CN111563635A - Pesticide residue detection system and method based on big data - Google Patents

Pesticide residue detection system and method based on big data Download PDF

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CN111563635A
CN111563635A CN202010547105.4A CN202010547105A CN111563635A CN 111563635 A CN111563635 A CN 111563635A CN 202010547105 A CN202010547105 A CN 202010547105A CN 111563635 A CN111563635 A CN 111563635A
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王伟
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Abstract

The invention discloses a pesticide residue detection system and method based on big data, relating to the technical field of big data, comprising a data processing module and a block chain module, the data processing module analyzes the influence degree of the current pesticide spraying data on the current pesticide residue data according to the influence degree of the historical pesticide spraying data on the historical pesticide residue data, the block chain module is used for uploading the processing data of the data processing module to the block chain, the invention is scientific and reasonable, the use is safe and convenient, through big data to the pesticide residue of agricultural product predict, compare with traditional utilization detecting instrument and check out test set to agricultural product pesticide residue, the testing process is simpler, need not utilize a large amount of instruments and equipment to detect, has reduced the detection cost, has shortened the length of time that pesticide residue detected for need not carry out the sampling of agricultural product.

Description

Pesticide residue detection system and method based on big data
Technical Field
The invention relates to the technical field of big data, in particular to a pesticide residue detection system and method based on big data.
Background
China is a big agricultural country, and with the continuous improvement of agricultural productivity, problems caused by large-scale agricultural production gradually emerge from the water surface, in order to ensure the yield and quality of agricultural products, pesticide is sprayed in the growth period of agricultural products, so as to ensure that the agricultural products are free from insect damage in the growth process, the yield is improved, however, the pesticide spraying can cause the root, stem, leaf and melon and fruit of the agricultural products to have pesticide residue, when the pesticide residue exceeds the standard, the human health can be harmed after eating, and therefore, the pesticide residue of the agricultural products needs to be detected;
the agricultural product pesticide residue detection is carried out in the mode of extracting, purifying and detecting samples of agricultural products in the prior art, the detection process is complex, the operation is complex, instruments of a large number of major are required to be used for detection, meanwhile, the agricultural products are required to be sampled, and when the agricultural product pesticide residue is unqualified, the standard reaching time of the pesticide residue cannot be known, the whole process of the agricultural product pesticide residue detection cannot be traced, the phenomenon that the agricultural products are picked when the agricultural product pesticide residue cannot reach the standard in order to obtain higher benefits can occur, the health of people is harmed, and therefore people need a pesticide residue detection system and method based on big data to solve the problems.
Disclosure of Invention
The invention aims to provide a pesticide residue detection system and method based on big data, and aims to solve the problems in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme: a pesticide residue detection system based on big data comprises a data input module, a data providing module, a data processing module and a block chain module;
the data input module is used for inputting the types of agricultural products and current pesticide spraying data into the pesticide residue detection system as the basis of pesticide residue detection of the agricultural products, the types of the agricultural products comprise rice, peanut, corn, apple, banana, orange and the like, the current pesticide spraying data refers to the data of the agricultural products needing pesticide residue detection when pesticide is sprayed, the data providing module is used for providing historical pesticide spraying data and historical pesticide residue data for pesticide residue detection of the agricultural products, the data processing module analyzes the influence degree of the current pesticide spraying data on the current pesticide residue data according to the influence degree of the historical pesticide spraying data on the historical pesticide residue data, prediction of the current pesticide residue data is realized, and the historical pesticide spraying data refers to the data of the same type of agricultural products stored in the database, the historical pesticide residue data refers to pesticide residue of the same type of agricultural products under the influence of the historical pesticide spraying data, which is stored in a database currently, the current pesticide residue data refers to pesticide residue under the influence of the current pesticide spraying data, and the block chain module is used for uploading the processing data of the data processing module to the block chain, so that malicious tampering on the current pesticide residue data is avoided, the current pesticide residue data is more real and reliable, and the safety of the agricultural products is ensured;
the output end of the data input module is electrically connected with the input end of the data processing module, the data processing module is electrically connected with the data providing module, and the output end of the data processing module is electrically connected with the input end of the block chain module.
According to the technical scheme, the current pesticide spraying data comprise current pesticide spraying time data, current pesticide spraying concentration data, current pesticide spraying amount data, current pesticide spraying wind speed data and current pesticide spraying rainfall data, and the historical pesticide spraying data comprise historical pesticide spraying time data, historical pesticide detection time data, historical pesticide spraying concentration data, historical pesticide spraying amount data, historical pesticide spraying wind speed data and historical pesticide spraying rainfall data.
According to the technical scheme, the data providing module comprises a database, a data calling unit and a data storage unit;
the data storage unit is used for storing the current pesticide spraying data and the current pesticide residue data which are processed by the data processing module in the database so as to be convenient for later-stage data retrieval and application, and the larger the data quantity stored in the database is, the more accurate the prediction result of the pesticide residue of agricultural products is;
the output end of the database is electrically connected with the input end of the data calling unit, the output end of the data calling unit is electrically connected with the input end of the data processing module, the output end of the data processing unit is electrically connected with the input end of the data storage unit, and the output end of the data storage unit is electrically connected with the input end of the database.
According to the technical scheme, the data processing module comprises a data analysis unit, a data calculation unit, a time prediction unit and a data summarization unit;
the data analysis unit analyzes the degree of influence of each of the historical pesticide spraying data on the historical pesticide residue data, the data calculation unit calculates the influence degree of each data in the current pesticide spraying data on the current pesticide residue data according to the analysis result of the data analysis unit, the data summarization unit summarizes the influence degree of each data in the current pesticide spraying data on the current pesticide residue data to obtain the final pesticide residue data, the final pesticide residue data refers to the current pesticide residue of the agricultural product predicted by the pesticide residue detection system according to the current pesticide spraying data, the time prediction unit predicts the shortest picking time of the crops according to the final pesticide residue data and the standard pesticide residue data, the standard pesticide residue data refers to the lowest pesticide residue of agricultural products, wherein pesticide residues cannot cause harm to human bodies;
the output end of the data providing module is electrically connected with the input ends of the data analyzing unit and the time predicting unit, the output end of the data analyzing unit is electrically connected with the input end of the data calculating unit, the output end of the data inputting module is electrically connected with the input end of the data calculating unit, the output end of the data calculating unit is electrically connected with the input end of the data summarizing unit, and the output end of the data summarizing unit is electrically connected with the input ends of the time predicting unit and the data providing unit.
According to the technical scheme, the block chain module comprises a two-dimensional code generation unit, a data uploading unit and block chain link points;
the two-dimensional code generating unit generates a two-dimensional code according to the current pesticide residue data and the shortest picking time of the agricultural products predicted by the data processing module, the two-dimensional code comprises the current pesticide residue data and the earliest selling time data of the agricultural products, the data uploading unit is used for uploading the two-dimensional code generated by the two-dimensional code generating unit to the block chain node, and the two-dimensional code with the shortest picking time of the agricultural products is uploaded to the block chain node, so that the malicious tampering of the shortest picking time of the agricultural products is avoided, and the pesticide residue of the agricultural products sold in the market reaches a standard amount;
the output end of the data processing module is electrically connected with the input end of the two-dimensional code generating unit, the output end of the two-dimensional code generating unit is electrically connected with the input end of the data uploading unit, and the output end of the data uploading unit is electrically connected with the input end of the block chain node.
A pesticide residue detection method based on big data comprises the following steps:
s1, inputting the type of agricultural products and current pesticide spraying data by using the data input module;
s2, calling historical pesticide residue data and historical pesticide spraying data of agricultural products of corresponding categories from the database by using the data calling unit;
s3, analyzing the influence degree of the historical pesticide spraying data of the agricultural products of the corresponding categories on the historical pesticide residue data by using a data analysis unit;
s4, calculating the influence degree of the current pesticide spraying data on the current pesticide residue data by using the data calculation unit;
s5, summarizing the influence degree of the current pesticide spraying data on the current pesticide residue data by using a data summarizing unit to obtain final pesticide residue data;
s6, according to the final pesticide residue data and the standard pesticide residue data, the shortest picking time of fruits and vegetables is predicted by using a time prediction unit, so that the situation that the fruits and vegetables are picked in advance for improving the quantity of agricultural products is avoided, and the situation that the agricultural products with the pesticide residue exceeding the standard are leaked to the market for sale is avoided;
s7, uploading the shortest picking time data to a block chain node by using a data uploading unit, so that malicious tampering of the detection data is avoided, the growth process of agricultural products is well documented, and people eat healthier food;
s8, the data storage unit is used for storing the pesticide residue analysis data in the database, so that historical pesticide spraying data and historical pesticide residue data in the database are continuously expanded, and the big data prediction analysis result is more accurate.
According to the above technical solution, in S1:
inputting the category X of agricultural products by using a data input module, wherein X can be rice, peanuts, corns, apples, bananas, oranges and the like, inputting the current pesticide spraying data of the agricultural products X by using the data input module, wherein the current pesticide spraying data comprises current pesticide spraying time data, current pesticide spraying concentration data, current pesticide spraying amount data, current pesticide spraying wind speed data and current pesticide spraying rainfall data, and the current pesticide spraying time data is t1The current pesticide spraying time data refers to the current pesticide spraying time point, and the current pesticide spraying concentration data is p1The current pesticide spraying concentration data refers to the concentration of pesticide during spraying, and the current pesticide spraying dosage data is y1The current pesticide spraying amount data refers to the pesticide volume per square meter of pesticide during spraying, and the current pesticide spraying wind speed data is v1The current pesticide spraying wind speed data refers to the wind speed of pesticide during spraying, and the current pesticide spraying rainfall data is j1The current rainfall data of pesticide spraying refers to the pesticide spraying time point t1To t2The amount of rainfall over a period of time.
According to the technical scheme, in S2-S3:
calling historical pesticide residue data of agricultural products X from a database by using a data calling unit to form a historical pesticide residue data set CCollection＝{C1,C2,C3,…,CnIn which C is1,C2,C3,…,CnRepresents the pesticide residue quantity of agricultural product X under n times of historical pesticide spraying data, wherein C1,C2,C3,…,CnAre all composed of pesticide detection concentrations at different time points, Ci＝{Ci1,Ci2,Ci3,…,CifIn which C isi1,Ci2,Ci3,…,CifObtaining pesticide residue data of different time points by a spectral analysis method in a growth cycle of crops;
and calling n times of historical pesticide spraying data corresponding to the n times of historical pesticide residue data from the database by using a data calling unit, wherein the historical pesticide spraying data comprises historical pesticide spraying time data, historical pesticide detection time data, historical pesticide spraying concentration data, historical pesticide spraying amount data, historical pesticide spraying wind speed data and historical pesticide spraying rainfall data.
Grouping the historical pesticide spraying data of each time into a set
Figure BDA0002541091420000071
Figure BDA0002541091420000072
Figure BDA0002541091420000073
Wherein,
Figure BDA0002541091420000074
n sets of historical pesticide spray data respectively representing agricultural products X;
computing a set according to the following formula
Figure BDA0002541091420000075
And collections
Figure BDA0002541091420000076
Figure BDA0002541091420000077
The influence coefficient of the stage difference between the historical pesticide spraying time data and the historical pesticide detection time data on the pesticide residue is as follows, the stage is different time periods:
Figure BDA0002541091420000078
wherein, azRepresenting the influence coefficient of the difference between the historical pesticide spraying time data and the historical pesticide detection time data on the pesticide residue, i and k ∈ [1, n]，CizAnd CkzRespectively represent a set CiData for detecting pesticide residue at the z-th time point in (1) and set CkDetection data of pesticide residue at the z-th time point in (T'i-Ti)≠(T′k-Tk)，Pi＝Pk，Yi＝Yk，Vi＝Vk，Ji＝Jk，Ci≠Ck；
Calculating the residual historical pesticide spraying data and the historical pesticide residue data in the database according to the formula to obtain a set a of influence coefficients of the difference between the historical pesticide spraying time data and the historical pesticide detection time data on the pesticide residue in unit timez set＝{az1,az2,az3,…,azs}；
Calculating the average value of the influence coefficient of the difference between the historical pesticide spraying time data and the historical pesticide detection time data on the pesticide residue in unit time according to the following formula:
Figure BDA0002541091420000081
wherein,
Figure BDA0002541091420000082
as the difference value between the historical pesticide spraying time data and the historical pesticide detection time data of the z time period in the growth cycle of the crops to the cropsMean value of the coefficient of influence of drug residue;
calculating the mean value of the influence coefficients of the difference values of the historical pesticide spraying time data and the historical pesticide detection time data of other time periods in the crop growth cycle on the pesticide residue in sequence to obtain a crop pesticide consumption rate set of different time periods
Figure BDA0002541091420000083
Wherein,
Figure BDA0002541091420000084
linear coefficients respectively representing the consumption rates of the pesticides in the crop bodies in different time periods;
therefore, the nonlinear relation of pesticide consumption in the crops is converted into a linear relation for calculation and processing, and the calculation and processing of the pesticide residue in the crops are facilitated;
the average value of the influence coefficients of the historical pesticide spraying amount data, the historical pesticide spraying wind speed data and the historical pesticide spraying rainfall data on the pesticide residue is sequentially subjected to the variable control method
Figure BDA0002541091420000092
And
Figure BDA0002541091420000093
performing calculation, wherein when the historical pesticide spraying concentration data and the current pesticide spraying concentration data p are called from the database by the data calling unit1And (5) the consistency is achieved.
Through calculating and analyzing the influence coefficient of each historical pesticide spraying data on the historical pesticide residue data, the current pesticide residue data can be calculated according to the influence of the current pesticide spraying data on the current pesticide residue data in the calculation of the historical big data, and through big data analysis, picking sampling of agricultural products is avoided, so that the pesticide residue on the agricultural products can be detected more quickly and quickly.
According to the technical scheme, in S4-S5:
the current pesticide spraying time data t is calculated according to the following formula1With current pesticide detection time data t3Calculating the current pesticide disappearance generated by the difference between the two:
Figure BDA0002541091420000091
wherein, BzData t representing the current pesticide spray timez-1With current pesticide detection time data tzThe current pesticide disappearance amount generated by the difference between the two;
and (3) sequentially calculating the pesticide disappearance of each time period:
Z＝B1+B2+B3+…+Bf；
wherein z represents the sum of the pesticide disappearance amounts in each time period;
the current pesticide spraying dosage data y is calculated according to the following formula1The current amount of pesticide lost produced was calculated:
Figure BDA0002541091420000101
wherein A is2Data y representing the current amount of pesticide sprayed1The current amount of pesticide lost;
the current pesticide spraying wind speed data v is obtained according to the following formula1The current amount of pesticide lost produced was calculated:
Figure BDA0002541091420000102
wherein A is3Data v representing current pesticide spraying wind speed1The current amount of pesticide lost;
spraying rainfall data j to the current pesticide according to the following formula1The current amount of pesticide lost produced was calculated:
Figure BDA0002541091420000103
wherein A is4Data y representing current rainfall of pesticide spraying1The current amount of pesticide lost;
using a data summarization unit to perform final pesticide residue data P according to the following formulaGeneral assemblyAnd (3) calculating:
Pgeneral assembly＝p1-Z-A2-A3-A4；
Wherein, PGeneral assemblyIndicating the current pesticide residue data at the time of detection.
And calculating the influence degree of each current pesticide spraying data on the current pesticide residue data to obtain final pesticide residue data, namely pesticide residue detection data.
According to the above technical solution, in S6:
the standard pesticide residue data is PSign boardCalculating the shortest picking time T of the agricultural product according to the following formula:
Figure BDA0002541091420000111
wherein T represents that agricultural products can be picked after the time of T from the beginning of pesticide residue detection,
Figure BDA0002541091420000112
represents PGeneral assemblyTo PSign boardTime period the rate of consumption of the pesticide in the crop body.
Compared with the prior art, the invention has the beneficial effects that:
1. the pesticide residue of agricultural products is predicted through big data, compared with the traditional detection of the pesticide residue of the agricultural products by using a detection instrument and detection equipment, the detection process is simpler, a large amount of instrument and equipment is not needed for detection, the detection cost is reduced, the pesticide residue detection time is shortened, the agricultural products are not needed to be sampled, the prediction site is not limited, the prediction of the pesticide residue of the agricultural products can be realized only by inputting the current pesticide spraying data, and the agricultural products are more convenient and rapid;
2. according to the prediction result of the pesticide residue of the agricultural product, the agricultural product pesticide residue data are compared with the standard pesticide residue data, the big data are utilized, the time for the pesticide residue of the agricultural product to reach the standard can be predicted, the picking time of the agricultural product can be known, the two-dimensional code is generated according to the predicted time and the pesticide residue detection result, the two-dimensional code data are uploaded to the block chain node, malicious tampering on the pesticide residue detection result and the picking time is avoided, the pesticide residue of the agricultural product which is leaked to the market for sale meets the standard, and the harm to a human body caused by the fact that the pesticide residue exceeds the standard is avoided.
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FIG. 1 is a schematic diagram of a module structure of a big data-based pesticide residue detection system according to the present invention;
FIG. 2 is a schematic diagram of a module connection structure of a big data-based pesticide residue detection system according to the present invention;
FIG. 3 is a schematic flow chart of the steps of a big data-based pesticide residue detection method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1 to 3, a pesticide residue detection system based on big data comprises a data input module, a data providing module, a data processing module and a block chain module;
the data input module is used for inputting the types of agricultural products and current pesticide spraying data into the pesticide residue detection system as the basis of pesticide residue detection of the agricultural products, the types of the agricultural products comprise rice, peanut, corn, apple, banana, orange and the like, the current pesticide spraying data refers to the data of the agricultural products needing pesticide residue detection when pesticide is sprayed, the data providing module is used for providing historical pesticide spraying data and historical pesticide residue data for pesticide residue detection of the agricultural products, the data processing module analyzes the influence degree of the current pesticide spraying data on the current pesticide residue data according to the influence degree of the historical pesticide spraying data on the historical pesticide residue data, prediction of the current pesticide residue data is realized, and the historical pesticide spraying data refers to the data of the same type of agricultural products stored in the database, the historical pesticide residue data refers to pesticide residue of the same type of agricultural products under the influence of the historical pesticide spraying data, which is stored in a database currently, the current pesticide residue data refers to pesticide residue under the influence of the current pesticide spraying data, and the block chain module is used for uploading the processing data of the data processing module to the block chain, so that malicious tampering on the current pesticide residue data is avoided, the current pesticide residue data is more real and reliable, and the safety of the agricultural products is ensured;
the output end of the data input module is electrically connected with the input end of the data processing module, the data processing module is electrically connected with the data providing module, and the output end of the data processing module is electrically connected with the input end of the block chain module.
The current pesticide spraying data comprise current pesticide spraying time data, current pesticide spraying concentration data, current pesticide spraying amount data, current pesticide spraying wind speed data and current pesticide spraying rainfall data, and the historical pesticide spraying data comprise historical pesticide spraying time data, historical pesticide detection time data, historical pesticide spraying concentration data, historical pesticide spraying amount data, historical pesticide spraying wind speed data and historical pesticide spraying rainfall data.
The data providing module comprises a database, a data calling unit and a data storage unit;
the data storage unit is used for storing the current pesticide spraying data and the current pesticide residue data which are processed by the data processing module in the database so as to be convenient for later-stage data retrieval and application, and the larger the data quantity stored in the database is, the more accurate the prediction result of the pesticide residue of agricultural products is;
the output end of the database is electrically connected with the input end of the data calling unit, the output end of the data calling unit is electrically connected with the input end of the data processing module, the output end of the data processing unit is electrically connected with the input end of the data storage unit, and the output end of the data storage unit is electrically connected with the input end of the database.
The data processing module comprises a data analysis unit, a data calculation unit, a time prediction unit and a data summarization unit;
the data analysis unit analyzes the degree of influence of each of the historical pesticide spraying data on the historical pesticide residue data, the data calculation unit calculates the influence degree of each data in the current pesticide spraying data on the current pesticide residue data according to the analysis result of the data analysis unit, the data summarization unit summarizes the influence degree of each data in the current pesticide spraying data on the current pesticide residue data to obtain the final pesticide residue data, the final pesticide residue data refers to the current pesticide residue of the agricultural product predicted by the pesticide residue detection system according to the current pesticide spraying data, the time prediction unit predicts the shortest picking time of the crops according to the final pesticide residue data and the standard pesticide residue data, the standard pesticide residue data refers to the lowest pesticide residue of agricultural products, wherein pesticide residues cannot cause harm to human bodies;
the output end of the data providing module is electrically connected with the input ends of the data analyzing unit and the time predicting unit, the output end of the data analyzing unit is electrically connected with the input end of the data calculating unit, the output end of the data inputting module is electrically connected with the input end of the data calculating unit, the output end of the data calculating unit is electrically connected with the input end of the data summarizing unit, and the output end of the data summarizing unit is electrically connected with the input ends of the time predicting unit and the data providing unit.
The block chain module comprises a two-dimensional code generation unit, a data uploading unit and block chain link points;
the two-dimensional code generating unit generates a two-dimensional code according to the current pesticide residue data of the agricultural products and the shortest picking time predicted by the data processing module, the two-dimensional code comprises the current pesticide residue data and the earliest selling time data of the agricultural products, and the data uploading unit is used for uploading the two-dimensional code generated by the two-dimensional code generating unit to a block chain node;
the output end of the data processing module is electrically connected with the input end of the two-dimensional code generating unit, the output end of the two-dimensional code generating unit is electrically connected with the input end of the data uploading unit, and the output end of the data uploading unit is electrically connected with the input end of the block chain node.
A pesticide residue detection method based on big data comprises the following steps:
s1, inputting the type of agricultural products and current pesticide spraying data by using the data input module;
s2, calling historical pesticide residue data and historical pesticide spraying data of agricultural products of corresponding categories from the database by using the data calling unit;
s3, analyzing the influence degree of the historical pesticide spraying data of the agricultural products of the corresponding categories on the historical pesticide residue data by using a data analysis unit;
s4, calculating the influence degree of the current pesticide spraying data on the current pesticide residue data by using the data calculation unit;
s5, summarizing the influence degree of the current pesticide spraying data on the current pesticide residue data by using a data summarizing unit to obtain final pesticide residue data;
s6, predicting the shortest picking time of fruits and vegetables by using a time prediction unit according to the final pesticide residue data and the standard pesticide residue data;
s7, uploading the shortest picking time length data to a block chain node by using a data uploading unit;
and S8, storing the pesticide residue analysis data in the database by using the data storage unit.
In S1:
inputting the category X of agricultural products by using a data input module, wherein X can be rice, peanuts, corns, apples, bananas, oranges and the like, inputting the current pesticide spraying data of the agricultural products X by using the data input module, wherein the current pesticide spraying data comprises current pesticide spraying time data, current pesticide spraying concentration data, current pesticide spraying amount data, current pesticide spraying wind speed data and current pesticide spraying rainfall data, and the current pesticide spraying time data is t1The current pesticide spraying time data refers to the current pesticide spraying time point, and the current pesticide spraying concentration data is p1The current pesticide spraying concentration data refers to the concentration of pesticide during spraying, and the current pesticide spraying dosage data is y1The current pesticide spraying amount data refers to the pesticide volume per square meter of pesticide during spraying, and the current pesticide spraying wind speed data is v1The current pesticide spraying wind speed data refers to the wind speed of pesticide during spraying, and the current pesticide spraying rainfall data is j1The current rainfall data of pesticide spraying refers to the pesticide spraying time point t1To t2The amount of rainfall over a period of time.
In S2-S3:
calling historical pesticide residue data of agricultural products X from a database by using a data calling unit to form a historical pesticide residue data set CCollection＝{C1,C2,C3,…,CnIn which C is1,C2,C3,…,CnRepresents the pesticide residue quantity of agricultural product X under n times of historical pesticide spraying data, wherein C1,C2,C3,…,CnAre all composed of pesticide detection concentrations at different time points, Ci＝{Ci1,Ci2,Ci3,…,CifIn which C isi1,Ci2,Ci3,…,CifObtaining pesticide residue data of different time points by a spectral analysis method in a growth cycle of crops;
and calling n times of historical pesticide spraying data corresponding to the n times of historical pesticide residue data from the database by using a data calling unit, wherein the historical pesticide spraying data comprises historical pesticide spraying time data, historical pesticide detection time data, historical pesticide spraying concentration data, historical pesticide spraying amount data, historical pesticide spraying wind speed data and historical pesticide spraying rainfall data.
Grouping the historical pesticide spraying data of each time into a set
Figure BDA0002541091420000171
Figure BDA0002541091420000172
Figure BDA0002541091420000173
Wherein,
Figure BDA0002541091420000174
n sets of historical pesticide spray data respectively representing agricultural products X;
computing a set according to the following formula
Figure BDA0002541091420000175
And collections
Figure BDA0002541091420000176
Figure BDA0002541091420000177
The influence coefficient of the periodic difference between the historical pesticide spraying time data and the historical pesticide detection time data on the pesticide residue is as follows, the periodic difference refers to different time periods:
Figure BDA0002541091420000181
wherein, azRepresenting the influence coefficient of the difference between the historical pesticide spraying time data and the historical pesticide detection time data on the pesticide residue, i and k ∈ [1, n]，CizAnd CkzRespectively represent a set CiData for detecting pesticide residue at the z-th time point in (1) and set CkDetection data of pesticide residue at the z-th time point in (T'i-Ti)≠(T′k-Tk)，Pi＝Pk，Yi＝Yk，Vi＝Vk，Ji＝Jk，Ci≠Ck；
Calculating the residual historical pesticide spraying data and the historical pesticide residue data in the database according to the formula to obtain a set a of influence coefficients of the difference between the historical pesticide spraying time data and the historical pesticide detection time data on the pesticide residue in unit timez set＝{az1,az2,az3,…,azs}；
Calculating the average value of the influence coefficient of the difference between the historical pesticide spraying time data and the historical pesticide detection time data on the pesticide residue in unit time according to the following formula:
Figure BDA0002541091420000182
wherein,
Figure BDA0002541091420000183
the average value of the influence coefficient of the difference value between the historical pesticide spraying time data and the historical pesticide detection time data of the z time period in the crop growth cycle on the pesticide residue is used as the average value;
calculating the mean value of the influence coefficients of the difference values of the historical pesticide spraying time data and the historical pesticide detection time data of other time periods in the crop growth cycle on the pesticide residue in sequence to obtain a crop pesticide consumption rate set of different time periods
Figure BDA0002541091420000191
Wherein,
Figure BDA0002541091420000192
linear coefficients respectively representing the consumption rates of the pesticides in the crop bodies in different time periods;
therefore, the nonlinear relation of pesticide consumption in the crops is converted into a linear relation for calculation and processing, and the calculation and processing of the pesticide residue in the crops are facilitated;
the average value of the influence coefficients of the historical pesticide spraying amount data, the historical pesticide spraying wind speed data and the historical pesticide spraying rainfall data on the pesticide residue is sequentially subjected to the variable control method
Figure BDA0002541091420000193
And
Figure BDA0002541091420000194
performing calculation, wherein when the historical pesticide spraying concentration data and the current pesticide spraying concentration data p are called from the database by the data calling unit1And (5) the consistency is achieved.
Through calculating and analyzing the influence coefficient of each historical pesticide spraying data on the historical pesticide residue data, the current pesticide residue data can be calculated according to the influence of the current pesticide spraying data on the current pesticide residue data in the calculation of the historical big data, and through big data analysis, picking sampling of agricultural products is avoided, so that the pesticide residue on the agricultural products can be detected more quickly and quickly.
In S4-S5:
the current pesticide spraying time data t is calculated according to the following formula1With current pesticide detection time data t3Calculating the current pesticide disappearance generated by the difference between the two:
Figure BDA0002541091420000195
wherein, BzData t representing the current pesticide spray timez-1With the current agricultureTime data t of drug testzThe current pesticide disappearance amount generated by the difference between the two;
and (3) sequentially calculating the pesticide disappearance of each time period:
Z＝B1+B2+B3+…+Bf；
wherein z represents the sum of the pesticide disappearance amounts in each time period;
by converting the consumption of the pesticide in the crop body into piecewise linearity, the pesticide residue in each time period can be calculated according to different influence coefficients, so that the error of pesticide residue detection can be effectively reduced, and the accuracy is improved;
the current pesticide spraying dosage data y is calculated according to the following formula1The current amount of pesticide lost produced was calculated:
Figure BDA0002541091420000201
wherein A is2Data y representing the current amount of pesticide sprayed1The current amount of pesticide lost;
the current pesticide spraying amount data is fixed when pesticide spraying is carried out, and cannot change along with the change of time, so that the calculation of influence coefficients on pesticide residues in crops in different time periods is not needed;
the current pesticide spraying wind speed data v is obtained according to the following formula1The current amount of pesticide lost produced was calculated:
Figure BDA0002541091420000202
wherein A is3Data v representing current pesticide spraying wind speed1The current amount of pesticide lost;
the current pesticide spraying wind speed data is fixed when pesticide spraying is carried out, and cannot change along with the change of time, so that the calculation of influence coefficients on pesticide residues in crops in different time periods is not needed;
spraying rainfall data j to the current pesticide according to the following formula1The current amount of pesticide lost produced was calculated:
Figure BDA0002541091420000211
wherein A is4Data y representing current rainfall of pesticide spraying1The current amount of pesticide lost;
the current rainfall data of pesticide spraying is fixed when the pesticide is sprayed, and cannot change along with the change of time, so that the calculation of influence coefficients on pesticide residues in crops in different time periods is not needed;
using a data summarization unit to perform final pesticide residue data P according to the following formulaGeneral assemblyAnd (3) calculating:
Pgeneral assembly＝p1-Z-A2-A3-A4；
Wherein, PGeneral assemblyIndicating the current pesticide residue data at the time of detection.
Influence coefficient A used for calculating pesticide residue in different time periodszDifferent, therefore, Z needs to be calculated in segments.
In S6:
the standard pesticide residue data is PSign boardCalculating the shortest picking time T of the agricultural product according to the following formula:
Figure BDA0002541091420000212
wherein T represents that agricultural products can be picked after the time of T from the beginning of pesticide residue detection,
Figure BDA0002541091420000213
represents PGeneral assemblyTo PSign boardTime period the rate of consumption of the pesticide in the crop body.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.

Claims (10)

1. A pesticide residue detecting system based on big data which characterized in that: the pesticide residue detection system comprises a data input module, a data providing module, a data processing module and a block chain module;
the data input module is used for inputting the type of agricultural products and current pesticide spraying data into the pesticide residue detection system to be used as a basis for detecting pesticide residues of the agricultural products, the data providing module is used for providing historical pesticide spraying data and historical pesticide residue data for pesticide residue detection of the agricultural products, the data processing module is used for analyzing the influence degree of the current pesticide spraying data on the current pesticide residue data according to the influence degree of the historical pesticide spraying data on the historical pesticide residue data, and the block chain module is used for uploading processing data of the data processing module to a block chain;
the output end of the data input module is electrically connected with the input end of the data processing module, the data processing module is electrically connected with the data providing module, and the output end of the data processing module is electrically connected with the input end of the block chain module.
2. The big data-based pesticide residue detection system according to claim 1, wherein: the current pesticide spraying data comprise current pesticide spraying time data, current pesticide spraying concentration data, current pesticide spraying amount data, current pesticide spraying wind speed data and current pesticide spraying rainfall data, and the historical pesticide spraying data comprise historical pesticide spraying time data, historical pesticide detection time data, historical pesticide spraying concentration data, historical pesticide spraying amount data, historical pesticide spraying wind speed data and historical pesticide spraying rainfall data.
3. The big data-based pesticide residue detection system according to claim 1, wherein: the data providing module comprises a database, a data calling unit and a data storage unit;
the data storage unit is used for storing the current pesticide spraying data and the current pesticide residue data processed by the data processing module in the database;
the output end of the database is electrically connected with the input end of the data calling unit, the output end of the data calling unit is electrically connected with the input end of the data processing module, the output end of the data processing unit is electrically connected with the input end of the data storage unit, and the output end of the data storage unit is electrically connected with the input end of the database.
4. The big data-based pesticide residue detection system according to claim 1, wherein: the data processing module comprises a data analysis unit, a data calculation unit, a time prediction unit and a data summarization unit;
the data analysis unit analyzes the influence degree of each datum in the historical pesticide spraying data on the historical pesticide residue data, the data calculation unit calculates the influence degree of each datum in the current pesticide spraying data on the current pesticide residue data according to the analysis result of the data analysis unit, the data summarization unit summarizes the influence degree of each datum in the current pesticide spraying data on the current pesticide residue data to obtain final pesticide residue data, and the time prediction unit predicts the shortest picking time of crops according to the final pesticide residue data and the standard pesticide residue data;
the output end of the data providing module is electrically connected with the input ends of the data analyzing unit and the time predicting unit, the output end of the data analyzing unit is electrically connected with the input end of the data calculating unit, the output end of the data inputting module is electrically connected with the input end of the data calculating unit, the output end of the data calculating unit is electrically connected with the input end of the data summarizing unit, and the output end of the data summarizing unit is electrically connected with the input ends of the time predicting unit and the data providing unit.
5. The big data-based pesticide residue detection system according to claim 1, wherein: the block chain module comprises a two-dimensional code generation unit, a data uploading unit and block chain link points;
the two-dimension code generating unit generates a two-dimension code according to the current pesticide residue data of the agricultural products predicted by the data processing module and the shortest picking time, and the data uploading unit is used for uploading the two-dimension code generated by the two-dimension code generating unit to the block chain node;
the output end of the data processing module is electrically connected with the input end of the two-dimensional code generating unit, the output end of the two-dimensional code generating unit is electrically connected with the input end of the data uploading unit, and the output end of the data uploading unit is electrically connected with the input end of the block chain node.
6. A pesticide residue detection method based on big data is characterized in that: the pesticide residue detection method comprises the following steps:
s1, inputting the type of agricultural products and current pesticide spraying data by using the data input module;
s2, calling historical pesticide residue data and historical pesticide spraying data of agricultural products of corresponding categories from the database by using the data calling unit;
s3, analyzing the influence degree of the historical pesticide spraying data of the agricultural products of the corresponding categories on the historical pesticide residue data by using a data analysis unit;
s4, calculating the influence degree of the current pesticide spraying data on the current pesticide residue data by using the data calculation unit;
s5, summarizing the influence degree of the current pesticide spraying data on the current pesticide residue data by using a data summarizing unit to obtain final pesticide residue data;
s6, predicting the shortest picking time of fruits and vegetables by using a time prediction unit according to the final pesticide residue data and the standard pesticide residue data;
s7, uploading the shortest picking time length data to a block chain node by using a data uploading unit;
and S8, storing the pesticide residue analysis data in the database by using the data storage unit.
7. The big data-based pesticide residue detection method according to claim 6, characterized in that: in S1:
inputting the type X of the agricultural product by using a data input module, and inputting the current pesticide spraying data of the agricultural product X by using the data input module, wherein the current pesticide spraying data comprises current pesticide spraying time data, current pesticide spraying concentration data, current pesticide spraying amount data, current pesticide spraying wind speed data and current pesticide spraying rainfall data, and the current pesticide spraying time data is t1The current pesticide spraying concentration data is p1The current pesticide spraying amount data is y1The current pesticide spraying wind speed data is v1The current rainfall data of pesticide spraying is j1。
8. The big data-based pesticide residue detection method according to claim 7, characterized in that: in S2-S3:
calling historical pesticide residue data of agricultural products X from a database by using a data calling unit to form a historical pesticide residue data set CCollection＝{C1,C2,C3,…,CnIn which C is1,C2,C3,…,CnIndicating agricultural product X pesticide history for n timesPesticide residue under spray data, wherein, C1,C2,C3,…,CnAre all composed of pesticide detection concentrations at different time points, Ci＝{Ci1,Ci2,Ci3,…,CifIn which C isi1,Ci2,Ci3,…,CifObtaining pesticide residue data of different time points by a spectral analysis method in a growth cycle of crops;
and calling n times of historical pesticide spraying data corresponding to the n times of historical pesticide residue data from the database by using a data calling unit, wherein the historical pesticide spraying data comprises historical pesticide spraying time data, historical pesticide detection time data, historical pesticide spraying concentration data, historical pesticide spraying amount data, historical pesticide spraying wind speed data and historical pesticide spraying rainfall data.
Grouping the historical pesticide spraying data of each time into a set
Figure FDA0002541091410000051
Figure FDA0002541091410000052
Figure FDA0002541091410000053
Wherein,
Figure FDA0002541091410000054
n sets of historical pesticide spray data respectively representing agricultural products X;
computing a set according to the following formula
Figure FDA0002541091410000055
And collections
Figure FDA0002541091410000056
Figure FDA0002541091410000057
Of the historical pesticideInfluence coefficient of the periodic difference between the spraying time data and the historical pesticide detection time data on the pesticide residue:
Figure FDA0002541091410000058
wherein, azRepresenting the influence coefficient of the difference between the historical pesticide spraying time data and the historical pesticide detection time data on the pesticide residue, i and k ∈ [1, n]，CizAnd CkzRespectively represent a set CiData for detecting pesticide residue at the z-th time point in (1) and set CkDetection data of pesticide residue at the z-th time point in (T'i-Ti)≠(T′k-Tk)，Pi＝Pk，Yi＝Yk，Vi＝Vk，Ji＝Jk，Ci≠Ck；
Calculating the residual historical pesticide spraying data and the historical pesticide residue data in the database according to the formula to obtain a set a of influence coefficients of the difference between the historical pesticide spraying time data and the historical pesticide detection time data on the pesticide residue in unit timez set＝{az1,az2,az3,…,azs}；
Calculating the average value of the influence coefficient of the difference between the historical pesticide spraying time data and the historical pesticide detection time data on the pesticide residue in unit time according to the following formula:
Figure FDA0002541091410000061
wherein,
Figure FDA0002541091410000062
the average value of the influence coefficient of the difference value between the historical pesticide spraying time data and the historical pesticide detection time data of the z time period in the crop growth cycle on the pesticide residue is used as the average value;
in turn for the growth period of cropsCalculating the mean value of the influence coefficients of the difference values of the historical pesticide spraying time data and the historical pesticide detection time data of other time periods in the period on the pesticide residue to obtain a set of crop pesticide consumption rates of different time periods
Figure FDA0002541091410000063
Wherein,
Figure FDA0002541091410000064
linear coefficients respectively representing the consumption rates of the pesticides in the crop bodies in different time periods;
the average value of the influence coefficients of the historical pesticide spraying amount data, the historical pesticide spraying wind speed data and the historical pesticide spraying rainfall data on the pesticide residue is sequentially subjected to the variable control method
Figure FDA0002541091410000065
And
Figure FDA0002541091410000066
performing calculation, wherein when the historical pesticide spraying concentration data and the current pesticide spraying concentration data p are called from the database by the data calling unit1And (5) the consistency is achieved.
9. The big data-based pesticide residue detection method according to claim 8, characterized in that: in S4-S5:
the current pesticide spraying time data t is calculated according to the following formula1With current pesticide detection time data t3Calculating the current pesticide disappearance generated by the difference between the two:
Figure FDA0002541091410000071
wherein, BzData t representing the current pesticide spray timez-1With current pesticide detection time data tzThe difference between the current pesticideLoss amount;
and (3) sequentially calculating the pesticide disappearance of each time period:
Z＝B1+B2+B3+…+Bf；
wherein z represents the sum of the pesticide disappearance amounts in each time period;
the current pesticide spraying dosage data y is calculated according to the following formula1The current amount of pesticide lost produced was calculated:
Figure FDA0002541091410000072
wherein A is2Data y representing the current amount of pesticide sprayed1The current amount of pesticide lost;
the current pesticide spraying wind speed data v is obtained according to the following formula1The current amount of pesticide lost produced was calculated:
Figure FDA0002541091410000073
wherein A is3Data v representing current pesticide spraying wind speed1The current amount of pesticide lost;
spraying rainfall data j to the current pesticide according to the following formula1The current amount of pesticide lost produced was calculated:
Figure FDA0002541091410000081
wherein A is4Data y representing current rainfall of pesticide spraying1The current amount of pesticide lost;
using a data summarization unit to perform final pesticide residue data P according to the following formulaGeneral assemblyAnd (3) calculating:
Pgeneral assembly＝p1-Z-A2-A3-A4；
Wherein, PGeneral assemblyIndicating the current pesticide residue data at the time of detection.
10. The big data-based pesticide residue detection method according to claim 9, characterized in that: in S6:
the standard pesticide residue data is PSign boardCalculating the shortest picking time T of the agricultural product according to the following formula:
Figure FDA0002541091410000082
wherein T represents that agricultural products can be picked after the time of T from the beginning of pesticide residue detection,
Figure FDA0002541091410000083
represents PGeneral assemblyTo PSign boardTime period the rate of consumption of the pesticide in the crop body.
CN202010547105.4A 2020-06-16 2020-06-16 Pesticide residue detection system and method based on big data Withdrawn CN111563635A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112150300A (en) * 2020-09-14 2020-12-29 安阳工学院 Accurate harvesting method based on pesticide residue statistics
CN117787510A (en) * 2024-02-28 2024-03-29 青岛小蜂生物科技有限公司 Optimization method of pesticide residue monitoring process based on time sequence predictive analysis

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112150300A (en) * 2020-09-14 2020-12-29 安阳工学院 Accurate harvesting method based on pesticide residue statistics
CN117787510A (en) * 2024-02-28 2024-03-29 青岛小蜂生物科技有限公司 Optimization method of pesticide residue monitoring process based on time sequence predictive analysis
CN117787510B (en) * 2024-02-28 2024-05-03 青岛小蜂生物科技有限公司 Optimization method of pesticide residue monitoring process based on time sequence predictive analysis

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