CN115983509A - Intelligent agricultural layout management system and method based on Internet of things - Google Patents
Intelligent agricultural layout management system and method based on Internet of things Download PDFInfo
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Abstract
The invention relates to the technical field of intelligent agriculture, in particular to an Internet of things-based intelligent agricultural layout management system and method. In the process of managing the agricultural layout, the influence of the difference of the terrain and the topography on the growth of the planted crops is considered, the influence of the difference of the growth states of different types of crops in the growth process is also considered, and the comprehensive influence value corresponding to each crop planting layout scheme in the area to be planted by the crops is analyzed to realize the effective screening of the crop layout schemes.
Description
Technical Field
The invention relates to the technical field of intelligent agriculture, in particular to a system and a method for managing intelligent agricultural layout based on the Internet of things.
Background
The rapid development of the information technology brings great convenience to production and life of people, and in the agricultural field, people can monitor the production state of crop production through the Internet of things, ensure that people can know the growth state of crops in real time, and are convenient for people to timely adjust the production environment of crops.
However, in the field of agricultural layout, in the prior art, the geographic information is only obtained by simply adopting a GIS technology, different planting areas are directly divided according to the obtained geographic information, the influence of the difference of the geographic topography on the growth of planted crops is not considered, and the influence of the difference of the growth states of different types of crops on each other is not considered in the growth process, so that the existing intelligent agricultural layout management system has a great defect.
Disclosure of Invention
The invention aims to provide an intelligent agricultural office layout management system and method based on the Internet of things, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an intelligent agricultural layout management method based on the Internet of things comprises the following steps:
s1, obtaining a to-be-planted area of crops, to-be-planted crop types and to-be-planted areas of the to-be-planted crop types, dividing the to-be-planted area of the crops into different sub-planting areas with the same specification, recording the ith sub-planting area as Ai, and recording the jth to-be-planted crop type as Bj;
s2, extracting the topography characteristics of each sub-planting area through topography monitoring equipment;
s3, acquiring topography characteristic deviation between adjacent sub-planting areas, analyzing the influence on the growth states of crops in the two sub-planting areas when various crop species are respectively planted in the adjacent sub-planting areas under the condition that the topography characteristic deviation is not changed according to historical planting data,
the influence value of the crop species Bj planted in Ai on the growth state of the crop species Bj1 planted in Ai in the adjacent sub-planting areas Ai and Ai1 is recorded as SZ Ai-Ai1 ；
S4, generating different crop planting layout schemes according to the planting area of each crop type to be planted, and predicting a comprehensive influence value corresponding to each crop planting layout scheme by combining the topographic features in each sub-planting area and the analysis result in the S3;
and S5, selecting a crop layout scheme with the minimum comprehensive influence value prediction result as an optimal crop planting layout scheme according to the comprehensive influence value prediction results respectively corresponding to the various crop planting layout schemes, and planting and managing the to-be-planted area of the crops according to the optimal crop planting layout scheme.
Further, the method for dividing the area to be planted by the crops into different sub-planting areas with the same specification in the step S1 comprises the following steps:
s1.1, acquiring the area to be planted of each crop type to be planted, and recording the area to be planted corresponding to Bj as MBj;
s1.2, acquiring the length corresponding to the to-be-planted area of the crops, marking the length as L1, acquiring the width corresponding to the to-be-planted area of the crops, marking the width as L2, and defaulting that the to-be-planted area of the crops is rectangular;
s1.3, acquiring a maximum common factor corresponding to the to-be-planted area of each to-be-planted crop type, and recording the maximum common factor as gys;
s1.4, acquiring a set of factors corresponding to L1 as L1Y, acquiring a set of factors corresponding to L2 as L2Y, and acquiring a data pair consisting of a k1 th element in the L1Y and a k2 th element in the L2Y as (L1 Yk1, L2Yk 2);
s1.5, select each data pair (L1 Yk1, L2Yk 2) satisfying the condition L1Yk1 × L2Yk2= gys, and mark the data pair having the smallest difference between k1 and k2 among the selected data pairs as (L1 Yk 1) min ，L2Yk2 min ) Will be (L1 Yk 1) min ，L2Yk2 min ) As a sub-planting-area specification reference, a sub-planting-areaThe length of the specification is L1Yk1 min And a width of L2Yk2 min A rectangular area of (a).
In the process of dividing the crop to-be-planted area into different sub-planting areas with the same specification in the S1, the problem of uniformly dividing the crop to-be-planted area is also considered, the problem of uniformly dividing the to-be-planted areas of various types of crops to be planted is also considered, only one type of crop to be planted in the same divided sub-planting area is ensured, and the monitoring and management of the crop growth environment and the growth state in each sub-planting area in the crop to-be-planted area in the subsequent process are facilitated.
Further, the method for extracting the topographic features of each sub-planting area through the topographic monitoring device in the step S2 includes the following steps:
s2.1, acquiring a point with the lowest relief in the area to be planted with the crops as a reference point, acquiring quantized values of the lowest relief point, the highest relief point and the average relief height in each sub-planting area relative to the altitude of the reference point respectively,
the quantized value of the altitude height of the highest point of the terrain relative to the reference point in the Ai is recorded as h1Ai,
the quantized value of the altitude of the lowest point of the terrain in Ai relative to the reference point is recorded as h2Ai,
the quantized value of the average terrain height in Ai relative to the altitude of the reference point is recorded as h3Ai,
the quantized value of the altitude of a certain relief point relative to the reference point is equal to the difference value of the altitude of the corresponding relief point and the altitude of the reference point;
s2.2, acquiring the terrain features of each sub-planting area, recording the terrain features corresponding to the ith sub-planting area as Ai as { h3Ai, h1Ai, h2Ai, WAi }, wherein WAi represents the terrain smoothness degree in Ai,
when h1Ai-h2Ai =0, WAi =1 is determined, indicating that the terrain is stable in Ai,
when h1Ai-h2Ai > 0, WAi = (h 3Ai-h2 Ai)/(h 1Ai-h2 Ai),
if WAi is less than 1, it indicates that the terrain is not stable in Ai and the terrain is convex,
if WAi is more than 1, the topography in Ai is unstable and concave;
in the process of acquiring the quantized value h3Ai of the average terrain height in the Ai relative to the reference point altitude, dividing the Ai into n rectangular areas with equal size, calculating the quantized value of the central point of each rectangular area relative to the reference point altitude, and recording the average value of the quantized values of the central points of the n rectangular areas in the Ai relative to the reference point altitude as h3Ai, wherein n is a preset constant in a database.
In the S2, in the process of extracting the topography characteristics of each sub-planting area through the topography monitoring equipment, h3Ai, h1Ai and h2Ai are obtained for the purpose of calculating WAi in the follow-up step, further judging the stability degree of the topography in the ith sub-planting area, and providing data reference for analyzing the influence condition of the growth state of crops in the two sub-planting areas when various crop species are planted in the adjacent sub-planting areas in the follow-up step.
Further, the step S3 of analyzing the influence on the growth state of the crops in the two sub-planting areas when the different topographic feature deviations are not changed and the respective crop species are planted in the adjacent sub-planting areas includes the following steps:
s3.1, acquiring terrain features of each sub-planting area, acquiring an average quantized value of each contact point of each two adjacent sub-planting areas relative to the altitude of a reference point, and recording the average quantized value of each contact point between the adjacent sub-planting areas Ai and Ai1 relative to the altitude of the reference point as hp Ai-Ai1 ；
S3.2, acquiring the terrain deviation DP of Ai relative to Ai1 in the adjacent sub-planting areas Ai and Ai1 Ai-Ai1 The feature of the terrain corresponding to Ai is expressed as { h3Ai, h1Ai, h2Ai, WAi }, and the feature of the terrain corresponding to Ai1 is expressed as { h3Ai1, h1Ai1, h2Ai1, WAi1},
DP Ai-Ai1 corresponding to a set { h3Ai-h3Ai1, gys XF (WAi, WAi1, h3Ai, h3Ai1, hp) Ai-Ai1 ）}，
Wherein, F (WAi, WAi1, h3Ai, h3Ai1, hp) Ai-Ai1 ) Representing the deviation coefficient of the terrain, gys is the area to be planted of various crop species to be plantedThe corresponding maximum common factor is used to determine,
comparison (h 3 Ai-hp) Ai-Ai1 ）×（h3Ai1-hp Ai-Ai1 ) The magnitude relationship between the value of (A) and (B) 0,
when (h 3 Ai-hp) Ai-Ai1 ）×（h3Ai1-hp Ai-Ai1 ) If the terrain is more than 0, judging that the terrain between the adjacent sub-planting areas Ai and Ai1 is in a V shape, selecting a terrain deviation coefficient corresponding to a data pair (WAi, WAi 1) in a V-shaped sub-database preset in a database,
when (h 3 Ai-hp) Ai-Ai1 ）×（h3Ai1-hp Ai-Ai1 ) When the terrain is less than or equal to 0, judging that the terrain between the adjacent sub-planting areas Ai and Ai1 is in a monotonous change trend, and selecting a terrain deviation coefficient corresponding to a data pair (WAi, WAi 1) in a sub-database which is preset in the database and is in the monotonous change trend;
s3.3, obtaining the terrain characteristic deviation value DP in the historical planting data Ai-Ai1 When the type of crop planted in Ai is Bj and the type of crop planted in Ai1 is Bj1, the difference between the average height Qai of the crop in Ai and the average height Qai1 of the crop in Ai1 on the t-th day is denoted as Qt Ai-Ai1 Recording the days corresponding to the overlapped time intervals in the crop growth cycles respectively corresponding to the Bj and the Bj1 as TB j-j1 ，1≤t≤TB j-j1 ；
Acquiring the height factor Q1t of Qai relative to Qai1 Ai-Ai1 ，
When Qt Ai-Ai1 When + h3Ai-h3Ai1 > 0, Q1t is determined Ai-Ai1 =Qt Ai-Ai1 +h3Ai-h3Ai1，
When Qt Ai-Ai1 When + h3Ai-h3Ai1 is less than or equal to 0, then Q1t is determined Ai-Ai1 = e, where e is an influence coefficient of Bj preset in the database with respect to Bj 1;
s3.4, the influence value of the crop species Bj planted in the Ai on the growth state of the crop species Bj1 planted in the Ai1 in the adjacent sub-planting areas Ai and Ai1 is obtained as SZ Ai-Ai1 ，
In the invention S3, the analysis is carried out to ensure that the deviation of the topographic features is not changedUnder the condition, when various crop species are planted in the adjacent sub-planting areas respectively, in the process of influencing the growth states of crops in the two sub-planting areas, the terrain deviation of Ai relative to Ai1 in the adjacent sub-planting areas Ai and Ai1 is obtained, and the situation that the terrain deviation coefficients caused by the terrain change situation between the adjacent sub-planting areas Ai and Ai1 are different is considered; acquiring the influence height factor of Qai relative to Qai1, considering the average height condition of different sub-planting areas and the difference of the heights of different crops corresponding to different crop species at the same time, and further providing a data basis for acquiring the influence value of the crop species Bj planted in Ai on the growth state of the crop species Bj1 planted in Ai in the adjacent sub-planting areas Ai and Ai1 in the subsequent step; obtaining gys × F (WAi, WAi1, h3Ai1, hp) Ai-Ai1 ) Considering that the terrain of the composition between the adjacent sub-planting areas is different, the range of the unit height influence on the crops in Ai1 in the influence height factor of QAi relative to QAi1 is different every day during the growth of the crops.
Further, the method for generating different crop planting layouts in S4 includes the following steps:
s4.1, acquiring the area to be planted of each type of crops to be planted, wherein the sum of the areas to be planted corresponding to each type of crops to be planted is equal to the area to be planted of the crops,
calculating a quotient obtained by dividing the area to be planted of each type of crops to be planted by the area corresponding to one sub-planting region to obtain the number of the sub-planting regions corresponding to each type of crops to be planted, and recording the number of the sub-planting regions corresponding to Bj as GBj;
s4.2, matching the corresponding crop species to be planted in each sub-planting area in the crop planting area to obtain different crop planting layout schemes, wherein the number of the sub-planting areas corresponding to the Bj in each crop planting scheme is equal to GBj, and one sub-planting area corresponds to one crop species to be planted.
In the different crop planting layout schemes generated in the S4 of the invention, when j1 represents the number of categories of the crop species to be planted, the number of the obtained crop planting layout schemes is
Further, the method for predicting the comprehensive influence value corresponding to each crop planting layout scheme in the step S4 includes the following steps:
s4-1, under the condition that the deviation of the topographic features is not changed, when various crop species are respectively planted in adjacent sub-planting areas, analysis results of the influence conditions on the crop growth states in the two sub-planting areas are obtained;
s4-2, acquiring the generated planting layout scheme of each crop;
s4-3, in the generated r-th crop planting layout scheme, the sum of the influence values of the crop species planted in each sub-planting region adjacent to Ai on the growth state of the crop species planted in Ai is recorded as SZYRAI;
s4-5, obtaining a comprehensive influence value SZYr corresponding to the r-th crop planting layout scheme,
wherein ui represents the total number of the sub-planting areas in the to-be-planted area of the crop.
In the process of predicting the comprehensive influence value corresponding to each crop planting layout scheme in the S4 of the present invention, because the same sub-planting region has a plurality of adjacent sub-planting regions, and the plurality of adjacent sub-planting regions all affect the growth state of the crop species in the sub-planting region, the sum SZYrAi of the influence values of the crop species planted in each sub-planting region adjacent to Ai on the growth state of the crop species planted in Ai needs to be calculated, which is used as the total influence condition of the crop species planted in each sub-planting region adjacent to Ai on the growth state of the crop species planted in Ai in the r-th crop planting layout scheme, and further provides data reference for subsequently and accurately calculating the comprehensive influence value corresponding to the r-th crop planting scheme.
Wisdom agricultural use layout management system based on thing networking, the system includes following module:
the planting area planning module acquires a region to be planted for crops, types of crops to be planted and a region to be planted for each type of crops to be planted, and divides the region to be planted for the crops into different sub-planting regions with the same specification;
the terrain feature extraction module is used for respectively extracting the terrain features of each sub-planting area through terrain monitoring equipment;
the growth state influence analysis module is used for acquiring the terrain characteristic deviation between adjacent sub-planting areas and analyzing the influence on the growth state of crops in the two sub-planting areas when various crop species are respectively planted in the adjacent sub-planting areas under the condition that the terrain characteristic deviation is not changed according to historical planting data;
the layout scheme influence prediction module generates different crop planting layout schemes according to the planting area of each crop species to be planted, and predicts a comprehensive influence value corresponding to each crop planting layout scheme by combining the terrain characteristics in each sub-planting region and the analysis result in the growth state influence analysis module;
and the planting management module selects a crop layout scheme with the minimum comprehensive influence value prediction result as an optimal crop planting layout scheme according to the comprehensive influence value prediction results respectively corresponding to various crop planting layout schemes, and plants and manages the to-be-planted area of the crops according to the optimal crop planting layout scheme.
Furthermore, the growth state influence analysis module comprises a topography feature acquisition unit, a topography feature deviation analysis unit, a topography deviation coefficient acquisition unit and a growth state influence value analysis unit,
the topography feature acquisition unit is used for acquiring topography features respectively corresponding to the two adjacent sub-planting areas;
the topography feature deviation analysis unit is used for acquiring deviation conditions between topography features respectively corresponding to two adjacent sub-planting areas;
the terrain deviation coefficient acquisition unit is used for acquiring a corresponding terrain deviation coefficient between two adjacent sub-planting areas;
the growth state influence value analysis unit is used for obtaining the growth state influence value of the crop species planted in one sub-planting area to the crop species planted in the other sub-planting area in the two adjacent sub-planting areas.
Furthermore, in the planting management process of the planting management module for the area to be planted with the crops according to the optimal planting layout scheme of the crops,
if the types of crops planted in each sub-planting area in the area to be planted with the crops are different from the types of the crops planted in the corresponding sub-planting area in the optimal crop planting layout scheme, the planting management module gives an early warning to an administrator;
otherwise, the planting management module does not give an early warning to the administrator.
Compared with the prior art, the invention has the following beneficial effects: in the process of managing the agricultural layout, the influence of the difference of the terrain and the topography on the growth of planted crops is considered, the influence of the difference of the growth states of different types of crops in the growth process is also considered, and the comprehensive influence value corresponding to each crop planting layout scheme in the area to be planted of the crops is analyzed to realize effective screening of the crop layout schemes and further realize effective management of the agricultural layout.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic structural diagram of an intelligent agricultural layout management system based on the Internet of things according to the present invention;
fig. 2 is a flow chart of the intelligent agricultural layout management method based on the internet of things according to the invention.
Detailed Description
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. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, the present invention provides a technical solution: an intelligent agricultural layout management method based on the Internet of things comprises the following steps:
s1, obtaining a to-be-planted area of crops, to-be-planted crop types and to-be-planted areas of the to-be-planted crop types, dividing the to-be-planted area of the crops into different sub-planting areas with the same specification, recording the ith sub-planting area as Ai, and recording the jth to-be-planted crop type as Bj;
the method for dividing the crop to-be-planted area into different sub-planting areas with the same specification in the S1 comprises the following steps:
s1.1, acquiring the area to be planted of each crop type to be planted, and recording the area to be planted corresponding to Bj as MBj;
s1.2, acquiring the length corresponding to the to-be-planted area of the crops, marking the length as L1, acquiring the width corresponding to the to-be-planted area of the crops, marking the width as L2, and defaulting that the to-be-planted area of the crops is rectangular;
s1.3, acquiring a maximum common factor corresponding to the to-be-planted area of each to-be-planted crop type, and recording the maximum common factor as gys;
s1.4, acquiring a set of factors corresponding to L1 as L1Y, acquiring a set of factors corresponding to L2 as L2Y, and acquiring a data pair consisting of a k1 th element in the L1Y and a k2 th element in the L2Y as (L1 Yk1, L2Yk 2);
s1.5, select each data pair (L1 Yk1, L2Yk 2) satisfying the condition L1Yk1 × L2Yk2= gys, and mark the data pair with the smallest difference between k1 and k2 in the selected data pair as (L1 Yk 1) min ，L2Yk2 min ) Will be (L1 Yk 1) min ，L2Yk2 min ) As a sub-planting-area specification reference, the sub-planting-area specification is L1Yk1 min And a width of L2Yk2 min A rectangular area of (a).
S2, extracting the terrain features of the sub-planting areas through terrain monitoring equipment;
the method for extracting the topographic features of each sub-planting area through the topographic monitoring equipment in the S2 comprises the following steps:
s2.1, acquiring a point with the lowest relief in the area to be planted with the crops as a reference point, acquiring quantized values of the lowest relief point, the highest relief point and the average relief height in each sub-planting area relative to the altitude of the reference point respectively,
the quantized value of the highest point of the terrain in Ai relative to the altitude of the reference point is recorded as h1Ai,
the quantized value of the altitude of the lowest point of the terrain in Ai relative to the reference point is recorded as h2Ai,
the quantized value of the average terrain height in Ai relative to the altitude of the reference point is recorded as h3Ai,
the quantized value of the altitude of a certain relief point relative to the reference point is equal to the difference value of the altitude of the corresponding relief point and the altitude of the reference point;
s2.2, acquiring the terrain features of each sub-planting area, recording the terrain features corresponding to the ith sub-planting area as Ai as { h3Ai, h1Ai, h2Ai, WAi }, wherein WAi represents the terrain smoothness degree in Ai,
when h1Ai-h2Ai =0, WAi =1 is determined, indicating that the terrain is stable in Ai,
when h1Ai-h2Ai > 0, WAi = (h 3Ai-h2 Ai)/(h 1Ai-h2 Ai),
if WAi is less than 1, it indicates that the terrain is not stable in Ai and the terrain is convex,
if WAi is more than 1, the terrain is unstable in Ai and concave;
in the process of acquiring the quantized value h3Ai of the average terrain height in the Ai relative to the reference point altitude, dividing the Ai into n rectangular areas with equal size, calculating the quantized value of the central point of each rectangular area relative to the reference point altitude, and recording the average value of the quantized values of the central points of the n rectangular areas in the Ai relative to the reference point altitude as h3Ai, wherein n is a preset constant in a database.
In this embodiment, when n is 9, if the quantized values of the altitudes of the center points of the 9 rectangular regions in A1 with respect to the reference point are 0.25, 0.2, 0.18, 0.23, 0.26, 0.32, 0.11, 0.15, and 0.19,
because (0.25 +0.2+0.18+0.23+0.26+0.32+0.11+0.15+ 0.19) ÷ 9=0.21,
the quantized value of the average geodetic height relative to the reference point altitude in A1 is determined to be 0.21.
S3, acquiring topography characteristic deviation between adjacent sub-planting areas, analyzing the influence on the growth states of crops in the two sub-planting areas when various crop species are respectively planted in the adjacent sub-planting areas under the condition that the topography characteristic deviation is not changed according to historical planting data,
the influence value of the crop species Bj planted in Ai on the growth state of the crop species Bj1 planted in Ai in the adjacent sub-planting areas Ai and Ai1 is recorded as SZ Ai-Ai1 ；
In the step S3, analyzing the influence of the different soil feature deviations on the growth states of the crops in the two sub-planting areas when the different crop species are respectively planted in the adjacent sub-planting areas comprises the following steps:
s3.1, acquiring the terrain features of each sub-planting area, obtaining the average quantized value of the altitude of each contact point of two adjacent sub-planting areas relative to the reference point, and recording the average quantized value of the altitude of each contact point between the adjacent sub-planting areas Ai and Ai1 relative to the reference point as hp Ai-Ai1 ；
S3.2, acquiring the terrain deviation DP of Ai relative to Ai1 in the adjacent sub-planting areas Ai and Ai1 Ai-Ai1 The topography features corresponding to Ai are recorded as { h3Ai, h1Ai, h2Ai, WAi }, the topography features corresponding to Ai1 are recorded as { h3Ai1, h1Ai1, h2Ai1, WAi1},
DP Ai-Ai1 corresponding to a set { h3Ai-h3Ai1, gys XF (WAi, WAi1, h3Ai, h3Ai1, hp) Ai-Ai1 ）}，
Wherein, F (WAi, WAi1, h3Ai, h3Ai1, hp) Ai-Ai1 ) Expressing the terrain deviation coefficient, gys is the maximum common factor corresponding to the planting area of various crop species to be planted,
comparison (h 3 Ai-hp) Ai-Ai1 ）×（h3Ai1-hp Ai-Ai1 ) The magnitude relationship between the first and second signals and 0,
when (h 3 Ai-hp) Ai-Ai1 ）×（h3Ai1-hp Ai-Ai1 ) If the terrain is larger than 0, judging that the terrain between the adjacent sub-planting areas Ai and Ai1 is V-shaped, selecting a terrain deviation coefficient corresponding to a data pair (WAi, WAi 1) in a V-shaped sub-database preset in a database,
when (h 3 Ai-hp) Ai-Ai1 ）×（h3Ai1-hp Ai-Ai1 ) When the terrain is less than or equal to 0, judging that the terrain between the adjacent sub-planting areas Ai and Ai1 is in a monotonous change trend, and selecting a terrain deviation coefficient corresponding to a data pair (WAi, WAi 1) in a sub-database which is preset in the database and is in the monotonous change trend;
s3.3, obtaining the terrain characteristic deviation value DP in the historical planting data Ai-Ai1 When the crop type is Bj and Bj1, the number of days corresponding to the time interval in which Bj and Bj1 respectively correspond to the crop growth cycles overlap is recorded as TB j-j1 ，1≤t≤TB j-j1 ，
Obtaining the difference value between the average height Qai of the crops in Ai and the average height Qai1 of the crops in Ai1 at the t day, and marking the difference value as Qt Ai-Ai1 ；
Acquiring the height factor Q1t of Qai relative to Qai1 Ai-Ai1 ，
When Qt Ai-Ai1 When + h3Ai-h3Ai1 > 0, Q1t is determined Ai-Ai1 =Qt Ai-Ai1 +h3Ai-h3Ai1，
When Qt Ai-Ai1 When + h3Ai-h3Ai1 is less than or equal to 0, then Q1t is determined Ai-Ai1 = e, where e is an influence coefficient of Bj preset in the database with respect to Bj 1;
s3.4, the influence value of the crop species Bj planted in the Ai on the growth state of the crop species Bj1 planted in the Ai1 in the adjacent sub-planting areas Ai and Ai1 is obtained as SZ Ai-Ai1 ，
S4, generating different crop planting layout schemes according to the planting area of each crop type to be planted, and predicting a comprehensive influence value corresponding to each crop planting layout scheme by combining the topographic features in each sub-planting area and the analysis result in the S3;
the method for generating different crop planting layout schemes in the S4 comprises the following steps:
s4.1, acquiring the area to be planted of each type of crops to be planted, wherein the sum of the areas to be planted corresponding to each type of crops to be planted is equal to the area to be planted of the crops,
calculating a quotient obtained by dividing the area to be planted of each type of crops to be planted by the area corresponding to one sub-planting region to obtain the number of the sub-planting regions corresponding to each type of crops to be planted, and recording the number of the sub-planting regions corresponding to Bj as GBj;
s4.2, matching the corresponding crop species to be planted in each sub-planting area in the crop planting area to obtain different crop planting layout schemes, wherein the number of the sub-planting areas corresponding to Bj in each crop planting scheme is equal to GBj, one sub-planting area corresponds to one crop species to be planted,
the obtained number of the crop planting layout schemes is
Wherein j1 represents the number of categories of crop species to be planted.
The method for predicting the comprehensive influence value corresponding to each crop planting layout scheme in the S4 comprises the following steps:
s4-1, under the condition that the deviation of the topographic features is not changed, when various crop species are respectively planted in adjacent sub-planting areas, analysis results of the influence conditions on the crop growth states in the two sub-planting areas are obtained;
s4-2, acquiring the generated planting layout scheme of each crop;
s4-3, acquiring the sum of the influence values of the crop species planted in each sub-planting area adjacent to Ai on the growth state of the crop species planted in Ai in the generated r-th crop planting layout scheme, and recording as SZYRAI;
s4-5, obtaining a comprehensive influence value SZYr corresponding to the r-th crop planting layout scheme,
wherein ui represents the total number of the sub-planting areas in the to-be-planted area of the crop.
And S5, selecting a crop layout scheme with the minimum comprehensive influence value prediction result as an optimal crop planting layout scheme according to the comprehensive influence value prediction results respectively corresponding to the various crop planting layout schemes, and planting and managing the to-be-planted area of the crops according to the optimal crop planting layout scheme.
Wisdom agricultural business use layout management system based on thing networking, the system includes following module:
the planting area planning module acquires a region to be planted for crops, types of crops to be planted and a region to be planted for each type of crops to be planted, and divides the region to be planted for the crops into different sub-planting regions with the same specification;
the terrain feature extraction module is used for respectively extracting the terrain features of each sub-planting area through terrain monitoring equipment;
the growth state influence analysis module is used for acquiring the terrain characteristic deviation between adjacent sub-planting areas and analyzing the influence condition on the growth state of crops in the two sub-planting areas when the terrain characteristic deviation is not changed and various crop species are respectively planted in the adjacent sub-planting areas according to historical planting data;
the layout scheme influence prediction module generates different crop planting layout schemes according to the planting area of each crop species to be planted, and predicts a comprehensive influence value corresponding to each crop planting layout scheme by combining the terrain characteristics in each sub-planting region and the analysis result in the growth state influence analysis module;
and the planting management module selects a crop layout scheme with the minimum comprehensive influence value prediction result as an optimal crop planting layout scheme according to the comprehensive influence value prediction results respectively corresponding to various crop planting layout schemes, and plants and manages the to-be-planted area of the crops according to the optimal crop planting layout scheme.
The growth state influence analysis module comprises a topography feature acquisition unit, a topography feature deviation analysis unit, a topography deviation coefficient acquisition unit and a growth state influence value analysis unit,
the topography feature acquisition unit is used for acquiring topography features respectively corresponding to the two adjacent sub-planting areas;
the topography characteristic deviation analysis unit is used for acquiring deviation conditions between topography characteristics respectively corresponding to two adjacent sub-planting areas;
the terrain deviation coefficient acquisition unit is used for acquiring a corresponding terrain deviation coefficient between two adjacent sub-planting areas;
the growth state influence value analysis unit is used for obtaining the growth state influence value of the crop species planted in one sub-planting area to the crop species planted in the other sub-planting area in the two adjacent sub-planting areas.
In the planting management process of the planting management module for the to-be-planted areas of the crops according to the optimal planting layout scheme of the crops,
if the types of crops planted in each sub-planting area in the area to be planted with the crops are different from the types of the crops planted in the corresponding sub-planting area in the optimal crop planting layout scheme, the planting management module gives an early warning to an administrator;
otherwise, the planting management module does not give an early warning to the administrator.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described above, or equivalents may be substituted for elements thereof. 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 (9)
1. An intelligent agricultural administration layout management method based on the Internet of things is characterized by comprising the following steps:
s1, obtaining a to-be-planted area of crops, to-be-planted crop types and to-be-planted areas of the to-be-planted crop types, dividing the to-be-planted area of the crops into different sub-planting areas with the same specification, recording the ith sub-planting area as Ai, and recording the jth to-be-planted crop type as Bj;
s2, extracting the topography characteristics of each sub-planting area through topography monitoring equipment;
s3, acquiring topography characteristic deviation between adjacent sub-planting areas, analyzing the influence on the growth state of crops in the two sub-planting areas when various crop species are respectively planted in the adjacent sub-planting areas under the condition that the topography characteristic deviation is not changed according to historical planting data,
the influence value of the crop species Bj planted in Ai on the growth state of the crop species Bj1 planted in Ai in the adjacent sub-planting areas Ai and Ai1 is recorded as SZ Ai-Ai1 ；
S4, generating different crop planting layout schemes according to the planting area of each crop type to be planted, and predicting a comprehensive influence value corresponding to each crop planting layout scheme by combining the topographic features in each sub-planting area and the analysis result in the S3;
and S5, selecting a crop layout scheme with the minimum comprehensive influence value prediction result as an optimal crop planting layout scheme according to the comprehensive influence value prediction results respectively corresponding to the various crop planting layout schemes, and planting and managing the to-be-planted area of the crops according to the optimal crop planting layout scheme.
2. The intelligent agricultural business layout management method based on the internet of things as claimed in claim 1, wherein: the method for dividing the area to be planted of the crops into different sub-planting areas with the same specification in S1 comprises the following steps:
s1.1, acquiring the area to be planted of each crop type to be planted, and recording the area to be planted corresponding to Bj as MBj;
s1.2, acquiring the length corresponding to the to-be-planted area of the crops, marking the length as L1, acquiring the width corresponding to the to-be-planted area of the crops, marking the width as L2, and defaulting that the to-be-planted area of the crops is rectangular;
s1.3, acquiring a maximum common factor corresponding to the to-be-planted area of each to-be-planted crop type, and recording the maximum common factor as gys;
s1.4, acquiring a set of factors corresponding to L1 as L1Y, acquiring a set of factors corresponding to L2 as L2Y, and acquiring a data pair consisting of a k1 th element in the L1Y and a k2 th element in the L2Y as (L1 Yk1, L2Yk 2);
s1.5, select each data pair (L1 Yk1, L2Yk 2) satisfying the condition L1Yk1 × L2Yk2= gys, and mark the data pair with the smallest difference between k1 and k2 in the selected data pair as (L1 Yk 1) min ，L2Yk2 min ) Will (L1 Yk 1) min ，L2Yk2 min ) As a sub-planting area specification reference, the sub-planting area specification is L1Yk1 in length min And a width of L2Yk2 min A rectangular area of (a).
3. The intelligent agricultural business layout management method based on the internet of things as claimed in claim 1, wherein: the method for extracting the topographic features of each sub-planting area through the topographic monitoring equipment in the S2 comprises the following steps:
s2.1, acquiring a point with the lowest relief in the area to be planted with the crops as a reference point, acquiring quantized values of the lowest relief point, the highest relief point and the average relief height in each sub-planting area relative to the altitude of the reference point respectively,
the quantized value of the altitude height of the highest point of the terrain relative to the reference point in the Ai is recorded as h1Ai,
the quantized value of the altitude height of the lowest point of topography in Ai relative to the reference point altitude is recorded as h2Ai,
the quantized value of the average terrain height in Ai relative to the altitude of the reference point is recorded as h3Ai,
the quantized value of the altitude of a certain relief point relative to the reference point is equal to the difference value of the altitude of the corresponding relief point and the altitude of the reference point;
s2.2, acquiring the terrain features of each sub-planting area, recording the terrain features corresponding to the ith sub-planting area as Ai as { h3Ai, h1Ai, h2Ai, WAi }, wherein WAi represents the terrain smoothness degree in Ai,
when h1Ai-h2Ai =0, WAi =1 is determined, indicating that the terrain is stable in Ai,
when h1Ai-h2Ai is more than 0, WAi = (h 3Ai-h2 Ai)/(h 1Ai-h2 Ai) is judged,
if WAi is less than 1, it indicates that the terrain is not stable in Ai and the terrain is convex,
if WAi is more than 1, the terrain is unstable in Ai and concave;
in the process of acquiring the quantized value h3Ai of the average terrain height in the Ai relative to the reference point altitude, dividing the Ai into n rectangular areas with equal size, calculating the quantized value of the central point of each rectangular area relative to the reference point altitude, and recording the average value of the quantized values of the central points of the n rectangular areas in the Ai relative to the reference point altitude respectively as h3Ai, wherein n is a preset constant in a database.
4. The intelligent agricultural business layout management method based on the internet of things as claimed in claim 3, wherein: in the step S3, analyzing the influence of the different soil feature deviations on the growth states of the crops in the two sub-planting areas when the different crop species are respectively planted in the adjacent sub-planting areas comprises the following steps:
s3.1, acquiring terrain features of each sub-planting area, acquiring an average quantized value of each contact point of each two adjacent sub-planting areas relative to the altitude of a reference point, and recording the average quantized value of each contact point between the adjacent sub-planting areas Ai and Ai1 relative to the altitude of the reference point as hp Ai-Ai1 ；
S3.2, acquiring the terrain deviation DP of Ai relative to Ai1 in the adjacent sub-planting areas Ai and Ai1 Ai-Ai1 The topography features corresponding to Ai are recorded as { h3Ai, h1Ai, h2Ai, WAi }, the topography features corresponding to Ai1 are recorded as { h3Ai1, h1Ai1, h2Ai1, WAi1},
DP Ai-Ai1 corresponding to a set { h3Ai-h3Ai1, gys XF (WAi, WAi1, h3Ai, h3Ai1, hp) Ai-Ai1 ）}，
Wherein, F (WAi, WAi1, h3Ai, h3Ai1, hp) Ai-Ai1 ) Expressing the terrain deviation coefficient, gys is the maximum common factor corresponding to the planting area of various crop species to be planted,
comparison (h 3 Ai-hp) Ai-Ai1 ）×（h3Ai1-hp Ai-Ai1 ) The magnitude relationship between the value of (A) and (B) 0,
when (h 3 Ai-hp) Ai-Ai1 ）×（h3Ai1-hp Ai-Ai1 ) If the terrain is more than 0, judging that the terrain between the adjacent sub-planting areas Ai and Ai1 is in a V shape, selecting a terrain deviation coefficient corresponding to a data pair (WAi, WAi 1) in a V-shaped sub-database preset in a database,
when (h 3 Ai-hp) Ai-Ai1 ）×（h3Ai1-hp Ai-Ai1 ) When the height is less than or equal to 0, judging that the adjacent sub-planting areas Ai and AThe terrain among the i1 is in a monotonous change trend, and a terrain deviation coefficient corresponding to a data pair (WAi, WAi 1) in a sub-database which is preset in the database and is in the monotonous change trend is selected;
s3.3, obtaining the terrain characteristic deviation value DP in the historical planting data Ai-Ai1 When the type of crop planted in Ai is Bj and the type of crop planted in Ai1 is Bj1, the difference between the average height Qai of the crop in Ai and the average height Qai1 of the crop in Ai1 on the t-th day is denoted as Qt Ai-Ai1 Recording the days corresponding to the overlapped time intervals in the crop growth cycles respectively corresponding to the Bj and the Bj1 as TB j-j1 ，1≤t≤TB j-j1 ；
Acquiring the influence height factor Q1t of Qai relative to Qai1 Ai-Ai1 ，
When Qt Ai-Ai1 When + h3Ai-h3Ai1 > 0, Q1t is determined Ai-Ai1 =Qt Ai-Ai1 +h3Ai-h3Ai1，
When Qt Ai-Ai1 When + h3Ai-h3Ai1 is less than or equal to 0, then Q1t is determined Ai-Ai1 = e, where e is an influence coefficient of Bj preset in the database with respect to Bj 1;
s3.4, the influence value of the crop species Bj planted in the Ai on the growth state of the crop species Bj1 planted in the Ai1 in the adjacent sub-planting areas Ai and Ai1 is obtained as SZ Ai-Ai1 ，
5. The intelligent agricultural business layout management method based on the internet of things as claimed in claim 1, wherein: the method for generating different crop planting layout schemes in the S4 comprises the following steps:
s4.1, acquiring the area to be planted of each type of crops to be planted, wherein the sum of the areas to be planted corresponding to each type of crops to be planted is equal to the area to be planted of the crops,
calculating a quotient obtained by dividing the area to be planted of each type of crops to be planted by the area corresponding to one sub-planting region to obtain the number of the sub-planting regions corresponding to each type of crops to be planted, and recording the number of the sub-planting regions corresponding to Bj as GBj;
s4.2, matching the corresponding crop species to be planted in each sub-planting area in the crop planting area to obtain different crop planting layout schemes, wherein the number of the sub-planting areas corresponding to the Bj in each crop planting scheme is equal to GBj, and one sub-planting area corresponds to one crop species to be planted.
6. The intelligent agricultural business layout management method based on the internet of things as claimed in claim 1, wherein: the method for predicting the comprehensive influence value corresponding to each crop planting layout scheme in the S4 comprises the following steps:
s4-1, under the condition that the deviation of the topographic features is not changed, when various crop species are respectively planted in adjacent sub-planting areas, analysis results of the influence conditions on the crop growth states in the two sub-planting areas are obtained;
s4-2, acquiring the generated planting layout scheme of each crop;
s4-3, acquiring the sum of the influence values of the crop species planted in each sub-planting area adjacent to Ai on the growth state of the crop species planted in Ai in the generated r-th crop planting layout scheme, and recording as SZYRAI;
s4-5, obtaining a comprehensive influence value SZYr corresponding to the r-th crop planting layout scheme,
wherein ui represents the total number of the sub-planting areas in the to-be-planted area of the crop.
7. Wisdom agricultural use layout management system based on thing networking, its characterized in that: the system comprises the following modules:
the planting area planning module acquires a to-be-planted area of crops, types of the crops to be planted and the to-be-planted area of each type of the crops to be planted, and divides the to-be-planted area of the crops into different sub-planting areas with the same specification;
the terrain feature extraction module is used for respectively extracting the terrain features of each sub-planting area through terrain monitoring equipment;
the growth state influence analysis module is used for acquiring the terrain characteristic deviation between adjacent sub-planting areas and analyzing the influence on the growth state of crops in the two sub-planting areas when various crop species are respectively planted in the adjacent sub-planting areas under the condition that the terrain characteristic deviation is not changed according to historical planting data;
the layout scheme influence prediction module generates different crop planting layout schemes according to the planting area of each crop species to be planted, and predicts a comprehensive influence value corresponding to each crop planting layout scheme by combining the terrain characteristics in each sub-planting region and the analysis result in the growth state influence analysis module;
and the planting management module selects a crop layout scheme with the minimum comprehensive influence value prediction result as an optimal crop planting layout scheme according to the comprehensive influence value prediction results respectively corresponding to various crop planting layout schemes, and plants and manages the crop to-be-planted area according to the optimal crop planting layout scheme.
8. The intelligent internet of things-based agricultural and horticultural layout management system of claim 7, wherein: the growth state influence analysis module comprises a topography feature acquisition unit, a topography feature deviation analysis unit, a topography deviation coefficient acquisition unit and a growth state influence value analysis unit,
the topography feature acquisition unit is used for acquiring topography features corresponding to two adjacent sub-planting areas respectively;
the topography feature deviation analysis unit is used for acquiring deviation conditions between topography features respectively corresponding to two adjacent sub-planting areas;
the terrain deviation coefficient acquisition unit is used for acquiring a corresponding terrain deviation coefficient between two adjacent sub-planting areas;
the growth state influence value analysis unit is used for obtaining the growth state influence value of the crop species planted in one sub-planting area to the crop species planted in the other sub-planting area in the two adjacent sub-planting areas.
9. The intelligent internet of things-based agricultural and horticultural layout management system of claim 7, wherein: in the planting management process of the planting management module for the to-be-planted areas of the crops according to the optimal planting layout scheme of the crops,
if the types of crops planted in each sub-planting area in the area to be planted with the crops are different from the types of the crops planted in the corresponding sub-planting area in the optimal crop planting layout scheme, the planting management module gives an early warning to an administrator;
otherwise, the planting management module does not give an early warning to the administrator.
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