CN117520469A - Agricultural Internet of things construction system and method thereof - Google Patents
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Abstract
The invention discloses an agricultural Internet of things construction system and a method thereof, and particularly relates to the technical field of agricultural Internet of things, wherein the agricultural Internet of things construction system comprises a data acquisition module, a data arrangement module, a comprehensive analysis module and a display module; the data acquisition modules comprise n sub-acquisition modules, each sub-acquisition module corresponds to only one soil dividing area, and each sub-acquisition module comprises m sub-acquisition units of the same type, a sub-acquisition unit of the type I and a sub-acquisition unit of the type II; the m first-class sub-acquisition units are used for acquiring soil information common to first-class crops and second-class crops; the method can be used for establishing the common soil information of the first-class crops and the second-class crops, collecting the growth information of the first-class crops and the correlation analysis method between the growth information of the second-class crops, and has great significance for optimizing the land resource management in the direction of research that the growth can be mutually promoted through the synergistic effect among the crops in limited soil.
Description
Technical Field
The invention relates to the technical field of agricultural Internet of things, in particular to an agricultural Internet of things construction system and method.
Background
With the development of science and technology, more and more scientific agriculture is developed, data can be acquired through a scientific monitoring means to provide powerful data support for subsequent conception and scientific planting, and soil is taken as a basic medium for crop growth, so that modern agriculture is very focused on monitoring the soil environment. The method is characterized in that technologies such as soil sensors and the like are widely adopted, experimental field research plans are often conducted in a scientific research stage, the aims of optimizing resource management, improving yield and quality and reducing environmental impact are fulfilled, research that growth can be mutually promoted through the synergistic effect among crops in limited soil is also a significant research direction, and how to screen valuable experimental fields of the experimental fields becomes an important factor of data support of subsequent research, so that a technical scheme is provided for better optimizing land resources.
Items of the invention
In order to achieve the above purpose, the present invention provides the following technical solutions:
the agricultural Internet of things construction system comprises a data acquisition module, a data arrangement module, a comprehensive analysis module and a display module, wherein the acquisition module, the data arrangement module, the comprehensive analysis module and the display module are connected through signals;
the data acquisition modules comprise n sub-acquisition modules, each sub-acquisition module corresponds to only one soil dividing area, and each sub-acquisition module comprises m sub-acquisition units of the same type, a sub-acquisition unit of the type I and a sub-acquisition unit of the type II; the m type sub-acquisition units are used for acquiring the common soil information of the first type crops and the second type crops, the I type sub-acquisition units are used for acquiring the growth information of the first type crops, and the II type sub-acquisition units are used for acquiring the growth information of the second type crops; the sub-acquisition module gathers soil information common to the first crop and the second crop, acquires growth information of the first crop and growth information of the second crop into a first-level data set and sends the first-level data set to the data arrangement module; m and n are integers greater than 1;
the data arrangement module comprises n sub-arrangement modules, each sub-arrangement module respectively carries out data arrangement operation on the first-level data set sent by the sub-acquisition module of the corresponding sequence number, deletes abnormal data in the first-level data set, classifies and arranges the first-level data set, gathers the abnormal data to obtain a second-level data set, and the data arrangement module combines the second-level data set and sends the second-level data set to the comprehensive analysis module;
the comprehensive analysis module calculates the synergistic value XTi of the first-class crops and the second-class crops according to the second-class data set, then performs descending order arrangement on the corresponding divided areas according to the synergistic value XTi, and generates the corresponding adjusting signal type according to the second-class data set;
the display module is used for displaying all the running states of the data acquisition module, the data arrangement module and the comprehensive analysis module.
In a preferred embodiment, the m sub-collection units are used for collecting soil information common to the first crop and the second crop, and the following steps are: each sub-collection unit is used for collecting soil data common to only one preset crop and two types of crops, and the soil data is calibrated to be TRm.
In a preferred embodiment, the type i sub-collection unit is used for collecting the growth information of a type of crop, and the type II sub-collection unit is used for collecting the growth information of a type of crop, which means that:
the I-type sub-acquisition unit acquires the growth data of all preset p items of one type of crops, and marks the growth data as SZ1p;
the II-type sub-acquisition unit acquires the growth data of all preset q projects of the second-class crops, and marks the growth data as SZ2q.
In a preferred embodiment, each sub-sorting module performs data sorting operation on the first-level data set sent by the sub-collecting module of the corresponding sequence number, deletes abnormal data in the first-level data set, sorts and sorts the first-level data set, and obtaining a second-level data set after summarizing means that:
step one, acquiring a plurality of acquired data acquired by a single sub-acquisition unit in t1 time, and counting the acquired data of each type to construct a similar data set;
step two, calculating the mean value Ji and standard deviation Bi of the similar data sets, wherein the mean value Ji is calculated according to the formula: ji=Sigma Xi represents the sum of all the collected data of the same type in a plurality of collected data collected by a single sub-collection unit in t1 time, and N represents the quantity of the collected data of the same type; the standard deviation Bi has the following calculation formula: />∑(Xi-Ji) 2 Representing the sum of squares of the differences between each type of acquired data and the mean value Ji;
step three, calculating a deviation score value PCi in a single sub-acquisition unit, wherein the calculation formula of the deviation score value PCi is as follows:comparing the partial difference value PCi with a preset abnormal comparison threshold A1, and if the partial difference value PCi exceeds the abnormal comparison threshold A1, determining that the acquired data Xi is abnormal data and deleting the acquired data Xi, otherwise, determining that the acquired data Xi is non-abnormal data and reserving the acquired data Xi;
and step four, summarizing all the data which are determined to be non-abnormal data and are reserved into the subtype data sets of the type of collected data, and summarizing the subtype data sets corresponding to all the type of sub-collection units to obtain a secondary data set.
In a preferred embodiment, the calculation of the synergistic value XTi of the first crop and the second crop by the comprehensive analysis module from the second data set means that:
step one, acquiring the same subtype data set corresponding to a preset collaborative factor, calculating an average value, processing, marking as a collaborative factor value YSi, and calculating the sum of all collaborative factor values YSi as a1;
step two, acquiring the same subtype data set corresponding to the preset antagonistic factors, calculating an average value, processing, marking the data set as an antagonistic factor value JKi, and calculating the sum of all antagonistic factor values JKi as a2;
step three, acquiring a preset non-antagonistic factor and the same subtype data set corresponding to the non-antagonistic factor, calculating an average value, processing, marking the data set as a non-interference factor value WGi, and calculating the sum of all non-interference factor values WGi as a3;
calculating the ratio value of a1 and a2 to the sum of a1, a2 and a3 respectively, and marking the ratio value as a synergy coefficient b1 and an antagonism coefficient b2 respectively;
step five, calculating the sum c1 of the products of all beneficial growth item parameters and the synergistic coefficient b1 in the crop growth data SZ1p, and calculating the sum c2 of the products of beneficial growth item parameters and the antagonistic coefficient b2 in the crop growth data SZ1p;
step six, calculating the sum c3 of the products of all beneficial growth item parameters and the synergistic coefficient b1 in the second crop growth data SZ2q, and the sum c4 of the products of beneficial growth item parameters and the antagonistic coefficient b2 in the second crop growth data SZ2 q;
step seven, calculating the synergistic value XTi of the first crop and the second crop, wherein xti=c1+c3-c 2-c4.
The comprehensive analysis module performs descending order arrangement on the corresponding divided areas according to the synergistic value XTi, which is that:
comparing the synergistic value XTi of the first crop and the second crop with a preset sequencing comparison value A2, and when the synergistic value XTi is not smaller than the preset sequencing comparison value A2, the corresponding divided areas of the synergistic value XTi participate in sequencing, otherwise, the sequencing is not performed.
In a preferred embodiment, the integrated analysis module generates the corresponding adjustment signal type from the secondary data set by:
comparing the collaborative factor value YSi with a collaborative adjustment threshold value ui corresponding to a preset collaborative factor, generating a type of adjustment signal when the ratio of the difference value of the collaborative factor value YSi and the collaborative adjustment threshold value ui to the collaborative adjustment threshold value ui exceeds r1 after absolute value processing, and otherwise, generating a type of adjustment signal;
comparing the antagonism factor value JKi with an antagonism adjustment threshold wi corresponding to a preset antagonism factor, generating a first type of adjustment signal when the ratio of the difference value of the antagonism factor value JKi and the antagonism adjustment threshold wi to the antagonism adjustment threshold wi exceeds r2 after absolute value processing, and generating a second type of adjustment signal otherwise;
comparing the non-interference factor value WGi with a preset non-antagonistic factor and a non-interference factor adjusting threshold vi corresponding to the non-antagonistic factor, generating a first type adjusting signal when the ratio of the difference value of the non-interference factor value WGi and the non-interference factor adjusting threshold vi to the non-antagonistic factor exceeds r3 after absolute value processing, and otherwise generating a second type adjusting signal.
The construction method of the agricultural Internet of things comprises the following steps:
step 1, collecting soil information common to one type of crops and two types of crops, growth information of one type of crops and growth information of two types of crops, and summarizing the soil information, the growth information and the growth information into a first-level data set;
step 2, performing data arrangement operation on the primary data set, deleting abnormal data in the primary data set, and classifying and arranging the primary data set to obtain a secondary data set;
and 3, calculating the synergistic value XTi of the first-class crops and the second-class crops according to the second-class data set, then arranging the corresponding divided areas in a descending order according to the synergistic value XTi, and generating the corresponding adjusting signal type according to the second-class data set.
The invention has the technical effects and advantages that:
the comprehensive analysis module generates the corresponding adjusting signal type according to the secondary data set, is favorable for researchers to better control the current soil environment through the adjusting signal so as to ensure the research of the whole test field and provide guarantee for the accuracy and the reliability of the follow-up data.
The invention aims to establish a method for analyzing the correlation between the soil information common to the first-class crops and the second-class crops, collecting the growth information of the first-class crops and the growth information of the second-class crops, and has great significance for optimizing land resource management, exploring the most suitable growth environment and finally improving the yield and quality in the direction of research that the growth can be mutually promoted through the synergistic effect among the crops in limited soil.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
fig. 1 is a schematic diagram of an agricultural internet of things construction system in the present invention.
Fig. 2 is a schematic diagram of an agricultural internet of things construction method in the invention.
Detailed Description
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.
Example 1
The agricultural Internet of things construction system comprises a data acquisition module, a data arrangement module, a comprehensive analysis module and a display module, wherein the acquisition module, the data arrangement module, the comprehensive analysis module and the display module are connected through signals;
the data acquisition modules comprise n sub-acquisition modules, each sub-acquisition module corresponds to only one soil dividing area, and each sub-acquisition module comprises m sub-acquisition units of the same type, a sub-acquisition unit of the type I and a sub-acquisition unit of the type II; the m type sub-acquisition units are used for acquiring the common soil information of the first type crops and the second type crops, the I type sub-acquisition units are used for acquiring the growth information of the first type crops, and the II type sub-acquisition units are used for acquiring the growth information of the second type crops; the sub-acquisition module gathers soil information common to the first crop and the second crop, acquires growth information of the first crop and growth information of the second crop into a first-level data set and sends the first-level data set to the data arrangement module; m and n are integers greater than 1; the data acquisition module further comprises a coding unit, and the coding unit is used for coding the soil dividing areas corresponding to the coding unit so as to display the soil dividing areas when the soil dividing areas are ordered subsequently;
the m sub-acquisition units are used for acquiring soil information common to the first crop and the second crop, and refer to the following steps: each sub-collection unit is used for collecting soil data common to only one preset crop and two types of crops, and the soil data is calibrated to be TRm.
The type I sub-acquisition unit is used for acquiring the growth information of one type of crops, and the type II sub-acquisition unit is used for acquiring the growth information of two types of crops and is used for acquiring the growth information of the two types of crops:
the I-type sub-acquisition unit acquires the growth data of all preset p items of one type of crops, and marks the growth data as SZ1p;
the II-type sub-acquisition unit acquires the growth data of all preset q projects of the second-class crops, and marks the growth data as SZ2q.
The data arrangement module comprises n sub-arrangement modules, each sub-arrangement module respectively carries out data arrangement operation on the first-level data set sent by the sub-acquisition module of the corresponding sequence number, deletes abnormal data in the first-level data set, classifies and arranges the first-level data set, gathers the abnormal data to obtain a second-level data set, and the data arrangement module combines the second-level data set and sends the second-level data set to the comprehensive analysis module;
the operation logic of the data arrangement module specifically comprises the following contents:
step one, acquiring a plurality of acquired data acquired by a single sub-acquisition unit in t1 time, and counting the acquired data of each type to construct a similar data set;
step two, calculating the mean value Ji and standard deviation B of the similar data setsi, the mean value Ji is calculated as:sigma Xi represents the sum of all the collected data of the same type in a plurality of collected data collected by a single sub-collection unit in t1 time, and N represents the quantity of the collected data of the same type; the standard deviation Bi has the following calculation formula: />∑(Xi-Ji) 2 Representing the sum of squares of the differences between each type of acquired data and the mean value Ji;
step three, calculating a deviation score value PCi in a single sub-acquisition unit, wherein the calculation formula of the deviation score value PCi is as follows:comparing the partial difference value PCi with a preset abnormal comparison threshold A1, and if the partial difference value PCi exceeds the abnormal comparison threshold A1, determining that the acquired data Xi is abnormal data and deleting the acquired data Xi, otherwise, determining that the acquired data Xi is non-abnormal data and reserving the acquired data Xi;
and step four, summarizing all the data which are determined to be non-abnormal data and are reserved into the subtype data sets of the type of collected data, and summarizing the subtype data sets corresponding to all the type of sub-collection units to obtain a secondary data set.
The comprehensive analysis module calculates the synergistic value XTi of the first-class crops and the second-class crops according to the second-class data set, then performs descending order arrangement on the corresponding divided areas according to the synergistic value XTi, and generates the corresponding adjusting signal type according to the second-class data set;
the operation logic of the comprehensive analysis module specifically comprises the following contents:
step one, acquiring the same subtype data set corresponding to a preset collaborative factor, calculating an average value, processing, marking as a collaborative factor value YSi, and calculating the sum of all collaborative factor values YSi as a1;
step two, acquiring the same subtype data set corresponding to the preset antagonistic factors, calculating an average value, processing, marking the data set as an antagonistic factor value JKi, and calculating the sum of all antagonistic factor values JKi as a2;
step three, acquiring a preset non-antagonistic factor and the same subtype data set corresponding to the non-antagonistic factor, calculating an average value, processing, marking the data set as a non-interference factor value WGi, and calculating the sum of all non-interference factor values WGi as a3;
calculating the ratio value of a1 and a2 to the sum of a1, a2 and a3 respectively, and marking the ratio value as a synergy coefficient b1 and an antagonism coefficient b2 respectively;
step five, calculating the sum c1 of the products of all beneficial growth item parameters and the synergistic coefficient b1 in the crop growth data SZ1p, and calculating the sum c2 of the products of beneficial growth item parameters and the antagonistic coefficient b2 in the crop growth data SZ1p;
step six, calculating the sum c3 of the products of all beneficial growth item parameters and the synergistic coefficient b1 in the second crop growth data SZ2q, and the sum c4 of the products of beneficial growth item parameters and the antagonistic coefficient b2 in the second crop growth data SZ2 q;
step seven, calculating the synergistic value XTi of the first crop and the second crop, wherein xti=c1+c3-c 2-c4.
Step eight, comparing the synergistic value XTi of the first crop and the second crop with a preset sequencing comparison value A2, and when the synergistic value XTi is not smaller than the preset sequencing comparison value A2, the dividing area corresponding to the synergistic value XTi participates in sequencing, otherwise, does not participate in sequencing;
step nine, comparing the collaborative factor value YSi with a collaborative adjustment threshold value ui corresponding to a preset collaborative factor, generating a type of adjustment signals when the ratio of the difference value of the collaborative factor value YSi and the collaborative adjustment threshold value ui to the collaborative adjustment threshold value ui exceeds r1 after absolute value processing, and otherwise, generating a type of adjustment signals;
comparing the antagonism factor value JKi with an antagonism adjustment threshold wi corresponding to a preset antagonism factor, generating a first type of adjustment signal when the ratio of the difference value of the antagonism factor value JKi and the antagonism adjustment threshold wi to the antagonism adjustment threshold wi exceeds r2 after absolute value processing, and generating a second type of adjustment signal otherwise;
comparing the non-interference factor value WGi with a preset non-antagonistic factor and a non-interference factor adjusting threshold vi corresponding to the non-antagonistic factor, generating a first type adjusting signal when the ratio of the difference value of the non-interference factor value WGi and the non-interference factor adjusting threshold vi to the non-antagonistic factor exceeds r3 after absolute value processing, and otherwise generating a second type adjusting signal.
Example 2
The construction method of the agricultural Internet of things construction system based on the first embodiment comprises the following steps:
step 1, collecting soil information common to one type of crops and two types of crops, growth information of one type of crops and growth information of two types of crops, and summarizing the soil information, the growth information and the growth information into a first-level data set; the method comprises the following steps: the agricultural Internet of things construction system comprises a data acquisition module, a data arrangement module, a comprehensive analysis module and a display module, wherein the acquisition module, the data arrangement module, the comprehensive analysis module and the display module are connected through signals, the data acquisition module comprises n sub-acquisition modules, each sub-acquisition module corresponds to only one soil dividing area, and each sub-acquisition module comprises m sub-acquisition units of the same type, a I-type sub-acquisition unit and a II-type sub-acquisition unit; the m type sub-acquisition units are used for acquiring the common soil information of the first type crops and the second type crops, the I type sub-acquisition units are used for acquiring the growth information of the first type crops, and the II type sub-acquisition units are used for acquiring the growth information of the second type crops; the sub-acquisition module gathers soil information common to the first crop and the second crop, acquires growth information of the first crop and growth information of the second crop into a first-level data set and sends the first-level data set to the data arrangement module; m and n are integers greater than 1; the display module is used for displaying all the running states of the data acquisition module, the data arrangement module and the comprehensive analysis module;
step 2, performing data arrangement operation on the primary data set, deleting abnormal data in the primary data set, and classifying and arranging the primary data set to obtain a secondary data set; the method comprises the following steps: the data arrangement module comprises n sub-arrangement modules, each sub-arrangement module respectively carries out data arrangement operation on the first-level data set sent by the sub-acquisition module of the corresponding sequence number, deletes abnormal data in the first-level data set, classifies and arranges the first-level data set, gathers the abnormal data to obtain a second-level data set, and the data arrangement module combines the second-level data set and sends the second-level data set to the comprehensive analysis module;
step 3, calculating the synergistic value XTi of the first-class crops and the second-class crops according to the second-class data set, then arranging the corresponding divided areas in a descending order according to the synergistic value XTi, and generating the corresponding adjusting signal types according to the second-class data set, wherein the adjusting signal types specifically are as follows: the comprehensive analysis module calculates the synergistic value XTi of the first crop and the second crop according to the second data set, then performs descending order arrangement on the corresponding divided areas according to the synergistic value XTi, and generates the corresponding adjusting signal type according to the second data set.
The m sub-acquisition units are used for acquiring soil information common to one type of crop and two types of crop, and the soil information common to the two types of crop is as follows: each sub-collection unit is used for collecting soil data common to only one preset crop and two types of crops, and calibrating the soil data into TRm, wherein m is 1, 2, 3, … … and e, and e is a positive integer, and the soil data are exemplified as follows: soil data TR1 represents the humidity of the soil, soil data TR2 represents the ph of the soil, soil data TR3 represents the temperature of the soil, and soil data TRm represents the mth soil-related data collected.
The type I sub-acquisition unit is used for acquiring the growth information of one type of crops, and the type II sub-acquisition unit is used for acquiring the growth information of two types of crops and is used for acquiring the growth information of the two types of crops: the I-type sub-acquisition unit acquires the growth data of all preset p items of one type of crops, and marks the growth data as SZ1p; p is 1, 2, 3, … …, e is a positive integer; the II-type sub-acquisition unit acquires the growth data of all preset q items of the second-class crops, the growth data are marked as SZ2q, q is 1, 2, 3, … … and e, and e is a positive integer.
Each sub-sorting module respectively carries out data sorting operation on the primary data sets sent by the sub-acquisition modules of the corresponding sequence numbers, deletes abnormal data in the primary data sets, sorts and sorts the primary data sets, and the step of obtaining the secondary data sets after summarizing is that:
step one, acquiring a plurality of acquired data acquired by a single sub-acquisition unit in t1 time, and counting the acquired data of each type to construct a similar data set; the same type refers to the same soil data TRm, and a single sub-acquisition unit can set a plurality of values corresponding to the soil data TRm acquired at different acquisition frequencies in t1 time, and the values are generally not less than 5 values;
step two, calculating the mean value Ji and standard deviation Bi of the similar data sets, wherein the mean value Ji is calculated according to the formula:sigma Xi represents the sum of all the collected data of the same type in a plurality of collected data collected by a single sub-collection unit in t1 time, and N represents the quantity of the collected data of the same type; the standard deviation Bi has the following calculation formula: />∑(Xi-Ji) 2 Representing the sum of squares of the differences between each type of acquired data and the mean value Ji;
step three, calculating a deviation score value PCi in a single sub-acquisition unit, wherein the calculation formula of the deviation score value PCi is as follows:comparing the partial difference value PCi with a preset abnormal comparison threshold A1, and if the partial difference value PCi exceeds the abnormal comparison threshold A1, determining that the acquired data Xi is abnormal data and deleting the acquired data Xi, otherwise, determining that the acquired data Xi is non-abnormal data and reserving the acquired data Xi; if the deviation value PCi exceeds the anomaly comparison threshold value A1, the abrupt change of the data is considered to be not normal, and the data is highly likely to cause interference or other adverse effects on the data analysis and research of researchers, so that the data is considered to be required to be removed, namely, the deletion behavior is made, otherwise, the data is reserved as effective research data;
and step four, summarizing all the data which are determined to be non-abnormal data and are reserved into the subtype data sets of the type of collected data, and summarizing the subtype data sets corresponding to all the type of sub-collection units to obtain a secondary data set.
The comprehensive analysis module calculates the synergistic value XTi of the first crop and the second crop according to the second data set, which is that:
step one, acquiring the same subtype data set corresponding to a preset collaborative factor, calculating an average value, processing, marking as a collaborative factor value YSi, and calculating the sum of all collaborative factor values YSi as a1;
step two, acquiring the same subtype data set corresponding to the preset antagonistic factors, calculating an average value, processing, marking the data set as an antagonistic factor value JKi, and calculating the sum of all antagonistic factor values JKi as a2;
step three, acquiring a preset non-antagonistic factor and the same subtype data set corresponding to the non-antagonistic factor, calculating an average value, processing, marking the data set as a non-interference factor value WGi, and calculating the sum of all non-interference factor values WGi as a3;
the method comprises the steps of collecting all growth data of p items preset by a type I sub-collecting unit, marking the growth data as SZ1p for illustration, when a type of crops and a type of crops are planted together, if SZ11 represents the germination rate of the type of crops, the germination rate of the type of crops is not lower than the germination rate of the type of crops when the crops are planted independently, determining the germination rate of the type of crops, namely the growth data SZ11, as a synergistic factor, and otherwise, determining as an antagonistic factor; if the SZ12 represents the yield of one type of crop, the yield of one type of crop is not lower than the yield of the one type of crop when the one type of crop is singly planted, the yield of one type of crop, namely the growth data SZ12, is considered as a synergistic factor, and otherwise, the yield of one type of crop is considered as an antagonistic factor; if SZ13 represents the flowering time of a crop, the flowering time of the crop is considered to be non-antagonistic factors and non-antagonistic factors, namely non-interference factors, no matter longer or shorter than the flowering time of the crop when the crop is singly planted;
calculating the ratio value of a1 and a2 to the sum of a1, a2 and a3 respectively, and marking the ratio value as a synergy coefficient b1 and an antagonism coefficient b2 respectively;
step five, calculating the sum c1 of the products of all beneficial growth item parameters and the synergistic coefficient b1 in the crop growth data SZ1p, and calculating the sum c2 of the products of beneficial growth item parameters and the antagonistic coefficient b2 in the crop growth data SZ1p;
step six, calculating the sum c3 of the products of all beneficial growth item parameters and the synergistic coefficient b1 in the second crop growth data SZ2q, and the sum c4 of the products of beneficial growth item parameters and the antagonistic coefficient b2 in the second crop growth data SZ2 q;
the method comprises the steps of collecting all preset p items of growth data of a crop by using an I-type sub-collecting unit, marking the growth data as SZ1p for illustration, if SZ13 represents flowering time of the crop, determining that the flowering time of the crop is longer or shorter than that of the crop when the crop is singly planted, namely, the flowering time of the crop is determined to be a non-antagonistic factor and a non-antagonistic factor, namely, a non-interference factor, and is a parameter except beneficial and harmful growth projects, if SZ11 represents the germination rate of the crop, determining that the germination rate of the crop is not lower than the germination rate of the crop when the crop is singly planted, determining that the growth data SZ11 is a synergistic factor, wherein the growth project corresponding to the growth data SZ11 is a beneficial growth project, a comparison table of growth project-beneficial parameter-harmful parameter is prestored in a comprehensive analysis module, and the sum of the beneficial parameter and the harmful parameter corresponding to the growth project is 1, and assuming that the germination rate of the crop corresponding to the growth project is 0.326 is determined to be the germination rate of the crop when the crop is singly planted, and the germination rate of the crop is determined to be 0;
step seven, calculating the synergistic value XTi of the first crop and the second crop, wherein xti=c1+c3-c 2-c4, i is 1, 2, 3, … …, e and e is a positive integer, and i only represents a number without specific meaning.
The comprehensive analysis module performs descending order arrangement on the corresponding divided areas according to the synergistic value XTi, which is that:
the method comprises the steps of comparing a synergistic value XTi of one type of crop with a preset sequencing contrast value A2, when the synergistic value XTi is not smaller than the preset sequencing contrast value A2, the partitioned area corresponding to the synergistic value XTi participates in sequencing, otherwise, does not participate in sequencing, when the synergistic value XTi is not smaller than the preset sequencing contrast value A2, the growth data of two types of crops in the partitioned area corresponding to the synergistic value XTi can be used as valuable research data, can be disclosed and reserved for an agricultural expert to research the optimal co-located environment of the two types of crops, is beneficial to popularization and planting, is beneficial to obtaining better crop growth performance in a limited soil area, provides a large number of reliable data supports for subsequent research and popularization work, and when the synergistic value XTi is smaller than the preset sequencing contrast value A2, the growth data of the two types of crops in the partitioned area corresponding to the synergistic value XTi are recognized as valuable research data.
The comprehensive analysis module generates a corresponding adjusting signal type according to the secondary data set, which is that:
comparing the collaborative factor value YSi with a collaborative adjustment threshold value ui corresponding to a preset collaborative factor, generating a type of adjustment signal when the ratio of the difference value of the collaborative factor value YSi and the collaborative adjustment threshold value ui to the collaborative adjustment threshold value ui exceeds r1 after absolute value processing, and otherwise, generating a type of adjustment signal;
comparing the antagonism factor value JKi with an antagonism adjustment threshold wi corresponding to a preset antagonism factor, generating a first type of adjustment signal when the ratio of the difference value of the antagonism factor value JKi and the antagonism adjustment threshold wi to the antagonism adjustment threshold wi exceeds r2 after absolute value processing, and generating a second type of adjustment signal otherwise;
comparing the non-interference factor value WGi with a preset non-antagonistic factor and a non-interference factor adjusting threshold vi corresponding to the non-antagonistic factor, generating a first type of adjusting signal when the ratio of the difference value of the non-interference factor value WGi and the non-interference factor adjusting threshold vi occupied by the non-antagonistic factor is greater than r3 after absolute value processing, and otherwise, generating a second type of adjusting signal;
when the display module displays that the growth data corresponding to the collaborative factor value YSi, the antagonistic factor value JKi and the non-interference factor value WGi generate a type of adjustment signal, irrigation or manual adjustment is not needed for the soil environment of the divided area, and when the growth data corresponding to the collaborative factor value YSi, the antagonistic factor value JKi and the non-interference factor value WGi generate a type of adjustment signal, irrigation or manual adjustment is needed for the soil environment of the divided area to ensure that the whole research is normally performed.
Example III
The generation of the corresponding adjustment signal type by the comprehensive analysis module according to the second data set in the first embodiment and the second embodiment refers to:
comparing the collaborative factor value YSi with a collaborative adjustment threshold value ui corresponding to a preset collaborative factor, generating a class-II adjustment signal when the ratio of the difference value of the collaborative factor value YSi and the collaborative adjustment threshold value ui to the collaborative adjustment threshold value ui exceeds r1 after absolute value processing, otherwise generating a class-II adjustment signal, wherein i is 1, 2, 3, … … and e, and e is a positive integer;
comparing the antagonism factor value JKi with a preset antagonism adjustment threshold wi corresponding to the antagonism factor, generating a first type of adjustment signal when the ratio of the difference value of the antagonism factor value JKi and the antagonism adjustment threshold wi exceeds r2 after absolute value processing, otherwise generating a second type of adjustment signal, wherein i is 1, 2, 3, … …, e and e is a positive integer;
comparing the non-interference factor value WGi with a preset non-antagonistic factor and a non-interference factor adjusting threshold vi corresponding to the non-antagonistic factor, wherein i is a positive integer, i is 1, 2, 3, … …, e, and when the ratio of the difference value of the two values occupying the non-interference factor adjusting threshold vi exceeds r3 after absolute value processing, generating a first class of adjusting signals, otherwise, generating a second class of adjusting signals.
Wherein r1, r2 and r3 are respectively preset signal thresholds corresponding to a cooperative adjustment threshold ui, an antagonistic adjustment threshold wi and a non-interference factor adjustment threshold vi, and r1, r2 and r3 are all percentages larger than 0 and smaller than 1.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. The agricultural Internet of things construction system is characterized by comprising a data acquisition module, a data arrangement module, a comprehensive analysis module and a display module;
the data acquisition modules comprise n sub-acquisition modules, each sub-acquisition module corresponds to only one soil dividing area, and each sub-acquisition module comprises m sub-acquisition units of the same type, a sub-acquisition unit of the type I and a sub-acquisition unit of the type II; the m type sub-acquisition units are used for acquiring the common soil information of the first type crops and the second type crops, the I type sub-acquisition units are used for acquiring the growth information of the first type crops, and the II type sub-acquisition units are used for acquiring the growth information of the second type crops; the sub-acquisition module gathers soil information common to the first crop and the second crop, acquires growth information of the first crop and growth information of the second crop into a first-level data set and sends the first-level data set to the data arrangement module; m and n are integers greater than 1;
the data arrangement module comprises n sub-arrangement modules, each sub-arrangement module respectively carries out data arrangement operation on the first-level data set sent by the sub-acquisition module of the corresponding sequence number, deletes abnormal data in the first-level data set, classifies and arranges the first-level data set, gathers the abnormal data to obtain a second-level data set, and the data arrangement module combines the second-level data set and sends the second-level data set to the comprehensive analysis module;
the comprehensive analysis module calculates the synergistic value XTi of the first-class crops and the second-class crops according to the second-class data set, then performs descending order arrangement on the corresponding divided areas according to the synergistic value XTi, and generates the corresponding adjusting signal type according to the second-class data set;
the display module is used for displaying all the running states of the data acquisition module, the data arrangement module and the comprehensive analysis module.
2. The agricultural internet of things construction system according to claim 1, wherein the m sub-collection units are configured to collect soil information common to the first crop and the second crop, which is: each sub-collection unit is used for collecting soil data common to only one preset crop and two types of crops, and the soil data is calibrated to be TRm.
3. The agricultural internet of things construction system of claim 1, wherein the type i sub-collection unit is configured to collect growth information of a type II crop, and the type II sub-collection unit is configured to collect growth information of a type II crop is defined as follows:
the I-type sub-acquisition unit acquires the growth data of all preset p items of one type of crops, and marks the growth data as SZ1p;
the II-type sub-acquisition unit acquires the growth data of all preset q projects of the second-class crops, and marks the growth data as SZ2q.
4. The agricultural internet of things construction system according to claim 1, wherein each sub-sorting module performs data sorting operation on the first-level data set sent by the sub-collection module of the corresponding sequence number, deletes abnormal data therein, sorts and sorts the first-level data set, and obtaining a second-level data set after summarizing means:
step one, acquiring a plurality of acquired data acquired by a single sub-acquisition unit in t1 time, and counting the acquired data of each type to construct a similar data set;
step two, calculating the mean value Ji and standard deviation Bi of the similar data sets, wherein the mean value Ji is calculated according to the formula:sigma Xi represents the sum of all the collected data of the same type in a plurality of collected data collected by a single sub-collection unit in t1 time, and N represents the quantity of the collected data of the same type; the standard deviation Bi has the following calculation formula: />∑(Xi-Ji) 2 Representing the sum of squares of the differences between each type of acquired data and the mean value Ji;
step three, calculating a deviation score value PCi in a single sub-acquisition unit, wherein the calculation formula of the deviation score value PCi is as follows:comparing the partial difference value PCi with a preset abnormal comparison threshold A1, and if the partial difference value PCi exceeds the abnormal comparison threshold A1, determining that the acquired data Xi is abnormal data and deleting the acquired data Xi, otherwise, determining that the acquired data Xi is non-abnormal data and reserving the acquired data Xi;
and step four, summarizing all the data which are determined to be non-abnormal data and are reserved into the subtype data sets of the type of collected data, and summarizing the subtype data sets corresponding to all the type of sub-collection units to obtain a secondary data set.
5. The agricultural internet of things construction system of claim 1, wherein the calculation of the synergistic value XTi of the first class and the second class of crops by the comprehensive analysis module according to the second class data set is:
step one, acquiring the same subtype data set corresponding to a preset collaborative factor, calculating an average value, processing, marking as a collaborative factor value YSi, and calculating the sum of all collaborative factor values YSi as a1;
step two, acquiring the same subtype data set corresponding to the preset antagonistic factors, calculating an average value, processing, marking the data set as an antagonistic factor value JKi, and calculating the sum of all antagonistic factor values JKi as a2;
step three, acquiring a preset non-antagonistic factor and the same subtype data set corresponding to the non-antagonistic factor, calculating an average value, processing, marking the data as a non-interference factor value WGi, and calculating the sum of all the non-interference factor values WGi as a3;
calculating the ratio value of a1 and a2 to the sum of a1, a2 and a3 respectively, and marking the ratio value as a synergy coefficient b1 and an antagonism coefficient b2 respectively;
step five, calculating the sum c1 of the products of all beneficial growth item parameters and the synergistic coefficient b1 in the crop growth data SZ1p, and calculating the sum c2 of the products of beneficial growth item parameters and the antagonistic coefficient b2 in the crop growth data SZ1p;
step six, calculating the sum c3 of the products of all beneficial growth item parameters and the synergistic coefficient b1 in the second crop growth data SZ2q, and the sum c4 of the products of beneficial growth item parameters and the antagonistic coefficient b2 in the second crop growth data SZ2 q;
step seven, calculating the synergistic value XTi of the first crop and the second crop, wherein xti=c1+c3-c 2-c4.
6. The agricultural internet of things construction system of claim 1, wherein the descending order of the corresponding divided regions by the comprehensive analysis module according to the synergistic value XTi means:
comparing the synergistic value XTi of the first crop and the second crop with a preset sequencing comparison value A2, and when the synergistic value XTi is not smaller than the preset sequencing comparison value A2, the corresponding divided areas of the synergistic value XTi participate in sequencing, otherwise, the sequencing is not performed.
7. The agricultural internet of things construction system of claim 1, wherein the comprehensive analysis module generating the corresponding adjustment signal type from the secondary data set is:
comparing the collaborative factor value YSi with a collaborative adjustment threshold value ui corresponding to a preset collaborative factor, generating a type of adjustment signal when the ratio of the difference value of the collaborative factor value YSi and the collaborative adjustment threshold value ui to the collaborative adjustment threshold value ui exceeds r1 after absolute value processing, and otherwise, generating a type of adjustment signal;
comparing the antagonism factor value JK i with an antagonism adjustment threshold w i corresponding to a preset antagonism factor, generating a first type of adjustment signal when the ratio of the difference value of the antagonism factor value JK i and the antagonism adjustment threshold wi to the antagonism adjustment threshold wi exceeds r2 after absolute value processing, and generating a second type of adjustment signal otherwise;
comparing the non-interference factor value WGi with a preset non-antagonistic factor and a non-interference factor adjusting threshold vi corresponding to the non-antagonistic factor, generating a first type adjusting signal when the ratio of the difference value of the non-interference factor value WGi and the non-interference factor adjusting threshold vi to the non-antagonistic factor exceeds r3 after absolute value processing, and otherwise generating a second type adjusting signal.
8. A construction method of the agricultural internet of things construction system according to any one of claims 1 to 7, comprising the steps of:
step 1, collecting soil information common to one type of crops and two types of crops, growth information of one type of crops and growth information of two types of crops, and summarizing the soil information, the growth information and the growth information into a first-level data set;
step 2, performing data arrangement operation on the primary data set, deleting abnormal data in the primary data set, and classifying and arranging the primary data set to obtain a secondary data set;
and 3, calculating the synergistic value XTi of the first-class crops and the second-class crops according to the second-class data set, then arranging the corresponding divided areas in a descending order according to the synergistic value XTi, and generating the corresponding adjusting signal type according to the second-class data set.
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