CN115866208B

CN115866208B - Agricultural perception monitoring system and application method thereof

Info

Publication number: CN115866208B
Application number: CN202211580904.7A
Authority: CN
Inventors: 卢述佳; 柳伟林
Original assignee: Shenzhen Haotai Technology Co ltd
Current assignee: Shenzhen Haotai Technology Co ltd
Priority date: 2022-12-09
Filing date: 2022-12-09
Publication date: 2023-09-26
Anticipated expiration: 2042-12-09
Also published as: CN117201738A; CN115866208A; CN117176912A

Abstract

The scheme provides an agricultural perception monitoring system, an image acquisition and distribution method based on a monitoring array, an unmanned aerial vehicle state adjustment method and a multi-view monitoring method based on the monitoring array, and relates to the technical field of agricultural analysis, and the agricultural perception monitoring system comprises the monitoring array, wherein the monitoring array is regularly arranged in a region to be detected, the monitoring array is connected with a back-end computing platform in a communication manner, the monitoring array comprises a plurality of sampling nodes, and the sampling nodes adjust the distribution state relative to the region to be detected according to the live rule of the region to be detected; the back-end computing platform comprises a terminal processor, the back-end computing platform is used for analyzing data acquired by the monitoring array, the back-end computing platform further comprises a site station arranged near the area to be tested, the monitoring array is connected with the site station through a local area network, and the site station is in communication connection with the back-end computing platform. The system has the characteristics of temporary construction and low construction cost, ensures the calculation accuracy and reliability, and can meet the requirement of temporary construction in remote areas economically.

Description

Agricultural perception monitoring system and application method thereof

Technical Field

The invention relates to the technical field of agricultural analysis, in particular to an agricultural perception monitoring system, an image acquisition and distribution method based on a monitoring array, an unmanned aerial vehicle state adjustment method and a multi-view monitoring method based on the monitoring array.

Background

The intelligent agriculture is to apply the Internet of things technology to the traditional agriculture, and the agricultural production is controlled by using the sensor and the software through the mobile platform or the computer platform, so that the traditional agriculture has more intelligence. Except for accurate sensing, control and decision management.

The intelligent agriculture management is applied to a large-area farmland, satellite remote sensing technology is often needed to participate, but the accuracy is poor under many conditions of satellite remote sensing application, accurate management and control and analysis cannot be achieved by adopting relative field images, acquisition, analysis and guidance are carried out on farmland production conditions by establishing monitoring stations of the farmland in some areas, but the cost of the farmland monitoring stations needed by complete intelligent agriculture is higher, the areas with poor economic conditions are difficult to bear, the acquisition accuracy can be reduced by establishing simple monitoring stations, analysis effects are affected, and accurate and high-definition effects are difficult to achieve.

Disclosure of Invention

The application provides an agricultural sensing monitoring system, an image acquisition and distribution method based on a monitoring array, an unmanned aerial vehicle state adjustment method and a multi-view monitoring method based on the monitoring array, wherein the system can be temporarily built, the setting cost is low, the image acquisition and distribution method based on the monitoring array can rapidly and stably acquire images of farmlands to be tested and process the images in batches, the unmanned aerial vehicle state adjustment method can increase the stability of sampling through a control method, the multi-view monitoring method based on the monitoring array can improve the precision of image acquisition, GPS signals are weaker when an area to be tested is in a remote area, and when unmanned aerial vehicles deviate, the unmanned aerial vehicle can perform self-reset through the method.

The technical scheme of the invention is as follows:

an agricultural perception monitoring system comprises a monitoring array which is regularly arranged in a region to be detected, wherein the monitoring array is in communication connection with a back-end computing platform,

the monitoring array comprises a plurality of sampling nodes, the sampling nodes adjust the distribution state of the area to be tested according to the live rule of the area to be tested, each sampling node comprises a power supply source, a node processing module, a camera module, a node memory and a node signal receiving and transmitting module, the camera module, the node memory and the node memory are connected with the node processing module, and the power supply source is in power supply connection with each module in the sampling nodes;

the back-end computing platform comprises a terminal processor, the back-end processor is connected with a back-end storage unit in a communication way, the back-end computing platform is used for analyzing the data acquired by the monitoring array,

the system comprises a ground storage, a ground processing module, a ground memory and a ground storage, wherein the ground storage is connected with the ground processing module, the ground station is arranged near an area to be detected, the ground station comprises a ground signal transceiver module, the node signal transceiver module of the monitoring array is connected with the ground signal transceiver module of the ground station through a local area network, and the ground station is in communication connection with the rear-end computing platform;

Each sampling node comprises a dynamic task distribution module and an image task primary processing module, the dynamic task distribution module is in control connection with the camera module, the image task primary processing module is in communication connection with the dynamic task distribution module and the camera module, the camera module transmits collected original image data to the image task primary processing module, the image task primary processing module transmits processed image information to the dynamic task distribution module, the image task primary processing module is respectively connected with other sampling nodes, field stations and back-end computing platforms in the monitoring array by means of the association docking unit, the local area network unit and the Internet unit, the dynamic task distribution module is respectively connected with other sampling nodes and field stations in the monitoring array by means of the association docking unit and the local area network unit,

the field station comprises a macroscopic state task distribution module and an image task secondary processing module, wherein the macroscopic state task distribution module is used for adjusting the whole visual field of the monitoring array, and the macroscopic state task distribution module and the image task secondary processing module are connected with the sampling node by virtue of the field signal receiving and transmitting module.

An image acquisition and distribution method based on a monitoring array, which is based on an agricultural perception monitoring system,

establishing basic data information, obtaining satellite photos and DEM data by a rear-end computing platform in butt joint with a geographic information system, establishing a topography of an area to be detected, obtaining NDVI data of the area to be detected by satellite remote sensing, and storing the topography and the NDVI data into a platform memory of the rear-end computing platform;

the system is pre-arranged, each sampling node is uniquely coded, a pre-arranged command identification code is arranged according to the code of each sampling node, the code of each sampling node and the pre-arranged command identification code are stored into a platform memory of the back-end computing platform,

the main processing module divides the ground memory into a ground information database, a ground key database, an array information database, an original image database and a secondary image database,

the site signal transceiver module of the site station is used for transmitting the topographic map and NDVI data to the site information base, the site signal transceiver module of the site station is used for transmitting the codes and the pre-command identification codes of each sampling node to the ground key database of the site station,

The node processing module divides the node memory into a command library, a node local information library and an original visual library,

the node processing module in each sampling node inputs the codes and the pre-command identification codes corresponding to the sampling node and the codes and the pre-command identification codes corresponding to the adjacent sampling nodes around to the local information base of the node through external equipment;

the array is arranged in front, the macroscopic state task allocation module is called, the macroscopic matrix distribution mode of the monitoring array is set according to the topographic map and the NDVI data, the arrangement information of the sampling nodes is set, the arrangement information is stored in an array information database of the site station and a platform memory of the rear end computing platform, and the monitoring array is started according to the arrangement information;

the monitoring is carried out, a monitoring array is used for fixedly shooting a selected area, overlapping shooting exists at the shooting juncture between adjacent sampling nodes and is used as an image splicing reference block, the monitoring array is used for sending image data to the field station through the local area network unit, each image is numbered according to codes of the sampling nodes, and the field station is used for storing the image data to an original image database;

The result processing, the site station retrieves the image information from the original image database, applies the image stitching technology to obtain the image data of the whole region to be detected, and stores the integrated image data into a secondary image database;

and outputting a result, performing recognition analysis on the integrated image data, outputting an analysis result through a man-machine interaction unit, calling an original image database and a secondary image database, and transmitting the original image data directly transmitted by a monitoring array and the secondary image data obtained through splicing to the back-end computing platform through the field signal receiving and transmitting module for storage and further image analysis.

The unmanned aerial vehicle state adjustment method is based on an agricultural perception monitoring system, positions of unmanned aerial vehicle monitoring nodes are determined and placed according to GPS, and the unmanned aerial vehicle state adjustment method is characterized in that a wireless signal range finder confirms distances adjacent to the unmanned aerial vehicle monitoring nodes, a node processing module sends ranging results to a physical action analysis module, and the physical action analysis module adjusts unmanned aerial vehicle flight states according to ranging commands.

A multi-view monitoring method based on a monitoring array is based on an agricultural perception monitoring system,

Step one, each sampling node receives image information acquired by adjacent sampling nodes by means of a temporary matrix network and performs coincidence analysis, the node processing module places the obtained image information and the image information sent by adjacent surrounding sampling nodes in a node memory, performs one-level coincidence analysis one by one, extracts coincident images for one-time counting marking, and places the node memory; the overlapping images which are counted for one time are subjected to overlapping ratio analysis to extract the image information which is overlapped with each other, a technical mark is added for one time, and the node memory is stored; the overlapping images which are counted twice are subjected to overlap ratio analysis, the image information which is overlapped again is added with a counting mark, and repeated for mu times until no overlapping image information exists, and the node processing module cuts out the images acquired by the node processing module according to the overlapping times to obtain the independent image information of each area;

step two, taking the interactive sampling node as a whole primary unit, taking the primary unit as a center, receiving two layers of images sent by sampling nodes adjacent to the primary unit by means of a temporary array network, performing coincidence analysis on the counted image fragments obtained in the step one and the two layers of images, extracting the images with coincidence, adding a counting mark once, placing the images in a node memory, and repeating the operation of the step one;

Step three, taking the sampling node interacted with the primary unit image in the step two as a secondary unit, taking the secondary unit as a center, receiving three-layer images sent by sampling nodes adjacent to the secondary unit by means of a temporary array network, carrying out overlap ratio analysis on the counted image fragments obtained in the step two and the three-layer images, and repeating the operation of the step two;

fourthly, performing operation xi times according to the first to third cycles until no overlapping image information is increased, recording overlapping information of each area of the image acquired by the node processing module, storing the overlapping information in an original visual library of the node, and calling the local area network unit to send the overlapping information to the field station;

and fifthly, the node processing module calls the sampling nodes with interaction between the dynamic task allocation module and the temporary array network in a butt joint mode, and performs corresponding multi-order shooting analysis on image fragments with different coincidence degrees according to the counting times.

The working principle and the beneficial effects of the application are as follows:

the monitoring array can be temporarily erected, can complete information acquisition and preliminary processing by matching with a site station and the Internet, is composed of a plurality of sampling nodes, is generally unmanned aerial vehicles, is placed above farmlands in a reasonable rule when farmlands are required to be analyzed, and can be used for continuously shooting and sampling an area to be monitored.

By applying the image acquisition and distribution method of the system, a command can be issued through shooting behaviors of a field station on a plurality of sampling nodes, a temporary network is formed among the sampling nodes, the association degree and cooperation coordination of the sampling nodes can be increased, and information of a farmland to be detected can be stably acquired by means of the monitoring array.

When the unmanned aerial vehicle state adjustment method is applied, the unmanned aerial vehicle monitoring nodes can realize position correction by sensing the positions of surrounding unmanned aerial vehicles when shaking, and when the field to be measured is remote, the position correction of the unmanned aerial vehicle can be realized without depending on GPS and the Internet.

The multi-view monitoring method based on the monitoring array is characterized in that a plurality of monocular cameras are mutually cooperated through a temporary array network to form a whole, multi-view investigation is carried out on an overlapping area, and the accuracy of an observation result is improved.

Drawings

The application will be described in further detail with reference to the drawings and the detailed description.

FIG. 1 is a block diagram of the agricultural perception monitoring system according to the present application;

FIG. 2 is a block diagram of a monitoring array in an agricultural perception monitoring system;

FIG. 3 is a flow chart of an image acquisition and distribution method based on a monitoring array in the application;

FIG. 4 is a block flow diagram of the array front-end arrangement in the monitoring array-based image acquisition and distribution method of the present invention;

FIG. 5 is a block flow diagram of detection execution in the monitoring array-based image acquisition and distribution method of the present invention;

FIG. 6 is a block flow diagram of result analysis in a monitoring array based image acquisition and distribution method of the present invention.

Detailed Description

The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

An agricultural perception monitoring system comprises a monitoring array which is regularly arranged in a region to be detected, the monitoring array is connected with a back-end computing platform in a communication way,

the monitoring array comprises a plurality of sampling nodes, the sampling nodes adjust the distribution state of the area to be tested according to the live rule of the area to be tested, each sampling node comprises a power supply source, a node processing module, a camera module, a node memory and a node signal receiving and transmitting module, wherein the camera module, the node memory and the node memory are connected with the node processing module, and the power supply source is in power supply connection with each module in the sampling nodes;

the field station comprises a field signal transceiver module, the node signal transceiver module of the monitoring array is connected with the field signal transceiver module of the field station by virtue of a local area network, and the field station is in communication connection with a rear-end computing platform.

The traditional agricultural image acquisition method is characterized in that an unmanned aerial vehicle is driven to fly through an area to be detected, a field is photographed when the unmanned aerial vehicle flies in a moving way, the error of the method is large, the image arrangement is difficult, each sampling node is fixed to adopt a pattern of a single area, the monitoring array is in a static state relative to a farmland, the position of each sampling node is fixed, the obtained image is more stable, a field station plays a role in macro management on the monitoring array, and because the operation speed of the monitoring array is limited, the field station can stably store and further analyze the image acquired by the monitoring array, can provide data and operation support for the field station by means of a rear end computing platform connected with a background through a network, and can generally use a wired network or the like.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. The specific names of the functional units and modules are also only used for distinguishing from each other, and are not used for limiting the protection scope of the application. For specific working processes of the units and modules in the system, reference may be made to corresponding processes in the following method embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms. The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

The node signal receiving and transmitting module comprises an internet unit, a local area network unit and an associated docking unit, wherein the sampling nodes are connected with the site station by means of communication of the local area network unit, the internet unit is used for connecting the internet and the back-end computing platform in a communication mode, the associated docking unit is used for forming communication connection with the associated docking units of other sampling nodes in the monitoring array, each sampling node forms a temporary array network by means of the associated docking unit, and the temporary array network is used for monitoring information interaction inside the array.

The system can provide various communication modes, sampling nodes can be directly connected with a back-end computing platform under the condition of good cellular network state, a local area network unit is a communication mode between a site station and each sampling node, information interaction among each sampling node can be realized through an association connection unit, and the association connection unit can use devices such as a wireless network bridge.

Each sampling node comprises an unmanned aerial vehicle monitoring node, the unmanned aerial vehicle monitoring node further comprises a sensing collection module and a physical action analysis module, the sensing collection module is connected with a signal input end of the physical action analysis module through signals, the sensing collection module is used for collecting state information of the unmanned aerial vehicle monitoring node, the physical action analysis module is connected with an unmanned aerial vehicle power controller through signals, the unmanned aerial vehicle power controller is controlled to be connected with an execution motor of the corresponding unmanned aerial vehicle monitoring node, and the physical action analysis module is connected with a site station and other sampling nodes through communication of a node signal transceiver module.

The sensing collection module further comprises a gyroscope, the GPS positioning device is used for positioning requirements of sampling nodes, the sensing collection module further comprises a temperature and humidity sensor, an air pressure sensor and the like, the sensing collection module can collect atmospheric environment conditions in real time and send environment conditions to a site station, environmental information can be collected, meanwhile, weather changes can be judged through data of the sensing module, and the state of the unmanned aerial vehicle detection nodes can be adjusted in real time when problems occur. The physical action analysis module can enable the unmanned aerial vehicle to automatically adjust the flight state of the unmanned aerial vehicle according to the change of the data of the sensing collection module. The dependence on communication equipment is reduced, and the intelligent effect is improved.

The perception collection module comprises a ranging unit arranged at the unmanned aerial vehicle monitoring node, the ranging unit comprises wireless signal rangefinders uniformly arranged at the center of the unmanned aerial vehicle monitoring node, the output ends of the wireless signal rangefinders are connected with the signal input ends of the physical action analysis module in a signal mode, and the wireless signal rangefinders establish identification channels with the ranging units of the adjacent unmanned aerial vehicle monitoring nodes.

The wireless signal range finders of each sampling node are connected with the nearest surrounding nodes, such as a checkerboard rectangular matrix, namely, each node is arranged in the middle of a rectangular grid, an information channel is established between the wireless signal range finders of each unmanned aerial vehicle and the wireless signal range finders of four surrounding nodes, for the hexagonal matrix, each node is arranged in the center of a hexagon, an information channel is established between the wireless signal range finders of each unmanned aerial vehicle and the wireless signal range finders of six surrounding nodes, each node only needs to calculate the distance condition of a local computer, and data are interacted through a temporary array network to further adjust.

Each sampling node comprises a dynamic task distribution module and an image task primary processing module, the dynamic task distribution module is in control connection with a camera module, the image task primary processing module is in communication connection with the dynamic task distribution module and the camera module, the camera module transmits collected original image data to the image task primary processing module, the image task primary processing module transmits processed image information to the dynamic task distribution module, the image task primary processing module is respectively connected with other sampling nodes, a site station and a rear-end computing platform in a monitoring array by means of an associated docking unit, a local area network unit and an Internet unit, the dynamic task distribution module is respectively connected with other sampling nodes and the site station in the monitoring array by means of the associated docking unit and the local area network unit, the site station comprises a macroscopic state task distribution module and an image task secondary processing module, the macroscopic state task distribution module is used for adjusting the overall arrangement mode of the monitoring array, and the macroscopic state task distribution module and the image task secondary processing module are connected with the sampling nodes by means of a site signal receiving and transmitting module.

The image task primary processing module is directly connected with the image pickup module and is used for receiving image data acquired by the image pickup module, so that an operation flow can be reduced, original image information is compressed and an original image after compression is transferred to a node memory, the compressed image is convenient for information interaction, the physical action analysis module can timely adjust shooting according to data of the image task primary processing module, and the blocking processing can reduce operation pressure to meet preliminary analysis of the image and reduce hardware pressure of cooperation operation of a monitoring array. The macro state task allocation module in the field station is used for adjusting the arrangement state of the monitoring array and is connected with man-machine interaction equipment, an operator can see the image information shot by each sampling node, and the suggestion of the view field and the angle of the camera module is provided by taking the image definition and the overlap ratio as indexes, and the macro state task allocation module can call the prepositive command identification code of the corresponding sampling node and send a command to the dynamic task allocation module of the corresponding node to make corresponding adjustment.

The functional units in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units. The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, and the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a USB flash disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier wave signal, a telecommunication signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.

the back-end computing platform is connected with the geographic information system, and after authorization, the data can be directly downloaded for reference, and the data of the geographic information system is used as a basic reference of a subsequent model.

The system is pre-arranged, uniquely codes each sampling node, and sets a pre-order identification code according to the code of each sampling node, the code of each sampling node and the pre-order identification code are stored in a platform memory of a back-end computing platform, a main processing module divides the ground memory into a field information base, a ground key database, an array information database, an original image database and a secondary image database, a field signal transceiver module of a field station is used for receiving topographic map and NDVI data and storing the topographic map and NDVI data in the field information base, a field signal transceiver module of the field station is used for receiving the code of each sampling node and the pre-order identification code and storing the codes in the ground key database of the field station,

Setting a switch for confirming a receiving command as a sampling node, namely a starting command, by uniquely naming each sampling node through codes and giving a pre-command identification code according to each code; meanwhile, the pre-command identification code is also used as a recognition mark of a site station or other nodes in the monitoring array for a specific node, and the unique code of the machine can be used as the pre-command identification code or the unique code can be encrypted regularly.

The field information base is used for storing information such as geographical information data, topographic map, NDVI data, annual climate conditions, crop planting types, crop yield, disaster conditions and the like of the farmland to be detected by the module; the ground key database is used for placing the coding information and the preposed command identification code of each sampling node, and can also be used for placing the unique code of each machine and the conversion rule of the preposed command identification code; the array information data are used for storing the serial numbers of the corresponding machines of each node in the arranged monitoring array and the arrangement method of the monitoring array. The original image database is used for storing original image data directly sent by the monitoring array and compressed image data; the secondary image database is used for storing the processed whole farmland image information.

The node processing module divides the node memory into a command library, a node local information library and an original visual library, and can be manually input or scanned code input through external equipment such as a man-machine interaction module, and the node processing module in each sampling node inputs codes and a front command identification code corresponding to the sampling node and codes and front command identification codes corresponding to adjacent nearest sampling nodes around the sampling node into the node local information library. The command library is used for storing a command program for controlling the actions of the sampling nodes, and the command library comprises actions of adaptively adjusting the cameras and the sampling nodes according to the collected images and the data of the sensibility collection module and is used for analyzing the actions sent by the site station.

And (3) preparing acquisition and analysis, connecting a back-end computing platform and a site station, and establishing connection among the back-end computing platform, the site station and the monitoring array.

The method comprises the steps of pre-setting an array, calling a macroscopic state task allocation module, setting a macroscopic matrix distribution mode of a monitoring array according to a topographic map and NDVI data, setting arrangement information of sampling nodes, storing the arrangement information in an array information database of a site station and a platform memory of a rear-end computing platform, and starting the monitoring array according to the arrangement information;

According to the arrangement mode of the monitoring array, sampling nodes can be horizontally arranged or arranged at equal altitude, the arrangement density of the sampling nodes is in direct proportion to the NDVI value, and operation can be carried out by a producer according to experience and actual farmland conditions, manual setting is carried out, and the sampling nodes are erected according to arrangement information. The arrangement mode is generally in the shape of a chessboard grid distribution or a hexagon grid distribution, the chessboard grid distribution is the most concise, the adjustment is easier, the requirement on the contact ratio of adjacent images is the lowest, the use of sampling nodes can be reduced, the method is suitable for large-area plain use, and the field of view area can be increased. The sampling of hexagonal grid distribution is more compact, the contact ratio is higher, the method is more suitable for being used under the conditions of more roads and rivers, triangular distribution can also be used, the coverage rate is high, more multi-view shooting and acquisition exist, errors can be avoided in a larger range, the pursuit of fineness is realized, and the method is suitable for being used in areas with high vegetation coverage rate and the like.

The monitoring is carried out, the monitoring array is used for fixedly shooting a selected area, adjacent sampling nodes are used as image splicing reference blocks when overlapping shooting exists at shooting junctions, the monitoring array is used for sending image data to a field station through a local area network unit, each image is numbered according to codes of the sampling nodes, and the field station stores the image data to an original image database;

During monitoring, each sampling node analyzes the selected agricultural area, takes the superposition position as a reference area and establishes a beacon, adjusts the size of the compressed image according to the actual level of the sampling node, performs superposition degree comparison and multi-order analysis on the sampling area of the temporary array network and other sampling nodes, sends the sampled image information to a field station, and further processes and processes the image by the field station.

the field station combines the images according to the arrangement mode of the sampling nodes, so that the dependence on the overlapping rate can be effectively reduced, the overlapping rate of the traditional image splicing identification needs to be more than 70%, the relation between the images is obtained according to the overlapping rate, and the overlapping rate is more than 40% by applying the scheme.

And outputting a result, carrying out recognition analysis on the integrated image data, outputting an analysis result through a man-machine interaction unit, calling an original image database and a secondary image database, and transmitting the original image data directly transmitted by the monitoring array and the secondary image data obtained through splicing to a rear-end computing platform through a field signal receiving and transmitting module for storage and further image analysis.

The spliced images are identified by using a multi-view camera shooting algorithm, most areas except the edge positions of the acting farmland are overlapped and shot, so that multi-view operation can be performed on the obtained secondary images, acquired secondary image information is transmitted to a rear-end computing platform, and the rear-end computing platform calls information data such as insect diseases, climate adjustment and the like in a rear-end database to identify and analyze the acquired images.

The array pre-placement step includes the steps of,

s3-1, a rear-end computing platform sets a macroscopic matrix distribution mode of a monitoring array according to a topographic map and NDVI data, stores the macroscopic matrix distribution mode into a rear-end memory, sends the macroscopic matrix distribution mode to a site station through the Internet, and a main processing module stores the macroscopic matrix distribution mode into an array information database;

s3-2, the main processing module calls topographic map data from a field information base, extracts the coding information of each node from a ground key database, calls a macro matrix distribution mode of a monitoring array from an array information database, places the information in a ground memory, takes the topographic map data as the bottom layer of the macro matrix distribution mode of the monitoring array, performs node sampling in a distribution map of the matrix according to the topographic map data to obtain the geographic coordinates of each node, distributes a geographic coordinate to each node along with a corresponding code, and accordingly obtains distribution information data of the codes of the sampling nodes on the topographic map, and stores the respective information data of the codes of the sampling nodes on the topographic map into the array information database;

S3-3, the main processing module extracts the pre-command identification code information from the ground key database to the ground memory, distributes the pre-command identification code to the coordinate information corresponding to the corresponding sampling node so that the pre-command identification code and the coordinate information correspond to each other one by one, and invokes the site signal transceiver module to send the coordinate data with the pre-command identification code to the monitoring array;

s3-4, the node processing module in each sampling node calls the preposed command identification code in the node local information base to the memory, the node processing module calls the local area network unit to receive the coordinate data sent by the S3-3 to the memory, the coordinate data is matched with the preposed command identification code to obtain the geographic coordinate information corresponding to the node, and the node processing module stores the obtained geographic coordinate information in the node local information base to obtain the spatial distribution position of each sampling node in the monitoring array.

The step of monitoring and executing includes the steps of,

s4-1, a node processing module in each sampling node calls a lens control module, the wide angle of the camera module is adjusted, the overlapping ratio of adjacent images is larger than a set value, and the lens control module sends a command of shooting frequency to the camera module;

S4-2, the node processing module copies the image information acquired by the camera module into double, one copy is stored into an original visual library, the other copy is directly placed in a node memory, the node processing module calls an image compression algorithm of the image task primary processing module to the memory, original image data in the node memory is compressed according to the image compression algorithm, the node processing module calls the front command identification codes of a local sampling node and an adjacent sampling node in the node local information library to be placed in the node memory, and in the scheme, the steps of image tasks and image extraction by the processing module can be omitted, so that the calculation flow is simplified;

s4-3, the node processing module arranges the prepositive command identification codes of adjacent sampling nodes in a OR relationship and takes the prepositive command identification codes as the image information to be sent, the node processing module sends the compressed image information to N nearest sampling nodes around by means of a temporary array network,

s4-4, the node processing module attaches the original image information with the corresponding prepositive command identification code of the sampling node, sends the original image information to a site through the local area network unit, the main processing module calls the site signal receiving and sending module to receive the image information with the prepositive command identification code, and the main processing module stores the image information with the prepositive command identification code into an original image database;

S4-5, the node processing module performs coincidence contrast on the compressed image data sent by the nearest N sampling nodes around and received by the association docking unit, and calls the dynamic task allocation module to adjust the wide angle range of the lens according to the coincidence contrast result.

Wherein S4-5 comprises S4-5-1, the node processing module calls the position information of the adjacent sampling node in the node local information base and the front order identification code to be placed in the node memory, the node processing module calls the coincidence judgment algorithm information of the image task first-level processing module to be placed in the node memory,

s4-5-2, selecting a received compressed image by the image task first-stage processing module, changing the size of the local image in equal proportion according to the level difference between the local image and a sampling node corresponding to the compressed image, obtaining the position of the sampling node corresponding to the image according to a preposed command identification code of the compressed image, translating the image acquired by the local image to the direction of the sampling node corresponding to the comparison image according to an image scale, calculating the proportion of the area of the overlapping area to the image acquired by the local image when the reference beacons overlap, and judging whether the overlapping area is between overlapping reference thresholds;

S4-5-3, if yes, the wide angle information in the direction is used as normal data record, otherwise, if the contact ratio is larger than the contact ratio reference threshold, the wide angle in the direction is recorded to be too large, if the contact ratio is smaller than the contact ratio reference threshold, the wide angle in the direction is recorded to be too small, the next sampling node is executed to send compressed images, and the S4-5-2 steps are repeated; if all are normal, skipping the step S4-5-4;

s4-5-4, weighting the coincidence degree of each sampling node by the dynamic task allocation module according to the ratio of the recording result and the coincidence degree in the S4-5-3, obtaining the adjustment degree of the wide angle of the camera module according to the weighting result, and commanding the camera module to adjust. The method comprises the steps of connecting adjacent sampling nodes by taking the positive north n and the positive west w of the nodes as two dimensions for calculating the coincidence degree, determining the number x of the sampling nodes, wherein the weight of each sampling node is 1/x, the cosine value multiplication weight value of a node connecting line and the calculated dimension is the weight of a certain adjacent sampling node on the selected dimension, multiplying the node connecting line and the coincidence degree of two images to obtain the coincidence degree weight of the adjacent sampling node, adding absolute values of the coincidence degree weights of all the sampling nodes to obtain a view coincidence degree calculation result of the selected sampling node, comparing the result with a coincidence degree threshold value, and adjusting the wide angle of the camera module. If the phase difference is serious, the camera shooting mode can be adjusted by manually selecting a corresponding sampling node to send command information to control a dynamic task allocation module of the sampling node through a macroscopic state task allocation module of the field station.

When the coincidence degree is calculated, the sizes of the images need to be adjusted, so that the images selected by sampling nodes with large wide angles or higher horizontal heights of each image in the same coordinate system need to be amplified by an image task and a processing module, and the sizes of the reference objects or beacons and the central sampling nodes are coincident. Similarly, if the level is low, it is necessary to reduce the image and store the scaled image in the original visual library.

The processing of the results may include the steps of,

s5-1, a main processing module calls a matrix filling algorithm of an image secondary processing module to be placed in a ground memory, the main processing module calls arrangement information of a monitoring array in an array information database to be placed in the ground memory, the matrix filling algorithm establishes a matrix frame matched with the arrangement rule of the monitoring array, a region to be filled exists in a node of the matrix frame, the main processing module calls a prepositive command identification code of each sampling node in a ground key database to be placed in the memory, and the image secondary processing module sets a corresponding prepositive command identification code in a corresponding region to be filled according to the matrix frame established in the S5-1;

s5-2, the image secondary processing module matches the preposed command identification code of the area to be filled with the preposed command identification code of each piece of image information, so that the images are filled in the matrix frame;

S5-3, the main processing module calls a coincidence degree processing algorithm of the image secondary processing module, marks the overlapping layer number of the overlapping areas of the adjacent image information, obtains complete site information, and stores the site information in a secondary image database.

By the method, a frame of an image can be established firstly, the frame corresponds to a corresponding sampling node at each position to be filled, the image of each sampling node is reasonably scaled according to the preposed command identification code and placed at the corresponding position in the frame, the superposition times of each area are calculated to obtain the superposition condition of each part in the secondary image information processed by the field station, and the sampling nodes involved in each superposition are recorded, so that the subsequent multi-view analysis is convenient.

And establishing a beacon used for ensuring proper coincidence ratio and delay of sampling of each sampling node in the monitoring array between the prepositive setting and the monitoring execution steps of the array, and placing image information which can be applied to periodically change at a fixed frequency at the juncture position of every two sampling nodes according to the macroscopic matrix distribution mode of the monitoring array.

The beacons release different optical signals at fixed frequency, the simplest can be two or numbers with different colors, so that the change rule of the beacons can be written into the local information base of the field station and the sampling nodes in advance by releasing the light with gradual change, the sampling nodes are used for comparing and distinguishing the acquisition of the beacons with other sampling nodes through a temporary array network, the image task first-stage processing module is used for comparing the acquired beacon information with the beacon information in the images of the other sampling nodes, and the shooting frequency is adjusted.

The unmanned aerial vehicle state adjustment method is based on an agricultural perception monitoring system, positions of unmanned aerial vehicle monitoring nodes are determined and placed according to GPS, a wireless signal range finder confirms distances between adjacent unmanned aerial vehicle monitoring nodes, a node processing module sends ranging results to a physical action analysis module, and the physical action analysis module adjusts the flight state of an unmanned aerial vehicle according to ranging commands.

In a general scene, the unmanned aerial vehicle is positioned by means of GPS, but GPS signals at some mountain areas are weak and poor in reliability, so that timely correction of node positions is realized through a temporary array network, and the position change of sampling nodes is judged according to acquired distance information through a ranging unit in sensing combination, which is generally a wireless signal range finder, an infrared or ultrasonic range finder and the like, so that the unmanned aerial vehicle is adaptively adjusted. The distance change data can also be transmitted to the field station through the local area network unit, and an operator can send an instruction to the sampling node in real time according to the distance information.

step one, each sampling node receives image information acquired by adjacent sampling nodes by means of a temporary matrix network, performs coincidence analysis, a node processing module places the obtained image information and the image information sent by adjacent surrounding sampling nodes in a node memory, performs one-level coincidence analysis one by one, extracts coincident images for one-time counting marking, and places the node memory; the overlapping images which are counted for one time are subjected to overlapping ratio analysis to extract the image information which is overlapped with each other, a technical mark is added for one time, and node memories are stored; the overlapping images which are counted twice are subjected to overlapping ratio analysis, the image information which is overlapped again is added with a counting mark, and repeated for mu times until no overlapping image information exists, and the node processing module cuts out the images acquired by the machine and obtains the independent image information of each area according to the overlapping times;

Step two, taking the interactive sampling node as a whole primary unit, taking the primary unit as a center, receiving two layers of images sent by sampling nodes adjacent to the primary unit by means of a temporary array network, performing coincidence analysis on the counted image fragments obtained in the step one and the two layers of images, extracting the images with coincidence, adding a counting mark once, placing the images in a node memory, and repeating the step one operation;

fourthly, performing operation xi times according to the first to third cycles until no overlapping image information is increased, recording overlapping information of each area of the image acquired by the node processing module, storing the overlapping information in an original visual library of the node, and calling a local area network unit to send the overlapping information to a site station;

and fifthly, the node processing module calls the dynamic task allocation module to butt joint the sampling nodes with interaction with the temporary array network, and performs corresponding multi-order shooting analysis on the image fragments with different coincidence degrees according to the counting times.

And marking the overlapping times of the overlapping region, and recording the codes of the related sampling nodes in each technical process, so that the selection of the nodes in multi-view imaging is facilitated.

The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.

Claims

1. An agricultural perception monitoring system is characterized by comprising a monitoring array which is regularly arranged in a region to be detected, wherein the monitoring array is in communication connection with a back-end computing platform,

the node signal receiving and transmitting module comprises an internet unit, a local area network unit and an associated docking unit, wherein the sampling nodes are in communication connection with the field station by means of the local area network unit, the internet unit is used for connecting the internet and in communication connection with the back-end computing platform, the associated docking unit is used for forming communication connection with the associated docking units of other sampling nodes in the monitoring array, each sampling node forms a temporary array network by means of the associated docking unit, and the temporary array network is used for monitoring information interaction in the array;

2. The agricultural perception monitoring system according to claim 1, wherein each of the sampling nodes comprises an unmanned aerial vehicle monitoring node, the unmanned aerial vehicle monitoring node further comprises a perception collection module and a physical action analysis module, the perception collection module is in signal connection with the signal input end of the physical action analysis module, the perception collection module is used for collecting state information of the unmanned aerial vehicle monitoring node, the physical action analysis module is in signal connection with an unmanned aerial vehicle power controller, the unmanned aerial vehicle power controller controls an execution motor connected with the corresponding unmanned aerial vehicle monitoring node, and the physical action analysis module is also in communication connection with the site station and other sampling nodes by means of the node signal transceiver module.

3. The agricultural perception monitoring system according to claim 2, wherein the perception collection module comprises a ranging unit arranged at the unmanned aerial vehicle monitoring node, the ranging unit comprises a wireless signal range finder uniformly arranged at the center of the unmanned aerial vehicle monitoring node, an output end of the wireless signal range finder is in signal connection with a signal input end of the physical action analysis module, and the wireless signal range finder establishes an identification channel with a ranging unit of an adjacent unmanned aerial vehicle monitoring node.

4. An image acquisition and distribution method based on a monitoring array, which is based on an agricultural perception monitoring system as claimed in claim 3, characterized in that,

the site signal receiving and transmitting module of the site station is used for receiving the topographic map and NDVI data and storing the topographic map and NDVI data in a site information base, the site signal receiving and transmitting module of the site station is used for receiving the codes and the pre-command identification codes of each sampling node and storing the codes and the pre-command identification codes in a ground key database of the site station,

5. The method of claim 4, wherein the pre-array setting step comprises,

s3-1, setting a macro matrix distribution mode of a monitoring array according to a topographic map and NDVI data by the rear end computing platform, storing the macro matrix distribution mode into a rear end memory, transmitting the macro matrix distribution mode to the site station through the Internet, and storing the macro matrix distribution mode into the array information database by the main processing module;

s3-2, the main processing module calls topographic map data from the site information database, extracts the coding information of each node from the ground key database, calls the macro matrix distribution mode of the monitoring array from the array information database, places the information in the ground memory, takes the topographic map data as the bottom layer of the macro matrix distribution mode of the monitoring array, performs node sampling in a distribution map of the matrix according to the topographic map data to obtain the geographic coordinates of each node, distributes a geographic coordinate to each node along with a corresponding code, and accordingly obtains the distribution information data of the codes of the sampling nodes on the topographic map, and stores the respective information data of the codes of the sampling nodes on the topographic map into the array information database;

S3-3, the main processing module extracts the pre-command identification code information from the ground key database to the ground memory, distributes the pre-command identification code to the coordinate information corresponding to the corresponding sampling node so that the pre-command identification code and the coordinate information correspond to each other one by one, and invokes the field signal transceiver module to send the coordinate data with the pre-command identification code to the monitoring array;

s3-4, the node processing module in each sampling node calls a preposed command identification code in the node local information base to a memory, the node processing module receives coordinate data sent by a field station in S3-3 from the local area network unit to the node memory, matches the coordinate data with the preposed command identification code to obtain geographic coordinate information corresponding to the node, and stores the obtained geographic coordinate information into the node local information base, so that the spatial distribution position of each sampling node in the monitoring array is obtained.

6. The method of claim 4, wherein the step of performing monitoring includes,

s4-1, the node processing module in each sampling node calls a lens control module, the wide angle of the camera module is adjusted so that the overlapping ratio of adjacent images is larger than a set value, and the lens control module sends a command of shooting frequency to the camera module;

S4-2, the node processing module copies the image information acquired by the camera module into duplicate, one copy is stored into an original visual library, the other copy is directly placed in a node memory, the node processing module calls an image compression algorithm of the image task primary processing module to the memory, original image data in the node memory is compressed according to the image compression algorithm, and the node processing module calls a pre-command identification code of a local sampling node and an adjacent sampling node in the node local information library and places the pre-command identification code in the node memory;

s4-3, the node processing module arranges the prepositive command identification codes of adjacent sampling nodes in an OR relationship and takes the prepositive command identification codes as the prepositions of the image information to be sent, the node processing module sends the compressed image information to N sampling nodes with nearest surroundings by means of the temporary array network,

s4-4, the node processing module attaches the original image information with a pre-command identification code corresponding to the sampling node, the original image information is sent to the site station through the local area network unit, the site signal receiving and transmitting module is called by the main processing module to receive the image information with the pre-command identification code, and the image information with the pre-command identification code is stored in the original image database by the main processing module;

S4-5, the node processing module compares the compressed image data received by the association docking unit and sent by the nearest N sampling nodes around the node processing module with the local image in a one-to-one mode, and calls the dynamic task allocation module to adjust the wide angle range of the lens according to the comparison result of the contact ratio.

7. The method of claim 4, wherein the processing results comprises,

s5-1, the main processing module calls a matrix filling algorithm of the image secondary processing module to be placed in a ground memory, the main processing module calls arrangement information of a monitoring array in the array information database to be placed in the ground memory, the matrix filling algorithm establishes a matrix frame matched with an arrangement rule of the monitoring array, a region to be filled exists at a node of the matrix frame, the main processing module calls a prepositive command identification code of each sampling node in the ground key database to be placed in the memory, and the image secondary processing module sets a corresponding prepositive command identification code in a corresponding region to be filled according to the matrix frame established in the S5-1;

S5-3, the main processing module calls a coincidence degree processing algorithm of the image secondary processing module, marks the overlapping layer number of the overlapping areas of the adjacent image information, obtains complete site information, and stores the site information in the secondary image database.

8. The method of claim 6, wherein, in S4-5,

s4-5-1, the node processing module calls the position information of the adjacent sampling node and the preposed command identification code in the node local information base to be placed in the node memory, the node processing module calls the coincidence judgment algorithm information of the image task first-stage processing module to be placed in the node memory,

s4-5-4, the dynamic task allocation module weights the coincidence ratio of each sampling node according to the recording result and the coincidence ratio in S4-5-3, obtains the adjustment degree of the wide angle of the camera module according to the weighted result, and commands the camera module to adjust.

9. The method of claim 6, further comprising establishing a beacon for ensuring proper coincidence and delay of sampling of each sampling node in the monitoring array between the array pre-setting and the monitoring executing steps,

and placing image information which can be applied to periodically change at a fixed frequency at the junction position of every two sampling nodes according to the macroscopic matrix distribution mode of the monitoring array.

10. An unmanned aerial vehicle state adjustment method, the method is based on the agricultural perception monitoring system according to claim 3, the unmanned aerial vehicle monitoring node position is determined and placed according to GPS, the method is characterized in that a wireless signal range finder confirms the distance between the unmanned aerial vehicle monitoring node and the unmanned aerial vehicle monitoring node, a node processing module sends the ranging result to a physical action analysis module, and the physical action analysis module adjusts the unmanned aerial vehicle flight state according to a ranging command unmanned aerial vehicle power controller.

11. A multi-view monitoring method based on a monitoring array, which is based on an agricultural perception monitoring system according to claim 1, characterized in that,