CN104821084A - Road section traffic index estimation system based on unmanned aerial vehicle measurement - Google Patents
Road section traffic index estimation system based on unmanned aerial vehicle measurement Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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Abstract
The invention relates to a road section traffic index estimation system based on unmanned aerial vehicle measurement, which is arranged on an unmanned aerial vehicle. The system comprises an aerial camera, an image processor, an air pressure height sensor and a main controller, wherein the aerial camera is connected with the image processor for sending a shot road section image to the image processor for image processing; the main controller is respectively connected with the image processor and the air pressure height sensor; and according to an image processing result of the image processor and an unmanned aerial vehicle height detected by the air pressure height sensor, the road section traffic index is estimated. Thus, traffic congestion information of a critical road section can be provided for a road traffic management department, the data are more intuitive and accurate, and the road traffic management department can make accurate and effective traffic handling measures.
Description
The divisional application of the patent of " the road section traffic volume index estimating system based on unmanned plane is measured " that the present invention is application number is 201410484449X, the applying date, to be September 20, denomination of invention in 2014 be.
Technical field
The present invention relates to unmanned plane fields of measurement, particularly relate to a kind of road section traffic volume index estimating system measured based on unmanned plane.
Background technology
Unmanned plane, i.e. unmanned spacecraft, its english abbreviation is " UAV ", is the not manned aircraft utilizing radio robot to handle with the presetting apparatus provided for oneself.Can be divided into from technical standpoint definition: this several large class of depopulated helicopter, unmanned fixed-wing aircraft, unmanned multi-rotor aerocraft, unmanned airship, unmanned parasol.Military unmanned air vehicle and civilian unmanned plane can be divided into from the classification of purposes aspect.Military aspect, can be used for battle reconnaissance and supervision, positioning school are penetrated, injured assessment, electronic warfare, and civilian aspect, can be used for border patrol, nuclear radiation detection, aeroplane photography, mineral exploration aviation, the condition of a disaster supervision, traffic patrolling and security monitoring.
The estimation that unmanned plane is applied to road section traffic volume index compensate for the blank that traffic control field is difficult to calculate key road segment traffic index largely.In the urban traffic environment of day by day blocking up, how providing the road section traffic volume index that quality is high, for vehicle driver provides important referential data, rationally carry out automatic shunt to city vehicle, is a difficult problem of puzzlement city traffic control department.The limited means calculating road section traffic volume index in prior art are only the traffic indexs that the GPS locator data turning back to traffic control traffic control monitor supervision platform by the taxi that urban inner is ten hundreds of calculates section, but there is following defect in such account form: (1) computational accuracy relies on larger to the quantity of taxi in section, but when wishing that the key road segment calculated does not have taxi to travel, the traffic index of this key road segment has no way of calculating; (2) data volume transmitted is comparatively large, and each taxi returns GPS locator data, causes traffic control traffic control monitor supervision platform to assume responsibility for great calculated load.
Therefore, traffic index unmanned plane measurement being applied to section calculates, build a kind of new road section traffic volume index estimating system, only by can complete the traffic index estimation of target road section to the image procossing of a two field picture, improve computational accuracy while reducing calculated amount, improve the reference value of road section traffic volume index.
Summary of the invention
In order to solve the problem, the invention provides a kind of road section traffic volume index estimating system measured based on unmanned plane, unmanned plane is introduced key road segment region by GPS locator data according to key road segment automatically, aerial camera and image processor is used to perform image taking and image procossing to key road segment scene, by the technological means of vehicle target identification, the traffic index of estimation key road segment, be convenient to vehicle supervision department and issue section congestion information timely and accurately, for the foundation that vehicle driver provides section to select, thus rationally wagon flow in city is shunted immediately, reach the technique effect solving urban congestion.
According to an aspect of the present invention, provide a kind of road section traffic volume index estimating system measured based on unmanned plane, described estimating system is arranged on unmanned plane, comprise aerial camera, image processor, pressure-altitude sensor and master controller, described aerial camera is connected with described image processor, the section image of shooting is sent to described image processor and performs image procossing, described master controller is connected with described image processor and described pressure-altitude sensor respectively, according to the unmanned plane height that processing result image and the described pressure-altitude sensor of described image processor detect, estimation road section traffic volume index.
More specifically, the described road section traffic volume index estimating system measured based on unmanned plane also comprises, twoway radio, and for receiving the control information that traffic control monitor supervision platform in ground sends, and traffic control monitor supervision platform sends measurement data earthward, GPS navigation equipment, for receiving the GPS locator data that GPS navigation satellite sends, unmanned plane power-equipment, to fly to target location for driving unmanned plane, described aerial camera is linear array digital aviation video camera, comprises undercarriage having shock absorption function, front cover glass, camera lens, filter and image-forming electron unit, for place, photographic subjects section scene to export section image, described image processor also comprises characteristic storing unit, has prestored section upper limit gray threshold, section lower limit gray threshold and vehicular characteristics data storehouse, has preserved each type of vehicle template image in described vehicular characteristics data storehouse, pavement section unit, be connected respectively with described aerial camera and described characteristic storing unit, receive described section image, the pixel identification of gray-scale value in the image of described section between section upper limit gray threshold and section lower limit gray threshold is formed section target subimage, vehicle data recognition unit, be connected respectively with described pavement section unit and described characteristic storing unit, receive described section target subimage, adopt the quantity of all kinds vehicle in section target subimage described in the identification of Hu's moment invariants evaluation algorithm based on described vehicular characteristics data storehouse, described estimating system also comprises light fixture, for providing floor light for the shooting of described aerial camera to target road section place scene, luminance sensor, for measuring the brightness data of unmanned plane position, described master controller and described twoway radio, described GPS navigation equipment, described unmanned plane power-equipment is connected respectively with described aerial camera, the GPS locator data of the target road section received from ground traffic control monitor supervision platform by described twoway radio is sent to described unmanned plane power-equipment and flies to directly over target road section to drive described unmanned plane, when the current unmanned plane GPS locator data that the described GPS navigation equipment received sends is consistent with the GPS locator data of target road section, described aerial camera is driven to perform the shooting of section image, drive described image processor to perform the image procossing of section image simultaneously, described master controller is also connected the quantity to receive all kinds vehicle with described vehicle data recognition unit, be connected to receive section target subimage with described pavement section unit, according to the unmanned plane height that described pressure-altitude sensor exports, calculate section area in the target subimage of section, and based on the quantity of described all kinds vehicle and the road section traffic volume index of described section area reckoning target road section, so that described road section traffic volume index is sent to ground traffic control monitor supervision platform by described twoway radio, wherein, described master controller is also connected respectively with described light fixture and described luminance sensor, with when described brightness data is less than predetermined luminance threshold value, starts described light fixture to provide floor light, described GPS navigation equipment, described unmanned plane power-equipment, described image processor and described master controller are all positioned at unmanned plane front end panel board, and described light fixture, described luminance sensor, described pressure-altitude sensor, described aerial camera and described twoway radio are all positioned on the fuselage of unmanned plane, describedly the quantity of all kinds vehicle in section target subimage described in the identification of Hu's moment invariants evaluation algorithm is adopted to comprise based on described vehicular characteristics data storehouse, described section target subimage is divided into multiple partial image again containing a vehicle, by seven of partial image again containing a vehicle not bending moment and each type of vehicle template image in described vehicular characteristics data storehouse seven not bending moment compare respectively, until inquire a certain type of vehicle template image, when the difference of seven not bending moments is all in respective predetermined difference value threshold value, then judge that the partial image again containing a vehicle comprises the vehicle of a certain type of vehicle template image corresponding types, described seven not bending moment be the feature of car modal image, there is translation, amplify, reduce and rotate all constant characteristic.
More specifically, in the described road section traffic volume index estimating system based on unmanned plane measurement, described image processor is the digital signal processor DSP of TMS9000 series.
More specifically, in the described road section traffic volume index estimating system based on unmanned plane measurement, described master controller is the Cortex-A53 processor of Acorn company.
More specifically, in the described road section traffic volume index estimating system based on unmanned plane measurement, described characteristic storing unit is a synchronous DRAM SDRAM (Synchronous DynamicRandom Access Memory).
More specifically, in the described road section traffic volume index estimating system based on unmanned plane measurement, described GPS navigation equipment, described image processor, described master controller are integrated on one piece of surface-mounted integrated circuit.
More specifically, in the described road section traffic volume index estimating system based on unmanned plane measurement, described pressure-altitude sensor measures unmanned plane height according to the change of unmanned plane position air pressure.
Accompanying drawing explanation
Below with reference to accompanying drawing, embodiment of the present invention are described, wherein:
Fig. 1 is the block diagram of the road section traffic volume index estimating system based on unmanned plane measurement illustrated according to an embodiment of the present invention.
Fig. 2 is the block diagram of the image processor of the road section traffic volume index estimating system based on unmanned plane measurement illustrated according to an embodiment of the present invention.
Embodiment
Below with reference to accompanying drawings the embodiment of the road section traffic volume index estimating system based on unmanned plane measurement of the present invention is described in detail.
Traffic congestion index is according to road situation, and the unimpeded or conceptual exponential quantity of blocking up of the concentrated expression road net that some cities are arranged, he is equivalent to jam situation digitizing.
It is 15 minutes that traffic index calculates minimum time unit, exponential quantity can reflect the running status of system-wide net real-time dynamicly, travelled frequently early by definition, the different measurement period of evening peak or peak etc. festivals or holidays, can obtain weekday rush average traffic index, day reflection one day typical traffic feature such as traffic index maximal value index.Traffic index be integrate traffic congestion spatial dimension, the duration, the order of severity comprehensive numerical value, traffic administration person and traffic participant can pass through traffic index, obtain the traffic behavior of system-wide net or Regional Road Network, to adopt an effective measure in time, reduce the generation of blocking up.Traffic index can help resident to judge line time consumption, and such as under unimpeded situation, the time of travelling frequently of going to work is 30 minutes, so when road network be in moderate block up time, the time advance that will reserve about 30 minutes is gone out in order to avoid late more.
Can by carrying out the traffic index that deep processing process obtains to the dynamic vehicle positional information (abbreviation floating car data) being distributed in streets and lanes, city, such as in Beijing, be to the data processing centre (DPC) of vehicle supervision department by the vehicle GPS passback dynamic data on more than 30,000, whole city taxi.Data processing centre (DPC) is first to vehicle position data process, obtain the travelling speed of difference in functionality grade road, then and data on flows different according to function path calculates this road shared weight in the whole network, finally by people, the perception of blocking up is judged, provide the index desired value being converted to 0-10.
But traffic index does not also mean that the speed of a motor vehicle, because path area is different, speed band is not identical to the impression of people.Such as 20 kilometers of speed hourly feel to be exactly heavy congestion on through street, and just feel ratio smoothly in the limited roads such as lane.Distinguishing these grades to calculate, needing staff to carry the instruments such as GPS, going around streets and lanes, calculated by with data by comparison presence afterwards, finally determine the traffic index of various different road.
As can be seen here, above-mentioned traffic index computing method need taxi data back, staff's on-site land survey, and data volume is large, and large to taxis quantity dependence, reliability is not high.
The road section traffic volume index estimating system measured based on unmanned plane of the present invention, can automatically control unmanned plane and sail for target road section, by image taking, image procossing and target identification, calculate the vehicle fleet size of each type in target road section, the section area of traffic index will be calculated according to flying height estimation simultaneously, thus calculate based on the traffic index that the vehicle data of each type and section area complete target road section, process data volume is less, processing mode more directly, more effective.
Fig. 1 is the block diagram of the road section traffic volume index estimating system based on unmanned plane measurement illustrated according to an embodiment of the present invention, as shown in Figure 1, described estimating system is installed on civilian unmanned plane, described estimating system comprises aerial camera 1, image processor 2, pressure-altitude sensor 3, master controller 4 and power-supply unit 5, described power-supply unit 5 provides power supply supply for other electronic equipments in described estimating system except described power-supply unit 5, described aerial camera 1 is connected with described image processor 2, the section image of shooting is sent to described image processor 2 and performs image procossing, described master controller 4 is connected with described image processor 2 and described pressure-altitude sensor 3 respectively, according to the unmanned plane height that processing result image and the described pressure-altitude sensor 3 of described image processor 2 detect, the road section traffic volume index of estimation target road section.Described target road section is generally the key road segment of vehicle supervision department's emphasis monitoring, or conventional method cannot measure the section of traffic index.
Then, more specific description is carried out to estimating system of the present invention.
The described road section traffic volume index estimating system measured based on unmanned plane also comprises twoway radio, and for receiving the control information that traffic control monitor supervision platform in ground sends, and traffic control monitor supervision platform sends measurement data earthward; GPS navigation equipment, for receiving the GPS locator data that GPS navigation satellite sends; Unmanned plane power-equipment, to fly to target location for driving unmanned plane; Described aerial camera 1 is linear array digital aviation video camera, comprises undercarriage having shock absorption function, front cover glass, camera lens, filter and image-forming electron unit, for place, photographic subjects section scene to export section image.
With reference to Fig. 2, described image processor 2 is specifically described, described image processor 2 comprises characteristic storing unit 21, prestore section upper limit gray threshold, section lower limit gray threshold and vehicular characteristics data storehouse, in described vehicular characteristics data storehouse, preserve each type of vehicle template image; 22 sections, road division unit, be connected respectively with described aerial camera 1 and described characteristic storing unit 21, receive described section image, the pixel identification of gray-scale value in the image of described section between section upper limit gray threshold and section lower limit gray threshold is formed section target subimage; Vehicle data recognition unit 23, be connected respectively with described pavement section unit 22 and described characteristic storing unit 21, receive described section target subimage, adopt the quantity of all kinds vehicle in section target subimage described in the identification of Hu's moment invariants evaluation algorithm based on described vehicular characteristics data storehouse.
Particularly, in described vehicle data recognition unit 23, the quantity of all kinds vehicle in section target subimage described in the identification of Hu's moment invariants evaluation algorithm is adopted to comprise based on described vehicular characteristics data storehouse, described section target subimage is divided into multiple partial image again containing a vehicle, by seven of partial image again containing a vehicle not bending moment and each type of vehicle template image in described vehicular characteristics data storehouse seven not bending moment compare respectively, until inquire a certain type of vehicle template image, when the difference of seven not bending moments is all in respective predetermined difference value threshold value, then judge that the partial image again containing a vehicle comprises the vehicle of a certain type of vehicle template image corresponding types, described seven not bending moment be the feature of car modal image, there is translation, amplify, reduce and rotate all constant characteristic.
Described estimating system also comprises light fixture, for providing floor light for the shooting of described aerial camera 1 pair of target road section place scene; Luminance sensor, for measuring the brightness data of unmanned plane position.
Described master controller 4 and described twoway radio, described GPS navigation equipment, described unmanned plane power-equipment is connected respectively with described aerial camera 1, the GPS locator data of the target road section received from ground traffic control monitor supervision platform by described twoway radio is sent to described unmanned plane power-equipment and flies to directly over target road section to drive described unmanned plane, when the current unmanned plane GPS locator data that the described GPS navigation equipment received sends is consistent with the GPS locator data of target road section, described aerial camera 1 is driven to perform the shooting of section image, drive described image processor 2 to perform the image procossing of section image simultaneously.
Described master controller 4 is also connected with described vehicle data recognition unit 23 quantity receiving all kinds vehicle, be connected to receive section target subimage with described pavement section unit 22, according to the unmanned plane height that described pressure-altitude sensor 3 exports, calculate section area in the target subimage of section, and based on the quantity of described all kinds vehicle and the road section traffic volume index of described section area reckoning target road section, so that described road section traffic volume index is sent to ground traffic control monitor supervision platform by described twoway radio.
Described master controller 4 is also connected respectively with described light fixture and described luminance sensor, with when described brightness data is less than predetermined luminance threshold value, starts described light fixture to provide floor light.
Described GPS navigation equipment, described unmanned plane power-equipment, described image processor 2, described master controller 4 and described power-supply unit 5 are all positioned at unmanned plane front end panel board, and described light fixture, described luminance sensor, described pressure-altitude sensor 3, described aerial camera 1 and described twoway radio are all positioned on the fuselage of unmanned plane.
Wherein, described image processor 2 can select the digital signal processor DSP of TMS9000 series, described master controller 4 can select the Cortex-A53 processor of Acorn company, it is a synchronous DRAM SDRAM (SynchronousDynamic Random Access Memory) that described characteristic storing unit 21 can be selected, in estimating system of the present invention, can select described GPS navigation equipment, described image processor 2, described master controller 4 and described power-supply unit 5 are integrated on one piece of surface-mounted integrated circuit, described pressure-altitude sensor 3 can be selected to measure unmanned plane height according to the change of unmanned plane position air pressure.
In addition, arm processor is the first item risc microcontroller of Acorn computing machine company limited design.More early be called Acorn RISC Machine.Arm processor itself is 32 designs, but is also equipped with 16 bit instruction collection, saves in general and reaches 35%, but can retain having superiority of 32 systems than 32 codes of equal value.The Jazelle technology of ARM makes Java accelerate to obtain the performance more much higher than the Java Virtual Machine (JVM) based on software, and equal non-Java accelerates nuclear phase than lower power consumption 80%.
Arm processor increases DSP instruction set on cpu function, provides 16 and 32 arithmetic operation capabilities of enhancing, improves performance and dirigibility.The debugging of chip device on the high integration slice that ARM also provides two forward position characteristics to carry out the dark embedded processor of subband, they are embedded ICE-RT logic and the grand core of Embedded Trace (ETMS) series.The product of ARM company after classical processor A RM11 uses Cortex name instead, and is divided into A, R and M tri-class, is intended to for various different market provides service.
Three large features of arm processor are: little power consumption function is strong, 16/32 two instruction set and affiliate numerous.Be in particular in: 1, little, the low-power consumption of volume, low cost, high-performance; 2, support Thumb (16)/ARM (32) two instruction set, energy is compatible 8/16 devices well; 3, use register in a large number, instruction execution speed is faster; 4, most of data manipulation completes all in a register; 5, addressing mode is simple flexibly, and execution efficiency is high; 6, instruction length is fixed
Support two kinds of instruction set in newer architecture of ARM microprocessor: ARM instruction set and Thumb instruction set.Wherein, ARM instruction is the length of 32, and Thumb instruction is 16 bit lengths.Thumb instruction set is the function subset of ARM instruction set, but compared with the ARM code of equivalence, can save the storage space of more than 30% ~ 40%, possesses all advantages of 32 codes simultaneously.
Adopt the road section traffic volume index estimating system measured based on unmanned plane of the present invention, complicated for existing road section traffic volume index estimating system estimation process, need image data amount excessive, calculate coarse technical matters, adopt the mode of unmanned plane test, build a kind of road section traffic volume index estimation platform relying on image procossing, directly aerial photographing is carried out to section image, image procossing and target identification, analysis by means of only a two field picture can obtain the traffic index of target road section, whole computation process desired data collection capacity is less, result of calculation is real-time, accurately, can administer for city traffic management department the urban congestion situation that day by day can't bear and effective data basis is provided.
Be understandable that, although the present invention with preferred embodiment disclose as above, but above-described embodiment and be not used to limit the present invention.For any those of ordinary skill in the art, do not departing under technical solution of the present invention ambit, the technology contents of above-mentioned announcement all can be utilized to make many possible variations and modification to technical solution of the present invention, or be revised as the Equivalent embodiments of equivalent variations.Therefore, every content not departing from technical solution of the present invention, according to technical spirit of the present invention to any simple modification made for any of the above embodiments, equivalent variations and modification, all still belongs in the scope of technical solution of the present invention protection.
Claims (2)
1. the road section traffic volume index estimating system measured based on unmanned plane, it is characterized in that, described estimating system is arranged on unmanned plane, comprise aerial camera, image processor, pressure-altitude sensor and master controller, described aerial camera is connected with described image processor, the section image of shooting is sent to described image processor and performs image procossing, described master controller is connected with described image processor and described pressure-altitude sensor respectively, according to the unmanned plane height that processing result image and the described pressure-altitude sensor of described image processor detect, estimation road section traffic volume index.
2., as claimed in claim 1 based on the road section traffic volume index estimating system that unmanned plane is measured, it is characterized in that, described estimating system also comprises:
Twoway radio, for receiving the control information that traffic control monitor supervision platform in ground sends, and traffic control monitor supervision platform sends measurement data earthward;
GPS navigation equipment, for receiving the GPS locator data that GPS navigation satellite sends;
Unmanned plane power-equipment, to fly to target location for driving unmanned plane;
Described aerial camera is linear array digital aviation video camera, comprises undercarriage having shock absorption function, front cover glass, camera lens, filter and image-forming electron unit, for place, photographic subjects section scene to export section image;
Described image processor also comprises
Characteristic storing unit, has prestored section upper limit gray threshold, section lower limit gray threshold and vehicular characteristics data storehouse, has preserved each type of vehicle template image in described vehicular characteristics data storehouse;
Pavement section unit, be connected respectively with described aerial camera and described characteristic storing unit, receive described section image, the pixel identification of gray-scale value in the image of described section between section upper limit gray threshold and section lower limit gray threshold is formed section target subimage;
Vehicle data recognition unit, be connected respectively with described pavement section unit and described characteristic storing unit, receive described section target subimage, adopt the quantity of all kinds vehicle in section target subimage described in the identification of Hu's moment invariants evaluation algorithm based on described vehicular characteristics data storehouse;
Light fixture, for providing floor light for the shooting of described aerial camera to target road section place scene;
Luminance sensor, for measuring the brightness data of unmanned plane position;
Described master controller and described twoway radio, described GPS navigation equipment, described unmanned plane power-equipment is connected respectively with described aerial camera, the GPS locator data of the target road section received from ground traffic control monitor supervision platform by described twoway radio is sent to described unmanned plane power-equipment and flies to directly over target road section to drive described unmanned plane, when the current unmanned plane GPS locator data that the described GPS navigation equipment received sends is consistent with the GPS locator data of target road section, described aerial camera is driven to perform the shooting of section image, drive described image processor to perform the image procossing of section image simultaneously, described master controller is also connected the quantity to receive all kinds vehicle with described vehicle data recognition unit, be connected to receive section target subimage with described pavement section unit, according to the unmanned plane height that described pressure-altitude sensor exports, calculate section area in the target subimage of section, and based on the quantity of described all kinds vehicle and the road section traffic volume index of described section area reckoning target road section, so that described road section traffic volume index is sent to ground traffic control monitor supervision platform by described twoway radio,
Wherein, described master controller is also connected respectively with described light fixture and described luminance sensor, with when described brightness data is less than predetermined luminance threshold value, starts described light fixture to provide floor light;
Wherein, described GPS navigation equipment, described unmanned plane power-equipment, described image processor and described master controller are all positioned at unmanned plane front end panel board, and described light fixture, described luminance sensor, described pressure-altitude sensor, described aerial camera and described twoway radio are all positioned on the fuselage of unmanned plane;
Wherein, the quantity of all kinds vehicle in section target subimage described in the identification of Hu's moment invariants evaluation algorithm is adopted to comprise based on described vehicular characteristics data storehouse, described section target subimage is divided into multiple partial image again containing a vehicle, by seven of partial image again containing a vehicle not bending moment and each type of vehicle template image in described vehicular characteristics data storehouse seven not bending moment compare respectively, until inquire a certain type of vehicle template image, when the difference of seven not bending moments is all in respective predetermined difference value threshold value, then judge that the partial image again containing a vehicle comprises the vehicle of a certain type of vehicle template image corresponding types, described seven not bending moment be the feature of car modal image, there is translation, amplify, reduce and rotate all constant characteristic,
Described pressure-altitude sensor measures unmanned plane height according to the change of unmanned plane position air pressure.
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Also Published As
| Publication number | Publication date |
|---|---|
| CN104821084B (en) | 2017-01-18 |
| CN104269054A (en) | 2015-01-07 |
| CN104794899A (en) | 2015-07-22 |
| CN104835322B (en) | 2016-03-09 |
| CN104794899B (en) | 2015-12-09 |
| CN104835322A (en) | 2015-08-12 |
| CN104269054B (en) | 2015-10-28 |
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