condition, and judges whether the passenger flow density of the monitored area meets the standard or not for the second time according to the passenger flow density difference value,
or, adopting a corresponding capacity scheduling strategy to evacuate passenger flow;
the first preset condition is that the passenger flow density of any monitoring area is larger than a preset passenger flow density standard;
the passenger flow density difference value is the difference value between the passenger flow density and a preset passenger flow density standard;
the passenger flow density of the monitoring area meets the standard, and whether the passenger flow density meets the standard is judged secondarily, wherein the passenger flow density difference is smaller than a first preset passenger flow density difference;
adopting a corresponding transport capacity scheduling strategy to meet the condition that the passenger flow density difference value is larger than or equal to a first preset passenger flow density difference value;
the capacity scheduling module is further provided with a second preset passenger flow density difference value, the first preset passenger flow density difference value is smaller than the second preset passenger flow density difference value, wherein the capacity scheduling module calculates the capacity difference value between the available capacity value and the standard available capacity value under the condition that the passenger flow density difference value is smaller than or equal to the second preset passenger flow density difference value, and adjusts the operation time interval of the corresponding capacity to the corresponding value according to the capacity difference value;
the capacity scheduling module is provided with a plurality of adjustment modes aiming at the operation time interval according to the capacity difference value, and the adjustment amplitude of each adjustment mode on the operation time interval is different;
when the passenger flow density difference value is larger than a second preset passenger flow density difference value, the transport capacity scheduling module is provided with a plurality of adjustment modes aiming at the number of trains according to the density secondary difference value, and the adjustment modes are different in adjustment amplitude of the number of trains;
the density secondary difference is the difference between the passenger flow density difference and a first preset passenger flow density difference;
the capacity scheduling module acquires the variation of the passenger flow density in unit time after capacity scheduling is completed, and if the variation is smaller than a preset variation standard, the capacity scheduling module judges and invokes the capacity of a monitoring area with the available capacity value meeting the standard;
the capacity scheduling module calculates the scheduling consumption parameter F of any monitoring area with the available capacity value meeting the standard according to the following formula, and sets
，
Wherein L is the distance between any monitoring area and the monitoring area with the capacity early warning, L0 is the preset standard distance, alpha is the distance weight coefficient, Y is the available capacity value of the corresponding monitoring area, Y0 is the standard available capacity value, and beta is the capacity weight coefficient;
and the capacity scheduling module performs descending order arrangement on the calculated scheduling consumption parameters, and schedules the capacity of the monitoring area corresponding to the minimum value of the scheduling consumption parameters to the monitoring area with capacity early warning.
2. The big data based comprehensive transportation hub capacity scheduling system of claim 1, wherein the passenger flow situation awareness module comprises:
the thermal imaging unit is used for carrying out thermal imaging on the passenger flow of the monitoring area and calculating a color difference value between the thermal imaging and a preset thermal imaging image;
and the passenger flow density analysis unit is connected with the thermal imaging unit and is used for calculating the passenger flow density of the monitoring area when the color difference value is greater than or equal to a preset color difference value standard.
3. The big data based comprehensive transportation hub capacity scheduling system of claim 2, wherein the capacity calculation module comprises:
the transport capacity calculation unit is used for calculating the available transport capacity value of any monitoring area when the passenger flow density of the monitoring area is larger than a preset passenger flow density standard;
and the capacity early warning unit is connected with the capacity calculation unit and is used for sending out capacity early warning when the available capacity value is lower than the standard available capacity value.
4. The comprehensive transportation hub capacity scheduling system based on big data according to claim 3, wherein the data analysis module obtains historical passenger flow data of the monitoring area, calculates a passenger flow average value of a corresponding time period of the historical year according to the current time characteristic, draws a passenger flow volume increase curve based on the passenger flow average value, calculates an average curvature of the increase curve, and calculates theoretical passenger flow density of the current time period according to the average curvature and the passenger flow volume average value of a corresponding time period of the previous year to make a secondary determination on whether the passenger flow density of the monitoring area meets the standard.
5. The comprehensive transportation hub traffic dispatching system based on big data according to claim 4, wherein the data analysis module compares the theoretical traffic density of the current period with the traffic density of the monitored area, if the theoretical traffic density of the current period is greater than or equal to the traffic density of the monitored area, the traffic dispatching module judges that the traffic density of the monitored area meets the standard, if the theoretical traffic density of the current period is less than the traffic density of the monitored area, the traffic dispatching module judges that the traffic density of the monitored area does not meet the standard, and adopts a corresponding traffic dispatching strategy to evacuate the traffic.
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