CN116192242A - Unmanned aerial vehicle low-altitude remote sensing data self-adaptive slicing processing algorithm - Google Patents

Unmanned aerial vehicle low-altitude remote sensing data self-adaptive slicing processing algorithm Download PDF

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CN116192242A
CN116192242A CN202310452690.3A CN202310452690A CN116192242A CN 116192242 A CN116192242 A CN 116192242A CN 202310452690 A CN202310452690 A CN 202310452690A CN 116192242 A CN116192242 A CN 116192242A
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remote sensing
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CN116192242B (en
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周志艳
周子滨
姜锐
何思敏
黄俊浩
罗锡文
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South China Agricultural University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/18502—Airborne stations
    • H04B7/18506—Communications with or from aircraft, i.e. aeronautical mobile service
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/25—Flow control; Congestion control with rate being modified by the source upon detecting a change of network conditions
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/36—Flow control; Congestion control by determining packet size, e.g. maximum transfer unit [MTU]
    • H04L47/365—Dynamic adaptation of the packet size
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract

The invention relates to the technical field of low-altitude remote sensing and slicing processing, in particular to an unmanned plane low-altitude remote sensing data self-adaptive slicing processing algorithm, which comprises the following steps of S1: the acquisition equipment is connected with the server to determine the data transmission rate; s2: determining a data processing rate of the server; s3: calculating the number of acquisition devices allowed to be connected at the same time by a server side; s4: determining the size of a fragmented data block; s5: receiving data in a slicing way, and creating a remote sensing data processing process to process the received data; s6: disconnecting the communication connection between the acquisition equipment and the server, and carrying out residual data block processing and remote sensing data synthesis; the algorithm can dynamically adjust the size of the received fragments, process remote sensing data in real time, effectively shorten the processing time after the data is received, and the fragments are processed in real time to enable the data to be deployed on a map in real time in a fragment mode, so that the purpose of real-time viewing is realized.

Description

Unmanned aerial vehicle low-altitude remote sensing data self-adaptive slicing processing algorithm
Technical Field
The invention relates to the technical field of low-altitude remote sensing and slicing processing, in particular to an unmanned plane low-altitude remote sensing data self-adaptive slicing processing algorithm.
Background
With the development of unmanned aerial vehicle technology, unmanned aerial vehicle low-altitude remote sensing technology has become an effective means for acquiring high-quality and high-resolution remote sensing data. Compared with traditional satellite remote sensing, the unmanned plane low-altitude remote sensing technology has the advantages of strong timeliness, small influence by atmospheric radiation, high spatial resolution, large data volume, good data quality and the like. Unmanned aerial vehicle low-altitude remote sensing technology has been successfully applied to various fields such as mapping, agriculture, urban planning, environmental monitoring and the like.
In the existing unmanned aerial vehicle low-altitude remote sensing operation, data acquired by unmanned aerial vehicle remote sensing data acquisition equipment are usually stored in an onboard memory card, after the operation is completed, the acquired data are transferred to a computer for remote sensing data post-processing, and the whole process is tedious and time-consuming. According to the traditional method, the unmanned plane is remotely monitored in a low-altitude mode for 1000 mu, the image data acquisition amount before splicing can reach 5000 pieces, and the post-processing work such as correction, splicing and analysis is completed by a common graphic workstation, so that more than 5 hours are required.
In the prior art, cases of transmitting and processing low-altitude remote sensing data exist, but most cases belong to processing work after the remote sensing flight operation is finished.
The invention patent application with the application number of CN202111372913.2 discloses a remote sensing data encryption transmission method and a remote sensing data encryption transmission system, which encrypt and upload data to a server in a fragmentation way by utilizing an RSA algorithm to realize the function of uploading remote sensing data, but uses a browser of a front-end computer as an object, and the transmission content is the remote sensing data processed after the flight operation is finished, so that the remote sensing data is encrypted and is not transmitted and processed in the low-altitude remote sensing operation period of an unmanned plane.
The invention patent application with the patent number of CN202211392395.5 discloses a remote sensing data slicing processing method and a terminal, wherein the method of slicing processing is used, remote sensing data is sliced and transmitted to different nodes for processing, and the remote sensing data processing rate is accelerated. However, in the technology, a plurality of servers are adopted to perform remote sensing data distributed processing, the remote sensing data are not segmented according to the actual working condition of the acquisition equipment and the processing speed of the servers, but the remote sensing data are simply generated in an acceleration mode by using the distributed processing of a plurality of node equipment, and the data cannot be transmitted and processed during the low-altitude remote sensing operation of the unmanned plane.
Disclosure of Invention
The invention aims to provide an unmanned plane low-altitude remote sensing data self-adaptive slicing processing algorithm so as to quickly and timely transmit and process remote sensing data during unmanned plane low-altitude remote sensing operation.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the self-adaptive slicing processing algorithm for the low-altitude remote sensing data of the unmanned aerial vehicle comprises the following steps:
s1: the acquisition equipment is connected with the server, and the data transmission rate is determined: communication connection is established between data acquisition equipment and a server of the unmanned plane through a network technology, and single data transmission quantity determined in the acquisition equipment is used for realizing the data transmissiondWith data transmission frequencyf Calculating to obtain the data transmission rate of the acquisition equipmentv ；
S2: determining a data processing rate of the server: after the acquisition equipment is successfully connected with the server, the server starts a simulation calculation process, and the simulation calculation process is performed according to simulation processingxProcessing time of the size of the partitioned data block, and calculating the analog data processing rate of the serverv x The saidx The sizes of the data blocks of the parts and the fragments are respectivelyq、2q、3q、...、xqTo avoid process congestion, the coefficients are adjusted according to the server processing datacAnalog data processing rate of serverv x Minimum value of (2)v min Thereby determining a server data processing ratev' ；
S3: calculating the number of acquisition devices allowed to be connected at the same time by a server: according to server data processing ratev' Data transmission rate of acquisition devicev Calculating the number of acquisition devices allowed to be connected at the same time by the servern The method comprises the steps of carrying out a first treatment on the surface of the Simultaneously calculating data transmission rate of acquisition equipmentv Data processing rate with serverv' Remainder at ratiom If (if)mIs set to be 0, the number of the components is set to be 0,nsubtracting 1; the number of connected acquisition devices of the current serverη< n ThenηAdding 1, and starting to receive data by a server;
s4: determining the size of the fragmented data block: according to the data transmission rate of the acquisition equipmentvServer data processing ratev' Number of connected acquisition devicesηAnd determined software initialization and load timesaObtaining the optimal time length under the current server working conditiont' To maximize server processing performance utilization and minimize fragmented data blocks, the received data block size is increasedsAnd the size of the data block that the server can handles' Equal, optimal time lengtht' Corresponding data block sizesOr (b)s' For optimal tile data block sized' ；
S5: and (3) data slicing receiving, and processing the received data by a newly built remote sensing data processing process: the server receives the data, when the data received by the server buffer area is equal to the current block size of the fragmented datad' When the remote sensing data processing method is used, the currently received data is stored, the data in the buffer area is emptied, a remote sensing data processing process is newly established, and the stored remote sensing data is processed; repeating S5 until the remote sensing data transmission is finished;
s6: and (3) disconnecting communication connection between the acquisition equipment and the server, and carrying out residual data block processing and remote sensing data synthesis: the acquisition equipment sends a request for ending transmission and disconnecting the network connection to the server, the server interrupts communication connection after receiving the request for disconnecting the network connection, and starts a new remote sensing data processing process to process the rest data blocks in the cache; and after the data processing is finished, merging and storing the remote sensing data obtained by all the segmentation processing.
In some embodiments, the remote sensing data processing includes any one or more of cosine correction, data location coordinate calculation, crop growth vector diagram and grid thematic diagram generation.
In some embodiments, the data transmission rate of the acquisition device in S1v The calculation formula is as follows:
Figure SMS_1
in the method, in the process of the invention,dthe unit is a preset single data transmission amount, and the unit is bytes;f is the data transmission frequency in hertz (Hz);v is the data transfer rate of the acquisition device in bytes/second (byte/s).
In some embodiments, the processing rate of each slice of the server in S2 simulates a data blockv x The calculation formula is as follows:
Figure SMS_2
in the method, in the process of the invention,v x is the current sequence number ofxIn bytes/second (bytes/s);t x is the current sequence number ofxAnalog data block processing time of (a);qis the analog data block size;xthe serial number of the analog data block is 1, the data block with serial number not being calculated, x is more than or equal to 2；
The server data processing rate
Figure SMS_3
The calculation formula is as follows: />
Figure SMS_4
In the method, in the process of the invention,c the server processes the data adjustment coefficient;v min is the minimum value of all analog data block processing rates in bytes/second (bytes/s);v' is the server data processing rate in bytes/second (byte/s).
In some embodiments, the server side in S3 allows the number of acquisition devices connected simultaneouslynThe calculation formula of (2) is as follows:
Figure SMS_5
in the method, in the process of the invention,v is the acquisition device data transfer rate in bytes/second (bytes/s);v' is the server data processing rate in bytes/second (bytes/s);n the number of the acquisition devices which are allowed to be connected at the same time by the server side is an integer;
Figure SMS_6
is the remainder of the ratio of the data processing rate of the server to the data transmission rate of the acquisition device, remainder +.>
Figure SMS_7
The calculation formula of (2) is as follows; />
Figure SMS_8
In some embodiments, the data block size received in S4sThe calculation formula is as follows:
Figure SMS_9
in the method, in the process of the invention,sthe unit is bytes;v is the acquisition device data transfer rate in bytes/second (bytes/s);t is the reception time in seconds(s);ηthe number of the current servers connected with the acquisition equipment;
the size of the data block which can be processed by the server in the S4s' The calculation formula is as follows:
Figure SMS_10
in the method, in the process of the invention,s' units are bytes;v' is the data processing rate of the server in bytes/second (byte/s);t is the reception time in seconds(s);
Figure SMS_11
is the preset software initialization and loading timeThe unit is seconds(s).
In some embodiments, in S4, the received data block is sizeds Data block size processable by servers' Equal time oft'The calculation formula is as follows:
Figure SMS_12
in the method, in the process of the invention,t' the time required for equalizing the received data and the processed data is expressed in seconds(s);a the software initialization time is preset, and the unit is seconds(s);v' is the data processing rate of the server in bytes/second (byte/s);v is the acquisition device data transfer rate in bytes/second (byte/s).
The invention has the technical effects and advantages that:
(1) According to the unmanned aerial vehicle low-altitude remote sensing data self-adaptive slicing processing algorithm, the size of a received slicing data block can be adjusted by self-adaptively matching the data transmission rate of the acquisition equipment and the data processing rate of the server, so that the processing performance utilization rate of the server is improved.
(2) According to the unmanned plane low-altitude remote sensing data self-adaptive slicing processing algorithm, the unmanned plane is subjected to data acquisition and data processing simultaneously during operation traveling, the neutral position of a server during equipment data acquisition operation is effectively utilized, and the processing time after data reception is greatly shortened.
(3) According to the unmanned aerial vehicle low-altitude remote sensing data self-adaptive slicing processing algorithm, the slicing real-time processing can enable remote sensing data to be deployed on a map in real time through small pictures, real-time checking is achieved, and remote sensing data acquisition operation is more visual.
Drawings
Fig. 1 is a schematic flow chart of an adaptive slicing processing algorithm for low-altitude remote sensing data of an unmanned aerial vehicle in a specific embodiment.
Fig. 2 is a block diagram of step S3 in an adaptive slicing processing algorithm for low-altitude remote sensing data of an unmanned aerial vehicle in a specific embodimentnIs a flow chart of calculation and judgment of (a)。
Fig. 3 is a flowchart of steps S5 and S6 of data receiving and processing in an adaptive slicing processing algorithm for low-altitude remote sensing data of an unmanned plane in a specific embodiment.
Detailed Description
Preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. There are shown in the drawings, embodiments of the invention which are presently preferred, it being understood, however, that the invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
It should be understood that although the terms "first," "second," "third," etc. may be used herein to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the invention. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Examples
The current technology of network technology is used to realize real-time transmission and processing of unmanned aerial vehicle remote sensing data, which is to process or generate the remote sensing data after the remote sensing unmanned aerial vehicle is operated, and the defects are that: the inability to perform data transmission and processing during the unmanned aerial vehicle low-altitude remote sensing operation results in a longer latency for generating remote sensing data after the acquisition operation is completed.
Aiming at the technical problems, the unmanned aerial vehicle low-altitude remote sensing data self-adaptive slicing processing algorithm disclosed by the embodiment, as shown in fig. 1, comprises the following steps:
s1: the acquisition equipment is connected with the server, and the data transmission rate is determined: communication connection is established between data acquisition equipment and a server of the unmanned plane through a network technology, and single data transmission quantity determined in the acquisition equipment is used for realizing the data transmissiond With data transmission frequencyf Calculating to obtain the data transmission rate of the acquisition equipmentv ；
S2: determining a data processing rate of the server: after the acquisition equipment is successfully connected with the server, the server starts a simulation calculation process, and the simulation calculation process is performed according to simulation processingx Processing time of the size of the partitioned data blocks, and calculating the analog data processing rate of the serverv x The saidx The sizes of the data blocks of the parts and the fragments are respectivelyq、2q、3q、...、xqTo avoid process congestion, the coefficients are adjusted according to the server processing datacAnalog data processing rate of serverv x Minimum value of (2)v min Thereby determining a server data processing ratev' ；
S3: calculating the number of acquisition devices which are allowed to be connected at the same time by a server side: as shown in fig. 2, according to the server data processing ratev' Data transmission rate of acquisition devicev Calculating the number of acquisition devices allowed to be connected at the same time by the servernThe method comprises the steps of carrying out a first treatment on the surface of the Simultaneously calculating data transmission rate of acquisition equipmentvData processing rate with serverv' Remainder at ratiomIf (if)mIs set to be 0, the number of the components is set to be 0,nsubtracting 1; if the current server is connected with the number of the acquisition devicesη<n ThenηAdding 1, and starting to receive data by a server;
s4: determining the size of the fragmented data block: according to the data transmission rate of the acquisition equipmentv Server data processing ratev' Number of connected acquisition devicesηAnd the determined softPart initialization and load timeaObtaining the optimal time length under the current server working conditiont' Maximizing server processing performance utilization and minimizing fragmented data blocks, even if the received data block sizesAnd the size of the data block that the server can handles' Equal, optimal time lengtht' Corresponding data block sizes Or (b)s' For optimal tile data block sized' ；
S5: and (3) data slicing receiving, and processing the received data by a newly built remote sensing data processing process: as shown in FIG. 3, the server receives data when the data received by the server buffer is equal to the current chunk sized' When the remote sensing data processing method is used, the currently received data is stored, the data in the buffer area is emptied, a remote sensing data processing process is newly established, and the stored remote sensing data is processed; repeating S5 until the remote sensing data transmission is finished;
s6: and (3) disconnecting communication connection between the acquisition equipment and the server, and carrying out residual data block processing and remote sensing data synthesis: as shown in fig. 3, the acquisition device sends a request for ending transmission and disconnecting the network connection to the server, and after receiving the request for disconnecting the network connection, the server interrupts the communication connection, starts a new process of remote sensing data processing, and processes the rest data blocks in the cache; and after the data processing is finished, merging and storing the remote sensing data obtained by all the segmentation processing.
In this embodiment, the remote sensing data processing includes any one or more of cosine correction, data position coordinate calculation, crop growth vector diagram and grid thematic diagram generation.
In this embodiment, the data transmission rate of the acquisition device in S1v The calculation formula is as follows:
Figure SMS_13
in the method, in the process of the invention,d the unit is a preset single data transmission amount, and the unit is bytes;f is the data transmission frequency in hertz (Hz);v is the data transfer rate of the acquisition device in bytes/second (byte/s).
In this embodiment, the processing rate of each slice of the server in S2 simulates a data blockv x The calculation formula is as follows:
Figure SMS_14
in the method, in the process of the invention,v x is the current sequence number ofxIn bytes/second (bytes/s);t x is the current sequence number ofxAnalog data block processing time of (a);q is the analog data block size;x is the analog data block sequence number, the data block with sequence number 1 is not calculated,x≥2；
the server data processing rate
Figure SMS_15
The calculation formula is as follows: />
Figure SMS_16
In the method, in the process of the invention,c is the data regulating coefficient processed by the serverv min Is the minimum value of all analog data block processing rates in bytes/second (bytes/s);v' is the server data processing rate in bytes/second (byte/s).
In some embodiments, the server side in S3 allows the number of acquisition devices connected simultaneouslyn The calculation formula of (2) is as follows:
Figure SMS_17
in the method, in the process of the invention,v is the acquisition device data transfer rate in bytes/second (bytes/s);v' is the server data processing rate in bytes/second (bytes/s);n the number of the acquisition devices which are allowed to be connected at the same time by the server side is an integer;
Figure SMS_18
is the remainder of the ratio of the data processing rate of the server to the data transmission rate of the acquisition deviceRemainder->
Figure SMS_19
The calculation formula of (2) is as follows; />
Figure SMS_20
In this embodiment, the size of the data block received in S4s The calculation formula is as follows:
Figure SMS_21
in the method, in the process of the invention,s the unit is bytes;v is the acquisition device data transfer rate in bytes/second (bytes/s);t is the reception time in seconds(s);ηthe number of the current servers connected with the acquisition equipment;
the size of the data block which can be processed by the server in the S4s' The calculation formula is as follows:
Figure SMS_22
in the method, in the process of the invention,s' units are bytes;v' is the data processing rate of the server in bytes/second (byte/s);t is the reception time in seconds(s);
Figure SMS_23
the preset software initialization and loading time is given in seconds(s).
In this embodiment, in S4, the received data block size is set tos Data block size processable by servers' Equal time oft'The calculation formula is as follows:
Figure SMS_24
in the method, in the process of the invention,t' the time required for equalizing the received data and the processed data is expressed in seconds(s);a the software initialization time is preset, and the unit is seconds(s);v' is of a server typeData processing rate in bytes/second (bytes/s);v is the acquisition device data transfer rate in bytes/second (byte/s).
Test examples
To verify the experimental effect of the present invention, the verification is illustratively performed in the following test:
the remote sensing data acquisition operation of the experiment is based on the algorithm of the embodiment 1, wherein the operation site is Qing Yuan city, yangshan county, guangdong province, the operation area is 544.5 mu, and the operation equipment is one remote sensing data acquisition equipment and one Tengxun cloud server; the specific equipment and server parameters are as follows:
the parameters and configuration information of the 4G communication module of the remote sensing data acquisition equipment are shown in table 1:
TABLE 1
Figure SMS_25
The cloud server configuration information is shown in table 2:
TABLE 2
Figure SMS_26
The specific implementation flow is as follows:
s1: the acquisition equipment is connected with the server, and the data transmission rate is determined: communication connection is established between data acquisition equipment and a server of the unmanned plane through a 4G mobile communication technology, and single data transmission quantity determined in the acquisition equipment is used for transmitting data according to the single datadWith data transmission frequencyf Calculating to obtain the data transmission rate of the acquisition equipmentv The method comprises the steps of carrying out a first treatment on the surface of the In this test, the single data transmission amount was determinedd 1,306 b, data transmission frequencyf 20 Hz, calculated according to the formulav Is 26,120 bytes/s,v the calculation formula of (2) is as follows:
Figure SMS_27
s2: determining a data processing rate of the server: after the acquisition equipment of the unmanned plane is successfully connected with the server, the server records the information of the connected equipmentBecause the processing rate of the server is affected by the real-time working condition of the server, in order to ensure that the processing rate of the server is closer to the remote sensing data processing rate of the current working condition of the server when the acquisition equipment is accessed, the server side can perform a calculation process of analog processing once after each connection is successful to recalculate the data processing rate, wherein the logic of the analog calculation process is to simulate the real processing flow, and the data processing rate is calculated by processingxThe data blocks are shown in Table 3 in this test examplex3, the analog data block sizes are respectivelyq、2q、3q，q239,616 b (i.e., 239,616 b, 479,232 b, 718,848 b, respectively, for 3 data), the server records its processing time ast 1 、t 2 、t 3 Then according tov x The calculation formula calculates the data processing rate of the increased portion of the data blockv x . During the operation of the algorithm, in order to prevent the situation of causing the process congestion of the server, it is necessary to ensure that after the operation of the processing program of the last fragment is finished, the processing program of the next fragment is continued to be operated, namely the processing rate of the server in the algorithmv'Must be lower than the actual server processing rate, and therefore, it is necessary to compare all the analog data block processing rates obtained and choose the minimum value among themv min . In this test example, the data block processing rate is simulatedv 2 、v 3 59,904 bytes/s, 79,872 bytes/s, respectively, the smallest of which is selectedv 2 As a means ofv min Then according to the minimum value of the processing ratev min Determined server process data adjustment coefficientscThe processing rate of the server can be calculatedv' ，cThe setting was made to be 0.9,v' is about 53,910 bytes/s,v x and (3) withv' The calculation formulas of (a) are respectively as follows:
Figure SMS_28
s3: meter with a meter bodyThe computing server side allows the number of acquisition devices connected simultaneously: server data processing rate obtained by the above two stepsv' Data transmission ratev Calculating the number of the maximum allowed simultaneous connection devicesn. In order to ensure the normal operation of the server, the situation that the next slice starts when the last slice is not processed does not occur, and the maximum number of the devices which are allowed to be connected simultaneously is calculatednRounding down. To prevent follow-upt'The case where the denominator is 0 occurs in the calculation,mwhen the number of the organic light emitting diode is 0, n minus 1. When (when)
Figure SMS_29
When the performance of the server is smaller than or equal to 0, the current server is limited in performance, and the algorithm is not allowed to be implemented. In the course of the test, the test pieces were mixed,n the result of the calculation of (2),m the calculated result of (2) is not0The number of connected acquisition devices of the current server acquired in real timeηIs set to be 0, the number of the components is set to be 0,η<nallowing the data reception to take place,η1 is added.n and mThe calculation formula of (2) is as follows:
Figure SMS_30
s4: determining the size of the fragmented data block: according to the data transmission rate of the acquisition equipmentv Server data processing ratev' Number of connected acquisition devicesηAnd determined software initialization and load timesaObtaining the optimal time length under the current server working conditiont' In order to make the processing performance utilization rate of the server reach the highest and the partitioned data blocks minimum, the size of the received data blocks is equal to the size of the data blocks which can be processed by the server, and the optimal duration is longt' Corresponding data block sizes Or (b)s' For optimal tile data block sized' The method comprises the steps of carrying out a first treatment on the surface of the In the course of this test, the test piece,v is set to 26,120 b/s,v' is 53, 910 b/s,ηis a number of 1, and is not limited by the specification,ain the case of 6, the number of the components is,t' 11s，And because of
Figure SMS_31
Can obtaind' 287,320 b, where->
Figure SMS_32
The calculation formulas of (a) are respectively as follows:
Figure SMS_33
/>
s5: and (3) data slicing receiving, and processing the received data by a newly built remote sensing data processing process: the server receives the data, when the data received by the server buffer area is equal to the current block size of the fragmented datad' When the remote sensing data processing method is used, the currently received data is stored, the data in the buffer area is emptied, a remote sensing data processing process is newly established, and the stored remote sensing data is processed; and S5, repeating until the remote sensing data transmission is finished. In this experiment, a total of 13 complete splits were received.
S6: and (3) disconnecting communication connection between the acquisition equipment and the server, and carrying out residual data block processing and remote sensing data synthesis: the acquisition equipment sends a request for ending transmission and disconnecting the network connection to the server, the server interrupts communication connection after receiving the request for disconnecting the network connection, and starts a new remote sensing data processing process to process the rest data blocks in the cache; and after the data processing is finished, merging and storing the remote sensing data obtained by all the segmentation processing. In this embodiment, the remaining data in the last cache is shared 270,337 b, which is 18s time consuming from the receipt of the request to disconnect to the end of the algorithm.
The unmanned aerial vehicle low-altitude remote sensing data self-adaptive fragment processing algorithm can dynamically adjust the size of the received fragments according to the processing rate of the server; the remote sensing data can be received and processed in real time, the data processing time is effectively shortened, and the problem that data acquisition and data processing can not be performed simultaneously during the operation advancing period of the unmanned plane in the prior art is solved.
In order to verify that the algorithm shortens the processing time after the data is received, the test also uses the same equipment and uses a conventional processing method to complete the same acquisition operation as a comparison group. The comparison group is different from the self-adaptive slicing processing algorithm in that the processing is concentrated after the data is received. In the comparison group processing method, the processing time after the data is received is 81 s, and the data processing time of the self-adaptive slicing processing algorithm is 18 s.
The relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present application unless it is specifically stated otherwise. Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description. Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but should be considered part of the specification where appropriate. In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
In the description of the present application, it should be understood that, where azimuth terms such as "front, rear, upper, lower, left, right", "transverse, vertical, horizontal", and "top, bottom", etc., indicate azimuth or positional relationships generally based on those shown in the drawings, only for convenience of description and simplification of the description, these azimuth terms do not indicate and imply that the apparatus or elements referred to must have a specific azimuth or be constructed and operated in a specific azimuth, and thus should not be construed as limiting the scope of protection of the present application; the orientation word "inner and outer" refers to inner and outer relative to the contour of the respective component itself.
Spatially relative terms, such as "above … …," "above … …," "upper surface at … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial location relative to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "above" or "over" other devices or structures would then be oriented "below" or "beneath" the other devices or structures. Thus, the exemplary term "above … …" may include both orientations of "above … …" and "below … …". The device may also be positioned in other different ways (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
In addition, the terms "first", "second", etc. are used to define the components, and are merely for convenience of distinguishing the corresponding components, and unless otherwise stated, the terms have no special meaning, and thus should not be construed as limiting the scope of the present application.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by 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 (7)

1. The unmanned aerial vehicle low-altitude remote sensing data self-adaptive slicing processing algorithm is characterized by comprising the following steps of:
s1: the acquisition equipment is connected with the server, and the data transmission rate is determined: communication connection is established between the data acquisition equipment and the server of the unmanned plane through a network technology, and the data transmission quantity d and the data transmission frequency are determined according to single times in the acquisition equipmentfCalculating to obtain the data transmission rate v of the acquisition equipment;
s2: determining a data processing rate of the server: after the acquisition equipment is successfully connected with the server, the server starts a simulation calculation process, and the simulation calculation process is performed according to simulation processingxProcessing time of the size of the data blockCalculating the analog data processing rate of the serverv x The method comprises the steps of carrying out a first treatment on the surface of the The saidxThe sizes of the data blocks of the parts and the fragments are respectivelyq、2q、3q、...、xqAdjusting coefficients based on server processing datacAnalog data processing rate of serverv x Minimum value of (2)v min Thereby determining a server data processing ratev'；
S3: calculating the number of acquisition devices which are allowed to be connected at the same time by a server side: according to server data processing ratev' Data transmission rate of acquisition devicevCalculating the number of acquisition devices allowed to be connected at the same time by the servernThe method comprises the steps of carrying out a first treatment on the surface of the Simultaneously calculating data transmission rate of acquisition equipmentvData processing rate with serverv' Remainder at ratiomIf (if)mIs set to be 0, the number of the components is set to be 0,nsubtracting 1; if the current server is connected with the number of the acquisition devicesη<nThenηAdding 1, and starting to receive data by a server;
s4: determining the size of the fragmented data block: according to the data transmission rate of the acquisition equipmentvServer data processing ratev' Number of connected acquisition devicesηAnd determined software initialization and load timesaObtaining the optimal time length under the current server working conditiont'Sizing received data blockssData block size processable by servers' Equal, optimal time lengtht' Corresponding data block sizesOr (b)s' For optimal tile data block sized'；
S5: and (3) data slicing receiving, and processing the received data by a newly built remote sensing data processing process: the server receives the data, when the data received by the server buffer area is equal to the current block size of the fragmented datad' When the remote sensing data processing method is used, the currently received data is stored, the data in the buffer area is emptied, a remote sensing data processing process is newly established, and the stored remote sensing data is processed; repeating S5 until the remote sensing data transmission is finished;
s6: and (3) disconnecting communication connection between the acquisition equipment and the server, and carrying out residual data block processing and remote sensing data synthesis: the acquisition equipment sends a request for disconnecting the network to the server, and after the server receives the request for disconnecting, the server starts a new remote sensing data processing process to process the rest data blocks in the cache; and after the data processing is finished, merging and storing the remote sensing data obtained by all the segmentation processing.
2. The unmanned aerial vehicle low-altitude remote sensing data adaptive slicing processing algorithm of claim 1, wherein the remote sensing data processing comprises any one or more of cosine correction, data position coordinate calculation, crop growth vector diagram and grid thematic map generation.
3. The unmanned aerial vehicle low altitude remote sensing data adaptive fragmentation processing algorithm according to claim 1, wherein the data transmission rate of the acquisition device in S1vThe calculation formula is as follows:
Figure QLYQS_1
in the method, in the process of the invention,dthe unit is a preset single data transmission amount, and the unit is bytes;fis the data transmission frequency in hertz;vis the data transfer rate of the acquisition device in bytes/second.
4. The unmanned aerial vehicle low-altitude remote sensing data adaptive fragmentation processing algorithm according to claim 1, wherein the processing rate of each fragmented analog data block of the server in S2v x The calculation formula is as follows:
Figure QLYQS_2
in the method, in the process of the invention,v x is the current sequence number ofxIs set in bytes/second;t x is the current sequence number ofxAnalog data block processing time of (a);qis the analog data block size;xis the analog data block sequence number, the data block with sequence number 1 is not calculated,x≥2；/>
the server data processing ratev' The calculation formula is as follows:
Figure QLYQS_3
in the method, in the process of the invention,cthe server processes the data adjustment coefficient;v min is the minimum value of all analog data block processing rates, in bytes/second;v' is the server data processing rate in bytes/second.
5. The unmanned aerial vehicle low-altitude remote sensing data adaptive fragmentation processing algorithm according to claim 1, wherein the server side in S3 allows the number of acquisition devices connected simultaneouslynThe calculation formula of (2) is as follows:
Figure QLYQS_4
in the method, in the process of the invention,vthe data transmission rate of the acquisition equipment is in bytes/second;v' is the server data processing rate in bytes/second;nthe number of the acquisition devices which are allowed to be connected at the same time by the server side is an integer; />
Figure QLYQS_5
The remainder of the ratio of the data processing rate of the server to the data transmission rate of the acquisition equipment is calculated by the following formula: />
Figure QLYQS_6
。
6. The unmanned aerial vehicle low altitude remote sensing data adaptive fragmentation processing algorithm of claim 1, wherein the data block size received in S4sThe calculation formula is as follows:
Figure QLYQS_7
in the method, in the process of the invention,sthe unit is bytes;vthe data transmission rate of the acquisition equipment is in bytes/second;tis the reception time in seconds;ηthe number of the current servers connected with the acquisition equipment;
the size of the data block which can be processed by the server in the S4s' The calculation formula is as follows:
Figure QLYQS_8
in the method, in the process of the invention,s' the unit is bytes;v' is the data processing rate of the server in bytes/second;tis the reception time in seconds; />
Figure QLYQS_9
The software initialization and loading time is preset, and the unit is seconds.
7. The unmanned aerial vehicle low altitude remote sensing data adaptive fragmentation processing algorithm according to claim 1, wherein in S4, the received data block size is set to besData block size processable by servers' Equal time oft' The calculation formula is as follows:
Figure QLYQS_10
in the method, in the process of the invention,t' the time required for equalizing the received data and the processed data is expressed in seconds; />
Figure QLYQS_11
The software initialization time is preset, and the unit is seconds;v' is the data processing rate of the server in bytes/second;vis the acquisition device data transfer rate in bytes/second. />
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