CN114418251B - Intelligent monitoring system and monitoring method for permanent basic farmland - Google Patents
Intelligent monitoring system and monitoring method for permanent basic farmland Download PDFInfo
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Abstract
The invention relates to an intelligent monitoring method for a permanent basic farmland, which comprises the following steps of S1, establishing a region to be monitored by a self-learning module according to the preliminary cruise of an unmanned aerial vehicle on the edge region of the basic farmland to be monitored; step S2, the cruise customizing module divides the area to be monitored into a plurality of subareas to be monitored according to the similarity between the area to be monitored acquired by the self-learning module and the area to be monitored acquired by the current basic farmland area stored by the storage module; step S3, the central control module obtains the complexity of the subarea to be monitored according to the area and the irregularity of the subarea to be monitored, and the central control module selects the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored according to the comparison between the complexity of the subarea to be monitored and the preset complexity; and step S4, the central control module acquires crop growth information data of the farmland in the current subarea to be monitored through the unmanned aerial vehicle, compares the crop growth information data with the crop growth information data predicted by the crop growth model library, and judges the farmland quality of the current subarea to be monitored.
Description
Technical Field
The invention relates to the field of intelligent monitoring, in particular to an intelligent monitoring system and method for permanent basic farmland.
Background
Agricultural problems are a fundamental problem for global sustainable development and also a fundamental industry in one country. Today, with the rapid development of intellectualization, fine agriculture becomes a new trend of agricultural development in the new century, and agricultural intellectualization also becomes a very important subject today. The real-time collection of the farmland information is the basic requirement of fine agriculture, a complete set of complete intelligent agricultural farmland data collection system is provided, whether each index information in the agricultural production process can be timely, accurately and efficiently obtained or not is judged, and the key of agricultural production management and decision making is improved.
The permanent basic farmland is a farmland which is determined by China according to the requirements of population and social and economic development in a certain period and cannot be occupied according to the general land utilization plan, high-quality farmlands which are easy to be occupied around cities are preferentially divided into the permanent basic farmland, the urbanization process is strictly controlled, the occupation of the farmlands, particularly the high-quality farmlands around the cities is accelerated, fertile farmland soil is reserved for descendants, the permanent basic farmland is mainly used for planting grain crops such as wheat, corn, rice and the like, and therefore the regional range of the permanent farmland and the planted crops are kept stable.
People can not monitor the growth state of crops in real time in the agricultural production process, and people can not know and improve in time when the water content of the crops changes, the growth of the crops is influenced, the modern agricultural production mode is difficult to meet, and the growth environment of the crops can not be monitored, so that nutrition and moisture can not be supplemented to the crops in time, a large amount of crops can not grow well, and the yield is low. Chinese patent ZL201610175984.6 discloses a remote wireless farmland monitoring system and method, which solves the technical problems of low utilization rate of irrigation water and low measurement accuracy, high investment cost and manpower consumption of the traditional agricultural irrigation water monitoring method by adopting a farmland information acquisition subsystem, an irrigation control subsystem, a wireless remote transmission unit and a cloud computing server, but still has the technical problem that accurate monitoring of the growth state of permanent basic farmland planting areas and crops cannot be performed in real time.
Disclosure of Invention
Therefore, the invention provides an intelligent monitoring system and a monitoring method for a permanent basic farmland, which can solve the technical problem that the monitoring area and the monitoring mode cannot be adjusted according to the complexity of the monitoring area of the permanent basic farmland.
To achieve the above objects, in one aspect, the present invention provides an intelligent monitoring method for permanent basic farmland, comprising:
step S1, the self-learning module performs preliminary cruising on the edge area of the basic farmland to be monitored according to the unmanned aerial vehicle to establish the area to be monitored;
step S2, the cruise customizing module divides the area to be monitored into a plurality of subareas to be monitored according to the similarity between the area to be monitored acquired by the self-learning module and the area to be monitored acquired by the current basic farmland area stored by the storage module;
step S3, the central control module obtains the complexity of the subarea to be monitored according to the area and the irregularity of the subarea to be monitored, and the central control module selects the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored according to the comparison between the complexity of the subarea to be monitored and the preset complexity;
and step S4, the central control module acquires the farmland crop growth information data of the current subarea to be monitored through the unmanned aerial vehicle, compares the obtained farmland crop growth information data with the current crop growth information data predicted by the crop growth model library, and judges the farmland quality of the current subarea to be monitored, wherein if the difference between the acquired farmland crop growth information data of the current subarea to be monitored and the predicted current crop growth information data of the central control module is smaller than a preset difference, the central control module judges that the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored is prolonged, the subarea to be monitored is re-divided to determine the current crop growth information data, if the difference between the acquired farmland crop growth information data of the current subarea to be monitored and the predicted current crop growth information data of the central control module is larger than the preset difference, the central control module judges that the farmland quality of the current subarea to be monitored does not accord with the standard.
Furthermore, the central control module takes the central point of the area to be monitored and the central point of the basic farmland area as the origin, and takes the pixel as the unit to establish a plane rectangular coordinate system, the central control module obtains the similarity s of the area to be monitored according to the gray value of each point of the area to be monitored and the basic farmland area, and sets s = to
Wherein xi is the gray value of the ith point of the basic farmland area, yi is the gray value of the ith point of the area to be monitored, and n is the total number of points of each area.
Further, the central control module presets a similarity S, the central control module compares the obtained similarity of the regions to be monitored with the preset similarity, and the obtained number of the subareas to be monitored is divided into the regions to be monitored, wherein,
when S is larger than or equal to S2, the central control module divides the area to be monitored into M0 subareas to be monitored;
when S1 < S < S2, the central control module divides an area to be monitored into M1 subareas to be monitored, and sets M1= M0 x (1+ (S2-S) x (S-S1)/(S1 x S2)), and if M1 is a non-integer, the whole is rounded upwards;
when S is less than or equal to S1, the central control module divides the region to be monitored into M2 subareas to be monitored, and M2= M0 x (1+ ((S1-S)/S1)2) If M1 is a non-integer, rounding up;
the central control module is preset with a similarity S, a first preset similarity S1 and a second preset similarity S2 are set, and M0 is used for presetting the number of the subareas to be monitored for the central control module.
Further, the central control module obtains complexity dj of a partition to be monitored currently, and sets dj = (1+ (gj-g0)/g0) × (1+ (mj-m0)/m0), wherein gj is a regularity of the j-th partition to be monitored, g0 is a regularity standard value, mj is an area of the j-th partition to be monitored, and m0 is an area standard value.
Further, the central control module obtains the regularity gi of the partition to be monitored according to the slope of each adjacent characteristic point of the contour of the partition to be monitored, and sets gi = (ki12-ki0)2+(ki23-ki0)2+···+(kiei1-ki0)2And/ei, wherein ki12 is the slope of the first characteristic point and the second characteristic point of the ith to-be-monitored area, ki23 is the slope of the second characteristic point and the third characteristic point of the ith to-be-monitored area, kiei1 is the slope of the eith characteristic point and the 1 st characteristic point of the ith to-be-monitored area, ki0 is the average value of the slopes of all adjacent characteristic points of the ith to-be-monitored area, and ei is the number of characteristic points of the ith to-be-monitored area.
Further, the central control module selects the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored according to the comparison between the complexity of the current subarea to be monitored and the preset complexity D, wherein,
when dj is less than or equal to D1, the central control module selects a first preset time T1 as the cruising staying time of the unmanned aerial vehicle in the jth to-be-monitored partition;
when D1 is more than dj and less than D2, the central control module selects a second preset time T2 as the cruising staying time of the unmanned aerial vehicle in the j-th subarea to be monitored;
when dj is larger than or equal to D2, the central control module selects a third preset time T3 as the cruising staying time of the unmanned aerial vehicle in the jth to-be-monitored partition;
the central control module presets time T, sets a first preset time T1 and a second preset time T2, presets complexity D, sets a first preset complexity D1 and a second preset complexity D2, j =1,2 · · Mr, r =0,1, 2.
Furthermore, the crop growth model base presets farmland crop growth information data A, the central control module compares the current farmland crop growth information data a of the subarea to be monitored with the preset farmland crop growth information data A to judge the farmland quality of the subarea to be monitored, wherein,
when a is not more than A1, the central control module analyzes the subarea to be monitored again;
when A1 is more than a and less than A2, the central control module judges that the quality of the subarea farmland to be monitored at present meets the preset standard;
when a is larger than or equal to A2, the central control module analyzes the subarea to be monitored again;
the crop growth model library farmland crop growth information data A sets first preset farmland crop growth information data A1, and second preset farmland crop growth information data A2.
Further, when the central control module determines to analyze the subarea to be monitored again, the central control module obtains a difference value Δ H between the farmland crop growth information data a of the current subarea to be monitored and the predicted current crop growth information data A0, sets Δ H = | a-A0|, the central control module compares the obtained difference value with a preset difference value H, and analyzes the subarea to be monitored again, wherein,
when the delta H is less than or equal to H1, the central control module shrinks the subarea to be monitored;
when H1 < [ delta ] H < H2, the central control module judges that the cruising staying time Tp of the unmanned aerial vehicle in the current subarea to be monitored is prolonged to Tp1, and sets Tp1= Tp x (1+ (H2-delta H) × ([ delta ] H-H1)/(H1 × H2));
when the delta H is larger than or equal to H2, the central control module of the central control module judges that the quality of the farmland of the current subarea to be monitored does not meet the standard;
the central control module presets a difference value H, and sets a first preset difference value H1 and a second preset difference value H2, wherein p =1,2 and 3.
Further, the central control module obtains that the absolute value of the difference between the farmland crop growth information data of the current subarea to be monitored and the predicted crop growth information data is smaller than or equal to a first preset difference, the central control module judges that the subarea to be monitored is reduced, the central control module increases the number Mr of the subareas to be monitored to Mr1, Mr1= Mr x (1+ (H1-delta H)/H1) is set, and if Mr1 is a non-integer, the integral is rounded upwards.
In another aspect, the present invention provides an intelligent monitoring system for permanent basic farmlands, which includes a storage module including a farmland basic information base and a crop growth model base, wherein the farmland basic information base stores basic information of each permanent basic farmland, the basic information of each permanent basic farmland includes farmland position, area, variety of planted crops, planting management mode, etc., and the crop growth model base stores growth information data of each growth period of each farmland crop;
the self-learning module is used for performing preliminary cruising on the edge area of the basic farmland to be monitored by the unmanned aerial vehicle to obtain the area to be monitored;
the cruise customizing module is used for acquiring the region similarity of the region to be monitored through the region to be monitored and the region of the permanent basic farmland, comparing the acquired region similarity with the preset region similarity, and dividing the region to be monitored into a plurality of subareas to be monitored;
the central control module acquires the complexity of the current subarea to be monitored according to the area and the rule of the current subarea to be monitored, the central control module compares the complexity of the current subarea to be monitored with a preset complexity, selects the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored, acquires the crop growth information data of the current subarea to be monitored, compares the crop growth information data with the crop growth information data predicted by the crop growth model base, judges whether the current crop growth meets the standard, if the current crop growth does not meet the preset standard, the central control module compares the difference value between the current crop growth information and the growth standard value with the preset difference value, wherein if the difference value between the current crop growth information and the growth standard value is smaller, the central control module determines the current crop growth information data by prolonging the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored or subdividing the subarea to be monitored, and if the difference value between the current crop growth information and the growth standard value is larger, the central control module judges that the quality of the farmland of the current subarea to be monitored is poor.
Compared with the prior art, the method has the advantages that the central control module is arranged, the central control module acquires the complexity of the subarea to be monitored according to the area and the irregularity of the subarea to be monitored, the central control module selects the cruising and staying time of the unmanned aerial vehicle in the current subarea to be monitored according to the complexity of the subarea to be monitored and the preset complexity, the central control module acquires the growth information data of the farmland crops in the current subarea to be monitored through the unmanned aerial vehicle, compares the growth information data with the predicted current crop growth information data of the crop growth model base, and judges the farmland quality of the current subarea to be monitored, wherein if the difference value between the acquired growth information data of the farmland crops in the current subarea to be monitored and the predicted current crop growth information data of the central control module is smaller than the preset difference value, the central control module judges that the cruising and staying time of the unmanned aerial vehicle in the current subarea to be monitored is prolonged, and the subarea to be monitored is subdivided to determine the current crop growth information data, and if the difference value between the farmland crop growth information data of the current subarea to be monitored acquired by the central control module and the current crop growth information data is predicted to be larger than the preset difference value, the central control module judges that the farmland quality of the current subarea to be monitored does not meet the standard.
Particularly, the method comprises the steps of acquiring similarity of a region to be monitored according to gray values of each point of the region to be monitored, which are established by a basic farmland region stored by a storage module and an unmanned aerial vehicle in a primary cruising mode, dividing the set similarity into two standards by a central control module, respectively comparing the acquired similarity of the region to be monitored with the two standards of the preset similarity by the central control module, and dividing the region to be monitored into the optimal number of partitions to be monitored, wherein if the similarity of the region to be monitored acquired by the central control module is greater than or equal to a second preset similarity, the similarity between the current region to be monitored and the basic farmland region stored by the storage module is higher, the central control module divides the region to be monitored into partitions according to the standard value of the preset partitions to be monitored, and if the similarity of the region to be monitored acquired by the central control module is between the first preset similarity and the second similarity, the difference between the current region to be monitored and the basic farmland region of the storage module is certain, the central control module divides the to-be-monitored area by adding a preset to-be-monitored partition quantity standard value so as to improve the accuracy of acquiring the divided to-be-monitored partition data, if the similarity of the to-be-monitored area acquired by the central control module is smaller than or equal to a first preset similarity, the situation that the current to-be-monitored area is greatly different from the basic farmland area of the storage module is shown, and the central control module needs to greatly improve the preset to-be-monitored partition quantity standard value so as to divide the to-be-monitored area based on the characteristic of invariability of the permanent basic farmland, so that the accurate analysis of the to-be-monitored area is realized.
Particularly, the regularity of the partition to be monitored is obtained according to the slope of the adjacent characteristic points of the partition to be monitored, and the regularity is used for evaluating the regularity of the profile of the partition to be monitored, namely when the slope change condition of each characteristic point of the profile of the partition to be monitored is more complex, the regularity of the profile of the current partition to be monitored is lower, and when the slope change condition of each characteristic point of the profile of the partition to be monitored is simpler, the regularity of the profile of the current partition to be monitored is higher. Furthermore, the area and the regularity of the subarea to be monitored are used for comprehensively evaluating the complexity of the subarea to be monitored so as to obtain the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored.
In particular, the invention acquires the complexity of the subarea to be monitored according to the rule of the subarea to be monitored and the area of the subarea to be monitored, the central control module compares the acquired complexity of the subarea to be monitored with a preset complexity to acquire the cruising staying time of the unmanned aerial vehicle on the subarea to be monitored so as to ensure that the unmanned aerial vehicle can have enough time to monitor the subarea to be monitored, wherein the complexity of the current subarea to be monitored acquired by the central control module is less than or equal to a first preset complexity to indicate that the current subarea to be monitored is not complicated, the central control module selects the first preset time as the cruising staying time of the unmanned aerial vehicle on the current subarea to be monitored so as to improve the cruising efficiency, the complexity of the current subarea to be monitored is between the first preset complexity and a second preset complexity to indicate that the current subarea to be monitored is complicated, and the central control module selects the second preset time as the cruising staying time of the unmanned aerial vehicle on the current subarea to be monitored, the complexity of the current subarea to be monitored is more than or equal to a second preset complexity, which shows that the situation of the current subarea to be monitored is very complex, and the central control module selects a third preset time as the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored so as to more accurately obtain the crop growth situation of the current subarea to be monitored.
In particular, the control module of the invention presets a difference value, compares the absolute value of the difference value between the current farmland crop growth information data of the subarea to be monitored and the current crop growth information data predicted to be compared with the preset difference value, analyzes the subareas to be monitored customized and divided by the cruise customization module again to determine whether the information acquisition of the current subarea to be monitored is accurate, wherein if the difference value acquired by the central control module is less than or equal to a first preset difference value, the current subarea to be monitored is not accurately acquired, the central control module judges that the number of the subareas is increased by taking the acquired difference value and the preset first difference value as a reference to reduce the area of each subarea to be monitored so as to more accurately acquire the farmland crop growth condition of the current area, and when the difference value acquired by the central control module is between the first preset difference value and a second preset difference value, the current subarea to be monitored is not accurately acquired, the central control module prolongs the cruising retention time of the unmanned aerial vehicle in the current subarea to be monitored so as to accurately acquire the farmland planting situation of the current subarea to be monitored, if the difference value acquired by the central control module is greater than or equal to a second preset difference value, the difference between the farmland crop planting situation of the current subarea to be monitored and the predicted planting data is larger, the central control module judges that the environment situation of the current farmland is poorer, and the soil and crops need to be maintained according to the water and fertilizer application situation of the central control module, so that the quality of the permanent farmland meets the standard.
Drawings
FIG. 1 is a schematic diagram of an intelligent monitoring system for permanent basic farmland according to an embodiment of the invention;
FIG. 2 illustrates an intelligent monitoring method for permanent basic farmland according to an embodiment of the invention;
FIG. 3 is a schematic diagram of an area to be monitored according to an embodiment of the present invention;
FIG. 4 is a schematic view of an area to be monitored according to another embodiment of the present invention;
fig. 5 is a schematic diagram of an irregular region to be monitored according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described below with reference to examples; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and do not limit the scope of the present invention.
It should be noted that in the description of the present invention, the terms of direction or positional relationship indicated by the terms "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, which are only for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1, it is shown that the intelligent monitoring system for permanent basic farmland according to the embodiment of the present invention comprises,
the storage module comprises a farmland basic information base and a crop growth model base, wherein the farmland basic information base stores basic information of each permanent basic farmland, the permanent farmland basic information comprises farmland positions, areas, planted crop varieties, planting management modes and the like, and the crop growth model base stores growth information data of each growth period of each farmland crop;
the self-learning module is used for performing preliminary cruising on the edge area of the basic farmland to be monitored by the unmanned aerial vehicle to obtain the area to be monitored;
the cruise customizing module is used for acquiring the region similarity of the region to be monitored through the region to be monitored and the region of the permanent basic farmland, comparing the acquired region similarity with the preset region similarity, and dividing the region to be monitored into a plurality of regions to be monitored;
the central control module acquires the complexity of the current subarea to be monitored according to the area and the rule of the current subarea to be monitored, the central control module compares the complexity of the current subarea to be monitored with a preset complexity, selects the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored, acquires the farmland crop growth information data of the current subarea to be monitored, compares the farmland crop growth information data with the current crop growth information data predicted by a crop growth model base, judges whether the current crop growth meets a standard, if the current crop growth does not meet the preset standard, the central control module compares the difference value between the current crop growth information and the growth standard value with a preset difference value, wherein if the difference value between the current crop growth information and the growth standard value is smaller, the central control module determines the current crop growth information data by prolonging the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored or reclassifying the subarea to be monitored, and if the difference value between the current crop growth information and the growth standard value is larger, the central control module judges that the quality of the farmland of the current subarea to be monitored is poor.
Specifically, the embodiment of the invention does not limit the data type of the farmland crop growth information, and the farmland crop growth situation, the plant height, the plant color, the crop growth density and the like in each growth period can be used for evaluating the data of the farmland crop growth condition.
Referring to fig. 2, a schematic diagram of an intelligent monitoring method for permanent basic farmland according to an embodiment of the present invention is shown, which includes,
step S1, the self-learning module carries out preliminary cruising on the edge area of the basic farmland to be monitored according to the unmanned aerial vehicle to establish the area to be monitored;
step S2, the cruise customizing module divides the area to be monitored into a plurality of subareas to be monitored according to the similarity between the area to be monitored acquired by the self-learning module and the area to be monitored acquired by the current basic farmland area stored by the storage module;
step S3, the central control module obtains the complexity of the subarea to be monitored according to the area and the irregularity of the subarea to be monitored, and the central control module selects the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored according to the comparison between the complexity of the subarea to be monitored and the preset complexity;
and step S4, the central control module acquires the growth information data of the farmland crops of the current subarea to be monitored through the unmanned aerial vehicle, compares the growth information data with the current crop growth information data predicted by the crop growth model base, and judges the farmland quality of the current subarea to be monitored, wherein if the difference between the acquired growth information data of the farmland crops of the current subarea to be monitored and the predicted growth information data of the current crops is smaller than a preset difference, the central control module judges that the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored is prolonged, the subarea to be monitored is re-divided to determine the current crop growth information data, if the difference between the acquired growth information data of the farmland crops of the current subarea to be monitored and the predicted growth information data of the current crops is larger than the preset difference, the central control module judges that the farmland quality of the current subarea to be monitored does not accord with the standard.
The central control module takes the central points of the area to be monitored and the basic farmland area as original points, establishes a plane rectangular coordinate system by taking pixels as units, and is used for establishing a plane rectangular coordinate system according to the area to be monitoredObtaining the similarity s of the region to be monitored with the gray value of each point of the basic farmland region, and setting s =
Wherein xi is the ith gray value of the basic farmland area, yi is the ith gray value of the area to be monitored, and n is the total number of points in each area.
Specifically, the central control module presets a similarity S, compares the obtained similarity of the region to be monitored with the preset similarity, and obtains the number of the subareas to be monitored to divide the region to be monitored, wherein,
when S is larger than or equal to S2, the central control module divides the area to be monitored into M0 subareas to be monitored;
when S1 < S < S2, the central control module divides an area to be monitored into M1 subareas to be monitored, and sets M1= M0 x (1+ (S2-S) x (S-S1)/(S1 x S2)), and if M1 is a non-integer, the whole is rounded upwards;
when S is less than or equal to S1, the central control module divides the region to be monitored into M2 subareas to be monitored, and M2= M0 x (1+ ((S1-S)/S1) is set2) If M1 is a non-integer, rounding up;
the central control module presets a similarity degree S, a first preset similarity degree S1 and a second preset similarity degree S2 are set, and M0 presets the number of partitions to be monitored for the central control module.
Specifically, the method includes the steps that the similarity of a region to be monitored is obtained according to gray values of all points of the region to be monitored, which are established by a basic farmland region stored by a storage module and an unmanned aerial vehicle in a primary cruising mode, a central control module divides the region to be monitored into two standards, the central control module obtains the optimal number of partitions to be monitored according to the obtained similarity of the region to be monitored and the two standards of the preset similarity, if the similarity of the region to be monitored obtained by the central control module is larger than or equal to a second preset similarity, the similarity of the current region to be monitored and the basic farmland region stored by the storage module is higher, the central control module divides the region to be monitored according to the standard value of the preset partitions to be monitored, and if the similarity of the region to be monitored obtained by the central control module is between the first preset similarity and the second similarity, the difference between the current region to be monitored and the basic farmland region of the storage module is shown, the central control module divides the area to be monitored by adding a preset number standard value of the area to be monitored so as to improve the accuracy of acquiring data of the divided area to be monitored, if the similarity of the area to be monitored acquired by the central control module is less than or equal to a first preset similarity, the fact that the current area to be monitored is greatly different from the basic farmland area of the storage module is shown, and the central control module needs to greatly improve the preset number standard value of the area to be monitored so as to divide the area to be monitored based on the unchangeable characteristic of the permanent basic farmland, so that accurate analysis of the area to be monitored is realized.
Specifically, the partition dividing method of the area to be monitored is not limited in the embodiments of the present invention, as long as the partition dividing method can reasonably divide each partition of the area to be monitored. The embodiment of the present invention provides a preferred implementation, please refer to fig. 3, which is a schematic diagram of an area to be monitored according to the embodiment of the present invention, wherein, the first subarea to be monitored 11, the second subarea to be monitored 12, the third subarea to be monitored 13, the fourth subarea to be monitored 14, the fifth subarea to be monitored 15, the sixth subarea to be monitored 16, the seventh subarea to be monitored 17 and the eighth subarea to be monitored 18, the cruise customization module is not limited to design the cruise route of the unmanned aerial vehicle in the embodiment of the invention, as long as the cruise customization module can perform cruise monitoring on each monitoring subarea, and aiming at the embodiment, the invention provides a preferable cruise route, the cruise sequence is from the first subarea to be monitored 11 to the second subarea to be monitored 12 to the third subarea to be monitored 13 to the fourth subarea to be monitored 14 to the eighth subarea to be monitored 18 to the seventh subarea to be monitored 17 to the sixth subarea to be monitored 16 to the fifth subarea to be monitored 15.
Please refer to fig. 4, which is a schematic diagram of a to-be-monitored area according to another embodiment of the present invention, including a ninth to-be-monitored partition 21, a tenth to-be-monitored partition 22, an eleventh to-be-monitored partition 23, a twelfth to-be-monitored partition 24, a thirteenth to-be-monitored partition 25, a fourteenth to-be-monitored partition 26, a fifteenth to-be-monitored partition 27, a sixteenth to-be-monitored partition 28, and a seventeenth to-be-monitored partition 29, wherein the unmanned aerial vehicle cruise wirelessly may be the ninth to-be-monitored partition 21 to the tenth to-be-monitored partition 22 to the eleventh to-be-monitored partition 23 to the twelfth to-be-monitored partition 24 to the thirteenth to-be-monitored partition 25 to the fourteenth to-monitored partition 27 to the seventeenth to-be-monitored partition 29.
Please refer to fig. 5, which is a schematic diagram of an irregular area to be monitored according to an embodiment of the present invention, including a thirty-first to-be-monitored partition 301, a thirty-second to-be-monitored partition 302, a thirty-third to-be-monitored partition 303, a thirty-fourth to-be-monitored partition 304, a thirty-fifth to-be-monitored partition 305, a thirty-sixth to-be-monitored partition 306, a thirty-seventh to-be-monitored partition 307, a thirty-eighth to-be-monitored partition 308, a thirty-ninth to-be-monitored partition 309, a forty-to-be-monitored partition 310, a forty-first to-be-monitored partition 311, a forty-second to-be-monitored partition 312, a forty-third to-be-monitored partition 313, and a forty-fourth to-be-monitored partition 314, wherein the unmanned aerial vehicle cruise radio may be configured from the thirty-first to thirty-second to-third to-fourth to-be-monitored partitions 301, 303, 304, the thirty-fifth to 305, the thirty-sixth to thirty-seventh to ninth to-to thirty-to-ninth to-third to-monitor partitions 314 The monitoring sections 309 to 310 to 311 to 312 to forty-second to-be-monitored sections to 313 to 314.
The central control module obtains complexity dj of a current partition to be monitored, and sets dj = (1+ (gj-g0)/g0) × (1+ (mj-m0)/m0), wherein gj is the regularity of the j-th partition to be monitored, g0 is a regularity standard value, mj is the area of the j-th partition to be monitored, and m0 is an area standard value.
The central control module obtains the regularity gi of the subarea to be monitored according to the slope of each adjacent characteristic point of the outline of the subarea to be monitored, and gi = (ki12-ki0)2+(ki23-ki0)2+···+(kiei1-ki0)2I/ei, wherein ki12 is the slope of the first characteristic point and the second characteristic point of the ith region to be monitored, ki23 is the slope of the second characteristic point and the third characteristic point of the ith region to be monitored, and kiei1 is the slope of the second characteristic point and the 1 st characteristic point of the ith region to be monitoredAnd ki0 is the average value of the slopes of the adjacent characteristic points of the ith to-be-monitored area, wherein ei is the number of the characteristic points of the ith to-be-monitored area.
Specifically, the regularity of the partition to be monitored is obtained according to the slope of the adjacent characteristic points of the partition to be monitored, and is used for evaluating the regularity of the contour of the partition to be monitored, namely when the slope change condition of each characteristic point of the contour of the partition to be monitored is complex, the regularity of the contour of the partition to be monitored is low, and when the slope change condition of each characteristic point of the contour of the partition to be monitored is simple, the regularity of the contour of the partition to be monitored is high. Furthermore, the area and the regularity of the subarea to be monitored are used for comprehensively evaluating the complexity of the subarea to be monitored so as to obtain the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored.
The central control module selects the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored according to the comparison between the complexity of the current subarea to be monitored and the preset complexity D, wherein,
when dj is less than or equal to D1, the central control module selects a first preset time T1 as the cruising staying time of the unmanned aerial vehicle in the jth subarea to be monitored;
when D1 is greater than dj and less than D2, the central control module selects second preset time T2 as the cruising staying time of the unmanned aerial vehicle in the jth subarea to be monitored;
when dj is larger than or equal to D2, the central control module selects a third preset time T3 as the cruising staying time of the unmanned aerial vehicle in the jth to-be-monitored partition;
the central control module presets time T, sets a first preset time T1 and a second preset time T2, presets complexity D, sets a first preset complexity D1 and a second preset complexity D2, j =1,2 · · Mr, r =0,1, 2.
Specifically, the invention obtains the complexity of the subarea to be monitored according to the rule of the subarea to be monitored and the area of the subarea to be monitored, the central control module compares the obtained complexity of the subarea to be monitored with a preset complexity to obtain the cruising staying time of the unmanned aerial vehicle on the subarea to be monitored so as to ensure that the unmanned aerial vehicle can have enough time to monitor the subarea to be monitored, wherein the complexity of the current subarea to be monitored, which is obtained by the central control module, is less than or equal to a first preset complexity, which indicates that the current subarea to be monitored is not complicated, the central control module selects the first preset time as the cruising staying time of the unmanned aerial vehicle on the current subarea to be monitored so as to improve the cruising efficiency, the complexity of the current subarea to be monitored is between the first preset complexity and a second preset complexity, which indicates that the current subarea to be monitored is complicated, and the central control module selects the second preset time as the cruising staying time of the unmanned aerial vehicle on the current subarea to be monitored, the complexity of the current subarea to be monitored is more than or equal to a second preset complexity, which shows that the situation of the current subarea to be monitored is very complex, and the central control module selects a third preset time as the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored so as to more accurately obtain the crop growth situation of the current subarea to be monitored.
The crop growth model base is preset with farmland crop growth information data A, the central control module compares the current farmland crop growth information data a of the subarea to be monitored with the preset farmland crop growth information data A to judge the farmland quality of the subarea to be monitored, wherein,
when a is not more than A1, the central control module analyzes the subarea to be monitored again;
when A1 is more than a and less than A2, the central control module judges that the quality of the subarea farmland to be monitored at present meets the preset standard;
when a is larger than or equal to A2, the central control module analyzes the subarea to be monitored again;
the crop growth model library farmland crop growth information data A sets first preset farmland crop growth information data A1, and second preset farmland crop growth information data A2.
Specifically, in the embodiment of the present invention, the crop growth model base stores the reference values of the growth information of each crop in multiple growth periods, the embodiment of the present invention does not limit the types and data values of the crop growth information data in each period stored in the crop growth model base, as long as the data can be used as a standard to evaluate the crop growth information data obtained by the crop planted in the sub-area to be monitored by the unmanned aerial vehicle in the monitoring period, and the embodiment of the present invention provides a preferred embodiment, if the crop planted in the farmland is wheat, the crop growth model base stores standard values of the growth data in each growth period of wheat, for example, the standard values of the growth data in each growth period of wheat, such as the growth information data (4-7) in the sowing period of wheat, the growth information data (1-2 cm) in the green turning period of wheat, the growth information data (1.5-2.5 cm) in the jointing period of wheat, and the growth information data (1.5-2.5 cm) in the jointing period of wheat, The heading stage can use the occurrence condition of wheat diseases and insect pests, the growth height of wheat and the heading number of wheat as growth information data and the like, if the current farmland planted crops are corn, the seedling height and emergence rate of the corn seeding stage are used as the growth information data, the plant height of the corn seedling stage is used as the growth information data, the plant height and stem thickness of the corn heading stage are used as the growth information data, and the plant height and the fruit ear amount of the corn mature stage are used as the growth information data and the like.
When the central control module judges that the subarea to be monitored is analyzed again, the central control module acquires the difference value delta H between the farmland crop growth information data a of the current subarea to be monitored and the predicted current crop growth information data A0, sets delta H = | a-A0|, the central control module compares the acquired difference value with the preset difference value H, and analyzes the subarea to be monitored again, wherein,
when the delta H is less than or equal to H1, the central control module shrinks the subarea to be monitored;
when H1 < [ delta ] H < H2, the central control module judges that the cruising staying time Tp of the unmanned aerial vehicle in the current subarea to be monitored is prolonged to Tp1, and sets Tp1= Tp x (1+ (H2-delta H) × ([ delta ] H-H1)/(H1 × H2));
when the delta H is larger than or equal to H2, the central control module of the central control module judges that the quality of the farmland of the current subarea to be monitored does not meet the standard;
the central control module presets a difference value H, and sets a first preset difference value H1 and a second preset difference value H2, wherein p =1,2 and 3.
The central control module obtains that the absolute value of the difference value between the farmland crop growth information data of the current subarea to be monitored and the predicted crop growth information data is smaller than or equal to a first preset difference value, the central control module judges that the subarea to be monitored is reduced, the central control module increases the number Mr of the subareas to be monitored to Mr1, Mr1= Mr x (1+ (H1-delta H)/H1) is set, and if Mr1 is a non-integer, the integral is rounded upwards.
Specifically, the control module of the invention presets a difference value, compares the absolute value of the difference value between the current farmland crop growth information data of the subarea to be monitored and the current crop growth information data predicted, analyzes the subareas to be monitored customized and divided by the cruise customization module again to determine whether the information acquisition of the farmland crop of the current subarea to be monitored is accurate, wherein if the difference value acquired by the central control module is less than or equal to a first preset difference value, the current subarea to be monitored is not accurately acquired, the central control module judges that the number of the subareas is increased by taking the acquired difference value and the preset first difference value as a reference to reduce the area of each subarea to be monitored so as to more accurately acquire the farmland crop growth condition of the current area, and when the difference value acquired by the central control module is between the first preset difference value and a second preset difference value, the current subarea to be monitored is not accurately acquired, the central control module prolongs the cruising retention time of the unmanned aerial vehicle in the current subarea to be monitored so as to accurately acquire the farmland planting situation of the current subarea to be monitored, if the difference value acquired by the central control module is greater than or equal to a second preset difference value, the difference between the farmland crop planting situation of the current subarea to be monitored and the predicted planting data is larger, the central control module judges that the environment situation of the current farmland is poorer, and the soil and crops need to be maintained according to the water and fertilizer application situation of the central control module, so that the quality of the permanent farmland meets the standard.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
Claims (10)
1. An intelligent monitoring method for permanent basic farmland, characterized by comprising:
step S1, the self-learning module performs preliminary cruising on the edge area of the basic farmland to be monitored according to the unmanned aerial vehicle to establish the area to be monitored;
step S2, the cruise customizing module divides the area to be monitored into a plurality of subareas to be monitored according to the similarity between the area to be monitored acquired by the self-learning module and the area to be monitored acquired by the current basic farmland area stored by the storage module;
step S3, the central control module acquires the complexity of the subarea to be monitored according to the area and the irregularity of the subarea to be monitored, the central control module compares the complexity of the subarea to be monitored with a preset complexity to select the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored, wherein the central control module acquires the complexity of the subarea to be monitored according to the regularity and the area of the subarea to be monitored, the central control module compares the acquired complexity of the subarea to be monitored with the preset complexity to acquire the cruising staying time of the unmanned aerial vehicle on the subarea to be monitored so as to ensure that the unmanned aerial vehicle can have enough time to monitor the subarea to be monitored, wherein the complexity of the current subarea to be monitored acquired by the central control module is less than or equal to the first preset complexity, and the central control module selects the first preset time as the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored, the method comprises the steps that the cruising efficiency is improved, the complexity of a current subarea to be monitored is obtained by the central control module to be between a first preset complexity and a second preset complexity, the second preset time is selected by the central control module to be used as the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored, the complexity of the current subarea to be monitored is more than or equal to the second preset complexity, the third preset time is selected by the central control module to be used as the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored, and the crop growth condition of the current subarea to be monitored is more accurately obtained;
step S4, the central control module obtains the growth information data of the farmland in the current subarea to be monitored through the unmanned aerial vehicle, compares the growth information data with the current crop growth information data predicted by the crop growth model base, and judges the farmland quality of the current subarea to be monitored, wherein if the difference between the obtained growth information data of the farmland in the current subarea to be monitored and the predicted growth information data of the current crop is smaller than a preset difference, the central control module judges to prolong the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored, the subarea to be monitored is re-divided to make clear the growth information data of the current crop, the central control module judges to increase the number of the subareas by taking the obtained difference and the preset first difference as the reference, so as to reduce the area of each subarea to be monitored, so as to more accurately obtain the growth condition of the farmland in the current area, and if the difference between the obtained growth information data of the farmland in the current subarea to be monitored and the predicted growth information data of the crop is larger than the predicted by the central control module And presetting a difference value, and judging that the quality of the farmland of the current subarea to be monitored does not meet the standard by the central control module.
2. The intelligent monitoring method for permanent basic farmland according to claim 1, wherein the central control module takes the central points of the area to be monitored and the basic farmland area as the origin, establishes a plane rectangular coordinate system by taking pixels as units, acquires the similarity s of the area to be monitored according to the gray values of the points of the area to be monitored and the basic farmland area, and sets
Wherein xi is the ith gray value of the basic farmland area, yi is the ith gray value of the area to be monitored, and n is the total number of points in each area.
3. The intelligent monitoring method for permanent basic farmland according to claim 2, characterized in that the central control module presets a similarity S, the central control module compares the obtained similarity of the areas to be monitored with the preset similarity, and obtains the number of the subareas to be monitored to divide the areas to be monitored, wherein,
when S is larger than or equal to S2, the central control module divides the area to be monitored into M0 subareas to be monitored;
when S1 < S < S2, the central control module divides an area to be monitored into M1 subareas to be monitored, and sets M1= M0 x (1+ (S2-S) x (S-S1)/(S1 x S2)), and if M1 is a non-integer, the whole is rounded upwards;
when S is less than or equal to S1, the central control module is used for monitoring the area to be monitoredDivision into M2 subareas to be monitored, setting M2= M0X (1+ ((S1-S)/S1)2) If M1 is a non-integer, rounding up;
the central control module presets a similarity degree S, a first preset similarity degree S1 and a second preset similarity degree S2 are set, and M0 presets the number of partitions to be monitored for the central control module.
4. The intelligent monitoring method for permanent basic farmland as claimed in claim 3, wherein the central control module obtains complexity dj of the partition to be monitored currently, and sets dj = (1+ (gj-g0)/g0) × (1+ (mj-m0)/m0), wherein gj is regularity of the j-th partition to be monitored, g0 is regularity standard value, mj is area of the j-th partition to be monitored, and m0 is area standard value.
5. The intelligent monitoring method for permanent basic farmland as claimed in claim 4, wherein the central control module obtains the regularity gi of the subarea to be monitored according to the slope of each adjacent characteristic point of the outline of the subarea to be monitored, and sets gi = (ki12-ki0)2+(ki23-ki0)2+···+(kiei1-ki0)2And/ei, wherein ki12 is the slope of the first characteristic point and the second characteristic point of the ith to-be-monitored area, ki23 is the slope of the second characteristic point and the third characteristic point of the ith to-be-monitored area, kiei1 is the slope of the first characteristic point and the 1 st characteristic point of the ith to-be-monitored area, ki0 is the average value of the slopes of all adjacent characteristic points of the ith to-be-monitored area, and ei is the number of characteristic points of the ith to-be-monitored area.
6. The intelligent monitoring method for permanent basic farmland according to claim 4, wherein the central control module selects the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored according to the comparison between the complexity of the current subarea to be monitored and the preset complexity D, wherein,
when dj is less than or equal to D1, the central control module selects a first preset time T1 as the cruising staying time of the unmanned aerial vehicle in the jth to-be-monitored partition;
when D1 is greater than dj and less than D2, the central control module selects second preset time T2 as the cruising staying time of the unmanned aerial vehicle in the jth subarea to be monitored;
when dj is larger than or equal to D2, the central control module selects a third preset time T3 as the cruising staying time of the unmanned aerial vehicle in the jth to-be-monitored partition;
the central control module presets time T, sets a first preset time T1 and a second preset time T2, presets complexity D, sets a first preset complexity D1 and a second preset complexity D2, j =1,2 · · Mr, r =0,1, 2.
7. The intelligent monitoring method for permanent basic farmland according to claim 6, wherein the crop growth model base presets farmland crop growth information data A, the central control module judges the farmland quality of the subarea to be currently monitored according to the comparison between the current subarea farmland crop growth information data a to be monitored and the presets farmland crop growth information data A, wherein,
when a is not more than A1, the central control module analyzes the subarea to be monitored again;
when A1 is more than a and less than A2, the central control module judges that the quality of the subarea farmland to be monitored at present meets the preset standard;
when a is more than or equal to A2, the central control module analyzes the subarea to be monitored again;
the crop growth model library farmland crop growth information data A sets first preset farmland crop growth information data A1, and second preset farmland crop growth information data A2.
8. The intelligent monitoring method for permanent basic farmland according to claim 7, wherein when the central control module determines to re-analyze the subarea to be monitored, the central control module obtains a difference Δ H between the farmland crop growth information data a of the subarea to be monitored currently and the predicted crop growth information data A0, sets Δ H = | a-A0|, compares the obtained difference with a preset difference H, and re-analyzes the subarea to be monitored, wherein,
when the delta H is less than or equal to H1, the central control module shrinks the subarea to be monitored;
when H1 < [ delta ] H < H2, the central control module judges that the cruising staying time Tp of the unmanned aerial vehicle in the current subarea to be monitored is prolonged to Tp1, and sets Tp1= Tp x (1+ (H2-delta H) × ([ delta ] H-H1)/(H1 × H2));
when the delta H is larger than or equal to H2, the central control module of the central control module judges that the quality of the farmland of the current subarea to be monitored does not meet the standard;
the central control module presets a difference value H, and sets a first preset difference value H1 and a second preset difference value H2, wherein p =1,2 and 3.
9. The intelligent monitoring method for permanent basic farmland as claimed in claim 8, wherein the central control module obtains the absolute value of the difference between the farmland crop growth information data of the current subarea to be monitored and the predicted crop growth information data of the current subarea to be monitored, the central control module determines to reduce the subarea to be monitored, the central control module increases the number Mr of the subareas to be monitored to Mr1, Mr1= Mr x (1+ (H1- Δ H)/H1) is set, and if Mr1 is a non-integer, the integer is rounded upwards.
10. An intelligent monitoring system for permanent basic farmland, the monitoring method of which adopts the monitoring method according to any one of claims 1 to 9, characterized by comprising,
the storage module comprises a farmland basic information base and a crop growth model base, wherein the farmland basic information base stores basic information of each permanent basic farmland, the permanent basic farmland basic information comprises farmland positions, areas, planted crop varieties and planting management modes, and the crop growth model base stores growth information data of each growth period of each farmland crop;
the self-learning module is used for performing preliminary cruising on the edge area of the basic farmland to be monitored by the unmanned aerial vehicle to obtain the area to be monitored;
the cruise customizing module is used for acquiring the region similarity of the region to be monitored through the region to be monitored and the region of the permanent basic farmland, comparing the acquired region similarity with the preset region similarity, and dividing the region to be monitored into a plurality of subareas to be monitored;
the central control module acquires the complexity of the current subarea to be monitored according to the area and the rule of the current subarea to be monitored, the central control module compares the complexity of the current subarea to be monitored with a preset complexity, selects the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored, acquires the crop growth information data of the current subarea to be monitored, compares the crop growth information data with the crop growth information data predicted by the crop growth model base, judges whether the current crop growth meets the standard, if the current crop growth does not meet the preset standard, the central control module compares the difference value between the current crop growth information and the growth standard value with the preset difference value, wherein if the difference value between the current crop growth information and the growth standard value is smaller, the central control module determines the current crop growth information data by prolonging the cruising staying time of the unmanned aerial vehicle in the current subarea to be monitored or subdividing the subarea to be monitored, and if the difference value between the current crop growth information and the growth standard value is larger, the central control module judges that the quality of the farmland of the current subarea to be monitored is poor.
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