CN108398384A - A kind of landslide downslide amount parameter remote rapid reconnaissance method based on big data - Google Patents
A kind of landslide downslide amount parameter remote rapid reconnaissance method based on big data Download PDFInfo
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Abstract
The invention belongs to telemetry techniques fields, disclose a kind of landslide downslide amount parameter remote rapid reconnaissance method based on big data, image collecting module, for obtaining relatively close sliding front and back two phase imaging times in landslide, remote sensing image data of the imaging resolution better than 1m and digital altitude data, digital elevation data include high-resolution satellite image stereogram data and the altitude data that airborne laser radar obtains；Data processing module, the data for being acquired to image collecting module carry out analyzing processing；Wireless communication module, for the data information of the processing of data processing module to be sent to wireless base station by wireless, data information is sent to Cloud Server module and is stored and calculated by wireless base station；Display module, for showing the front and back landslide surface number elevation model of sliding and landslide downslide amount parameter.The present invention provides data processing speed；It is more embodied by building 3 D Remote Sensing module to build digital elevation model simultaneously, facilitates exploration.
Description
Technical field
The invention belongs to telemetry techniques field more particularly to a kind of landslide downslide amount parameter remotes based on big data
Rapid reconnaissance method.
Background technology
Landslide refers to the soil body or rock mass on slope, by river degradation, groundwater activities, rainwater immersion, earthquake and people
Work cuts the influence of the factors such as slope, under the effect of gravity, along certain Weak face either weak band integrally or dispersedly along slope
The natural phenomena of slide downward.Rock (soil) body of movement is known as conjugating body or slide mass, the rock that underlies (soil) body not moved are known as
Slider bed.With mankind's activity increasingly frequently, the engineering constructions such as railway, highway, mine, water conservancy, workshop make landslide disaster
Probability increasingly increases.However, when existing telemetry techniques are handled in face of mass data, speed is slow, and efficiency is low；Pass through simultaneously
Remote sensing directly acquires image and is not enough embodied, and is not easy to survey.
In conclusion problem of the existing technology is：When existing telemetry techniques are handled in face of mass data, speed
Degree is slow, and efficiency is low；Image is directly acquired by remote sensing to be not enough embodied, be not easy to survey simultaneously.
Invention content
In view of the problems of the existing technology, the present invention provides a kind of landslide downslide amount parameter remote based on big data
Rapid reconnaissance method.
The invention is realized in this way a kind of landslide downslide amount parameter remote rapid reconnaissance system packet based on big data
It includes：
Image collecting module is connect with data processing module, relatively close for the front and back two phase imaging times of acquisition landslide sliding,
Remote sensing image data of the imaging resolution better than 1m and digital altitude data, digital elevation data include high-resolution satellite image
The altitude data that stereogram data and airborne laser radar obtain；
The specific method is as follows for the carry out rectangle partitioning algorithm of the image collecting module：
Step 1, image transmitting terminal obtain the resolution ratio of screen first, obtain 0~C of range and the row scanning of column scan
0~R of range；
The data of current frame image conservation zone are saved in previous frame image buffering area by step 2, transmitting terminal；It intercepts and captures current
Screen bitmaps data and be stored in current frame image buffering area；
Step 3, transmitting terminal initializes variation rectangular area top left co-ordinate first and bottom right angular coordinate is (0,0), next time
Sweep starting point coordinate is (0,0), and row is unchanged to be identified as true, updates the range of the range and row scanning of column scan；
Step 4 judges whether to be expert in scanning range, not exist, jumps to step 10；
Step 5 judges whether within the scope of column scan, does not exist, and jumps to step 8；Within the scope of column scan using every
Row direct comparison method is detected current sampling point；Value is different, sets the unchanged mark of row to false first, then sentences
Whether disconnected be the first variation sampled point detected, be using sample point coordinate as the top left co-ordinate for changing rectangular area,
It is not first variation sampled point, the coordinate of the coordinate in the rectangle lower right corner and the point relatively and is maximized as new rectangle
Bottom right angular coordinate, then judge whether the sampled point is first variation sampled point of one's own profession, it is that the ordinate just by the sampled point is same
The ordinate in the rectangle upper left corner is compared and is minimized the top left co-ordinate of more new change rectangular area；It is worth identical, needs
Judge that row is unchanged and identifies whether, for false, if it is false, starting point of the record coordinate as scanning next time detects it is most
Latter row sampled point jumps to step 7 using last row sample point coordinate as the starting point of scanning next time；
Row coordinate is moved to right N row, jumps to step 5 and detect next sampled point by step 6；
Step 7, one's own profession detection finish, and the next time of the next sweep starting point coordinate of one's own profession and lastrow record is scanned
Point coordinates compares, and is maximized as new next sweep starting point coordinate, and line number adds 1, jump to step 4 from next line from
Head starts from left to right to detect；
Step 8, judge go it is unchanged identify whether as true and variation rectangular area top left co-ordinate be not (0,0), no
It is true, line number adds 1, jumps to step 4；It is true, then shows that full line without different pixels, has obtained the square of a variation
Shape region unit；Obtained variation rectangular area block upper left corner ordinate be moved to the left N row, lower right corner ordinate move right N row
To include image boundary information；
Step 9 records the variation rectangular area coordinate detected and corresponding next sweep starting point coordinate, judges to work as
The range of preceding column scan whether 0~C and row scanning range whether 0~R, be, setting mark show the variation that current detection goes out
Rectangular area mark detects that then line number adds 1 to jump to step 4 to detect next change since next line for the first time
The rectangular area block of change；Until detecting the range beyond row scanning；
Step 10 after this detection, handles next sweep starting point all in this detection, calculates down
The set of secondary scanning range；The ordinate for first next sweep starting point that this is detected is first checked for whether than last row
The ordinate of sampled point is small, is not, which completes, and detects the ordinate of next next sweep starting point；It is, with first
The abscissa in the secondary variation rectangular area upper left corner detected is abscissa, is scanned relevant next time with currently changing rectangular area
The ordinate of starting point coordinate is ordinate, generates the top left co-ordinate of a next scanning range；With the change detected for the first time
The abscissa for changing the rectangular area lower right corner is abscissa, and a scanning next time model is generated by ordinate of the maximum number of column C of screen
The bottom right angular coordinate enclosed；Then handle second next sweep starting point, until next sweep starting point all in this detection all
It is treated as stopping；
Step 11 detects scanning area all in next scanning range set, is primarily based on next scanning range collection
The width and height of first scanning area in conjunction, the range of raw row scan and column scan repeat step 3 and are examined to step 10
The rectangular area block changed in first scanning area is surveyed, second scanning area is then handled, until next scanning range collection
Until all scanning areas are all detected in conjunction；
Step 12 repeats step 10 to step 11, obtains the variation rectangular area block of scanning range next time, until
The ordinate of all next sweep starting points is greater than or equal to the ordinate of last row sampled point, and entire screen detection finishes；
Step 13 has obtained the not overlapping rectangles for the area minimum that all frame images change relative to previous frame image
The set in region, checks the rectangular area in the set, and two rectangle its upper left corner ordinates are identical with lower right corner ordinate, and
The lower right corner abscissa of one rectangle is adjacent with another rectangle upper left corner abscissa, merges into a rectangle, then recompresses
And the set for the sending rectangular area image data that is included and respective coordinates are to client；
Step 14, image receiving terminal will be based on each rectangular region image data and corresponding seat after the data decompression of reception
Mark is integrated into previous frame image and shows；
Step 15 repeated step 2 every T seconds and arrives step 14, according to difference and the requirements of bandwidth of application scenarios,
It adjusts to interval time T；
Data processing module is connect with image collecting module, wireless communication module, display module, for image collection
The data of module acquisition carry out analyzing processing；
The image combining method of told data processing module has photographic equipment, is obtained respectively by the photographic equipment
The different two images of exposure time, include the following steps：
The short image of the image that time for exposure is grown and time for exposure is respectively labeled as H images and L images by S1；
S2 obtains the YCbCr triple channel components of the H images and L images respectively, and carries out gradient calculating to each component
The triple channel component Grad of the H images and each location of pixels in L images is obtained afterwards；
The ladder of the H images that S3 successively obtains S2 steps and the same location of pixels per same component in L images
Degree is compared and carries out weights modification, obtains H images weight matrix corresponding with L images；Compare for GYH (m, n)
It is compared in the case of identical m, n with GCrL (m, n) with GYL (m, n), GCbH (m, n) and GCbL (m, n), GCrH (m, n),
Wherein, m indicates that the m rows of image H or image L, n indicate the n-th row of image H or image L；When carrying out weights modification, when two
Image gradient difference takes identical weights, as 0.5 when within the 1/3 of greatest gradient difference；Conversely, when gradient difference is more than maximum
Gradient difference 1/3 when, big to Grad weights of the imparting more than 0.5, small weights of the imparting less than 0.5 of Grad；Finally
Obtain the corresponding weight matrix YA (m, n) of two width figures, CbA (m, n), CrA (m, n) and YB (m, n), CbB (m, n), CrB (m, n)；
S4 multiplies the H images and the pixel of each same pixel position of the respective YCbCr triple channel components of L images respectively
With its corresponding weights；
The product that S5 obtains S4 carries out summation process, finally obtains triple channel component and synthesizes new image；
Wireless communication module is connect with data processing module, is wirelessly connected with wireless base station, is used for data processing module
The data information of processing wireless base station is sent to by wireless, data information is sent to Cloud Server mould by wireless base station
Block is stored and is calculated；
The normalization Higher Order Cumulants equation group construction method of the wireless communication module time-frequency overlapped signal includes：
The signal model for receiving signal is expressed as：
R (t)=x1(t)+x2(t)+…+xn(t)+v(t)
Wherein, xi(t) it is each signal component of time-frequency overlapped signal, each component signal is independently uncorrelated, and n is time-frequency weight
The number of folded signal component, θkiIndicate the modulation to each signal component carrier phase, fciFor carrier frequency, AkiBelieve for i-th
Amplitude number at the k moment, TsiFor Baud Length, pi(t) it is raised cosine shaping filter function that rolloff-factor is α, andN (t) is that mean value is 0, variance σ2Stationary white Gaussian noise；
The Higher Order Cumulants formula of mixed signal is as follows：
Both sides simultaneously divided by mixed signal second moment k/2 powers：
It is further deformed into：
WhereinWithIndicate the ratio and noise power of each component signal power and general power and the ratio of general power
Value, is expressed asAnd λv；The Higher Order Cumulants of white Gaussian noise are 0, and above formula is expressed as：
Structure normalization Higher Order Cumulants equation group as a result,：
Display module is connect with data processing module, for showing the front and back landslide surface number elevation model of sliding and cunning
Slope downslide amount parameter.
A kind of landslide downslide amount parameter remote rapid reconnaissance method based on big data includes the following steps：
Step 1 obtains the front and back two phase imaging times of landslide sliding compared with close, imaging resolution is excellent by image collecting module
Remote sensing image data in 1m and digital altitude data, and it is sent to data processing module；
Step 2, data processing module carry out analyzing processing to the data that image collecting module acquires；
The data information that data processing module is handled is sent to wirelessly by step 3, wireless communication module by wireless
Data information is sent to Cloud Server module and is stored and calculated by base station, wireless base station,
Step 4 passes through the front and back landslide surface number elevation model of display module display sliding and landslide downslide amount parameter.
Further, the data processing module includes：Structure 3 D Remote Sensing module, judges mould at landslide boundary division module
Block；
3 D Remote Sensing module is built, for building 3 D Remote Sensing interpretation theme, at two phase remote sensing images of acquisition
Reason, establishes digital elevation model using two issue word altitude datas of acquisition, utilizes two phase digital elevation models and processing respectively
Remote sensing image afterwards builds 3 D Remote Sensing interpretation theme respectively；
Landslide boundary division module, based on 3 D Remote Sensing interpretation theme before sliding, establishes landslide for delimiting landslide boundary
Interpretation mark delimit landslide boundary and extracts landslide boundary elevation；
Whether judgment module completely disengages in landslide boundary for slip mass after differentiating sliding, if not completely disengaging entrance
It obtains slip mass rear and cuts outlet information, the front and back surface number elevation model that comes down of extraction sliding is gone to if completely disengaging.
Further, the Cloud Server module includes：Data memory module, big data computing module；
Data memory module, for being stored to data processing module Law of DEM Data；
Big data computing module carries out landslide downslide amount parameter for gathering network computing resource timely.
Advantages of the present invention and good effect are：The present invention can be with by Cloud Server mould big data computing module in the block
Gathering network is so computing resource provides data processing speed to remote sensing image progress data processing；It is three-dimensional by building simultaneously
Remote sensing module is more embodied to build digital elevation model, facilitates exploration.
Description of the drawings
Fig. 1 is that the present invention implements the landslide downslide amount parameter remote rapid reconnaissance method flow based on big data provided
Figure.
Fig. 2 is that the present invention implements the landslide downslide amount parameter remote rapid reconnaissance system structure frame based on big data provided
Figure.
In Fig. 2：1, image collecting module；2, data processing module；3, wireless communication module；4, display module；5, wireless
Base station；6, Cloud Server module.
Specific implementation mode
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
Limit the present invention.
Below in conjunction with the accompanying drawings and specific embodiment is further described the application principle of the present invention.
As shown in Figure 1, the landslide downslide amount parameter remote rapid reconnaissance side provided in an embodiment of the present invention based on big data
Method includes the following steps：
Step S101, the relatively close, imaging resolution by the front and back two phase imaging times of image collecting module acquisition landslide sliding
Remote sensing image data better than 1m and digital altitude data, and it is sent to data processing module；
Step S102, data processing module carry out analyzing processing to the data that image collecting module acquires；
The data information that data processing module is handled is sent to nothing by step S103, wireless communication module by wireless
Data information is sent to Cloud Server module and is stored and calculated by line base station, wireless base station,
Step S104 passes through the front and back landslide surface number elevation model of display module display sliding and landslide downslide amount ginseng
Number.
As shown in Fig. 2, the landslide downslide amount parameter remote rapid reconnaissance system provided by the invention based on big data includes：
Image collecting module 1, data processing module 2, wireless communication module 3, display module 4, wireless base station 5, Cloud Server module 6.
Image collecting module 1 is connect with data processing module 2, for obtain the front and back two phase imaging times of landslide sliding compared with
Closely, remote sensing image data of the imaging resolution better than 1m and digital altitude data, digital elevation data include high-resolution satellite
The altitude data that view stereoscopic picture obtains data and airborne laser radar；
Data processing module 2 is connect with image collecting module 1, wireless communication module 3, display module 4, for image
The data that acquisition module 1 acquires carry out analyzing processing；
Wireless communication module 3 is connect with data processing module 2, is wirelessly connected with wireless base station 5, is used for data processing
The data information of the processing of module 2 is sent to wireless base station 5 by wireless, and data information is sent to cloud by wireless base station 5
Server module 6 is stored and is calculated；
Display module 4 is connect with data processing module 2, for show the front and back landslide surface number elevation model of sliding and
Come down downslide amount parameter.
Data processing module 2 provided by the invention includes：It builds 3 D Remote Sensing module, landslide boundary division module, judge
Module；
3 D Remote Sensing module is built, for building 3 D Remote Sensing interpretation theme, at two phase remote sensing images of acquisition
Reason, establishes digital elevation model using two issue word altitude datas of acquisition, utilizes two phase digital elevation models and processing respectively
Remote sensing image afterwards builds 3 D Remote Sensing interpretation theme respectively；
Landslide boundary division module, based on 3 D Remote Sensing interpretation theme before sliding, establishes landslide for delimiting landslide boundary
Interpretation mark delimit landslide boundary and extracts landslide boundary elevation；
Whether judgment module completely disengages in landslide boundary for slip mass after differentiating sliding, if not completely disengaging entrance
It obtains slip mass rear and cuts outlet information, the front and back surface number elevation model that comes down of extraction sliding is gone to if completely disengaging.
Cloud Server module 6 provided by the invention includes：Data memory module, big data computing module；
Data memory module, for being stored to data processing module Law of DEM Data；
Big data computing module carries out landslide downslide amount parameter for gathering network computing resource timely.
Image collecting module is connect with data processing module, relatively close for the front and back two phase imaging times of acquisition landslide sliding,
Remote sensing image data of the imaging resolution better than 1m and digital altitude data, digital elevation data include high-resolution satellite image
The altitude data that stereogram data and airborne laser radar obtain；
The specific method is as follows for the carry out rectangle partitioning algorithm of the image collecting module：
Step 1, image transmitting terminal obtain the resolution ratio of screen first, obtain 0~C of range and the row scanning of column scan
0~R of range；
The data of current frame image conservation zone are saved in previous frame image buffering area by step 2, transmitting terminal；It intercepts and captures current
Screen bitmaps data and be stored in current frame image buffering area；
Step 3, transmitting terminal initializes variation rectangular area top left co-ordinate first and bottom right angular coordinate is (0,0), next time
Sweep starting point coordinate is (0,0), and row is unchanged to be identified as true, updates the range of the range and row scanning of column scan；
Step 4 judges whether to be expert in scanning range, not exist, jumps to step 10；
Step 5 judges whether within the scope of column scan, does not exist, and jumps to step 8；Within the scope of column scan using every
Row direct comparison method is detected current sampling point；Value is different, sets the unchanged mark of row to false first, then sentences
Whether disconnected be the first variation sampled point detected, be using sample point coordinate as the top left co-ordinate for changing rectangular area,
It is not first variation sampled point, the coordinate of the coordinate in the rectangle lower right corner and the point relatively and is maximized as new rectangle
Bottom right angular coordinate, then judge whether the sampled point is first variation sampled point of one's own profession, it is that the ordinate just by the sampled point is same
The ordinate in the rectangle upper left corner is compared and is minimized the top left co-ordinate of more new change rectangular area；It is worth identical, needs
Judge that row is unchanged and identifies whether, for false, if it is false, starting point of the record coordinate as scanning next time detects it is most
Latter row sampled point jumps to step 7 using last row sample point coordinate as the starting point of scanning next time；
Row coordinate is moved to right N row, jumps to step 5 and detect next sampled point by step 6；
Step 7, one's own profession detection finish, and the next time of the next sweep starting point coordinate of one's own profession and lastrow record is scanned
Point coordinates compares, and is maximized as new next sweep starting point coordinate, and line number adds 1, jump to step 4 from next line from
Head starts from left to right to detect；
Step 8, judge go it is unchanged identify whether as true and variation rectangular area top left co-ordinate be not (0,0), no
It is true, line number adds 1, jumps to step 4；It is true, then shows that full line without different pixels, has obtained the square of a variation
Shape region unit；Obtained variation rectangular area block upper left corner ordinate be moved to the left N row, lower right corner ordinate move right N row
To include image boundary information；
Step 9 records the variation rectangular area coordinate detected and corresponding next sweep starting point coordinate, judges to work as
The range of preceding column scan whether 0~C and row scanning range whether 0~R, be, setting mark show the variation that current detection goes out
Rectangular area mark detects that then line number adds 1 to jump to step 4 to detect next change since next line for the first time
The rectangular area block of change；Until detecting the range beyond row scanning；
Step 10 after this detection, handles next sweep starting point all in this detection, calculates down
The set of secondary scanning range；The ordinate for first next sweep starting point that this is detected is first checked for whether than last row
The ordinate of sampled point is small, is not, which completes, and detects the ordinate of next next sweep starting point；It is, with first
The abscissa in the secondary variation rectangular area upper left corner detected is abscissa, is scanned relevant next time with currently changing rectangular area
The ordinate of starting point coordinate is ordinate, generates the top left co-ordinate of a next scanning range；With the change detected for the first time
The abscissa for changing the rectangular area lower right corner is abscissa, and a scanning next time model is generated by ordinate of the maximum number of column C of screen
The bottom right angular coordinate enclosed；Then handle second next sweep starting point, until next sweep starting point all in this detection all
It is treated as stopping；
Step 11 detects scanning area all in next scanning range set, is primarily based on next scanning range collection
The width and height of first scanning area in conjunction, the range of raw row scan and column scan repeat step 3 and are examined to step 10
The rectangular area block changed in first scanning area is surveyed, second scanning area is then handled, until next scanning range collection
Until all scanning areas are all detected in conjunction；
Step 12 repeats step 10 to step 11, obtains the variation rectangular area block of scanning range next time, until
The ordinate of all next sweep starting points is greater than or equal to the ordinate of last row sampled point, and entire screen detection finishes；
Step 13 has obtained the not overlapping rectangles for the area minimum that all frame images change relative to previous frame image
The set in region, checks the rectangular area in the set, and two rectangle its upper left corner ordinates are identical with lower right corner ordinate, and
The lower right corner abscissa of one rectangle is adjacent with another rectangle upper left corner abscissa, merges into a rectangle, then recompresses
And the set for the sending rectangular area image data that is included and respective coordinates are to client；
Step 14, image receiving terminal will be based on each rectangular region image data and corresponding seat after the data decompression of reception
Mark is integrated into previous frame image and shows；
Step 15 repeated step 2 every T seconds and arrives step 14, according to difference and the requirements of bandwidth of application scenarios,
It adjusts to interval time T；
The image combining method of told data processing module has photographic equipment, is obtained respectively by the photographic equipment
The different two images of exposure time, include the following steps：
The short image of the image that time for exposure is grown and time for exposure is respectively labeled as H images and L images by S1；
S2 obtains the YCbCr triple channel components of the H images and L images respectively, and carries out gradient calculating to each component
The triple channel component Grad of the H images and each location of pixels in L images is obtained afterwards；
The ladder of the H images that S3 successively obtains S2 steps and the same location of pixels per same component in L images
Degree is compared and carries out weights modification, obtains H images weight matrix corresponding with L images；Compare for GYH (m, n)
It is compared in the case of identical m, n with GCrL (m, n) with GYL (m, n), GCbH (m, n) and GCbL (m, n), GCrH (m, n),
Wherein, m indicates that the m rows of image H or image L, n indicate the n-th row of image H or image L；When carrying out weights modification, when two
Image gradient difference takes identical weights, as 0.5 when within the 1/3 of greatest gradient difference；Conversely, when gradient difference is more than maximum
Gradient difference 1/3 when, big to Grad weights of the imparting more than 0.5, small weights of the imparting less than 0.5 of Grad；Finally
Obtain the corresponding weight matrix YA (m, n) of two width figures, CbA (m, n), CrA (m, n) and YB (m, n), CbB (m, n), CrB (m, n)；
S4 multiplies the H images and the pixel of each same pixel position of the respective YCbCr triple channel components of L images respectively
With its corresponding weights；
The product that S5 obtains S4 carries out summation process, finally obtains triple channel component and synthesizes new image；
The normalization Higher Order Cumulants equation group construction method of the wireless communication module time-frequency overlapped signal includes：
The signal model for receiving signal is expressed as：
R (t)=x1(t)+x2(t)+…+xn(t)+v(t)
Wherein, xi(t) it is each signal component of time-frequency overlapped signal, each component signal is independently uncorrelated, and n is time-frequency weight
The number of folded signal component, θkiIndicate the modulation to each signal component carrier phase, fciFor carrier frequency, AkiBelieve for i-th
Amplitude number at the k moment, TsiFor Baud Length, pi(t) it is raised cosine shaping filter function that rolloff-factor is α, andN (t) is that mean value is 0, variance σ2Stationary white Gaussian noise；
The Higher Order Cumulants formula of mixed signal is as follows：
Both sides simultaneously divided by mixed signal second moment k/2 powers：
It is further deformed into：
WhereinWithIndicate the ratio and noise power of each component signal power and general power and the ratio of general power
Value, is expressed asAnd λv；The Higher Order Cumulants of white Gaussian noise are 0, and above formula is expressed as：
Structure normalization Higher Order Cumulants equation group as a result,：
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
All any modification, equivalent and improvement etc., should all be included in the protection scope of the present invention made by within refreshing and principle.
Claims (4)
1. a kind of landslide downslide amount parameter remote rapid reconnaissance system based on big data, which is characterized in that described based on big number
According to landslide downslide amount parameter remote rapid reconnaissance system include：
Image collecting module is connect with data processing module, for two phase imaging times to be relatively close before and after the sliding of acquisition landslide, are imaged
Remote sensing image data of the resolution ratio better than 1m and digital altitude data, digital elevation data include high-resolution satellite image solid
As the altitude data obtained to data and airborne laser radar；
The specific method is as follows for the carry out rectangle partitioning algorithm of the image collecting module：
Step 1, image transmitting terminal obtain the resolution ratio of screen first, obtain the range 0 of the 0~C of range and row scanning of column scan
~R；
The data of current frame image conservation zone are saved in previous frame image buffering area by step 2, transmitting terminal；Intercept and capture current screen
Curtain bitmap data is simultaneously stored in current frame image buffering area；
Step 3, transmitting terminal initializes variation rectangular area top left co-ordinate first and bottom right angular coordinate is (0,0), scanning next time
Starting point coordinate is (0,0), and row is unchanged to be identified as true, updates the range of the range and row scanning of column scan；
Step 4 judges whether to be expert in scanning range, not exist, jumps to step 10；
Step 5 judges whether within the scope of column scan, does not exist, and jumps to step 8；Using straight every row within the scope of column scan
Comparison method is connect to be detected current sampling point；Value is different, sets the unchanged mark of row to false first, and then judgement is
No is the first variation sampled point detected, is not to be using sample point coordinate as the top left co-ordinate of variation rectangular area
The coordinate of the coordinate in the rectangle lower right corner and the point relatively and is maximized as new rectangle bottom right by first variation sampled point
Angular coordinate, then judge whether the sampled point is first variation sampled point of one's own profession, it is the same rectangle of ordinate just by the sampled point
The ordinate in the upper left corner is compared and is minimized the top left co-ordinate of more new change rectangular area；It is worth identical, needs to judge
Row is unchanged to identify whether detect it is last if it is the starting point that false, record coordinate are scanned as next time for false
Row sampled point jumps to step 7 using last row sample point coordinate as the starting point of scanning next time；
Row coordinate is moved to right N row, jumps to step 5 and detect next sampled point by step 6；
Step 7, one's own profession detection finish, and the next sweep starting point of the next sweep starting point coordinate of one's own profession and lastrow record is sat
Mark compares, and is maximized as new next sweep starting point coordinate, and line number adds 1, jumps to step 4 and is from the beginning opened from next line
Beginning is from left to right detected；
Step 8, judge go it is unchanged identify whether as true and variation rectangular area top left co-ordinate be not (0,0), be not
True, line number add 1, jump to step 4；It is true, then shows that full line without different pixels, has obtained the rectangle of a variation
Region unit；Obtained variation rectangular area block upper left corner ordinate be moved to the left N row, lower right corner ordinate move right N arrange with
Including image boundary information；
Step 9, records the variation rectangular area coordinate detected and corresponding next sweep starting point coordinate, and forefront is worked as in judgement
The range of scanning whether 0~C and row scanning range whether 0~R, be, setting mark show the variation rectangle that current detection goes out
Area identification is to detect for the first time, and then line number adds 1 to jump to step 4 to detect next variation since next line
Rectangular area block；Until detecting the range beyond row scanning；
Step 10 after this detection, handles next sweep starting point all in this detection, calculates and sweep next time
Retouch the set of range；The ordinate for first next sweep starting point that this is detected is first checked for whether than last row sampling
The ordinate of point is small, is not, which completes, and detects the ordinate of next next sweep starting point；It is, to examine for the first time
The abscissa in the variation rectangular area upper left corner measured is abscissa, currently to change the relevant next sweep starting point in rectangular area
The ordinate of coordinate is ordinate, generates the top left co-ordinate of a next scanning range；With the variation square detected for the first time
The abscissa in the shape region lower right corner is abscissa, using the maximum number of column C of screen as one next scanning range of ordinate generation
Bottom right angular coordinate；Then second next sweep starting point is handled, until next sweep starting point all in this detection is all located
Until reason；
Step 11 detects scanning area all in next scanning range set, is primarily based in next scanning range set
The width and height of first scanning area, the range of raw row scan and column scan repeat step 3 and detect the to step 10
The rectangular area block changed in one scanning area then handles second scanning area, until in next scanning range set
Until all scanning areas are all detected；
Step 12 repeats step 10 to step 11, obtains the variation rectangular area block of scanning range next time, until all
The ordinate of next sweep starting point be greater than or equal to the ordinate of last row sampled point, the detection of entire screen finishes；
Step 13 has obtained the not overlapping rectangles region for the area minimum that all frame images change relative to previous frame image
Set, check the rectangular area in the set, two rectangle its upper left corner ordinates are identical with lower right corner ordinate, and one
The lower right corner abscissa of rectangle is adjacent with another rectangle upper left corner abscissa, merges into a rectangle, and then recompression is concurrent
Send image data that the set of rectangular area is included and respective coordinates to client；
Step 14, image receiving terminal will be based on each rectangular region image data after the data decompression of reception and respective coordinates are whole
It is bonded in previous frame image and shows；
Step 15 repeated step 2 every T seconds to step 14, according to the difference of application scenarios and the requirement of bandwidth, between pair
It adjusts every time T；
Data processing module is connect with image collecting module, wireless communication module, display module, for image collecting module
The data of acquisition carry out analyzing processing；
The image combining method of told data processing module has photographic equipment, exposure is obtained respectively by the photographic equipment
The different two images of duration, include the following steps：
The short image of the image that time for exposure is grown and time for exposure is respectively labeled as H images and L images by S1；
S2 obtains the YCbCr triple channel components of the H images and L images respectively, and is obtained after carrying out gradient calculating to each component
To the triple channel component Grad of each location of pixels in the H images and L images；
The gradient of H images that S3 successively obtains S2 steps and the same location of pixels per same component in L images into
It goes relatively and carries out weights modification, obtain H images weight matrix corresponding with L images；Compare for GYH (m, n) and GYL
(m, n), GCbH (m, n) are compared with GCbL (m, n), GCrH (m, n) and GCrL (m, n) in the case of identical m, n, wherein m
Indicate that the m rows of image H or image L, n indicate the n-th row of image H or image L；When carrying out weights modification, when two image ladders
Degree difference takes identical weights, as 0.5 when within the 1/3 of greatest gradient difference；Conversely, when gradient difference is poor more than greatest gradient
1/3 when, big to Grad weights of the imparting more than 0.5, small weights of the imparting less than 0.5 of Grad；Finally obtain two
The corresponding weight matrix YA (m, n) of width figure, CbA (m, n), CrA (m, n) and YB (m, n), CbB (m, n), CrB (m, n)；
The pixel of the H images and each same pixel position of the respective YCbCr triple channel components of L images is multiplied by it by S4 respectively
Corresponding weights；
The product that S5 obtains S4 carries out summation process, finally obtains triple channel component and synthesizes new image；
Wireless communication module is connect with data processing module, is wirelessly connected with wireless base station, is used for the place of data processing module
The data information of reason is sent to wireless base station by wireless, wireless base station by data information be sent to Cloud Server module into
Row is stored and is calculated；
The normalization Higher Order Cumulants equation group construction method of the wireless communication module time-frequency overlapped signal includes：
The signal model for receiving signal is expressed as：
R (t)=x1(t)+x2(t)+…+xn(t)+v(t)
Wherein, xi(t) it is each signal component of time-frequency overlapped signal, each component signal is independently uncorrelated, and n is time-frequency overlapping letter
The number of number component, θkiIndicate the modulation to each signal component carrier phase, fciFor carrier frequency, AkiExist for i-th of signal
The amplitude at k moment, TsiFor Baud Length, pi(t) it is raised cosine shaping filter function that rolloff-factor is α, andN (t) is that mean value is 0, variance σ2Stationary white Gaussian noise；
The Higher Order Cumulants formula of mixed signal is as follows：
Both sides simultaneously divided by mixed signal second moment k/2 powers：
It is further deformed into：
WhereinWithIndicate each component signal power and the ratio and noise power of general power and the ratio of general power, point
It is not expressed asAnd λv；The Higher Order Cumulants of white Gaussian noise are 0, and above formula is expressed as：
Structure normalization Higher Order Cumulants equation group as a result,：
Display module is connect with data processing module, for showing under the front and back landslide surface number elevation model of sliding and landslide
Sliding amount parameter.
2. the landslide downslide amount parameter remote rapid reconnaissance system based on big data as described in claim 1, which is characterized in that
The data processing module includes：Build 3 D Remote Sensing module, landslide boundary division module, judgment module；
Structure 3 D Remote Sensing module is handled two phase remote sensing images of acquisition for building 3 D Remote Sensing interpretation theme, profit
Digital elevation model is established respectively with two issue word altitude datas of acquisition, and using two phase digital elevation models and that treated is distant
Sense image builds 3 D Remote Sensing interpretation theme respectively；
Landslide boundary division module, based on 3 D Remote Sensing interpretation theme before sliding, establishes landslide interpretation for delimiting landslide boundary
Mark delimit landslide boundary and extracts landslide boundary elevation；
Whether judgment module completely disengages in landslide boundary for slip mass after differentiating sliding, if not completely disengaging into acquisition
Slip mass rear and outlet information is cut, the front and back surface number elevation model that comes down of extraction sliding is gone to if completely disengaging.
3. the landslide downslide amount parameter remote rapid reconnaissance system based on big data as described in claim 1, which is characterized in that
The Cloud Server module includes：Data memory module, big data computing module；
Data memory module, for being stored to data processing module Law of DEM Data；
Big data computing module carries out landslide downslide amount parameter for gathering network computing resource timely.
4. a kind of landslide downslide amount parameter remote rapid reconnaissance system based on big data as described in claim 1 is counted based on big
According to landslide downslide amount parameter remote rapid reconnaissance method, which is characterized in that the landslide downslide amount parameter based on big data
Remote sensing rapid reconnaissance method includes the following steps：
Step 1, by the front and back two phase imaging times of image collecting module acquisition landslide sliding, relatively close, imaging resolution is better than 1m
Remote sensing image data and digital altitude data, and be sent to data processing module；
Step 2, data processing module carry out analyzing processing to the data that image collecting module acquires；
The data information that data processing module is handled is sent to wireless base by step 3, wireless communication module by wireless
It stands, data information is sent to Cloud Server module and is stored and calculated by wireless base station,
Step 4 passes through the front and back landslide surface number elevation model of display module display sliding and landslide downslide amount parameter.
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