CN116975789A - Intelligent farmland field analysis method, system and medium based on big data - Google Patents
Intelligent farmland field analysis method, system and medium based on big data Download PDFInfo
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Abstract
The invention discloses a farmland field intelligent analysis method, a farmland field intelligent analysis system and a farmland field intelligent analysis medium based on big data. Acquiring target farmland area information, and constructing a model according to the target farmland area information to form a farmland model based on three dimensions; acquiring soil monitoring data of each sub-area in a target farmland area, importing the soil monitoring data into a farmland model, and carrying out regional data continuity analysis and prediction based on a linear regression prediction algorithm to obtain continuous humidity prediction data of each sub-area; and acquiring crop growth information of different subareas in the target farmland area, and analyzing soil irrigation requirements based on the crop growth information and the soil continuity prediction data to obtain a farmland regulation and control scheme. The invention can realize accurate monitoring and control of farmlands.
Description
Technical Field
The invention relates to the field of data analysis, in particular to a farmland field intelligent analysis method, system and medium based on big data.
Background
The production efficiency of agricultural products is a major concern in China, and the production efficiency and land utilization of farmlands are important influences on the agricultural products.
However, the method is limited by the prior art, the informatization degree of the farmland field is low, the analysis capability of monitoring data is weak, and the monitoring prediction of continuous data such as humidity, temperature and the like is insufficient, so that the farmland utilization rate and the production efficiency are further reduced. Therefore, there is a need for an efficient intelligent analysis method for farmland sites.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a farmland field intelligent analysis method, a farmland field intelligent analysis system and a farmland field intelligent analysis medium based on big data.
The first aspect of the invention provides an intelligent farmland field analysis method based on big data, which comprises the following steps:
acquiring target farmland area information, and constructing a model according to the target farmland area information to form a farmland model based on three dimensions;
acquiring soil monitoring data of each sub-area in a target farmland area, importing the soil monitoring data into a farmland model, and carrying out regional data continuity analysis and prediction based on a linear regression prediction algorithm to obtain continuous humidity prediction data of each sub-area;
And acquiring crop growth information of different subareas in the target farmland area, and analyzing soil irrigation requirements based on the crop growth information and the soil continuity prediction data to obtain a farmland regulation and control scheme.
In this scheme, obtain target farmland regional information, according to target farmland regional information carries out the model construction and forms the farmland model based on three-dimensional, specifically does:
acquiring target farmland area information;
the target farmland area information comprises area, area outline and crop distribution information in the area;
and constructing a farmland model based on three dimensions according to the target farmland area information.
In this scheme, obtain the soil monitoring data of each subregion in the target farmland region, with the soil monitoring data import farmland model, based on linear regression prediction algorithm, carry out regional data continuity analysis and prediction, obtain the continuous humidity prediction data of each subregion, specifically be:
based on the positions of the existing monitoring points in the target farmland area, combining a farmland model, and carrying out grid division on the target farmland area to obtain a plurality of subareas, wherein each subarea corresponds to one monitoring point;
acquiring soil monitoring data of each sub-area in real time, wherein the soil monitoring data comprise soil humidity and air temperature;
Based on a farmland model, all monitoring points are connected with each other in adjacent points, and adjacent judgment standards are that the subareas corresponding to the two monitoring points are adjacent, namely the two monitoring points are adjacent;
forming a monitoring data network through connection of adjacent points, wherein each monitoring point has corresponding soil monitoring data in the monitoring data network, and the monitoring data network comprises a plurality of continuous horizontal line segments and continuous vertical line segments;
extracting soil humidity data of all monitoring points in a continuous transverse line segment from a monitoring data network, and sequencing the soil humidity data according to geographic position continuity to obtain ordered humidity data;
calculating the data interpolation N in the interpolation line segment based on the length of the continuous transverse line segment and the predicted data interval;
based on the ordered humidity data and the data interpolation quantity N, carrying out data interpolation prediction calculation by a polynomial-based linear regression interpolation method to obtain N humidity data and filling the N humidity data into the continuous transverse line segments;
and extracting all continuous horizontal line segments and continuous vertical line segments from the monitoring data network to carry out continuous data filling so as to obtain the monitoring data network with certain continuous data.
In this scheme, in the monitoring data network, draw out the soil humidity data of all monitoring points in a continuous horizontal line section, still include:
Extracting soil humidity data of all monitoring points in one continuous transverse line segment from a monitoring data network, and marking all corresponding subareas in the one continuous transverse line segment to obtain a plurality of selected subareas;
calculating variance values based on the planting density and the soil humidity in the selected subarea to obtain a first variance value and a second variance value;
judging whether the first variance value and the second variance value are both larger than a preset variance value, if so, screening a pair of adjacent monitoring points with the maximum humidity difference value based on the continuous transverse line section;
and cutting the continuous transverse line segments based on a pair of adjacent monitoring points with the maximum humidity difference value to form two continuous transverse line segments, and respectively carrying out continuous data predictive analysis on the two continuous transverse line segments.
In this scheme, obtain the soil monitoring data of each subregion in target farmland region, with soil monitoring data import farmland model, based on linear regression prediction algorithm, carry out regional data continuity analysis and prediction, obtain the continuous humidity prediction data of each subregion, still include:
acquiring planting density, illumination time length and air temperature information of each sub-area in a historical time period;
Acquiring soil humidity change data of monitoring points in each sub-area after irrigation in a historical time period;
taking the planting density, the illumination time length and the air temperature as independent variables, taking the soil humidity change data as dependent variables, performing predictive training based on multiple linear regression, performing predictive model fitting through optimizing regression coefficients and a gradient descent algorithm, obtaining a multiple predictive equation, and taking the multiple predictive equation as a soil humidity predictive equation.
In this scheme, acquire the crop growth information of different subregions in the target farmland region, carry out soil irrigation demand analysis based on crop growth information and soil continuity forecast data, obtain farmland regulation and control scheme, specifically do:
acquiring crop information in a target farmland area, and generating a search tag based on the crop information;
acquiring crop image big data, and performing related image retrieval from the crop image big data based on the retrieval tag to obtain retrieval image data;
carrying out data ordering on the search image data, and carrying out association mapping on growth images of different stages of crops and growth stage information based on the search image data to obtain crop growth association data;
Acquiring farmland image data of different subareas in a target farmland area, and performing image similarity calculation and growth stage evaluation based on the farmland image data and crop growth related data to obtain crop growth information;
each sub-area corresponds to one crop growth information;
marking one sub-area as the current sub-area;
in a monitoring data network, acquiring continuous humidity prediction data in all line segments in the current subarea range;
the continuous humidity prediction data includes a plurality of humidity values;
acquiring corresponding soil irrigation demand and soil humidity demand information according to crop growth information in the current subarea;
based on the continuous humidity prediction data, judging whether the continuous humidity prediction data accords with the soil humidity demand information;
marking and integrating humidity values which do not meet requirements in the continuous humidity prediction data to obtain abnormal humidity data;
extracting the position of a monitoring data network where each humidity value in abnormal humidity data is located, and marking to obtain a plurality of soil abnormal points;
and connecting lines based on the plurality of soil abnormal points to obtain a soil abnormal region.
In this scheme, acquire the crop growth information of different subregions in the target farmland region, based on crop growth information carries out soil irrigation demand analysis with soil continuity forecast data, obtains farmland regulation and control scheme, still includes:
Acquiring planting density, illumination time length and air temperature information in a current subarea in real time;
the planting density, the illumination time length and the air temperature information are used as parameters to be imported into a soil humidity prediction equation to conduct humidity prediction for a preset time, and humidity change prediction data are obtained;
analyzing the soil irrigation requirements of the abnormal region and the non-abnormal region of the current subarea according to the soil abnormal region, the humidity change prediction data, and obtaining soil regulation and control information;
and analyzing the soil regulation information of all subareas in the target farmland area, and carrying out scheme integration based on all the soil regulation information to form a farmland regulation scheme.
The second aspect of the invention also provides a farmland field intelligent analysis system based on big data, which comprises: the intelligent farmland field analysis program based on big data is executed by the processor to realize the following steps:
acquiring target farmland area information, and constructing a model according to the target farmland area information to form a farmland model based on three dimensions;
acquiring soil monitoring data of each sub-area in a target farmland area, importing the soil monitoring data into a farmland model, and carrying out regional data continuity analysis and prediction based on a linear regression prediction algorithm to obtain continuous humidity prediction data of each sub-area;
And acquiring crop growth information of different subareas in the target farmland area, and analyzing soil irrigation requirements based on the crop growth information and the soil continuity prediction data to obtain a farmland regulation and control scheme.
In this scheme, obtain target farmland regional information, according to target farmland regional information carries out the model construction and forms the farmland model based on three-dimensional, specifically does:
acquiring target farmland area information;
the target farmland area information comprises area, area outline and crop distribution information in the area;
and constructing a farmland model based on three dimensions according to the target farmland area information.
The third aspect of the present invention also provides a computer readable storage medium, wherein the computer readable storage medium includes a big data based intelligent analysis program for farm land, and when the big data based intelligent analysis program is executed by a processor, the steps of the big data based intelligent analysis method are implemented.
The invention discloses a farmland field intelligent analysis method, a farmland field intelligent analysis system and a farmland field intelligent analysis medium based on big data. Acquiring target farmland area information, and constructing a model according to the target farmland area information to form a farmland model based on three dimensions; acquiring soil monitoring data of each sub-area in a target farmland area, importing the soil monitoring data into a farmland model, and carrying out regional data continuity analysis and prediction based on a linear regression prediction algorithm to obtain continuous humidity prediction data of each sub-area; and acquiring crop growth information of different subareas in the target farmland area, and analyzing soil irrigation requirements based on the crop growth information and the soil continuity prediction data to obtain a farmland regulation and control scheme. The invention can realize accurate monitoring and control of farmlands.
Drawings
FIG. 1 shows a flow chart of a farmland field intelligent analysis method based on big data;
FIG. 2 shows a flow chart of the farmland model construction of the present application;
FIG. 3 shows a flow chart for obtaining a soil moisture prediction equation of the present application;
FIG. 4 shows a block diagram of a farmland field intelligent analysis system based on big data of the present application.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced in other ways than those described herein, and therefore the scope of the present application is not limited to the specific embodiments disclosed below.
FIG. 1 shows a flow chart of a farmland field intelligent analysis method based on big data.
As shown in fig. 1, the first aspect of the present application provides a method for intelligent analysis of a farmland field based on big data, comprising:
S102, acquiring target farmland area information, and constructing a model according to the target farmland area information to form a three-dimensional farmland model;
s104, acquiring soil monitoring data of each sub-area in the target farmland area, importing the soil monitoring data into a farmland model, and carrying out regional data continuity analysis and prediction based on a linear regression prediction algorithm to obtain continuous humidity prediction data of each sub-area;
and S106, acquiring crop growth information of different subregions in the target farmland region, and analyzing soil irrigation requirements based on the crop growth information and the soil continuity prediction data to obtain a farmland regulation and control scheme.
FIG. 2 shows a flow chart of the farmland model construction of the present invention.
According to the embodiment of the invention, the target farmland area information is obtained, and model building is carried out according to the target farmland area information to form a farmland model based on three dimensions, specifically comprising the following steps:
s202, acquiring target farmland area information;
s204, the target farmland area information comprises area, area outline and in-area crop distribution information;
s206, building a three-dimensional farmland model according to the target farmland area information.
It should be noted that, farmland model is a visual three-dimensional model, can carry out information visualization to the farmland to let the user look over farmland condition, especially farmland soil irrigation condition, humidity, temperature condition more directly perceivedly.
According to the embodiment of the invention, the soil monitoring data of each sub-area in the target farmland area is obtained, the soil monitoring data is imported into a farmland model, and the continuous analysis and prediction of regional data are carried out based on a linear regression prediction algorithm to obtain continuous humidity prediction data of each sub-area, wherein the continuous humidity prediction data comprise:
based on the positions of the existing monitoring points in the target farmland area, combining a farmland model, and carrying out grid division on the target farmland area to obtain a plurality of subareas, wherein each subarea corresponds to one monitoring point;
acquiring soil monitoring data of each sub-area in real time, wherein the soil monitoring data comprise soil humidity and air temperature;
based on a farmland model, all monitoring points are connected with each other in adjacent points, and adjacent judgment standards are that the subareas corresponding to the two monitoring points are adjacent, namely the two monitoring points are adjacent;
forming a monitoring data network through connection of adjacent points, wherein each monitoring point has corresponding soil monitoring data in the monitoring data network, and the monitoring data network comprises a plurality of continuous horizontal line segments and continuous vertical line segments;
Extracting soil humidity data of all monitoring points in a continuous transverse line segment from a monitoring data network, and sequencing the soil humidity data according to geographic position continuity to obtain ordered humidity data;
calculating the data interpolation N in the interpolation line segment based on the length of the continuous transverse line segment and the predicted data interval;
based on the ordered humidity data and the data interpolation quantity N, carrying out data interpolation prediction calculation by a polynomial-based linear regression interpolation method to obtain N humidity data and filling the N humidity data into the continuous transverse line segments;
and extracting all continuous horizontal line segments and continuous vertical line segments from the monitoring data network to carry out continuous data filling so as to obtain the monitoring data network with certain continuous data.
It should be noted that the predicted data interval may be 1 to 5 meters. The monitoring point is provided with soil monitoring equipment, and the soil monitoring equipment is fixed on the monitoring point and can acquire relevant monitoring data in real time.
The monitoring data network is a network diagram formed by connecting monitoring points, can be displayed in a farmland model, and can be used for analyzing, filling and extracting continuous data; in the embodiment of the invention, the subareas are square examples, the monitoring points are square center points, so that after all the monitoring points are connected, a rectangular network can be formed, the rectangular network is provided with a plurality of transverse lines and vertical lines, the junction point of each grid is the monitoring point, before data are not filled, the monitoring data network only has data at the junction point of the grid (namely the monitoring point position), after continuous data prediction and data filling are carried out, filled data are arranged at the line segments of the monitoring data network, the data intervals in the line segments are determined by the predicted data intervals, and further, the data density in the monitoring data network can be determined by the predicted data intervals. In addition, when the user needs monitoring data with higher continuity, the predicted data interval can be properly reduced, and the data density can be increased.
It is worth mentioning that in the environmental monitoring work of carrying out farmland, because limited by the hardware level, the monitoring point is difficult to cover the whole farmland area, and be difficult to place monitoring facilities in certain special farmland position, this just has led to the data in the monitoring farmland soil to exist the region that can not cover, and the data scope of monitoring point is less, regional data between monitoring point and the monitoring point is difficult to evaluate, do not have better continuity data to carry out detailed soil data description to the farmland area, further lead to the inaccurate and inaccurate condition of irrigation, fertilization.
In the invention, a monitoring data network is formed by forming the connection monitoring points, the data macro-visualization can be carried out on the whole farmland area through the monitoring data network, and the continuity data prediction and filling can be carried out on the basis of the existing monitoring data through the monitoring data network, so that more accurate and precise prediction data can be obtained, and the precise farmland irrigation can be carried out on the basis of the prediction data. Particularly, in the middle area between one sub-area and the other sub-area, the monitoring data of the predicted middle area can break the limit of the monitoring point to a certain extent, and the accurate prediction of the monitoring blind area is realized.
According to an embodiment of the present invention, in the monitoring data network, soil humidity data of all monitoring points in a continuous transverse line segment is extracted, and the method further includes:
extracting soil humidity data of all monitoring points in one continuous transverse line segment from a monitoring data network, and marking all corresponding subareas in the one continuous transverse line segment to obtain a plurality of selected subareas;
calculating variance values based on the planting density and the soil humidity in the selected subarea to obtain a first variance value and a second variance value;
judging whether the first variance value and the second variance value are both larger than a preset variance value, if so, screening a pair of adjacent monitoring points with the maximum humidity difference value based on the continuous transverse line section;
and cutting the continuous transverse line segments based on a pair of adjacent monitoring points with the maximum humidity difference value to form two continuous transverse line segments, and respectively carrying out continuous data predictive analysis on the two continuous transverse line segments.
It should be noted that, in the farmland, the planting densities in different areas affect the absorption and release of moisture in the soil, so that the humidity fluctuation of different planting densities is different, and if one-time continuous data prediction is performed based on different planting densities, a larger error may occur in the predicted filling value. Therefore, the method does not need to divide certain line segments and separate continuous data prediction by performing variance calculation on the planting density in the line segments and the related monitoring data (soil humidity), if the variance value is large, so that more accurate prediction data can be obtained. By the method, the monitoring data in any small area range in the whole farmland area can be accurately predicted, so that accurate farmland regulation and control are realized.
Fig. 3 shows a flow chart for obtaining the soil moisture prediction equation of the present invention.
According to an embodiment of the present invention, the method for acquiring soil monitoring data of each sub-area in a target farmland area, importing the soil monitoring data into a farmland model, and performing regional data continuity analysis and prediction based on a linear regression prediction algorithm to obtain continuous humidity prediction data of each sub-area, further includes:
s302, acquiring planting density, illumination duration and air temperature information of each sub-area in a historical time period;
s304, acquiring soil humidity change data of monitoring points in each sub-area after irrigation in a historical time period;
s306, taking the planting density, the illumination time length and the air temperature as independent variables, taking the soil humidity change data as dependent variables, performing predictive training based on multiple linear regression, performing predictive model fitting through optimizing regression coefficients and a gradient descent algorithm, obtaining a multiple predictive equation, and taking the multiple predictive equation as a soil humidity predictive equation.
In the soil humidity prediction equation, four parameters including planting density, illumination time, air temperature parameter and current soil humidity are input, soil humidity change prediction data is output, and prediction time is set by a user as parameter input.
According to the embodiment of the invention, the crop growth information of different subregions in the target farmland area is obtained, and the soil irrigation demand analysis is performed based on the crop growth information and the soil continuity prediction data to obtain a farmland regulation and control scheme, which specifically comprises the following steps:
acquiring crop information in a target farmland area, and generating a search tag based on the crop information;
acquiring crop image big data, and performing related image retrieval from the crop image big data based on the retrieval tag to obtain retrieval image data;
carrying out data ordering on the search image data, and carrying out association mapping on growth images of different stages of crops and growth stage information based on the search image data to obtain crop growth association data;
acquiring farmland image data of different subareas in a target farmland area, and performing image similarity calculation and growth stage evaluation based on the farmland image data and crop growth related data to obtain crop growth information;
each sub-area corresponds to one crop growth information;
marking one sub-area as the current sub-area;
in a monitoring data network, acquiring continuous humidity prediction data in all line segments in the current subarea range;
The continuous humidity prediction data includes a plurality of humidity values;
acquiring corresponding soil irrigation demand and soil humidity demand information according to crop growth information in the current subarea;
based on the continuous humidity prediction data, judging whether the continuous humidity prediction data accords with the soil humidity demand information;
marking and integrating humidity values which do not meet requirements in the continuous humidity prediction data to obtain abnormal humidity data;
extracting the position of a monitoring data network where each humidity value in abnormal humidity data is located, and marking to obtain a plurality of soil abnormal points;
and connecting lines based on the plurality of soil abnormal points to obtain a soil abnormal region.
The crop information includes information such as crop type and name. The growth phase information comprises information such as growth cycle number, growth nutrition requirement and the like. The continuous humidity prediction data comprises a plurality of humidity values, wherein each humidity value is a certain data value in all line segments in the current subarea range. The method comprises the step of connecting the abnormal points of the soil to obtain a soil abnormal region, and particularly a region formed by surrounding the abnormal points of the soil in a space through the connecting lines. The soil abnormal region is a humidity abnormal region, and accurate regulation and control on farmlands can be realized by carrying out irrigation scheme analysis on the basis of the position of the abnormal region.
According to an embodiment of the present invention, the method for obtaining crop growth information of different subregions in a target farmland area, performing soil irrigation demand analysis based on the crop growth information and soil continuity prediction data, and obtaining a farmland regulation and control scheme, further includes:
acquiring planting density, illumination time length and air temperature information in a current subarea in real time;
the planting density, the illumination time length and the air temperature information are used as parameters to be imported into a soil humidity prediction equation to conduct humidity prediction for a preset time, and humidity change prediction data are obtained;
analyzing the soil irrigation requirements of the abnormal region and the non-abnormal region of the current subarea according to the soil abnormal region, the humidity change prediction data, and obtaining soil regulation and control information;
and analyzing the soil regulation information of all subareas in the target farmland area, and carrying out scheme integration based on all the soil regulation information to form a farmland regulation scheme.
The planting density, the illumination time and the air temperature information are important information for influencing the humidity change of farmland soil.
It should be noted that, according to the present invention, it is possible to predict the continuity of the data with linear variation, in this embodiment, the humidity data, and also the data of temperature, temperature difference, relative humidity, etc.
According to an embodiment of the present invention, further comprising:
inputting a monitoring demand area in a target farmland area;
calculating the number M of random points based on the area size of the monitoring demand area;
m random point arrangement is carried out in the monitoring demand area, and M farmland position points are obtained by combining a farmland model;
acquiring a monitoring data network of a target farmland in real time;
combining the farmland model, and forming a corresponding circular area based on a preset radius by taking one farmland position point as a center point;
acquiring a line segment of the monitoring data network in the circular area and marking the line segment to obtain a coincident line segment;
based on a monitoring data network, extracting and averaging all monitoring data in the overlapped line segments to obtain averaged data, and taking the averaged data as soil monitoring data of the farmland position points;
analyzing all M farmland position points to obtain M soil monitoring data;
and taking the M pieces of soil monitoring data as real-time monitoring data of the monitoring demand area.
By the method, monitoring data can be acquired in any required area, so that regional analysis requirements on a target farmland are improved. To ensure data accuracy, the size of the monitoring demand region is generally smaller than the size of the sub-region. The larger the area of the monitoring demand area, the larger M. The coincident line segments are a plurality of line segments in the monitoring data network and are in a circular area. The monitoring data is data predicted and filled through the continuity data.
FIG. 4 shows a block diagram of a farmland field intelligent analysis system based on big data of the present invention.
The second aspect of the present invention also provides an intelligent analysis system 4 for farmland sites based on big data, the system comprising: the intelligent analysis program of the farmland field based on big data is implemented by the processor when executed by the processor as follows:
acquiring target farmland area information, and constructing a model according to the target farmland area information to form a farmland model based on three dimensions;
acquiring soil monitoring data of each sub-area in a target farmland area, importing the soil monitoring data into a farmland model, and carrying out regional data continuity analysis and prediction based on a linear regression prediction algorithm to obtain continuous humidity prediction data of each sub-area;
and acquiring crop growth information of different subareas in the target farmland area, and analyzing soil irrigation requirements based on the crop growth information and the soil continuity prediction data to obtain a farmland regulation and control scheme.
According to the embodiment of the invention, the target farmland area information is obtained, and model building is carried out according to the target farmland area information to form a farmland model based on three dimensions, specifically comprising the following steps:
Acquiring target farmland area information;
the target farmland area information comprises area, area outline and crop distribution information in the area;
and constructing a farmland model based on three dimensions according to the target farmland area information.
It should be noted that, farmland model is a visual three-dimensional model, can carry out information visualization to the farmland to let the user look over farmland condition, especially farmland soil irrigation condition, humidity, temperature condition more directly perceivedly.
According to the embodiment of the invention, the soil monitoring data of each sub-area in the target farmland area is obtained, the soil monitoring data is imported into a farmland model, and the continuous analysis and prediction of regional data are carried out based on a linear regression prediction algorithm to obtain continuous humidity prediction data of each sub-area, wherein the continuous humidity prediction data comprise:
based on the positions of the existing monitoring points in the target farmland area, combining a farmland model, and carrying out grid division on the target farmland area to obtain a plurality of subareas, wherein each subarea corresponds to one monitoring point;
acquiring soil monitoring data of each sub-area in real time, wherein the soil monitoring data comprise soil humidity and air temperature;
based on a farmland model, all monitoring points are connected with each other in adjacent points, and adjacent judgment standards are that the subareas corresponding to the two monitoring points are adjacent, namely the two monitoring points are adjacent;
Forming a monitoring data network through connection of adjacent points, wherein each monitoring point has corresponding soil monitoring data in the monitoring data network, and the monitoring data network comprises a plurality of continuous horizontal line segments and continuous vertical line segments;
extracting soil humidity data of all monitoring points in a continuous transverse line segment from a monitoring data network, and sequencing the soil humidity data according to geographic position continuity to obtain ordered humidity data;
calculating the data interpolation N in the interpolation line segment based on the length of the continuous transverse line segment and the predicted data interval;
based on the ordered humidity data and the data interpolation quantity N, carrying out data interpolation prediction calculation by a polynomial-based linear regression interpolation method to obtain N humidity data and filling the N humidity data into the continuous transverse line segments;
and extracting all continuous horizontal line segments and continuous vertical line segments from the monitoring data network to carry out continuous data filling so as to obtain the monitoring data network with certain continuous data.
It should be noted that the predicted data interval may be 1 to 5 meters. The monitoring point is provided with soil monitoring equipment, and the soil monitoring equipment is fixed on the monitoring point and can acquire relevant monitoring data in real time.
The monitoring data network is a network diagram formed by connecting monitoring points, can be displayed in a farmland model, and can be used for analyzing, filling and extracting continuous data; in the embodiment of the invention, the subareas are square examples, the monitoring points are square center points, so that after all the monitoring points are connected, a rectangular network can be formed, the rectangular network is provided with a plurality of transverse lines and vertical lines, the junction point of each grid is the monitoring point, before data are not filled, the monitoring data network only has data at the junction point of the grid (namely the monitoring point position), after continuous data prediction and data filling are carried out, filled data are arranged at the line segments of the monitoring data network, the data intervals in the line segments are determined by the predicted data intervals, and further, the data density in the monitoring data network can be determined by the predicted data intervals. In addition, when the user needs monitoring data with higher continuity, the predicted data interval can be properly reduced, and the data density can be increased.
It is worth mentioning that in the environmental monitoring work of carrying out farmland, because limited by the hardware level, the monitoring point is difficult to cover the whole farmland area, and be difficult to place monitoring facilities in certain special farmland position, this just has led to the data in the monitoring farmland soil to exist the region that can not cover, and the data scope of monitoring point is less, regional data between monitoring point and the monitoring point is difficult to evaluate, do not have better continuity data to carry out detailed soil data description to the farmland area, further lead to the inaccurate and inaccurate condition of irrigation, fertilization.
In the invention, a monitoring data network is formed by forming the connection monitoring points, the data macro-visualization can be carried out on the whole farmland area through the monitoring data network, and the continuity data prediction and filling can be carried out on the basis of the existing monitoring data through the monitoring data network, so that more accurate and precise prediction data can be obtained, and the precise farmland irrigation can be carried out on the basis of the prediction data. Particularly, in the middle area between one sub-area and the other sub-area, the monitoring data of the predicted middle area can break the limit of the monitoring point to a certain extent, and the accurate prediction of the monitoring blind area is realized.
According to an embodiment of the present invention, in the monitoring data network, soil humidity data of all monitoring points in a continuous transverse line segment is extracted, and the method further includes:
extracting soil humidity data of all monitoring points in one continuous transverse line segment from a monitoring data network, and marking all corresponding subareas in the one continuous transverse line segment to obtain a plurality of selected subareas;
calculating variance values based on the planting density and the soil humidity in the selected subarea to obtain a first variance value and a second variance value;
judging whether the first variance value and the second variance value are both larger than a preset variance value, if so, screening a pair of adjacent monitoring points with the maximum humidity difference value based on the continuous transverse line section;
and cutting the continuous transverse line segments based on a pair of adjacent monitoring points with the maximum humidity difference value to form two continuous transverse line segments, and respectively carrying out continuous data predictive analysis on the two continuous transverse line segments.
It should be noted that, in the farmland, the planting densities in different areas affect the absorption and release of moisture in the soil, so that the humidity fluctuation of different planting densities is different, and if one-time continuous data prediction is performed based on different planting densities, a larger error may occur in the predicted filling value. Therefore, the method does not need to divide certain line segments and separate continuous data prediction by performing variance calculation on the planting density in the line segments and the related monitoring data (soil humidity), if the variance value is large, so that more accurate prediction data can be obtained. By the method, the monitoring data in any small area range in the whole farmland area can be accurately predicted, so that accurate farmland regulation and control are realized.
According to an embodiment of the present invention, the method for acquiring soil monitoring data of each sub-area in a target farmland area, importing the soil monitoring data into a farmland model, and performing regional data continuity analysis and prediction based on a linear regression prediction algorithm to obtain continuous humidity prediction data of each sub-area, further includes:
acquiring planting density, illumination time length and air temperature information of each sub-area in a historical time period;
acquiring soil humidity change data of monitoring points in each sub-area after irrigation in a historical time period;
taking the planting density, the illumination time length and the air temperature as independent variables, taking the soil humidity change data as dependent variables, performing predictive training based on multiple linear regression, performing predictive model fitting through optimizing regression coefficients and a gradient descent algorithm, obtaining a multiple predictive equation, and taking the multiple predictive equation as a soil humidity predictive equation.
In the soil humidity prediction equation, four parameters including planting density, illumination time, air temperature parameter and current soil humidity are input, soil humidity change prediction data is output, and prediction time is set by a user as parameter input.
According to the embodiment of the invention, the crop growth information of different subregions in the target farmland area is obtained, and the soil irrigation demand analysis is performed based on the crop growth information and the soil continuity prediction data to obtain a farmland regulation and control scheme, which specifically comprises the following steps:
acquiring crop information in a target farmland area, and generating a search tag based on the crop information;
acquiring crop image big data, and performing related image retrieval from the crop image big data based on the retrieval tag to obtain retrieval image data;
carrying out data ordering on the search image data, and carrying out association mapping on growth images of different stages of crops and growth stage information based on the search image data to obtain crop growth association data;
acquiring farmland image data of different subareas in a target farmland area, and performing image similarity calculation and growth stage evaluation based on the farmland image data and crop growth related data to obtain crop growth information;
each sub-area corresponds to one crop growth information;
marking one sub-area as the current sub-area;
in a monitoring data network, acquiring continuous humidity prediction data in all line segments in the current subarea range;
The continuous humidity prediction data includes a plurality of humidity values;
acquiring corresponding soil irrigation demand and soil humidity demand information according to crop growth information in the current subarea;
based on the continuous humidity prediction data, judging whether the continuous humidity prediction data accords with the soil humidity demand information;
marking and integrating humidity values which do not meet requirements in the continuous humidity prediction data to obtain abnormal humidity data;
extracting the position of a monitoring data network where each humidity value in abnormal humidity data is located, and marking to obtain a plurality of soil abnormal points;
and connecting lines based on the plurality of soil abnormal points to obtain a soil abnormal region.
The crop information includes information such as crop type and name. The growth phase information comprises information such as growth cycle number, growth nutrition requirement and the like. The continuous humidity prediction data comprises a plurality of humidity values, wherein each humidity value is a certain data value in all line segments in the current subarea range. The method comprises the step of connecting the abnormal points of the soil to obtain a soil abnormal region, and particularly a region formed by surrounding the abnormal points of the soil in a space through the connecting lines. The soil abnormal region is a humidity abnormal region, and accurate regulation and control on farmlands can be realized by carrying out irrigation scheme analysis on the basis of the position of the abnormal region.
According to an embodiment of the present invention, the method for obtaining crop growth information of different subregions in a target farmland area, performing soil irrigation demand analysis based on the crop growth information and soil continuity prediction data, and obtaining a farmland regulation and control scheme, further includes:
acquiring planting density, illumination time length and air temperature information in a current subarea in real time;
the planting density, the illumination time length and the air temperature information are used as parameters to be imported into a soil humidity prediction equation to conduct humidity prediction for a preset time, and humidity change prediction data are obtained;
analyzing the soil irrigation requirements of the abnormal region and the non-abnormal region of the current subarea according to the soil abnormal region, the humidity change prediction data, and obtaining soil regulation and control information;
and analyzing the soil regulation information of all subareas in the target farmland area, and carrying out scheme integration based on all the soil regulation information to form a farmland regulation scheme.
The planting density, the illumination time and the air temperature information are important information for influencing the humidity change of farmland soil.
It should be noted that, according to the present invention, it is possible to predict the continuity of the data with linear variation, in this embodiment, the humidity data, and also the data of temperature, temperature difference, relative humidity, etc.
According to an embodiment of the present invention, further comprising:
inputting a monitoring demand area in a target farmland area;
calculating the number M of random points based on the area size of the monitoring demand area;
m random point arrangement is carried out in the monitoring demand area, and M farmland position points are obtained by combining a farmland model;
acquiring a monitoring data network of a target farmland in real time;
combining the farmland model, and forming a corresponding circular area based on a preset radius by taking one farmland position point as a center point;
acquiring a line segment of the monitoring data network in the circular area and marking the line segment to obtain a coincident line segment;
based on a monitoring data network, extracting and averaging all monitoring data in the overlapped line segments to obtain averaged data, and taking the averaged data as soil monitoring data of the farmland position points;
analyzing all M farmland position points to obtain M soil monitoring data;
and taking the M pieces of soil monitoring data as real-time monitoring data of the monitoring demand area.
By the method, monitoring data can be acquired in any required area, so that regional analysis requirements on a target farmland are improved. To ensure data accuracy, the size of the monitoring demand region is generally smaller than the size of the sub-region. The larger the area of the monitoring demand area, the larger M. The coincident line segments are a plurality of line segments in the monitoring data network and are in a circular area. The monitoring data is data predicted and filled through the continuity data.
The third aspect of the present invention also provides a computer readable storage medium, wherein the computer readable storage medium includes a big data based intelligent analysis program for farm land, and when the big data based intelligent analysis program is executed by a processor, the steps of the big data based intelligent analysis method are implemented.
The invention discloses a farmland field intelligent analysis method, a farmland field intelligent analysis system and a farmland field intelligent analysis medium based on big data. Acquiring target farmland area information, and constructing a model according to the target farmland area information to form a farmland model based on three dimensions; acquiring soil monitoring data of each sub-area in a target farmland area, importing the soil monitoring data into a farmland model, and carrying out regional data continuity analysis and prediction based on a linear regression prediction algorithm to obtain continuous humidity prediction data of each sub-area; and acquiring crop growth information of different subareas in the target farmland area, and analyzing soil irrigation requirements based on the crop growth information and the soil continuity prediction data to obtain a farmland regulation and control scheme. The invention can realize accurate monitoring and control of farmlands.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A farmland field intelligent analysis method based on big data is characterized by comprising the following steps:
acquiring target farmland area information, and constructing a model according to the target farmland area information to form a farmland model based on three dimensions;
acquiring soil monitoring data of each sub-area in a target farmland area, importing the soil monitoring data into a farmland model, and carrying out regional data continuity analysis and prediction based on a linear regression prediction algorithm to obtain continuous humidity prediction data of each sub-area;
and acquiring crop growth information of different subareas in the target farmland area, and analyzing soil irrigation requirements based on the crop growth information and the soil continuity prediction data to obtain a farmland regulation and control scheme.
2. The intelligent analysis method for farmland based on big data according to claim 1, wherein the obtaining of the target farmland area information, and the model building according to the target farmland area information form a farmland model based on three dimensions, specifically comprises:
Acquiring target farmland area information;
the target farmland area information comprises area, area outline and crop distribution information in the area;
and constructing a farmland model based on three dimensions according to the target farmland area information.
3. The intelligent analysis method for farmland based on big data according to claim 2, wherein the method is characterized in that the soil monitoring data of each sub-area in the target farmland area is obtained, the soil monitoring data is imported into a farmland model, and continuous analysis and prediction of regional data are performed based on a linear regression prediction algorithm, so as to obtain continuous humidity prediction data of each sub-area, specifically:
based on the positions of the existing monitoring points in the target farmland area, combining a farmland model, and carrying out grid division on the target farmland area to obtain a plurality of subareas, wherein each subarea corresponds to one monitoring point;
acquiring soil monitoring data of each sub-area in real time, wherein the soil monitoring data comprise soil humidity and air temperature;
based on a farmland model, all monitoring points are connected with each other in adjacent points, and adjacent judgment standards are that the subareas corresponding to the two monitoring points are adjacent, namely the two monitoring points are adjacent;
Forming a monitoring data network through connection of adjacent points, wherein each monitoring point has corresponding soil monitoring data in the monitoring data network, and the monitoring data network comprises a plurality of continuous horizontal line segments and continuous vertical line segments;
extracting soil humidity data of all monitoring points in a continuous transverse line segment from a monitoring data network, and sequencing the soil humidity data according to geographic position continuity to obtain ordered humidity data;
calculating the data interpolation N in the interpolation line segment based on the length of the continuous transverse line segment and the predicted data interval;
based on the ordered humidity data and the data interpolation quantity N, carrying out data interpolation prediction calculation by a polynomial-based linear regression interpolation method to obtain N humidity data and filling the N humidity data into the continuous transverse line segments;
and extracting all continuous horizontal line segments and continuous vertical line segments from the monitoring data network to carry out continuous data filling so as to obtain the monitoring data network with certain continuous data.
4. The intelligent analysis method for farmland sites based on big data according to claim 3, wherein the method for extracting soil humidity data of all monitoring points in a continuous transverse line segment in the monitoring data network further comprises:
Extracting soil humidity data of all monitoring points in one continuous transverse line segment from a monitoring data network, and marking all corresponding subareas in the one continuous transverse line segment to obtain a plurality of selected subareas;
calculating variance values based on the planting density and the soil humidity in the selected subarea to obtain a first variance value and a second variance value;
judging whether the first variance value and the second variance value are both larger than a preset variance value, if so, screening a pair of adjacent monitoring points with the maximum humidity difference value based on the continuous transverse line section;
and cutting the continuous transverse line segments based on a pair of adjacent monitoring points with the maximum humidity difference value to form two continuous transverse line segments, and respectively carrying out continuous data predictive analysis on the two continuous transverse line segments.
5. The intelligent analysis method for farmland based on big data according to claim 4, wherein the acquiring the soil monitoring data of each sub-area in the target farmland area, importing the soil monitoring data into a farmland model, and performing regional data continuity analysis and prediction based on a linear regression prediction algorithm to obtain continuous humidity prediction data of each sub-area, further comprises:
Acquiring planting density, illumination time length and air temperature information of each sub-area in a historical time period;
acquiring soil humidity change data of monitoring points in each sub-area after irrigation in a historical time period;
taking the planting density, the illumination time length and the air temperature as independent variables, taking the soil humidity change data as dependent variables, performing predictive training based on multiple linear regression, performing predictive model fitting through optimizing regression coefficients and a gradient descent algorithm, obtaining a multiple predictive equation, and taking the multiple predictive equation as a soil humidity predictive equation.
6. The intelligent analysis method for farmland based on big data according to claim 5, wherein the obtaining of crop growth information of different subregions in the target farmland area, and the analyzing of soil irrigation demand based on the crop growth information and the predicted data of soil continuity, obtain a farmland regulation scheme, specifically comprises:
acquiring crop information in a target farmland area, and generating a search tag based on the crop information;
acquiring crop image big data, and performing related image retrieval from the crop image big data based on the retrieval tag to obtain retrieval image data;
Carrying out data ordering on the search image data, and carrying out association mapping on growth images of different stages of crops and growth stage information based on the search image data to obtain crop growth association data;
acquiring farmland image data of different subareas in a target farmland area, and performing image similarity calculation and growth stage evaluation based on the farmland image data and crop growth related data to obtain crop growth information;
each sub-area corresponds to one crop growth information;
marking one sub-area as the current sub-area;
in a monitoring data network, acquiring continuous humidity prediction data in all line segments in the current subarea range;
the continuous humidity prediction data includes a plurality of humidity values;
acquiring corresponding soil irrigation demand and soil humidity demand information according to crop growth information in the current subarea;
based on the continuous humidity prediction data, judging whether the continuous humidity prediction data accords with the soil humidity demand information;
marking and integrating humidity values which do not meet requirements in the continuous humidity prediction data to obtain abnormal humidity data;
extracting the position of a monitoring data network where each humidity value in abnormal humidity data is located, and marking to obtain a plurality of soil abnormal points;
And connecting lines based on the plurality of soil abnormal points to obtain a soil abnormal region.
7. The intelligent analysis method for farmland based on big data according to claim 6, wherein the obtaining of crop growth information of different subregions in the target farmland area, and the analyzing of soil irrigation demand based on the crop growth information and the predicted data of soil continuity, further comprises:
acquiring planting density, illumination time length and air temperature information in a current subarea in real time;
the planting density, the illumination time length and the air temperature information are used as parameters to be imported into a soil humidity prediction equation to conduct humidity prediction for a preset time, and humidity change prediction data are obtained;
analyzing the soil irrigation requirements of the abnormal region and the non-abnormal region of the current subarea according to the soil abnormal region, the humidity change prediction data, and obtaining soil regulation and control information;
and analyzing the soil regulation information of all subareas in the target farmland area, and carrying out scheme integration based on all the soil regulation information to form a farmland regulation scheme.
8. A farmland place intelligent analysis system based on big data, which is characterized in that the system comprises: the intelligent farmland field analysis program based on big data is executed by the processor to realize the following steps:
Acquiring target farmland area information, and constructing a model according to the target farmland area information to form a farmland model based on three dimensions;
acquiring soil monitoring data of each sub-area in a target farmland area, importing the soil monitoring data into a farmland model, and carrying out regional data continuity analysis and prediction based on a linear regression prediction algorithm to obtain continuous humidity prediction data of each sub-area;
and acquiring crop growth information of different subareas in the target farmland area, and analyzing soil irrigation requirements based on the crop growth information and the soil continuity prediction data to obtain a farmland regulation and control scheme.
9. The intelligent farmland field analysis system based on big data according to claim 8, wherein the obtaining of the target farmland area information, and the model building according to the target farmland area information form a three-dimensional farmland model, specifically:
acquiring target farmland area information;
the target farmland area information comprises area, area outline and crop distribution information in the area;
and constructing a farmland model based on three dimensions according to the target farmland area information.
10. A computer readable storage medium, wherein the computer readable storage medium includes a big data based intelligent analysis program for farm land, and the big data based intelligent analysis program is executed by a processor to implement the steps of the big data based intelligent analysis method for farm land according to any of claims 1 to 7.
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