CN114815857A - Intelligent agricultural machinery management method and system based on Beidou navigation and cloud platform - Google Patents

Intelligent agricultural machinery management method and system based on Beidou navigation and cloud platform Download PDF

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CN114815857A
CN114815857A CN202210737995.4A CN202210737995A CN114815857A CN 114815857 A CN114815857 A CN 114815857A CN 202210737995 A CN202210737995 A CN 202210737995A CN 114815857 A CN114815857 A CN 114815857A
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CN114815857B (en
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邓维爱
吴铭基
李华栈
叶师曈
彭文斌
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Guangdong Bangsheng Beidou Technology Co ltd
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Abstract

The invention provides an intelligent agricultural machinery management method and system based on Beidou navigation and a cloud platform, and relates to the technical field of intelligent agriculture. Aiming at each agricultural mechanical device, acquiring current device position information of the agricultural mechanical device based on deployed Beidou positioning devices, and determining whether two target agricultural mechanical devices which have an incidence relation with each other exist in a plurality of agricultural mechanical devices based on the current device position information; for each target agricultural mechanical device, acquiring a device environment monitoring image set of the target agricultural mechanical device based on image acquisition devices deployed on the target agricultural mechanical device; and respectively generating motion control parameters corresponding to the two target agricultural mechanical devices based on the equipment environment monitoring images included in the equipment environment monitoring image set of each target agricultural mechanical device. Based on the method, the problem of low reliability of agricultural machinery management in the prior art can be solved.

Description

Intelligent agricultural machinery management method and system based on Beidou navigation and cloud platform
Technical Field
The invention relates to the technical field of intelligent agriculture, in particular to an intelligent agricultural machinery management method and system based on Beidou navigation and a cloud platform.
Background
In the agricultural structure adjustment, the informatization and the intellectualization of the agricultural machinery are key, wherein the position information of the agricultural machinery is the basis of the informatization and the intellectualization of the agricultural machinery. Moreover, the development of the satellite navigation technology provides a feasible solution for acquiring the position information of the agricultural mechanical equipment, for example, the position of the agricultural mechanical equipment can be determined in real time through the satellite navigation technology, so that the automatic and intelligent motion control of the agricultural mechanical equipment is realized. However, since the precision of the satellite navigation technology is not too high, especially when the agricultural machinery equipment works in a remote agricultural area, the problems of sending collision and the like between the equipment are easily caused by the low precision of the navigation technology, and therefore, a technical scheme capable of reliably managing the agricultural machinery equipment needs to be improved.
Disclosure of Invention
In view of this, the invention aims to provide an intelligent agricultural machinery management method and system based on Beidou navigation and a cloud platform, so as to solve the problem of low reliability of agricultural machinery management in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
the utility model provides an intelligent agricultural machinery management method based on beidou navigation, is applied to intelligent agricultural machinery management cloud platform, intelligent agricultural machinery management cloud platform communication connection has a plurality of beidou positioning device and a plurality of image acquisition equipment, has deployed a beidou positioning device and an image acquisition equipment on each agricultural machinery equipment, intelligent agricultural machinery management method based on beidou navigation includes:
for each piece of agricultural mechanical equipment, acquiring equipment current position information of the agricultural mechanical equipment based on the Beidou positioning equipment deployed on the agricultural mechanical equipment, and determining whether two pieces of target agricultural mechanical equipment which have an association relationship with each other exist in the plurality of pieces of agricultural mechanical equipment or not based on the equipment current position information of each piece of agricultural mechanical equipment, wherein an equipment position distance between the two pieces of target agricultural mechanical equipment is smaller than a preset position distance threshold;
if two target agricultural mechanical devices which have an incidence relation with each other exist in the plurality of agricultural mechanical devices, acquiring a device environment monitoring image set of each target agricultural mechanical device based on the image acquisition device deployed on the target agricultural mechanical device, wherein the device environment monitoring image set comprises a plurality of frames of device environment monitoring images;
and respectively generating motion control parameters corresponding to the two target agricultural mechanical devices based on the multi-frame device environment monitoring images included in the device environment monitoring image set of each target agricultural mechanical device, wherein the two target agricultural mechanical devices do not collide with each other when moving based on the corresponding motion control parameters.
In some preferred embodiments, in the above intelligent farm machinery management method based on beidou navigation, the step of obtaining, for each of the agricultural mechanical devices, device current location information of the agricultural mechanical device based on the beidou positioning device deployed on the agricultural mechanical device, and determining whether there are two target agricultural mechanical devices in the plurality of agricultural mechanical devices that have an association relationship with each other based on the device current location information of each of the agricultural mechanical devices includes:
for each agricultural mechanical device, acquiring current device position information of the agricultural mechanical device based on the Beidou positioning device deployed on the agricultural mechanical device;
for every two pieces of agricultural mechanical equipment in the plurality of pieces of agricultural mechanical equipment, determining the current equipment position distance between the two pieces of agricultural mechanical equipment based on the equipment current position information of the two pieces of agricultural mechanical equipment, and determining the relative size relation between the equipment position distance and a pre-configured position distance threshold, wherein the position distance threshold is determined based on the positioning accuracy of the Beidou positioning equipment;
for each two pieces of agricultural mechanical equipment in the plurality of pieces of agricultural mechanical equipment, if the current equipment position distance between the two pieces of agricultural mechanical equipment is smaller than the position distance threshold value, determining the two pieces of agricultural mechanical equipment as two pieces of target agricultural mechanical equipment which are in incidence relation with each other;
for every two pieces of agricultural mechanical equipment in the plurality of pieces of agricultural mechanical equipment, if the current equipment position distance between the two pieces of agricultural mechanical equipment is not smaller than the position distance threshold, the two pieces of agricultural mechanical equipment are determined as two pieces of target agricultural mechanical equipment which do not have a relationship with each other.
In some preferred embodiments, in the above intelligent farm machinery management method based on beidou navigation, if there are two target farm machinery devices in the plurality of farm machinery devices that have an association relationship with each other, for each of the target farm machinery devices, the step of acquiring, based on the image acquisition device deployed on the target farm machinery device, a device environment monitoring image set of the target farm machinery device includes:
if two target agricultural mechanical devices which have an incidence relation with each other exist in the plurality of agricultural mechanical devices, sending the generated image monitoring notification information to image acquisition equipment deployed on each target agricultural mechanical device aiming at each target agricultural mechanical device, wherein each image acquisition equipment is used for acquiring the equipment environment where the corresponding target agricultural mechanical device is located after receiving the image monitoring notification information to obtain a corresponding equipment environment monitoring image;
and acquiring an equipment environment monitoring image which is acquired and sent by image acquisition equipment deployed on the target agricultural mechanical equipment aiming at each target agricultural mechanical equipment, and forming a corresponding equipment environment monitoring image set based on the equipment environment monitoring image.
In some preferred embodiments, in the above intelligent agricultural machinery management method based on beidou navigation, the step of obtaining, for each target agricultural mechanical device, a device environment monitoring image acquired and sent by an image acquisition device deployed on the target agricultural mechanical device, and forming a corresponding device environment monitoring image set based on the device environment monitoring image includes:
acquiring a device environment monitoring image currently acquired by image acquisition equipment deployed on the target agricultural mechanical equipment aiming at each target agricultural mechanical equipment, wherein each image acquisition equipment is used for sending the currently acquired device environment monitoring image to the intelligent agricultural machinery management cloud platform after acquiring one frame of device environment monitoring image based on the image monitoring notification information;
analyzing two frames of equipment environment monitoring images acquired by the two target agricultural mechanical equipment currently, determining whether the same partial equipment environment exists between the equipment environments corresponding to the two target agricultural mechanical equipment currently, and sending generated image monitoring stopping information to each target agricultural mechanical equipment when the same partial equipment environment exists between the equipment environments corresponding to the two target agricultural mechanical equipment currently, wherein each target agricultural mechanical equipment is used for stopping image acquisition on the equipment environment where the corresponding target agricultural mechanical equipment is located after receiving the image monitoring stopping information;
and for each target agricultural mechanical device, forming a device environment monitoring image set corresponding to the target agricultural mechanical device based on each frame of device environment monitoring image acquired by the image acquisition device deployed on the target agricultural mechanical device.
In some preferred embodiments, in the above intelligent agricultural machinery management method based on beidou navigation, the step of obtaining, for each target agricultural mechanical device, a device environment monitoring image acquired and sent by an image acquisition device deployed on the target agricultural mechanical device, and forming a corresponding device environment monitoring image set based on the device environment monitoring image further includes:
for each target agricultural mechanical device, after acquiring a device environment monitoring image currently acquired by an image acquisition device deployed on the target agricultural mechanical device, determining whether the currently acquired device environment monitoring image belongs to a first acquired frame of device environment monitoring image, and generating image acquisition frame rate adjustment information corresponding to the target agricultural mechanical device when the currently acquired device environment monitoring image belongs to the first acquired frame of device environment monitoring image;
and sending the image acquisition frame rate adjustment information corresponding to each target agricultural mechanical device to image acquisition devices deployed on the target agricultural mechanical device, wherein each image acquisition device is used for sequentially increasing the image acquisition frame rate based on the image acquisition frame rate adjustment information and acquiring images based on the increased image acquisition frame rate.
In some preferred embodiments, in the above intelligent agricultural machinery management method based on beidou navigation, the step of generating the motion control parameters corresponding to the two target agricultural mechanical devices respectively based on the multiple frames of device environment monitoring images included in the device environment monitoring image set of each target agricultural mechanical device includes:
for each target agricultural mechanical device, determining device motion direction information corresponding to the target agricultural mechanical device based on a multi-frame device environment monitoring image included in a device environment monitoring image set of the target agricultural mechanical device, and performing motion track prediction processing on the target agricultural mechanical device based on the device motion direction information and current first device position information of the target agricultural mechanical device to obtain a predicted motion track corresponding to the target agricultural mechanical device;
determining whether a track coincidence point exists between two predicted motion tracks corresponding to the two target agricultural mechanical devices, and respectively generating motion control parameters corresponding to the two target agricultural mechanical devices when the track coincidence point exists between the two predicted motion tracks, wherein the motion control parameters are used for controlling the motion direction of the corresponding target agricultural mechanical devices, so that no track coincidence point exists between new predicted motion tracks formed based on the motion direction.
In some preferred embodiments, in the above intelligent agricultural machinery management method based on beidou navigation, for each target agricultural mechanical device, determining device motion direction information corresponding to the target agricultural mechanical device based on a multi-frame device environment monitoring image included in a device environment monitoring image set of the target agricultural mechanical device, and performing motion trajectory prediction processing on the target agricultural mechanical device based on the device motion direction information and current first device position information of the target agricultural mechanical device to obtain a predicted motion trajectory corresponding to the target agricultural mechanical device includes:
for each target agricultural mechanical device, performing image screening operation on multi-frame device environment monitoring images included in the device environment monitoring image set of the target agricultural mechanical device to obtain multi-frame target device environment monitoring images corresponding to the target agricultural mechanical device;
aiming at each two adjacent frames of target equipment environment monitoring images corresponding to each target agricultural mechanical equipment, respectively segmenting the two frames of target equipment environment monitoring images to obtain a plurality of corresponding first monitoring subimages and a plurality of corresponding second monitoring subimages, and determining a plurality of frames of corresponding target first monitoring subimages and a plurality of frames of corresponding target second monitoring subimages based on whether each frame of first monitoring subimage and each frame of second monitoring subimage corresponding to the two frames of target equipment environment monitoring images are the same, wherein the first monitoring subimage belongs to a previous frame in the two frames of target device environment monitoring images, the second monitoring subimage belongs to the next frame in the two frames of target equipment environment monitoring images, each frame of target first monitoring subimage has at least one same second monitoring subimage, and each frame of target second monitoring subimage has at least one same first monitoring subimage;
determining a first relative position relationship between corresponding multi-frame target first monitoring subimages aiming at each two adjacent frames of target equipment environment monitoring images, determining a second relative position relationship between corresponding multi-frame target second monitoring subimages, determining a candidate multi-frame second monitoring subimage with the first relative position relationship in the multi-frame second monitoring subimages which are the same as the multi-frame target first monitoring subimages, and determining a candidate multi-frame first monitoring subimage with the second relative position relationship in the multi-frame first monitoring subimages which are the same as the multi-frame target second monitoring subimages;
determining a first intersection between a corresponding multi-frame target first monitoring subimage and a multi-frame candidate first monitoring subimage aiming at each two adjacent frames of target equipment environment monitoring images, and determining a second intersection between the corresponding multi-frame target second monitor sub-image and the multi-frame candidate second monitor sub-image, determining the same monitoring subimage between the first intersection and the second intersection, determining equipment motion direction information of the corresponding target agricultural mechanical equipment in a time period corresponding to the two frames of target equipment environment monitoring images based on the positions of the monitoring subimages in the two frames of target equipment environment monitoring images respectively, and determining equipment motion direction information of each target agricultural mechanical equipment based on equipment motion direction information corresponding to each two adjacent frames of target equipment environment monitoring images in a multi-frame target equipment environment monitoring image corresponding to each target agricultural mechanical equipment;
and for each target agricultural mechanical device, performing motion track prediction processing on the target agricultural mechanical device based on the device motion direction information corresponding to the target agricultural mechanical device and the current first device position information to obtain a predicted motion track corresponding to the target agricultural mechanical device.
The invention also provides an intelligent agricultural machinery management system based on Beidou navigation, which is applied to an intelligent agricultural machinery management cloud platform, wherein the intelligent agricultural machinery management cloud platform is in communication connection with a plurality of Beidou positioning devices and a plurality of image acquisition devices, each agricultural mechanical device is provided with one Beidou positioning device and one image acquisition device, and the intelligent agricultural machinery management system based on Beidou navigation comprises:
the agricultural mechanical equipment determining module is used for acquiring equipment current position information of each piece of agricultural mechanical equipment based on the Beidou positioning equipment deployed on the agricultural mechanical equipment and determining whether two pieces of target agricultural mechanical equipment which have an association relationship with each other exist in a plurality of pieces of agricultural mechanical equipment or not based on the equipment current position information of each piece of agricultural mechanical equipment, wherein the equipment position distance between the two pieces of target agricultural mechanical equipment is smaller than a preset position distance threshold value;
an environment monitoring image obtaining module, configured to, if two target agricultural mechanical devices that have an association relationship with each other exist in the plurality of agricultural mechanical devices, obtain, for each target agricultural mechanical device, an equipment environment monitoring image set of the target agricultural mechanical device based on the image acquisition device deployed on the target agricultural mechanical device, where the equipment environment monitoring image set includes multiple frames of equipment environment monitoring images;
and the motion control parameter generating module is used for respectively generating motion control parameters corresponding to the two target agricultural mechanical devices based on the multi-frame device environment monitoring images included in the device environment monitoring image set of each target agricultural mechanical device, wherein when the two target agricultural mechanical devices respectively move based on the corresponding motion control parameters, the two target agricultural mechanical devices do not collide with each other.
In some preferred embodiments, in the above intelligent agricultural machinery management system based on beidou navigation, the motion control parameter generation module is specifically configured to:
for each target agricultural mechanical device, determining device motion direction information corresponding to the target agricultural mechanical device based on a multi-frame device environment monitoring image included in a device environment monitoring image set of the target agricultural mechanical device, and performing motion track prediction processing on the target agricultural mechanical device based on the device motion direction information and current first device position information of the target agricultural mechanical device to obtain a predicted motion track corresponding to the target agricultural mechanical device;
determining whether a track coincidence point exists between two predicted motion tracks corresponding to the two target agricultural mechanical devices, and respectively generating motion control parameters corresponding to the two target agricultural mechanical devices when the track coincidence point exists between the two predicted motion tracks, wherein the motion control parameters are used for controlling the motion direction of the corresponding target agricultural mechanical devices, so that no track coincidence point exists between new predicted motion tracks formed based on the motion direction.
The invention also provides an intelligent agricultural machinery management cloud platform which is used for executing the intelligent agricultural machinery management method based on the Beidou navigation.
According to the intelligent agricultural machinery management method, system and cloud platform based on Beidou navigation, the current position information of the agricultural mechanical equipment is obtained based on deployed Beidou positioning equipment for each agricultural mechanical equipment, whether two target agricultural mechanical equipment with an incidence relation exists in a plurality of agricultural mechanical equipment or not is determined based on the current position information of the equipment, then an equipment environment monitoring image set of the target agricultural mechanical equipment is obtained based on image acquisition equipment deployed on the target agricultural mechanical equipment for each target agricultural mechanical equipment, and finally, motion control parameters corresponding to the two target agricultural mechanical equipment are respectively generated based on equipment environment monitoring images included in the equipment environment monitoring image set of each target agricultural mechanical equipment. According to the agricultural machinery management system and method, the image monitoring technology is further integrated on the basis of the Beidou positioning technology, so that the control precision of the movement control of the agricultural machinery equipment can be improved to a certain extent, and the problem of low reliability of agricultural machinery management in the prior art is solved.
Drawings
Fig. 1 is a structural block diagram of an intelligent farm machinery management cloud platform provided by an embodiment of the present invention.
Fig. 2 is a schematic flow chart of steps included in the intelligent agricultural machinery management method based on Beidou navigation provided by the embodiment of the invention.
Fig. 3 is a schematic diagram of modules included in the intelligent agricultural machinery management system based on Beidou navigation provided by the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
As shown in fig. 1, an embodiment of the invention provides an intelligent farm machinery management cloud platform. Wherein the intelligent farm machinery management cloud platform can comprise a memory and a processor.
In detail, the memory and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, they may be electrically connected to each other via one or more communication buses or signal lines. The memory can have stored therein at least one software function (computer program) which can be present in the form of software or firmware. The processor can be used for executing the executable computer program stored in the memory, so that the intelligent agricultural machinery management method based on Beidou navigation provided by the embodiment of the invention is realized.
Alternatively, in one possible implementation, the Memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Read Only Memory (EPROM), an electrically Erasable Read Only Memory (EEPROM), and the like.
Optionally, in a possible implementation manner, the Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), a System on Chip (SoC), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.
Moreover, the structure shown in fig. 1 is only an illustration, and the intelligent farm machinery management cloud platform may further include more or fewer components than those shown in fig. 1, or have a different configuration from that shown in fig. 1, for example, may include a communication unit for information interaction with other devices.
Alternatively, in a possible implementation manner, the intelligent agricultural machinery management cloud platform can be a server with data processing capability.
With reference to fig. 2, an embodiment of the invention further provides an intelligent agricultural machinery management method based on Beidou navigation, which can be applied to the intelligent agricultural machinery management cloud platform. The method steps defined by the relevant flow of the intelligent agricultural machinery management method based on the Beidou navigation satellite system can be realized by the intelligent agricultural machinery management cloud platform. The intelligent agricultural machinery management cloud platform is in communication connection with a plurality of Beidou positioning devices and a plurality of image acquisition devices, and each agricultural mechanical device is provided with one Beidou positioning device and one image acquisition device. The specific process shown in FIG. 2 will be described in detail below.
Step S110, aiming at each agricultural mechanical device, obtaining device current position information of the agricultural mechanical device based on the Beidou positioning device deployed on the agricultural mechanical device, and determining whether two target agricultural mechanical devices which have an incidence relation with each other exist in a plurality of agricultural mechanical devices based on the device current position information of each agricultural mechanical device.
In the embodiment of the invention, the intelligent agricultural machinery management cloud platform can acquire the current position information of the agricultural mechanical equipment based on the Beidou positioning equipment deployed on the agricultural mechanical equipment for each piece of agricultural mechanical equipment, and determine whether two target agricultural mechanical equipment with a correlation relationship exist in a plurality of pieces of agricultural mechanical equipment based on the current position information of the agricultural mechanical equipment. Wherein a device location distance between the two target agricultural machine devices is less than a preconfigured location distance threshold.
Step S120, if there are two target agricultural mechanical devices in the plurality of agricultural mechanical devices that have an association relationship with each other, acquiring, for each of the target agricultural mechanical devices, a device environment monitoring image set of the target agricultural mechanical device based on the image acquisition device deployed on the target agricultural mechanical device.
In the embodiment of the present invention, when two target agricultural mechanical devices having an association relationship exist in the plurality of agricultural mechanical devices, the intelligent agricultural machinery management cloud platform may acquire, for each target agricultural mechanical device, a device environment monitoring image set of the target agricultural mechanical device based on the image acquisition device deployed on the target agricultural mechanical device. The device environment monitoring image set comprises a plurality of frames of device environment monitoring images.
Step S130, based on the multiple frames of device environment monitoring images included in the device environment monitoring image set of each target agricultural mechanical device, respectively generating motion control parameters corresponding to the two target agricultural mechanical devices.
In an embodiment of the present invention, the intelligent agricultural machinery management cloud platform may generate motion control parameters corresponding to the two target agricultural mechanical devices respectively based on the multiple frames of device environment monitoring images included in the device environment monitoring image set of each target agricultural mechanical device, where when the two target agricultural mechanical devices move respectively based on the corresponding motion control parameters, no collision occurs between the two target agricultural mechanical devices.
Based on the method, for each agricultural mechanical device, the device current position information of the agricultural mechanical device is obtained based on the deployed Beidou positioning device, whether two target agricultural mechanical devices which have an incidence relation with each other exist in the plurality of agricultural mechanical devices is determined based on the device current position information, then, for each target agricultural mechanical device, the device environment monitoring image set of the target agricultural mechanical device is obtained based on the image acquisition device deployed on the target agricultural mechanical device, and finally, the motion control parameters corresponding to the two target agricultural mechanical devices are respectively generated based on the device environment monitoring images included in the device environment monitoring image set of each target agricultural mechanical device. Therefore, on the basis of the Beidou positioning technology, the image monitoring technology is further integrated, the control precision of the motion control of the agricultural mechanical equipment can be improved to a certain extent, and the problem of low reliability of agricultural machinery management in the prior art is solved.
Alternatively, in a possible implementation, the step S110 in the foregoing implementation may further include the following:
firstly, aiming at each agricultural mechanical device, acquiring current device position information of the agricultural mechanical device based on the Beidou positioning device deployed on the agricultural mechanical device;
secondly, for every two agricultural mechanical devices in the plurality of agricultural mechanical devices, determining a current device position distance between the two agricultural mechanical devices based on device current position information of the two agricultural mechanical devices, and determining a relative size relationship between the device position distance and a pre-configured position distance threshold, wherein the position distance threshold is determined based on the positioning accuracy of the Beidou positioning device (if the positioning accuracy is higher, the position distance threshold is smaller, a negative correlation relationship can be obtained);
then, for each two pieces of agricultural mechanical equipment in the plurality of pieces of agricultural mechanical equipment, if the current equipment position distance between the two pieces of agricultural mechanical equipment is smaller than the position distance threshold, determining the two pieces of agricultural mechanical equipment as two pieces of target agricultural mechanical equipment which have a correlation relationship with each other;
finally, for every two agricultural mechanical devices in the plurality of agricultural mechanical devices, if the current device location distance between the two agricultural mechanical devices is not less than the location distance threshold (that is, the device location distance is greater than or equal to the location distance threshold), the two agricultural mechanical devices are determined as two target agricultural mechanical devices which do not have a relationship with each other (at this time, because the location distance is still relatively large, motion control can be continuously performed based on the positioning data of the Beidou positioning device).
Alternatively, in a possible implementation, the step S120 in the foregoing implementation may further include the following:
firstly, if two target agricultural mechanical devices which have an association relationship with each other exist in the plurality of agricultural mechanical devices, sending generated image monitoring notification information to image acquisition devices deployed on the target agricultural mechanical devices aiming at each target agricultural mechanical device, wherein each image acquisition device is used for acquiring the device environment where the corresponding target agricultural mechanical device is located after receiving the image monitoring notification information to obtain a corresponding device environment monitoring image;
then, for each target agricultural mechanical device, acquiring a device environment monitoring image acquired and sent by an image acquisition device deployed on the target agricultural mechanical device, and forming a corresponding device environment monitoring image set based on the device environment monitoring image.
Optionally, in a possible implementation manner, the step of, for each target agricultural mechanical device, acquiring a device environment monitoring image acquired and sent by an image acquisition device deployed on the target agricultural mechanical device, and forming a corresponding device environment monitoring image set based on the device environment monitoring image in the foregoing implementation manner may further include the following steps:
firstly, for each target agricultural mechanical device, acquiring a device environment monitoring image currently acquired by an image acquisition device deployed on the target agricultural mechanical device, wherein each image acquisition device is used for sending the currently acquired device environment monitoring image to the intelligent agricultural machinery management cloud platform (namely acquiring one frame and sending one frame) after acquiring one frame of device environment monitoring image based on the image monitoring notification information;
secondly, analyzing two frames of equipment environment monitoring images acquired by the two target agricultural mechanical equipment currently, determining whether the same partial equipment environment exists between the equipment environments corresponding to the two target agricultural mechanical equipment currently, and sending generated image monitoring stop information to each target agricultural mechanical equipment when the same partial equipment environment exists between the equipment environments corresponding to the two target agricultural mechanical equipment currently, wherein each target agricultural mechanical equipment is used for stopping image acquisition on the equipment environment where the corresponding target agricultural mechanical equipment is located after receiving the image monitoring stop information;
and finally, forming an equipment environment monitoring image set corresponding to the target agricultural mechanical equipment based on each frame of acquired equipment environment monitoring image acquired by the image acquisition equipment deployed on the target agricultural mechanical equipment.
Optionally, in a possible implementation manner, the step of, for each target agricultural mechanical device, acquiring a device environment monitoring image acquired and sent by an image acquisition device deployed on the target agricultural mechanical device, and forming a corresponding device environment monitoring image set based on the device environment monitoring image in the foregoing implementation manner may further include the following steps:
firstly, for each target agricultural mechanical device, after acquiring a device environment monitoring image currently acquired by an image acquisition device deployed on the target agricultural mechanical device, determining whether the currently acquired device environment monitoring image belongs to a first acquired frame of device environment monitoring image, and generating image acquisition frame rate adjustment information corresponding to the target agricultural mechanical device when the currently acquired device environment monitoring image belongs to the first acquired frame of device environment monitoring image;
secondly, for each target agricultural mechanical device, sending the image acquisition frame rate adjustment information corresponding to the target agricultural mechanical device to image acquisition devices deployed on the target agricultural mechanical device, wherein each image acquisition device is configured to sequentially increase an image acquisition frame rate based on the image acquisition frame rate adjustment information, and perform image acquisition based on the increased image acquisition frame rate (for example, acquiring one frame every 0.5s, 0.3s, 0.1s, 0.08s, and the like).
Alternatively, in a possible implementation, the step S130 in the foregoing implementation may further include the following:
firstly, for each target agricultural mechanical device, determining device motion direction information corresponding to the target agricultural mechanical device based on a multi-frame device environment monitoring image included in a device environment monitoring image set of the target agricultural mechanical device, and performing motion track prediction processing on the target agricultural mechanical device based on the device motion direction information and current first device position information of the target agricultural mechanical device to obtain a predicted motion track corresponding to the target agricultural mechanical device;
secondly, determining whether a track coincident point exists between two predicted motion tracks corresponding to the two target agricultural mechanical devices, and respectively generating motion control parameters corresponding to the two target agricultural mechanical devices when the track coincident point exists between the two predicted motion tracks, wherein the motion control parameters are used for controlling the motion direction of the corresponding target agricultural mechanical devices, so that no track coincident point exists between new predicted motion tracks formed based on the motion direction.
Optionally, in a possible implementation manner, the step of determining, for each target agricultural mechanical device, device motion direction information corresponding to the target agricultural mechanical device based on multiple frames of device environment monitoring images included in the device environment monitoring image set of the target agricultural mechanical device, and performing motion trajectory prediction processing on the target agricultural mechanical device based on the device motion direction information and the current first device location information of the target agricultural mechanical device to obtain a predicted motion trajectory corresponding to the target agricultural mechanical device may further include the following steps:
firstly, aiming at each target agricultural mechanical device, carrying out image screening operation on multi-frame device environment monitoring images included in a device environment monitoring image set of the target agricultural mechanical device to obtain multi-frame target device environment monitoring images corresponding to the target agricultural mechanical device;
secondly, aiming at each two adjacent frames of target equipment environment monitoring images corresponding to each target agricultural mechanical equipment, respectively segmenting the two frames of target equipment environment monitoring images to obtain a plurality of corresponding first monitoring subimages and a plurality of corresponding second monitoring subimages, and determining a plurality of frames of corresponding target first monitoring subimages and a plurality of frames of corresponding target second monitoring subimages based on whether each frame of first monitoring subimage and each frame of second monitoring subimage corresponding to the two frames of target equipment environment monitoring images are the same, wherein the first monitoring subimage belongs to a previous frame in the two frames of target device environment monitoring images, the second monitoring subimage belongs to the next frame in the two frames of target equipment environment monitoring images, each frame of target first monitoring subimage has at least one same second monitoring subimage, and each frame of target second monitoring subimage has at least one same first monitoring subimage;
then, for each two adjacent frames of target device environment monitoring images, determining a first relative position relationship between the corresponding multiple frames of target first monitoring subimages (in the corresponding target device environment monitoring images) (e.g. a certain number of pixel points are located right above one frame of target first monitoring subimage in another frame of target first monitoring subimage), and determines a second relative positional relationship between the corresponding multi-frame target second monitoring sub-images (in the corresponding target device environment monitoring images), and, determining a plurality of frames of candidate second monitoring subimages with the first relative position relation in a plurality of frames of second monitoring subimages which are the same as the plurality of frames of target first monitoring subimages, determining a plurality of frames of candidate first monitoring subimages with the second relative position relation in a plurality of frames of first monitoring subimages which are the same as the plurality of frames of target second monitoring subimages;
then, aiming at each two adjacent frames of target equipment environment monitoring images, determining a first intersection between a corresponding multi-frame target first monitoring subimage and a multi-frame candidate first monitoring subimage, determining a second intersection between a corresponding multi-frame target second monitoring subimage and a multi-frame candidate second monitoring subimage, determining the same monitoring subimage between the first intersection and the second intersection, determining equipment motion direction information of the corresponding target agricultural machinery equipment in a time period corresponding to the two frames of target equipment environment monitoring images based on the positions of the monitoring subimages in the two frames of target equipment environment monitoring images, and then respectively based on the equipment motion direction information corresponding to each two adjacent frames of target equipment environment monitoring images in the multi-frame target equipment environment monitoring images corresponding to each target agricultural machinery equipment, determining equipment motion direction information of each target agricultural mechanical equipment;
finally, for each target agricultural mechanical device, based on the device motion direction information corresponding to the target agricultural mechanical device and the current first device position information, motion trajectory prediction processing is performed on the target agricultural mechanical device to obtain a predicted motion trajectory corresponding to the target agricultural mechanical device (i.e., extending with the first device position information as a starting point and the device motion direction information as a direction).
Alternatively, in a possible implementation, the image filtering operation in the foregoing implementation may further include the following steps:
firstly, in the multi-frame device environment monitoring images, sequentially determining a plurality of frames of first device environment monitoring images, and segmenting the multi-frame device environment monitoring images based on the plurality of frames of first device environment monitoring images to form a plurality of image sequences, wherein a first frame of the first device environment monitoring image is a frame of device environment monitoring image with a time sequence which is closest to the first frame of device environment monitoring image in the multi-frame device environment monitoring images and completely different from the device environment corresponding to the first frame of device environment monitoring image, each frame of first device environment monitoring image except the first frame of device environment monitoring image is a frame of device environment monitoring image with a time sequence which is closest to the first device environment monitoring image of the previous frame and completely different from the device environment corresponding to the first device environment monitoring image, a first frame of equipment environment monitoring image in each image sequence is a first frame of equipment environment monitoring image in the multiple frames of equipment environment monitoring images or the first equipment environment monitoring image;
secondly, for each two adjacent frames of device environment monitoring images in each image sequence of the plurality of image sequences, determining moving distance information of the corresponding target agricultural mechanical device within the corresponding time based on timestamp information corresponding to the two frames of device environment monitoring images, and determining a corresponding first partial image and a corresponding second partial image in the two frames of device environment monitoring images respectively based on the moving distance information (that is, when the target agricultural mechanical device performs linear motion, the first partial image and the second partial image completely coincide, that is, device environments corresponding to the first partial image and the second partial image belong to the same), wherein the first partial image is a partial image on a side far away from the target agricultural mechanical device in a previous frame of device environment monitoring image in the two frames of device environment monitoring images, the second partial image is a partial image close to one side of the target agricultural mechanical equipment in the next frame of equipment environment monitoring image in the two frames of equipment environment monitoring images;
then, for each two adjacent frames of device environment monitoring images in each image sequence of the plurality of image sequences, calculating an image similarity between the first partial image and the second partial image corresponding to the two frames of device environment monitoring images, and determining a deviation degree value of the corresponding target agricultural mechanical device in the movement direction of the two frames of device environment monitoring images in the corresponding time based on the image similarity, wherein the deviation degree value and the image similarity have a negative correlation (i.e., the larger the image similarity is, the smaller the deviation degree value is);
then, for each two adjacent frames of device environment monitoring images in each image sequence of the plurality of image sequences, determining a relative size relationship between a deviation degree value corresponding to the two frames of device environment monitoring images and a preset deviation degree threshold, when the deviation degree value is smaller than the deviation degree threshold, taking the previous frame of device environment monitoring image in the two frames of device environment monitoring images as a candidate device environment monitoring image, and when the deviation degree value is larger than or equal to the deviation degree threshold, taking the two frames of device environment monitoring images as the candidate device environment monitoring image;
then, based on the corresponding time stamp information, sequencing the determined multiple frames of the candidate device environment monitoring images to form a corresponding image screening sequence, and aiming at every two adjacent frames of candidate equipment environment monitoring images in the image screening sequence, calculating the image similarity between the two frames of candidate equipment environment monitoring images, and calculating the average value of the image similarity between every two frames of candidate equipment environment monitoring images to obtain the corresponding similarity average value, and determining the corresponding target sampling parameter based on the similarity average value, the target sampling parameters and the similarity mean value have positive correlation (that is, the larger the similarity mean value is, the fewer the number of the target equipment environment monitoring images obtained by sampling is, and the smaller the similarity mean value is, the more the number of the target equipment environment monitoring images obtained by sampling is);
and finally, sampling the image screening sequence based on the target sampling parameters to obtain a corresponding multi-frame target equipment environment monitoring image.
With reference to fig. 3, an embodiment of the invention further provides an intelligent agricultural machinery management system based on Beidou navigation, which can be applied to the motion state identification cloud platform. The intelligent agricultural machinery management system based on Beidou navigation can comprise an agricultural mechanical equipment determining module (as shown in step S110), an environment monitoring image obtaining module (as shown in step S120) and a motion control parameter generating module (as shown in step S130).
Optionally, in a possible implementation manner, the agricultural mechanical equipment determining module is configured to, for each piece of agricultural mechanical equipment, obtain equipment current location information of the agricultural mechanical equipment based on the beidou positioning device deployed on the agricultural mechanical equipment, and determine whether two target agricultural mechanical equipment having an association relationship with each other exist in the plurality of pieces of agricultural mechanical equipment based on the equipment current location information of each piece of agricultural mechanical equipment, where an equipment location distance between the two target agricultural mechanical equipment is smaller than a preconfigured location distance threshold. The environment monitoring image obtaining module is configured to, if two target agricultural mechanical devices having an association relationship with each other exist in the plurality of agricultural mechanical devices, obtain, for each target agricultural mechanical device, an equipment environment monitoring image set of the target agricultural mechanical device based on the image acquisition device deployed on the target agricultural mechanical device, where the equipment environment monitoring image set includes multiple frames of equipment environment monitoring images. The motion control parameter generating module is configured to generate motion control parameters corresponding to the two target agricultural mechanical devices respectively based on the multiple frames of device environment monitoring images included in the device environment monitoring image sets of each target agricultural mechanical device, where when the two target agricultural mechanical devices move respectively based on the corresponding motion control parameters, no collision occurs between the two target agricultural mechanical devices.
Optionally, in a possible implementation manner, the motion control parameter generating module is specifically configured to: for each target agricultural mechanical device, determining device motion direction information corresponding to the target agricultural mechanical device based on a multi-frame device environment monitoring image included in a device environment monitoring image set of the target agricultural mechanical device, and performing motion track prediction processing on the target agricultural mechanical device based on the device motion direction information and current first device position information of the target agricultural mechanical device to obtain a predicted motion track corresponding to the target agricultural mechanical device; determining whether a track coincidence point exists between two predicted motion tracks corresponding to the two target agricultural mechanical devices, and respectively generating motion control parameters corresponding to the two target agricultural mechanical devices when the track coincidence point exists between the two predicted motion tracks, wherein the motion control parameters are used for controlling the motion direction of the corresponding target agricultural mechanical devices, so that no track coincidence point exists between new predicted motion tracks formed based on the motion direction.
In conclusion, the invention provides an intelligent agricultural machinery management method, system and cloud platform based on Beidou navigation, the current position information of each agricultural mechanical device can be obtained based on the deployed Beidou positioning device aiming at each agricultural mechanical device, and determining whether there are two target agricultural machine devices having an association relationship with each other among the plurality of agricultural machine devices based on the device current position information, then, for each target agricultural machinery, a device environment monitoring image set of the target agricultural machinery is obtained based on the image acquisition device deployed on the target agricultural machinery, and finally, the motion control parameters corresponding to the two target agricultural mechanical devices can be respectively generated based on the device environment monitoring images included in the device environment monitoring image set of each target agricultural mechanical device. Therefore, on the basis of the Beidou positioning technology, the image monitoring technology is further integrated, the control precision of the motion control of the agricultural mechanical equipment can be improved to a certain extent, and the problem of low reliability of agricultural machinery management in the prior art is solved.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. The utility model provides an intelligent agricultural machinery management method based on beidou navigation, its characterized in that is applied to intelligent agricultural machinery management cloud platform, intelligent agricultural machinery management cloud platform communication connection has a plurality of beidou positioning device and a plurality of image acquisition equipment, deploys a beidou positioning device and an image acquisition equipment on each agricultural machinery equipment, intelligent agricultural machinery management method based on beidou navigation includes:
for each piece of agricultural mechanical equipment, acquiring equipment current position information of the agricultural mechanical equipment based on the Beidou positioning equipment deployed on the agricultural mechanical equipment, and determining whether two pieces of target agricultural mechanical equipment which have an association relationship with each other exist in the plurality of pieces of agricultural mechanical equipment or not based on the equipment current position information of each piece of agricultural mechanical equipment, wherein an equipment position distance between the two pieces of target agricultural mechanical equipment is smaller than a preset position distance threshold;
if two target agricultural mechanical devices which have an incidence relation with each other exist in the plurality of agricultural mechanical devices, acquiring a device environment monitoring image set of each target agricultural mechanical device based on the image acquisition device deployed on the target agricultural mechanical device, wherein the device environment monitoring image set comprises a plurality of frames of device environment monitoring images;
and respectively generating motion control parameters corresponding to the two target agricultural mechanical devices based on the multiple frames of equipment environment monitoring images included in the equipment environment monitoring image set of each target agricultural mechanical device, wherein when the two target agricultural mechanical devices respectively move based on the corresponding motion control parameters, collision cannot occur between the two target agricultural mechanical devices.
2. The intelligent agricultural machinery management method based on Beidou navigation and according to claim 1, wherein the step of acquiring the current position information of each agricultural mechanical device based on the Beidou positioning device deployed on the agricultural mechanical device and determining whether two target agricultural mechanical devices having an association relationship with each other exist in a plurality of agricultural mechanical devices based on the current position information of each agricultural mechanical device comprises the following steps:
for each agricultural mechanical device, acquiring current device position information of the agricultural mechanical device based on the Beidou positioning device deployed on the agricultural mechanical device;
for every two pieces of agricultural mechanical equipment in the plurality of pieces of agricultural mechanical equipment, determining the current equipment position distance between the two pieces of agricultural mechanical equipment based on the equipment current position information of the two pieces of agricultural mechanical equipment, and determining the relative size relation between the equipment position distance and a pre-configured position distance threshold, wherein the position distance threshold is determined based on the positioning accuracy of the Beidou positioning equipment;
for each two pieces of agricultural mechanical equipment in the plurality of pieces of agricultural mechanical equipment, if the current equipment position distance between the two pieces of agricultural mechanical equipment is smaller than the position distance threshold value, determining the two pieces of agricultural mechanical equipment as two pieces of target agricultural mechanical equipment which are in incidence relation with each other;
for every two pieces of agricultural mechanical equipment in the plurality of pieces of agricultural mechanical equipment, if the current equipment position distance between the two pieces of agricultural mechanical equipment is not smaller than the position distance threshold, the two pieces of agricultural mechanical equipment are determined as two pieces of target agricultural mechanical equipment which do not have a relationship with each other.
3. The intelligent agricultural machinery management method based on Beidou navigation according to claim 1, wherein if there are two target agricultural mechanical devices in the plurality of agricultural mechanical devices which have an association relationship with each other, the step of acquiring, for each target agricultural mechanical device, a device environment monitoring image set of the target agricultural mechanical device based on the image acquisition device deployed on the target agricultural mechanical device comprises:
if two target agricultural mechanical devices which have an incidence relation with each other exist in the plurality of agricultural mechanical devices, sending the generated image monitoring notification information to image acquisition equipment deployed on each target agricultural mechanical device aiming at each target agricultural mechanical device, wherein each image acquisition equipment is used for acquiring the equipment environment where the corresponding target agricultural mechanical device is located after receiving the image monitoring notification information to obtain a corresponding equipment environment monitoring image;
and acquiring an equipment environment monitoring image which is acquired and sent by image acquisition equipment deployed on the target agricultural mechanical equipment aiming at each target agricultural mechanical equipment, and forming a corresponding equipment environment monitoring image set based on the equipment environment monitoring image.
4. The intelligent agricultural machinery management method based on Beidou navigation according to claim 3, wherein the step of obtaining the equipment environment monitoring image collected and sent by the image collecting equipment deployed on the target agricultural machinery equipment and forming the corresponding equipment environment monitoring image set based on the equipment environment monitoring image for each target agricultural machinery equipment comprises:
acquiring a device environment monitoring image currently acquired by image acquisition equipment deployed on the target agricultural mechanical equipment aiming at each target agricultural mechanical equipment, wherein each image acquisition equipment is used for sending the currently acquired device environment monitoring image to the intelligent agricultural machinery management cloud platform after acquiring one frame of device environment monitoring image based on the image monitoring notification information;
analyzing two frames of equipment environment monitoring images acquired by the two target agricultural mechanical equipment currently, determining whether the same partial equipment environment exists between the equipment environments corresponding to the two target agricultural mechanical equipment currently, and sending generated image monitoring stopping information to each target agricultural mechanical equipment when the same partial equipment environment exists between the equipment environments corresponding to the two target agricultural mechanical equipment currently, wherein each target agricultural mechanical equipment is used for stopping image acquisition on the equipment environment where the corresponding target agricultural mechanical equipment is located after receiving the image monitoring stopping information;
and for each target agricultural mechanical device, forming a device environment monitoring image set corresponding to the target agricultural mechanical device based on each frame of device environment monitoring image acquired by the image acquisition device deployed on the target agricultural mechanical device.
5. The intelligent agricultural machinery management method based on Beidou navigation according to claim 4, wherein the step of obtaining the equipment environment monitoring image collected and sent by the image collecting equipment deployed on the target agricultural machinery equipment and forming the corresponding equipment environment monitoring image set based on the equipment environment monitoring image for each target agricultural machinery equipment further comprises:
for each target agricultural mechanical device, after acquiring a device environment monitoring image currently acquired by an image acquisition device deployed on the target agricultural mechanical device, determining whether the currently acquired device environment monitoring image belongs to a first acquired frame of device environment monitoring image, and generating image acquisition frame rate adjustment information corresponding to the target agricultural mechanical device when the currently acquired device environment monitoring image belongs to the first acquired frame of device environment monitoring image;
and sending the image acquisition frame rate adjustment information corresponding to each target agricultural mechanical device to image acquisition devices deployed on the target agricultural mechanical device, wherein each image acquisition device is used for sequentially increasing the image acquisition frame rate based on the image acquisition frame rate adjustment information and acquiring images based on the increased image acquisition frame rate.
6. The intelligent agricultural machinery management method based on Beidou navigation according to any one of claims 1 to 5, wherein the step of respectively generating motion control parameters corresponding to the two target agricultural mechanical devices based on the plurality of frames of device environment monitoring images included in the device environment monitoring image set of each target agricultural mechanical device comprises:
for each target agricultural mechanical device, determining device motion direction information corresponding to the target agricultural mechanical device based on a multi-frame device environment monitoring image included in a device environment monitoring image set of the target agricultural mechanical device, and performing motion track prediction processing on the target agricultural mechanical device based on the device motion direction information and current first device position information of the target agricultural mechanical device to obtain a predicted motion track corresponding to the target agricultural mechanical device;
determining whether a track coincidence point exists between two predicted motion tracks corresponding to the two target agricultural mechanical devices, and respectively generating motion control parameters corresponding to the two target agricultural mechanical devices when the track coincidence point exists between the two predicted motion tracks, wherein the motion control parameters are used for controlling the motion direction of the corresponding target agricultural mechanical devices, so that no track coincidence point exists between new predicted motion tracks formed based on the motion direction.
7. The intelligent agricultural machinery management method based on Beidou navigation according to claim 6, wherein for each target agricultural mechanical device, the step of determining the device motion direction information corresponding to the target agricultural mechanical device based on the multi-frame device environment monitoring image included in the device environment monitoring image set of the target agricultural mechanical device, and performing motion trajectory prediction processing on the target agricultural mechanical device based on the device motion direction information and the current first device position information of the target agricultural mechanical device to obtain the predicted motion trajectory corresponding to the target agricultural mechanical device comprises:
for each target agricultural mechanical device, performing image screening operation on multi-frame device environment monitoring images included in the device environment monitoring image set of the target agricultural mechanical device to obtain multi-frame target device environment monitoring images corresponding to the target agricultural mechanical device;
aiming at each two adjacent frames of target equipment environment monitoring images corresponding to each target agricultural mechanical equipment, respectively segmenting the two frames of target equipment environment monitoring images to obtain a plurality of corresponding first monitoring subimages and a plurality of corresponding second monitoring subimages, and determining a plurality of frames of corresponding target first monitoring subimages and a plurality of frames of corresponding target second monitoring subimages based on whether each frame of first monitoring subimage and each frame of second monitoring subimage corresponding to the two frames of target equipment environment monitoring images are the same, wherein the first monitoring subimage belongs to a previous frame in the two frames of target device environment monitoring images, the second monitoring subimage belongs to the next frame in the two frames of target equipment environment monitoring images, each frame of target first monitoring subimage has at least one same second monitoring subimage, and each frame of target second monitoring subimage has at least one same first monitoring subimage;
determining a first relative position relationship between corresponding multi-frame target first monitoring subimages aiming at each two adjacent frames of target equipment environment monitoring images, determining a second relative position relationship between corresponding multi-frame target second monitoring subimages, determining a candidate multi-frame second monitoring subimage with the first relative position relationship in the multi-frame second monitoring subimages which are the same as the multi-frame target first monitoring subimages, and determining a candidate multi-frame first monitoring subimage with the second relative position relationship in the multi-frame first monitoring subimages which are the same as the multi-frame target second monitoring subimages;
determining a first intersection between a corresponding multi-frame target first monitoring subimage and a multi-frame candidate first monitoring subimage aiming at each two adjacent frames of target equipment environment monitoring images, and determining a second intersection between the corresponding multi-frame target second monitor sub-image and the multi-frame candidate second monitor sub-image, determining the same monitoring subimage between the first intersection and the second intersection, determining equipment motion direction information of the corresponding target agricultural mechanical equipment in a time period corresponding to the two frames of target equipment environment monitoring images based on the positions of the monitoring subimages in the two frames of target equipment environment monitoring images respectively, and determining equipment motion direction information of each target agricultural mechanical equipment based on equipment motion direction information corresponding to each two adjacent frames of target equipment environment monitoring images in a multi-frame target equipment environment monitoring image corresponding to each target agricultural mechanical equipment;
and for each target agricultural mechanical device, performing motion track prediction processing on the target agricultural mechanical device based on the device motion direction information corresponding to the target agricultural mechanical device and the current first device position information to obtain a predicted motion track corresponding to the target agricultural mechanical device.
8. The utility model provides an intelligent agricultural machinery management system based on beidou navigation, its characterized in that is applied to intelligent agricultural machinery management cloud platform, intelligent agricultural machinery management cloud platform communication connection has a plurality of big dipper positioning device and a plurality of image acquisition equipment, and it has a big dipper positioning device and an image acquisition equipment to deploy on each agricultural machinery equipment, intelligent agricultural machinery management system based on beidou navigation includes:
the agricultural mechanical equipment determining module is used for acquiring equipment current position information of each piece of agricultural mechanical equipment based on the Beidou positioning equipment deployed on the agricultural mechanical equipment and determining whether two pieces of target agricultural mechanical equipment which have an association relationship with each other exist in a plurality of pieces of agricultural mechanical equipment or not based on the equipment current position information of each piece of agricultural mechanical equipment, wherein the equipment position distance between the two pieces of target agricultural mechanical equipment is smaller than a preset position distance threshold value;
an environment monitoring image obtaining module, configured to, if two target agricultural mechanical devices that have an association relationship with each other exist in the plurality of agricultural mechanical devices, obtain, for each target agricultural mechanical device, an equipment environment monitoring image set of the target agricultural mechanical device based on the image acquisition device deployed on the target agricultural mechanical device, where the equipment environment monitoring image set includes multiple frames of equipment environment monitoring images;
and the motion control parameter generation module is used for respectively generating motion control parameters corresponding to the two target agricultural mechanical devices based on the multi-frame device environment monitoring images included in the device environment monitoring image set of each target agricultural mechanical device, wherein when the two target agricultural mechanical devices respectively move based on the corresponding motion control parameters, the two target agricultural mechanical devices do not collide with each other.
9. The intelligent agricultural machinery management system based on Beidou navigation of claim 8, wherein the motion control parameter generation module is specifically used for:
for each target agricultural mechanical device, determining device motion direction information corresponding to the target agricultural mechanical device based on a multi-frame device environment monitoring image included in a device environment monitoring image set of the target agricultural mechanical device, and performing motion track prediction processing on the target agricultural mechanical device based on the device motion direction information and current first device position information of the target agricultural mechanical device to obtain a predicted motion track corresponding to the target agricultural mechanical device;
determining whether a track coincidence point exists between two predicted motion tracks corresponding to the two target agricultural mechanical devices, and respectively generating motion control parameters corresponding to the two target agricultural mechanical devices when the track coincidence point exists between the two predicted motion tracks, wherein the motion control parameters are used for controlling the motion direction of the corresponding target agricultural mechanical devices, so that no track coincidence point exists between new predicted motion tracks formed based on the motion direction.
10. An intelligent agricultural machinery management cloud platform is used for executing the intelligent agricultural machinery management method based on Beidou navigation and defined by any one of claims 1 to 7.
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