CN115983509B - Intelligent agriculture layout management system and method based on Internet of things - Google Patents
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Abstract
The invention relates to the technical field of intelligent agriculture, in particular to a layout management system and a layout management method for intelligent agriculture based on the Internet of things. In the process of managing the agricultural layout, the invention not only considers the influence of the differences of topography and topography on the growth of the planted crops, but also considers the influence of the differences of growth states of different crops in the growth process, and realizes the effective screening of the crop layout scheme by analyzing the comprehensive influence value corresponding to each crop planting layout scheme in the region where the crops are to be planted.
Description
Technical Field
The invention relates to the technical field of intelligent agriculture, in particular to a layout management system and method for intelligent agriculture based on the Internet of things.
Background
The rapid development of the informatization technology brings great convenience to the production and life of people, and in the agricultural field, people can monitor the production state of crop production through the Internet of things, so that people can know the growth state of crops in real time, and people can adjust the production environment of the crops in time conveniently.
However, in the field of agricultural layout, in the prior art, only the geographic information is simply obtained by adopting the GIS technology, different planting areas are directly divided according to the obtained geographic information, the influence of the difference of the geographic features on the growth of planted crops is not considered, and the influence of the difference of the growth states on different crops in the growth process is caused, so that the existing intelligent agricultural layout management system has a larger defect.
Disclosure of Invention
The invention aims to provide a layout management system and method for intelligent agriculture based on the Internet of things, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the intelligent agriculture layout management method based on the Internet of things comprises the following steps:
s1, acquiring a crop to-be-planted area, crop types to be planted and to-be-planted areas of each crop type to be planted, dividing the crop to-be-planted area into different sub-planting areas with the same specification, marking an ith sub-planting area as Ai, and marking a jth crop type to be planted as Bj;
s2, extracting the relief features of each sub-planting area through a terrain monitoring device;
s3, obtaining the feature deviation of the land features between adjacent sub-planting areas, analyzing the influence conditions of the crop growth states in the two sub-planting areas when each crop species is planted in each adjacent sub-planting area under the condition that the feature deviation of the land features is unchanged according to the 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 Ai1 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 areas of each crop species to be planted, and predicting the comprehensive influence value corresponding to each crop planting layout scheme by combining the relief features in each sub-planting region and the analysis results in S3;
s5, selecting a crop layout scheme with the smallest 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 performing planting management on a region to be planted of crops according to the optimal crop planting layout scheme.
Further, the method for dividing the crop to-be-planted area into different sub-planted 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 marking the area to be planted corresponding to Bj as MBj;
s1.2, acquiring the length corresponding to the crop to-be-planted area, marking the length as L1, acquiring the width corresponding to the crop to-be-planted area, marking the width as L2, and defaulting the crop to-be-planted area to be rectangular;
s1.3, obtaining the maximum common factors corresponding to the areas to be planted of various crops to be planted, and marking the maximum common factors as gys;
s1.4, acquiring a set of factors corresponding to L1, namely L1Y, acquiring a set of factors corresponding to L2, namely L2Y, and acquiring a data pair formed by a kth 1 element in L1Y and a kth 2 element in L2Y, namely (L1 Yk1, L2Yk 2);
s1.5, selecting each data pair (L1 Yk1, L2Yk 2) meeting the condition L1Yk1, L2Yk2= gys, and recording the data pair with the smallest difference between the selected data pair k1 and k2 as (L1 Yk 1) min ，L2Yk2 min ) Will (L1 Yk 1) min ，L2Yk2 min ) As a reference for the sub-planting area specification, the sub-planting area specification is L1Yk1 min And has a width of L2Yk2 min Is a rectangular region of the panel.
In the process of dividing the crop to-be-planted area into different sub-planting areas with the same specification, the method provided by the invention not only considers the problem of uniformly dividing the crop to-be-planted area, but also considers the problem of uniformly dividing the to-be-planted areas of various crop types, so that only one crop type to be planted is planted in the same sub-planting area, 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 relief features of each sub-planting area by the terrain monitoring equipment in the step S2 comprises the following steps:
s2.1, acquiring the lowest point of the topography in the crop to-be-planted area as a reference point, acquiring quantized values of the lowest point of the topography, the highest point of the topography and the average topography height in each sub-planted area relative to the altitude of the reference point,
the quantized value of the altitude of the highest point of the topography in Ai relative to the reference point is recorded as h1Ai,
the quantized value of the altitude of the lowest point of the topography in Ai relative to the reference point is recorded as h2Ai,
the quantized value of the average topography height in Ai relative to the reference point altitude is recorded as h3Ai,
the quantized value of the altitude of a certain geographic point relative to the reference point is equal to the difference value between the altitude of the corresponding geographic point and the altitude of the reference point;
s2.2, obtaining the topography features of each sub-planting area, marking the topography features corresponding to the ith sub-planting area as Ai as { h3Ai, h1Ai, h2Ai, WAi }, WAi representing the topography stability degree in Ai,
when h1ai-h2ai=0, it is determined WAi =1, indicating that the topography in Ai is stationary,
when h1Ai-h2Ai > 0, the determination WAi = (h 3Ai-h2 Ai)/(h 1Ai-h2 Ai),
if WAi is less than 1, the topography in Ai is unstable and the topography is convex,
if WAi is more than 1, the topography in Ai is unstable and concave;
in the process of obtaining the quantized value h3Ai of the average topography height relative to the reference point altitude in Ai, dividing 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 Ai relative to the reference point altitude as h3Ai, wherein n is a preset constant in a database.
In the process of extracting the relief features of each sub-planting area through the terrain monitoring equipment in the S2, h3Ai, h1Ai and h2Ai are acquired for subsequent calculation WAi so as to judge the stability of the relief in the ith sub-planting area, and data reference is provided for analyzing the influence condition of the growth state of crops in two sub-planting areas when each crop species is respectively planted in adjacent sub-planting areas in the subsequent step.
Further, the step S3 of analyzing the influence on the growth state of the crops in the two sub-planting areas when each crop species is planted in each adjacent sub-planting area under the condition that the deviation of the topography characteristic is unchanged includes the following steps:
s3.1, obtaining the geographical features of each sub-planting area and the average quantized value of each contact point of two adjacent sub-planting areas relative to the altitude of the reference point, and marking the average quantized value of each contact point between adjacent sub-planting areas Ai and Ai1 relative to the altitude of the reference point as hp Ai-Ai1 ；
S3.2, acquiring the topography deviation DP of Ai relative to Ai1 in adjacent sub-planting areas Ai and Ai1 Ai-Ai1 The corresponding topography features of Ai are marked as { h3Ai, h1Ai, h2Ai, WAi }, the corresponding topography features of Ai1 are marked as { h3Ai1, h1Ai1, h2Ai1, WAi },
DP Ai-Ai1 corresponds to a set of { h3Ai-h3Ai1, gys. F (WAi, WAi1, h3Ai1, hp) Ai-Ai1 ）}，
Wherein F (WAi, WAi1, h3Ai, h3Ai1, hp) Ai-Ai1 ) Representing the relief deviation coefficient, gys is the maximum common factor corresponding to the area to be planted of various crop types to be planted,
comparison (h 3 Ai-hp) Ai-Ai1 ）*（h3Ai1-hp Ai-Ai1 ) A magnitude relation with 0 is provided,
when (h 3Ai-hp Ai-Ai1 ）*（h3Ai1-hp Ai-Ai1 ) When the difference is more than 0, judging that the topography between adjacent sub-planting areas Ai and Ai1 is V-shaped, selecting the topography deviation coefficient corresponding to the data pair (WAi, WAi 1) in the preset V-shaped sub-database in the database,
when (h 3Ai-hp Ai-Ai1 ）*（h3Ai1-hp Ai-Ai1 ) When the difference is less than or equal to 0, judging that the topography between adjacent sub-planting areas Ai and Ai1 is in a monotone change trend, and selecting a topography deviation coefficient corresponding to a data pair (WAi, WAi 1) in a sub-database which is preset in the database and is in the monotone change trend;
s3.3, in the process of obtaining the historical planting data,the deviation value of the topography characteristic is DP Ai-Ai1 When the crop species planted in Ai is Bj and the crop species 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 at the t-th day is denoted as Qt Ai-Ai1 The corresponding days of the overlapping time intervals in the crop growth periods corresponding to Bj and Bj1 are recorded 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 If +h3Ai-h3Ai1 > 0, then Q1t is determined Ai-Ai1 =Qt Ai-Ai1 +h3Ai-h3Ai1，
When Qt Ai-Ai1 If +h3Ai-h3Ai1 is less than or equal to 0, then Q1t is determined Ai-Ai1 E, the e is the influence coefficient of preset Bj relative to Bj1 in the database;
s3.4, obtaining 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 as SZ Ai-Ai1 ，
In the method, under the condition that the feature deviation of the terrain is unchanged, when each crop type is planted in each adjacent sub-planting area, in the process of influencing the growth state of the crops in the two sub-planting areas, the feature deviation of Ai relative to Ai1 in each adjacent sub-planting area Ai and Ai1 is acquired, and the situation that the feature deviation coefficients are different due to the feature change condition between the adjacent sub-planting areas Ai and Ai1 is considered; acquiring an influence height factor of the QAi relative to the QAi1, wherein the average height condition of different sub-planting areas and the difference of the heights of the crops corresponding to different crop types at the same time are considered, so that a data basis is provided for acquiring an influence value of the crop type Bj planted in the Ai on the growth state of the crop type Bj1 planted in the Ai1 in the adjacent sub-planting areas Ai and Ai1 in the subsequent step; obtaining gys F (WAi, WAi1, h3Ai, h3Ai1, hp) Ai-Ai1 ) Taking into account adjacent seed plantingIf the configuration of the areas is different from one another, the range of the crop in Ai1 is different from one another in the height factor of the influence of QAi to QAi1 per day during the growth of the crop.
Further, the method for generating different crop planting layout schemes in S4 includes the following steps:
s4.1, obtaining the area to be planted of each crop to be planted, wherein the sum of the areas to be planted corresponding to each crop to be planted is equal to the area to be planted of the crops,
calculating the quotient obtained by dividing the area to be planted of each crop type to be planted by the corresponding area of one sub-planting area to obtain the number of sub-planting areas corresponding to each crop type to be planted, and recording the number of sub-planting areas corresponding to Bj as GBj;
s4.2, matching corresponding crop types to be planted in each sub-planting area in the crop planting area to obtain different crop planting layout schemes, wherein the number of sub-planting areas corresponding to Bj in each crop planting scheme is equal to GBj, and one sub-planting area corresponds to one crop type to be planted.
In the method S4 for generating different crop planting layout schemes, when j1 represents the number of categories of the crop category 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 S4 includes the following steps:
s4-1, acquiring analysis results of influence conditions of crop growth states in two sub-planting areas when each crop species is planted in each adjacent sub-planting area under the condition that the topography characteristic deviation is unchanged;
s4-2, obtaining the generated planting layout schemes of the crops;
s4-3, obtaining the sum of influence values of 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 marking the sum 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 sub-planting areas in the area where crops are to be planted.
In the process of predicting the comprehensive influence value corresponding to each crop planting layout scheme in the S4, as a plurality of adjacent sub-planting areas exist in the same sub-planting area, the adjacent sub-planting areas can influence the growth state of crop species in the sub-planting area, and then the sum SZYrAI of the influence values of the crop species planted in each sub-planting area adjacent to the Ai on the growth state of the crop species planted in the Ai is needed to be calculated, the sum SZYrAI is taken as the total influence condition of the crop species planted in each sub-planting area adjacent to the Ai on the growth state of the crop species planted in the Ai in the r-th crop planting layout scheme, and further, the data reference is provided for the follow-up accurate calculation of the comprehensive influence value corresponding to the r-th crop planting layout scheme.
Intelligent agriculture is with overall arrangement management system based on thing networking, the system includes following module:
the planting area planning module is used for acquiring a crop to-be-planted area, crop types to be planted and to-be-planted areas of each crop type to be planted, and dividing the crop to-be-planted area into different sub-planting areas with the same specification;
the relief feature extraction module is used for extracting the relief features of each sub-planting area through the relief monitoring equipment;
the growth state influence analysis module acquires the feature deviation of the land between adjacent sub-planting areas and analyzes the influence conditions of the growth states of the crops in the two sub-planting areas when each crop species is planted in each adjacent sub-planting area under the condition that the feature deviation of the land is unchanged according to the historical planting data;
the layout scheme influence prediction module is used for generating different crop planting layout schemes according to the planting areas of each crop type to be planted, and predicting comprehensive influence values corresponding to each crop planting layout scheme by combining the topography characteristics in each sub-planting area and the analysis results in the growth state influence analysis module;
and the planting management module selects a crop layout scheme with the smallest 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 performs planting management on a crop area to be planted according to the optimal crop planting layout scheme.
Further, 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 relief feature acquisition unit is used for acquiring the relief features corresponding to the two adjacent sub-planting areas respectively;
the topography characteristic deviation analysis unit is used for acquiring deviation conditions between topography characteristics corresponding to two adjacent sub-planting areas respectively;
the relief deviation coefficient acquisition unit is used for acquiring the corresponding relief deviation coefficient between two adjacent sub-planting areas;
the growth state influence value analysis unit is used for acquiring the growth state influence value of the crop species planted in one of the two adjacent sub-planting areas on the crop species planted in the other sub-planting area.
Further, in the process that the planting management module performs planting management on the area to be planted of the crops according to the optimal crop planting layout scheme,
if the types of crops planted in each sub-planting area in the crop to-be-planted area are different from the types of 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 an administrator.
Compared with the prior art, the invention has the following beneficial effects: in the process of managing the agricultural layout, the invention not only considers the influence of the difference of the topography and the topography on the growth of the planted crops, but also considers the influence of the difference of the growth states of different crops in the growth process, and realizes the effective screening of the agricultural layout scheme by analyzing the comprehensive influence value corresponding to each crop planting layout scheme in the region where the crops are to be planted, thereby realizing the effective management of the agricultural layout.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a layout management system for intelligent agriculture based on the Internet of things;
fig. 2 is a flow chart of a layout management method for intelligent agriculture based on the internet of things.
Description of the embodiments
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: the intelligent agriculture layout management method based on the Internet of things comprises the following steps:
s1, acquiring a crop to-be-planted area, crop types to be planted and to-be-planted areas of each crop type to be planted, dividing the crop to-be-planted area into different sub-planting areas with the same specification, marking an ith sub-planting area as Ai, and marking a jth crop type to be planted as Bj;
the method for dividing the crop area to be planted 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 marking the area to be planted corresponding to Bj as MBj;
s1.2, acquiring the length corresponding to the crop to-be-planted area, marking the length as L1, acquiring the width corresponding to the crop to-be-planted area, marking the width as L2, and defaulting the crop to-be-planted area to be rectangular;
s1.3, obtaining the maximum common factors corresponding to the areas to be planted of various crops to be planted, and marking the maximum common factors as gys;
s1.4, acquiring a set of factors corresponding to L1, namely L1Y, acquiring a set of factors corresponding to L2, namely L2Y, and acquiring a data pair formed by a kth 1 element in L1Y and a kth 2 element in L2Y, namely (L1 Yk1, L2Yk 2);
s1.5, selecting each data pair (L1 Yk1, L2Yk 2) meeting the condition L1Yk1, L2Yk2= gys, and recording the data pair with the smallest difference between the selected data pair k1 and k2 as (L1 Yk 1) min ，L2Yk2 min ) Will (L1 Yk 1) min ，L2Yk2 min ) As a reference for the sub-planting area specification, the sub-planting area specification is L1Yk1 min And has a width of L2Yk2 min Is a rectangular region of the panel.
S2, extracting the relief features of each sub-planting area through a terrain monitoring device;
the method for extracting the relief features of each sub-planting area through the terrain monitoring equipment in the S2 comprises the following steps:
s2.1, acquiring the lowest point of the topography in the crop to-be-planted area as a reference point, acquiring quantized values of the lowest point of the topography, the highest point of the topography and the average topography height in each sub-planted area relative to the altitude of the reference point,
the quantized value of the altitude of the highest point of the topography in Ai relative to the reference point is recorded as h1Ai,
the quantized value of the altitude of the lowest point of the topography in Ai relative to the reference point is recorded as h2Ai,
the quantized value of the average topography height in Ai relative to the reference point altitude is recorded as h3Ai,
the quantized value of the altitude of a certain geographic point relative to the reference point is equal to the difference value between the altitude of the corresponding geographic point and the altitude of the reference point;
s2.2, obtaining the topography features of each sub-planting area, marking the topography features corresponding to the ith sub-planting area as Ai as { h3Ai, h1Ai, h2Ai, WAi }, WAi representing the topography stability degree in Ai,
when h1ai-h2ai=0, it is determined WAi =1, indicating that the topography in Ai is stationary,
when h1Ai-h2Ai > 0, the determination WAi = (h 3Ai-h2 Ai)/(h 1Ai-h2 Ai),
if WAi is less than 1, the topography in Ai is unstable and the topography is convex,
if WAi is more than 1, the topography in Ai is unstable and concave;
in the process of obtaining the quantized value h3Ai of the average topography height relative to the reference point altitude in Ai, dividing 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 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 altitude of the central point of the 9 rectangular areas in A1 relative to the reference point are 0.25, 0.2, 0.18, 0.23, 0.26, 0.32, 0.11, 0.15, 0.19,
because (0.25 +0.2+0.18+0.23+0.26+0.32+0.11+0.15+ 0.19)/(9 = 0.21,
then it is determined that the quantized value of the average topography height in A1 relative to the reference point altitude is 0.21.
S3, obtaining the feature deviation of the land features between adjacent sub-planting areas, analyzing the influence conditions of the crop growth states in the two sub-planting areas when each crop species is planted in each adjacent sub-planting area under the condition that the feature deviation of the land features is unchanged according to the 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 Ai1 in the adjacent sub-planting areas Ai and Ai1 is recorded as SZ Ai-Ai1 ；
In the step S3, under the condition that the feature deviation of the topography is unchanged, when each crop type is planted in each adjacent sub-planting area, the influence condition on the growth state of the crops in the two sub-planting areas is analyzed, and the method comprises the following steps:
s3.1, obtaining the geographical features of each sub-planting area and the average quantized value of each contact point of two adjacent sub-planting areas relative to the altitude of the reference point, and marking the average quantized value of each contact point between adjacent sub-planting areas Ai and Ai1 relative to the altitude of the reference point as hp Ai-Ai1 ；
S3.2, acquiring the topography deviation DP of Ai relative to Ai1 in adjacent sub-planting areas Ai and Ai1 Ai-Ai1 The corresponding topography features of Ai are marked as { h3Ai, h1Ai, h2Ai, WAi }, the corresponding topography features of Ai1 are marked as { h3Ai1, h1Ai1, h2Ai1, WAi },
DP Ai-Ai1 corresponds to a set of { h3Ai-h3Ai1, gys. F (WAi, WAi1, h3Ai1, hp) Ai-Ai1 ）}，
Wherein F (WAi, WAi1, h3Ai, h3Ai1, hp) Ai-Ai1 ) Representing the relief deviation coefficient, gys is the maximum common factor corresponding to the area to be planted of various crop types to be planted,
comparison (h 3 Ai-hp) Ai-Ai1 ）*（h3Ai1-hp Ai-Ai1 ) A magnitude relation with 0 is provided,
when (h 3Ai-hp Ai-Ai1 ）*（h3Ai1-hp Ai-Ai1 ) When the number of the adjacent sub-planting areas Ai and Ai1 is more than 0, judging that the topography between the adjacent sub-planting areas Ai and Ai1 is V-shaped, and selecting a topography deviation system corresponding to a data pair (WAi, WAi 1) in a preset V-shaped sub-database in the databaseThe number of the product is the number,
when (h 3Ai-hp Ai-Ai1 ）*（h3Ai1-hp Ai-Ai1 ) When the difference is less than or equal to 0, judging that the topography between adjacent sub-planting areas Ai and Ai1 is in a monotone change trend, and selecting a topography deviation coefficient corresponding to a data pair (WAi, WAi 1) in a sub-database which is preset in the database and is in the monotone change trend;
s3.3, in the historical planting data, the deviation value of the topography characteristic is DP Ai-Ai1 When the crop species planted in Ai is Bj and the crop species planted in Ai1 is Bj1, the number of days corresponding to the time interval overlapping in the crop growth cycle corresponding to Bj and Bj1 is denoted as TB j-j1 ，1≤t≤TB j-j1 ，
Obtaining the difference 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 as Qt Ai-Ai1 ；
Acquiring the influence height factor Q1t of QAi relative to QAi1 Ai-Ai1 ，
When Qt Ai-Ai1 If +h3Ai-h3Ai1 > 0, then Q1t is determined Ai-Ai1 =Qt Ai-Ai1 +h3Ai-h3Ai1，
When Qt Ai-Ai1 If +h3Ai-h3Ai1 is less than or equal to 0, then Q1t is determined Ai-Ai1 E, the e is the influence coefficient of preset Bj relative to Bj1 in the database;
s3.4, obtaining 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 as SZ Ai-Ai1 ，
S4, generating different crop planting layout schemes according to the planting areas of each crop species to be planted, and predicting the comprehensive influence value corresponding to each crop planting layout scheme by combining the relief features in each sub-planting region and the analysis results in S3;
the method for generating different crop planting layout schemes in the step S4 comprises the following steps:
s4.1, obtaining the area to be planted of each crop to be planted, wherein the sum of the areas to be planted corresponding to each crop to be planted is equal to the area to be planted of the crops,
calculating the quotient obtained by dividing the area to be planted of each crop type to be planted by the corresponding area of one sub-planting area to obtain the number of sub-planting areas corresponding to each crop type to be planted, and recording the number of sub-planting areas corresponding to Bj as GBj;
s4.2, matching the corresponding crop types to be planted in each sub-planting area in the crop planting area to obtain different crop planting layout schemes, wherein the number of sub-planting areas corresponding to Bj in each crop planting scheme is equal to GBj, one sub-planting area corresponds to one crop type to be planted,
the number of the obtained 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, acquiring analysis results of influence conditions of crop growth states in two sub-planting areas when each crop species is planted in each adjacent sub-planting area under the condition that the topography characteristic deviation is unchanged;
s4-2, obtaining the generated planting layout schemes of the crops;
s4-3, obtaining the sum of influence values of 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 marking the sum 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 sub-planting areas in the area where crops are to be planted.
S5, selecting a crop layout scheme with the smallest 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 performing planting management on a region to be planted of crops according to the optimal crop planting layout scheme.
Intelligent agriculture is with overall arrangement management system based on thing networking, the system includes following module:
the planting area planning module is used for acquiring a crop to-be-planted area, crop types to be planted and to-be-planted areas of each crop type to be planted, and dividing the crop to-be-planted area into different sub-planting areas with the same specification;
the relief feature extraction module is used for extracting the relief features of each sub-planting area through the relief monitoring equipment;
the growth state influence analysis module acquires the feature deviation of the land between adjacent sub-planting areas and analyzes the influence conditions of the growth states of the crops in the two sub-planting areas when each crop species is planted in each adjacent sub-planting area under the condition that the feature deviation of the land is unchanged according to the historical planting data;
the layout scheme influence prediction module is used for generating different crop planting layout schemes according to the planting areas of each crop type to be planted, and predicting comprehensive influence values corresponding to each crop planting layout scheme by combining the topography characteristics in each sub-planting area and the analysis results in the growth state influence analysis module;
and the planting management module selects a crop layout scheme with the smallest 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 performs planting management on a crop area to be planted according to the optimal crop planting layout scheme.
The growth state influence analysis module comprises a relief feature acquisition unit, a relief feature deviation analysis unit, a relief deviation coefficient acquisition unit and a growth state influence value analysis unit,
the relief feature acquisition unit is used for acquiring the relief features corresponding to the two adjacent sub-planting areas respectively;
the topography characteristic deviation analysis unit is used for acquiring deviation conditions between topography characteristics corresponding to two adjacent sub-planting areas respectively;
the relief deviation coefficient acquisition unit is used for acquiring the corresponding relief deviation coefficient between two adjacent sub-planting areas;
the growth state influence value analysis unit is used for acquiring the growth state influence value of the crop species planted in one of the two adjacent sub-planting areas on the crop species planted in the other sub-planting area.
In the process of planting and managing the crop to-be-planted areas by the planting and managing module according to the optimal crop planting layout scheme,
if the types of crops planted in each sub-planting area in the crop to-be-planted area are different from the types of 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 an administrator.
It is noted that relational terms such as first and second, and the like are 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. Moreover, 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: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. The intelligent agriculture layout management method based on the Internet of things is characterized by comprising the following steps of:
s1, acquiring a crop to-be-planted area, crop types to be planted and to-be-planted areas of each crop type to be planted, dividing the crop to-be-planted area into different sub-planting areas with the same specification, marking an ith sub-planting area as Ai, and marking a jth crop type to be planted as Bj;
s2, extracting the relief features of each sub-planting area through a terrain monitoring device;
s3, obtaining the feature deviation of the land features between adjacent sub-planting areas, analyzing the influence conditions of the crop growth states in the two sub-planting areas when each crop species is planted in each adjacent sub-planting area under the condition that the feature deviation of the land features is unchanged according to the 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 Ai1 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 areas of each crop species to be planted, and predicting the comprehensive influence value corresponding to each crop planting layout scheme by combining the relief features in each sub-planting region and the analysis results in S3;
s5, selecting a crop layout scheme with the smallest 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 performing planting management on a crop area to be planted according to the optimal crop planting layout scheme;
the method for extracting the relief features of each sub-planting area through the terrain monitoring equipment in the S2 comprises the following steps:
s2.1, acquiring the lowest point of the topography in the crop to-be-planted area as a reference point, acquiring quantized values of the lowest point of the topography, the highest point of the topography and the average topography height in each sub-planted area relative to the altitude of the reference point,
the quantized value of the altitude of the highest point of the topography in Ai relative to the reference point is recorded as h1Ai,
the quantized value of the altitude of the lowest point of the topography in Ai relative to the reference point is recorded as h2Ai,
the quantized value of the average topography height in Ai relative to the reference point altitude is recorded as h3Ai,
the quantized value of the altitude of a certain geographic point relative to the reference point is equal to the difference value between the altitude of the corresponding geographic point and the altitude of the reference point;
s2.2, obtaining the topography features of each sub-planting area, marking the topography features corresponding to the ith sub-planting area as Ai as { h3Ai, h1Ai, h2Ai, WAi }, WAi representing the topography stability degree in Ai,
when h1ai-h2ai=0, it is determined WAi =1, indicating that the topography in Ai is stationary,
when h1Ai-h2Ai > 0, the determination WAi = (h 3Ai-h2 Ai)/(h 1Ai-h2 Ai),
if WAi is less than 1, the topography in Ai is unstable and the topography is convex,
if WAi is more than 1, the topography in Ai is unstable and concave;
in the process of obtaining a quantized value h3Ai of the average topography height relative to the reference point altitude in Ai, dividing 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 Ai relative to the reference point altitude as h3Ai, wherein n is a preset constant in a database;
in the step S3, under the condition that the feature deviation of the topography is unchanged, when each crop type is planted in each adjacent sub-planting area, the influence condition on the growth state of the crops in the two sub-planting areas is analyzed, and the method comprises the following steps:
s3.1, obtaining the geographical features of each sub-planting area and the average quantized value of each contact point of two adjacent sub-planting areas relative to the altitude of the reference point, and marking the average quantized value of each contact point between adjacent sub-planting areas Ai and Ai1 relative to the altitude of the reference point as hp Ai-Ai1 ；
S3.2, acquiring the topography deviation DP of Ai relative to Ai1 in adjacent sub-planting areas Ai and Ai1 Ai-Ai1 The corresponding topography features of Ai are marked as { h3Ai, h1Ai, h2Ai, WAi }, the corresponding topography features of Ai1 are marked as { h3Ai1, h1Ai1, h2Ai1, WAi },
DP Ai-Ai1 corresponds to a set of { h3Ai-h3Ai1, gys. F (WAi, WAi1, h3Ai1, hp) Ai-Ai1 ）}，
Wherein F (WAi, WAi1, h3Ai, h3Ai1, hp) Ai-Ai1 ) Representing the relief deviation coefficient, gys is the maximum common factor corresponding to the area to be planted of various crop types to be planted,
comparison (h 3 Ai-hp) Ai-Ai1 ）*（h3Ai1-hp Ai-Ai1 ) A magnitude relation with 0 is provided,
when (h 3Ai-hp Ai-Ai1 ）*（h3Ai1-hp Ai-Ai1 ) When the difference is more than 0, judging that the topography between adjacent sub-planting areas Ai and Ai1 is V-shaped, selecting the topography deviation coefficient corresponding to the data pair (WAi, WAi 1) in the preset V-shaped sub-database in the database,
when (h 3Ai-hp Ai-Ai1 ）*（h3Ai1-hp Ai-Ai1 ) When the difference is less than or equal to 0, judging that the topography between adjacent sub-planting areas Ai and Ai1 is in a monotone change trend, and selecting the land corresponding to the data pair (WAi, WAi 1) in the sub-database which is preset in the database and is in the monotone change trendPotential deviation coefficient;
s3.3, in the historical planting data, the deviation value of the topography characteristic is DP Ai-Ai1 When the crop species planted in Ai is Bj and the crop species 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 at the t-th day is denoted as Qt Ai-Ai1 The corresponding days of the overlapping time intervals in the crop growth periods corresponding to Bj and Bj1 are recorded 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 If +h3Ai-h3Ai1 > 0, then Q1t is determined Ai-Ai1 =Qt Ai-Ai1 +h3Ai-h3Ai1，
When Qt Ai-Ai1 If +h3Ai-h3Ai1 is less than or equal to 0, then Q1t is determined Ai-Ai1 E, the e is the influence coefficient of preset Bj relative to Bj1 in the database;
s3.4, obtaining 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 as SZ Ai-Ai1 ，
The method for generating different crop planting layout schemes in the step S4 comprises the following steps:
s4.1, obtaining the area to be planted of each crop to be planted, wherein the sum of the areas to be planted corresponding to each crop to be planted is equal to the area to be planted of the crops,
calculating the quotient obtained by dividing the area to be planted of each crop type to be planted by the corresponding area of one sub-planting area to obtain the number of sub-planting areas corresponding to each crop type to be planted, and recording the number of sub-planting areas corresponding to Bj as GBj;
s4.2, matching corresponding crop types to be planted in each sub-planting area in the crop planting area to obtain different crop planting layout schemes, wherein the number of sub-planting areas corresponding to Bj in each crop planting scheme is equal to GBj, and one sub-planting area corresponds to one crop type 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, acquiring analysis results of influence conditions of crop growth states in two sub-planting areas when each crop species is planted in each adjacent sub-planting area under the condition that the topography characteristic deviation is unchanged;
s4-2, obtaining the generated planting layout schemes of the crops;
s4-3, obtaining the sum of influence values of 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 marking the sum 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 sub-planting areas in the area where crops are to be planted.
2. The layout management method for intelligent agriculture based on the internet of things according to claim 1, wherein the method comprises the following steps: the method for dividing the crop area to be planted 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 marking the area to be planted corresponding to Bj as MBj;
s1.2, acquiring the length corresponding to the crop to-be-planted area, marking the length as L1, acquiring the width corresponding to the crop to-be-planted area, marking the width as L2, and defaulting the crop to-be-planted area to be rectangular;
s1.3, obtaining the maximum common factors corresponding to the areas to be planted of various crops to be planted, and marking the maximum common factors as gys;
s1.4, acquiring a set of factors corresponding to L1, namely L1Y, acquiring a set of factors corresponding to L2, namely L2Y, and acquiring a data pair formed by a kth 1 element in L1Y and a kth 2 element in L2Y, namely (L1 Yk1, L2Yk 2);
s1.5, selecting each data pair (L1 Yk1, L2Yk 2) meeting the condition L1Yk1, L2Yk2= gys, and recording the data pair with the smallest difference between the selected data pair k1 and k2 as (L1 Yk 1) min ，L2Yk2 min ) Will (L1 Yk 1) min ，L2Yk2 min ) As a reference for the sub-planting area specification, the sub-planting area specification is L1Yk1 min And has a width of L2Yk2 min Is a rectangular region of the panel.
3. The layout management system for intelligent agriculture based on the internet of things, which applies the layout management method for intelligent agriculture based on the internet of things according to any one of claims 1-2, is characterized in that: the system comprises the following modules:
the planting area planning module is used for acquiring a crop to-be-planted area, crop types to be planted and to-be-planted areas of each crop type to be planted, and dividing the crop to-be-planted area into different sub-planting areas with the same specification;
the relief feature extraction module is used for extracting the relief features of each sub-planting area through the relief monitoring equipment;
the growth state influence analysis module acquires the feature deviation of the land between adjacent sub-planting areas and analyzes the influence conditions of the growth states of the crops in the two sub-planting areas when each crop species is planted in each adjacent sub-planting area under the condition that the feature deviation of the land is unchanged according to the historical planting data;
the layout scheme influence prediction module is used for generating different crop planting layout schemes according to the planting areas of each crop type to be planted, and predicting comprehensive influence values corresponding to each crop planting layout scheme by combining the topography characteristics in each sub-planting area and the analysis results in the growth state influence analysis module;
and the planting management module selects a crop layout scheme with the smallest 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 performs planting management on a crop area to be planted according to the optimal crop planting layout scheme.
4. The layout management system for intelligent agriculture based on the internet of things according to claim 3, wherein: the growth state influence analysis module comprises a relief feature acquisition unit, a relief feature deviation analysis unit, a relief deviation coefficient acquisition unit and a growth state influence value analysis unit,
the relief feature acquisition unit is used for acquiring the relief features corresponding to the two adjacent sub-planting areas respectively;
the topography characteristic deviation analysis unit is used for acquiring deviation conditions between topography characteristics corresponding to two adjacent sub-planting areas respectively;
the relief deviation coefficient acquisition unit is used for acquiring the corresponding relief deviation coefficient between two adjacent sub-planting areas;
the growth state influence value analysis unit is used for acquiring the growth state influence value of the crop species planted in one of the two adjacent sub-planting areas on the crop species planted in the other sub-planting area.
5. The layout management system for intelligent agriculture based on the internet of things according to claim 3, wherein: in the process of planting and managing the crop to-be-planted areas by the planting and managing module according to the optimal crop planting layout scheme,
if the types of crops planted in each sub-planting area in the crop to-be-planted area are different from the types of 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 an administrator.
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