Agricultural product pesticide residue detection and analysis system and method based on big data

Abstract

The application relates to the technical field of agricultural products, in particular to an agricultural product pesticide residue detection and analysis system and method based on big data, which comprises a sensor unit, which collects environmental monitoring data of a target area in the complete growth cycle of agricultural products; a pesticide detection unit, which obtains pesticide residue detection data of the agricultural products from a cloud storage platform; a data processing unit, which is used for data preprocessing of the environmental monitoring data and the pesticide residue detection data; a data analysis unit, which is used for determining the final quality grade of the agricultural products in the target area according to the environmental monitoring preprocessing data and the pesticide residue detection preprocessing data; and a data display unit, which is used for publishing and displaying the final quality grade of the agricultural products in the target area. The application integrates the environmental monitoring and pesticide residue detection data, accurately evaluates the quality safety grade, and provides a comprehensive quality management tool for agricultural producers.

Description

Technical Field

[0001] This invention relates to the field of agricultural product technology, and more specifically, to a system and method for detecting and analyzing pesticide residues in agricultural products based on big data. Background Technology

[0002] In recent years, public concern about food safety has been steadily increasing. Pesticide residues in agricultural products are directly related to public health, leading to increased demand and stricter regulatory requirements. Big data technology can provide more comprehensive and timely data analysis, helping to more effectively monitor and control pesticide residues in agricultural products.

[0003] The development of advanced detection technologies and data processing methods has provided opportunities for establishing efficient pesticide residue detection systems for agricultural products. The application of modern laboratory techniques, sensor networks, and remote sensing technologies has made it possible to collect large amounts of data, while the application of big data technology supports the efficient processing and analysis of this data. The application of big data technology helps promote sustainable agricultural development. By monitoring and controlling pesticide residues, the quality of agricultural products can be improved, adverse environmental impacts can be reduced, and the sustainability of agricultural production can be promoted. Summary of the Invention

[0004] In view of this, the present invention proposes a big data-based pesticide residue detection and analysis system and method for agricultural products, mainly to solve the problem of how to assess the quality level of agricultural products based on pesticide residue detection data using big data.

[0005] In one aspect, this invention proposes a big data-based pesticide residue detection and analysis system for agricultural products, the system comprising:

[0006] The sensor unit is used to collect environmental monitoring data of the target area during the entire growth cycle of agricultural products;

[0007] The pesticide detection unit is used to obtain pesticide residue detection data of the agricultural products from the cloud storage platform;

[0008] The data processing unit is used to preprocess the environmental monitoring data and pesticide residue detection data to obtain preprocessed environmental monitoring data and preprocessed pesticide residue detection data.

[0009] The data analysis unit is used to determine the final quality and safety level of agricultural products in the target area based on the environmental monitoring pretreatment data and pesticide residue detection pretreatment data.

[0010] The data display unit is used to publish and display data based on the final quality and safety level of agricultural products in the target area.

[0011] In some embodiments of this application, when the data analysis unit is used to determine the final quality and safety level of agricultural products in the target area based on the environmental monitoring preprocessing data and pesticide residue detection preprocessing data, it includes:

[0012] The total number of pesticide residue detection anomalies is obtained based on the pesticide residue detection data and denoted as Ab.

[0013] When Ab = 0, the data analysis unit determines that the agricultural product is qualified and makes a final determination of the quality and safety level of the agricultural product.

[0014] When Ab≠0, the data analysis unit determines that the agricultural product is unqualified and does not determine the final quality and safety level of the agricultural product.

[0015] In some embodiments of this application, when the data analysis unit determines that the agricultural product is qualified when Ab = 0 and performs the final quality and safety level determination of the agricultural product, it includes:

[0016] Based on the pesticide residue detection data, obtain a detection value for a pesticide detection item, denoted as F;

[0017] Obtain the qualified value corresponding to the pesticide test item, denoted as Fx, and F≥Fx;

[0018] Based on the qualified value Fx corresponding to the pesticide test item, the first preset qualified test threshold F1, the second preset qualified test threshold F2, the third preset qualified test threshold F3, and the fourth preset qualified test threshold F4 corresponding to the pesticide test item are preset, and F1 > F2 > F3 > F4 = Fx; the first preset quality and safety level M1, the second preset quality and safety level M2, the third preset quality and safety level M3, and the fourth preset quality and safety level M4 are preset, and M1 > M2 > M3 > M4;

[0019] When F≥F1, the data analysis unit selects the first preset quality and safety level M1 as the quality and safety level of the pesticide detection item;

[0020] When F1>F≥F2, the data analysis unit selects the second preset quality and safety level M2 as the quality and safety level of the pesticide detection item;

[0021] When F2>F≥F3, the data analysis unit selects the third preset quality and safety level M3 as the quality and safety level of the pesticide test item;

[0022] When F3 > F ≥ F4, the data analysis unit selects the fourth preset quality and safety level M4 as the quality and safety level of the pesticide detection item.

[0023] In some embodiments of this application, after the data analysis unit selects the i-th preset quality and safety level Mi as the quality and safety level of the pesticide detection item, i = 1, 2, 3, 4, it further includes:

[0024] The data analysis unit obtains all pesticide testing items based on the pesticide residue detection data, and sequentially determines the quality and safety level Mi corresponding to all the pesticide testing items.

[0025] In some embodiments of this application, after determining the quality and safety level Mi corresponding to all the pesticide testing items, the data analysis unit further includes:

[0026] Obtain the number of pesticide testing items included when the quality and safety level Mi = 1, and denote it as N1;

[0027] Obtain the number of pesticide testing items included when the quality and safety level Mi = 2, and denote it as N2;

[0028] Obtain the number of pesticide testing items included when the quality and safety level Mi = 3, and denote it as N3;

[0029] Obtain the number of pesticide testing items included when the quality and safety level Mi = 4, and denote it as N4;

[0030] Extract the maximum value from N1, N2, N3, and N4, and obtain the corresponding quality and safety level Mi based on the maximum value. Use the quality and safety level Mi obtained from the maximum value as the initial quality and safety level of the agricultural product.

[0031] In some embodiments of this application, after the data analysis unit obtains the quality and safety level Mi corresponding to the maximum value as the initial quality and safety level of the agricultural product, it further includes:

[0032] The total precipitation in the target area during the agricultural product growth cycle is obtained based on the environmental monitoring data and denoted as C.

[0033] Pre-set the minimum precipitation threshold Ca;

[0034] When C > Ca, the data analysis unit adjusts the initial quality and safety level Mi of the agricultural product once;

[0035] When C≤Ca, the data analysis unit does not adjust the initial quality and safety level Mi of the agricultural product, but directly uses the initial quality and safety level Mi as the final quality and safety level of the agricultural product.

[0036] In some embodiments of this application, when C > Ca, the data analysis unit adjusts the initial quality and safety level Mi of the agricultural product, including:

[0037] A first preset precipitation threshold C1 and a second preset precipitation threshold C2 are preset, where C1 > C2 and C2 = Ca;

[0038] When C2 < C ≤ C1, the data analysis unit will raise the initial quality and safety level Mi of the agricultural product by one level, not exceeding the first preset quality and safety level M1.

[0039] When C1 < C, the data analysis unit will raise the initial quality and safety level Mi of the agricultural product by two levels, with the maximum not exceeding the first preset quality and safety level M1;

[0040] Furthermore, the data analysis unit uses the initial quality and safety level Mi of the agricultural product after one adjustment as the adjusted quality and safety level Ma of the agricultural product.

[0041] In some embodiments of this application, after the data analysis unit obtains the adjusted quality and safety level Ma of the agricultural product, it further includes:

[0042] The average daily air humidity of the target area during the agricultural product growth cycle is obtained based on the environmental monitoring data and denoted as D.

[0043] The average daily air temperature of the target area during the agricultural product growth cycle is obtained based on the environmental monitoring data and denoted as E.

[0044] Pre-set the maximum daily average air humidity threshold Da, and pre-set the maximum daily average air temperature threshold Ea;

[0045] When either D > Da or E > Ea is satisfied, the data analysis unit performs a secondary adjustment on the initial quality and safety level Mi of the agricultural product;

[0046] When neither D > Da nor E > Ea is satisfied, the data analysis unit does not make a secondary adjustment to the initial quality and safety level Mi of the agricultural product, and takes the adjusted quality and safety level Ma of the agricultural product as the final quality and safety level of the agricultural product.

[0047] In some embodiments of this application, when the data analysis unit performs a secondary adjustment to the initial quality and safety level Mi of agricultural products, it includes:

[0048] When only one of D > Da and E > Ea is satisfied, the data analysis unit will raise the quality and safety level Ma of the agricultural product by one level, not exceeding the first preset quality and safety level M1.

[0049] When two of D > Da and E > Ea are satisfied, the data analysis unit will raise the quality and safety level Ma of the agricultural product by two levels, with the maximum not exceeding the first preset quality and safety level M1;

[0050] Furthermore, the data analysis unit uses the initial quality and safety level Mi of the agricultural product after the second adjustment as the final quality and safety level of the agricultural product.

[0051] In another aspect, this invention proposes a method for detecting and analyzing pesticide residues in agricultural products based on big data, the method comprising:

[0052] Collect environmental monitoring data of the target area during the complete growth cycle of agricultural products, and obtain pesticide residue detection data of the agricultural products from the cloud storage platform;

[0053] The environmental monitoring data and pesticide residue detection data are preprocessed to obtain preprocessed environmental monitoring data and preprocessed pesticide residue detection data.

[0054] The final quality and safety level of agricultural products in the target area is determined based on the environmental monitoring pretreatment data and pesticide residue detection pretreatment data.

[0055] The final quality and safety level of agricultural products in the target region will be published and displayed.

[0056] Compared with existing technologies, this invention offers the following advantages: First, it provides real-time and accurate environmental data by collecting environmental monitoring data of the target area throughout the entire growth cycle of agricultural products. This helps to precisely control the agricultural production environment and improve the yield and quality of agricultural products. The pesticide residue detection unit acquires pesticide residue detection data of agricultural products from a cloud storage platform. The data processing unit preprocesses the environmental monitoring data and pesticide residue detection data, generating preprocessed environmental monitoring data and preprocessed pesticide residue detection data. This optimizes the data to improve data quality and reduce noise, providing a more reliable foundation for subsequent data analysis. Based on the preprocessed environmental monitoring data and preprocessed pesticide residue detection data, the data analysis unit determines the final quality and safety level of agricultural products in the target area. Finally, the data display unit publishes and displays the final quality and safety level of agricultural products in the target area, showcasing the quality and safety level of agricultural products to farmers, consumers, agricultural decision-makers, and other stakeholders, thus promoting sustainable agricultural development. Attached Figure Description

[0057] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. In the drawings:

[0058] Figure 1 A functional block diagram of a big data-based pesticide residue detection and analysis system for agricultural products provided in an embodiment of the present invention;

[0059] Figure 2 The flowchart illustrates a method for detecting and analyzing pesticide residues in agricultural products based on big data, as provided in this embodiment of the invention. Detailed Implementation

[0060] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0061] See Figure 1 As shown, this invention proposes a big data-based pesticide residue detection and analysis system for agricultural products, which includes:

[0062] The sensor unit is used to collect environmental monitoring data of the target area during the entire growth cycle of agricultural products;

[0063] The pesticide detection unit is used to obtain pesticide residue detection data of the agricultural products from the cloud storage platform;

[0064] The data processing unit is used to preprocess the environmental monitoring data and pesticide residue detection data to obtain preprocessed environmental monitoring data and preprocessed pesticide residue detection data.

[0065] The data analysis unit is used to determine the final quality and safety level of agricultural products in the target area based on the environmental monitoring pretreatment data and pesticide residue detection pretreatment data.

[0066] The data display unit is used to publish and display data based on the final quality and safety level of agricultural products in the target area.

[0067] In some embodiments of this application, when the data analysis unit is used to determine the final quality and safety level of agricultural products in the target area based on the environmental monitoring preprocessing data and pesticide residue detection preprocessing data, it includes:

[0068] The total number of pesticide residue detection anomalies is obtained based on the pesticide residue detection data and denoted as Ab.

[0069] When Ab = 0, the data analysis unit determines that the agricultural product is qualified and makes a final determination of the quality and safety level of the agricultural product.

[0070] When Ab≠0, the data analysis unit determines that the agricultural product is unqualified and does not determine the final quality and safety level of the agricultural product.

[0071] In some embodiments of this application, when the data analysis unit determines that the agricultural product is qualified when Ab = 0 and performs the final quality and safety level determination of the agricultural product, it includes:

[0072] Based on the pesticide residue detection data, obtain a detection value for a pesticide detection item, denoted as F;

[0073] Obtain the qualified value corresponding to the pesticide test item, denoted as Fx, and F≥Fx;

[0074] Based on the qualified value Fx corresponding to the pesticide test item, the first preset qualified test threshold F1, the second preset qualified test threshold F2, the third preset qualified test threshold F3, and the fourth preset qualified test threshold F4 corresponding to the pesticide test item are preset, and F1 > F2 > F3 > F4 = Fx; the first preset quality and safety level M1, the second preset quality and safety level M2, the third preset quality and safety level M3, and the fourth preset quality and safety level M4 are preset, and M1 > M2 > M3 > M4;

[0075] When F≥F1, the data analysis unit selects the first preset quality and safety level M1 as the quality and safety level of the pesticide detection item;

[0076] When F1>F≥F2, the data analysis unit selects the second preset quality and safety level M2 as the quality and safety level of the pesticide detection item;

[0077] When F2>F≥F3, the data analysis unit selects the third preset quality and safety level M3 as the quality and safety level of the pesticide test item;

[0078] When F3 > F ≥ F4, the data analysis unit selects the fourth preset quality and safety level M4 as the quality and safety level of the pesticide detection item.

[0079] Understandably, in this embodiment, by processing pesticide residue detection data through the data analysis unit, the system can automatically determine the quality and safety level of agricultural products, thereby reducing the need for manual intervention and improving efficiency. Based on the quality and safety level of pesticide detection items, the system not only determines whether agricultural products are qualified, but also determines the quality and safety level according to the specific values ​​of pesticide detection items. This fine classification can more accurately reflect the actual quality status of agricultural products.

[0080] Furthermore, for each pesticide test item, the system provides preset acceptable test thresholds and corresponding quality and safety levels, which can be adjusted according to actual conditions, making the system more flexible and adaptable. The system divides the test item values ​​into multiple quality and safety levels, from the first preset quality and safety level to the fourth preset quality and safety level, which helps to more meticulously assess and manage the quality of agricultural products.

[0081] In some embodiments of this application, after the data analysis unit selects the i-th preset quality and safety level Mi as the quality and safety level of the pesticide detection item, i = 1, 2, 3, 4, it further includes:

[0082] The data analysis unit obtains all pesticide testing items based on the pesticide residue detection data, and sequentially determines the quality and safety level Mi corresponding to all the pesticide testing items.

[0083] In some embodiments of this application, after determining the quality and safety level Mi corresponding to all the pesticide testing items, the data analysis unit further includes:

[0084] Obtain the number of pesticide testing items included when the quality and safety level Mi = 1, and denote it as N1;

[0085] Obtain the number of pesticide testing items included when the quality and safety level Mi = 2, and denote it as N2;

[0086] Obtain the number of pesticide testing items included when the quality and safety level Mi = 3, and denote it as N3;

[0087] Obtain the number of pesticide testing items included when the quality and safety level Mi = 4, and denote it as N4;

[0088] Extract the maximum value from N1, N2, N3, and N4, and obtain the corresponding quality and safety level Mi based on the maximum value. Use the quality and safety level Mi obtained from the maximum value as the initial quality and safety level of the agricultural product.

[0089] Understandably, by acquiring the number of pesticide testing items included in each quality and safety level, this embodiment enables the system to comprehensively assess the quality status of agricultural products at different levels, providing more specific and detailed information. By extracting the maximum value among N1, N2, N3, and N4, the initial quality and safety level is dynamically determined, allowing for more flexible determination of the initial quality and safety level based on the number of testing items in different levels, making the system more adaptable.

[0090] Furthermore, by considering the number of test items under each quality and safety level, the system can more accurately reflect the overall quality status of agricultural products. This helps decision-makers formulate relevant policies or take necessary measures more scientifically. By automatically counting the number of test items under each level and selecting the maximum value, the system simplifies the process of determining the initial quality and safety level, reduces the burden of manual intervention, and improves operational efficiency.

[0091] In some embodiments of this application, after the data analysis unit obtains the quality and safety level Mi corresponding to the maximum value as the initial quality and safety level of the agricultural product, it further includes:

[0092] The total precipitation in the target area during the agricultural product growth cycle is obtained based on the environmental monitoring data and denoted as C.

[0093] Pre-set the minimum precipitation threshold Ca;

[0094] When C > Ca, the data analysis unit adjusts the initial quality and safety level Mi of the agricultural product once;

[0095] When C≤Ca, the data analysis unit does not adjust the initial quality and safety level Mi of the agricultural product, but directly uses the initial quality and safety level Mi as the final quality and safety level of the agricultural product.

[0096] In some embodiments of this application, when C > Ca, the data analysis unit adjusts the initial quality and safety level Mi of the agricultural product, including:

[0097] A first preset precipitation threshold C1 and a second preset precipitation threshold C2 are preset, where C1 > C2 and C2 = Ca;

[0098] When C2 < C ≤ C1, the data analysis unit will raise the initial quality and safety level Mi of the agricultural product by one level, not exceeding the first preset quality and safety level M1.

[0099] When C1 < C, the data analysis unit will raise the initial quality and safety level Mi of the agricultural product by two levels, with the maximum not exceeding the first preset quality and safety level M1;

[0100] Furthermore, the data analysis unit uses the initial quality and safety level Mi of the agricultural product after one adjustment as the adjusted quality and safety level Ma of the agricultural product.

[0101] Understandably, by incorporating precipitation as a factor in this embodiment, the system can adjust the initial quality and safety level of agricultural products based on the precipitation conditions of the target area. This mechanism can more comprehensively reflect the impact of environmental factors on agricultural product quality. By setting preset precipitation thresholds and adjustment rules, the system can dynamically adjust the initial quality and safety level of agricultural products. This makes the system more intelligent and capable of making flexible adjustments based on actual conditions. Since different regions have different climatic conditions, incorporating precipitation considerations can improve the system's adaptability, allowing it to adjust the quality and safety level of agricultural products more specifically. By setting a first preset precipitation threshold C1 and a second preset precipitation threshold C2, the system limits the adjustment range, ensuring that the adjusted quality and safety level does not exceed the first preset quality and safety level M1. This helps maintain the rationality of the adjustment.

[0102] In some embodiments of this application, after the data analysis unit obtains the adjusted quality and safety level Ma of the agricultural product, it further includes:

[0103] The average daily air humidity of the target area during the agricultural product growth cycle is obtained based on the environmental monitoring data and denoted as D.

[0104] The average daily air temperature of the target area during the agricultural product growth cycle is obtained based on the environmental monitoring data and denoted as E.

[0105] Pre-set the maximum daily average air humidity threshold Da, and pre-set the maximum daily average air temperature threshold Ea;

[0106] When either D > Da or E > Ea is satisfied, the data analysis unit performs a secondary adjustment on the initial quality and safety level Mi of the agricultural product;

[0107] When neither D > Da nor E > Ea is satisfied, the data analysis unit does not make a secondary adjustment to the initial quality and safety level Mi of the agricultural product, and takes the adjusted quality and safety level Ma of the agricultural product as the final quality and safety level of the agricultural product.

[0108] In some embodiments of this application, when the data analysis unit performs a secondary adjustment to the initial quality and safety level Mi of agricultural products, it includes:

[0109] When only one of D > Da and E > Ea is satisfied, the data analysis unit will raise the quality and safety level Ma of the agricultural product by one level, not exceeding the first preset quality and safety level M1.

[0110] When two of D > Da and E > Ea are satisfied, the data analysis unit will raise the quality and safety level Ma of the agricultural product by two levels, with the maximum not exceeding the first preset quality and safety level M1;

[0111] Furthermore, the data analysis unit uses the initial quality and safety level Mi of the agricultural product after the second adjustment as the final quality and safety level of the agricultural product.

[0112] It is understandable that by incorporating daily average air humidity and daily average air temperature as factors in this embodiment, the system can perform a secondary adjustment to the quality and safety level of agricultural products based on environmental factors after the initial adjustment. This mechanism allows the system to more comprehensively consider the impact of environmental factors on agricultural products. By setting a maximum threshold of daily average air humidity (Da) and a maximum threshold of daily average air temperature (Ea), and adjusting them according to the number of conditions met, the system can dynamically adjust the quality and safety level of agricultural products. This makes the system more intelligent and adaptable. By setting adjustment rules, the system limits the range of adjustment, ensuring that the adjusted quality and safety level does not exceed the first preset quality and safety level M1. This helps maintain the rationality of the adjustment.

[0113] Furthermore, considering the different climatic conditions in various regions, incorporating factors such as air humidity and temperature can improve the system's adaptability, enabling it to more effectively adjust the quality and safety levels of agricultural products. By comprehensively considering multiple environmental factors, the system's decision-making becomes more holistic, enhancing the scientific assessment of agricultural product quality.

[0114] See Figure 2 As shown, in another aspect, this embodiment provides a method for detecting and analyzing pesticide residues in agricultural products based on big data. The method includes:

[0115] S101: Collect environmental monitoring data of the target area during the complete growth cycle of agricultural products, and obtain pesticide residue detection data of the agricultural products from the cloud storage platform;

[0116] S102: Perform data preprocessing on the environmental monitoring data and pesticide residue detection data to obtain preprocessed environmental monitoring data and preprocessed pesticide residue detection data;

[0117] S103: Determine the final quality and safety level of agricultural products in the target area based on the environmental monitoring pretreatment data and pesticide residue detection pretreatment data;

[0118] S104: Publish and display the final quality and safety level of agricultural products in the target area.

[0119] It is understood that this embodiment provides agricultural producers with comprehensive quality management tools by integrating environmental monitoring and pesticide residue detection data, as well as performing data preprocessing and accurate assessment of quality and safety levels. This helps improve the quality and market competitiveness of agricultural products, while also promoting sustainable agricultural production.

[0120] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0122] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0123] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A big data-based pesticide residue detection and analysis system for agricultural products, characterized in that, include: The sensor unit is used to collect environmental monitoring data of the target area during the entire growth cycle of agricultural products; The pesticide detection unit is used to obtain pesticide residue detection data of the agricultural products from the cloud storage platform; The data processing unit is used to preprocess the environmental monitoring data and pesticide residue detection data to obtain preprocessed environmental monitoring data and preprocessed pesticide residue detection data. The data analysis unit is used to determine the final quality and safety level of agricultural products in the target area based on the environmental monitoring pretreatment data and pesticide residue detection pretreatment data. The data display unit is used to publish and display data based on the final quality and safety level of agricultural products in the target area. The data analysis unit determines the final quality and safety level of agricultural products in the target area based on the environmental monitoring preprocessing data and pesticide residue detection preprocessing data, including: The total number of pesticide residue anomalies is obtained from the pesticide residue detection data and denoted as Ab. When Ab = 0, the data analysis unit determines that the agricultural product is qualified. When determining the final quality and safety level of the agricultural product, the following steps are taken: Based on the pesticide residue detection data, obtain a detection value for a pesticide detection item, denoted as F; Obtain the qualified value corresponding to the pesticide test item, denoted as Fx, and F≥Fx; Based on the qualified value Fx corresponding to the pesticide test item, the first preset qualified test threshold F1, the second preset qualified test threshold F2, the third preset qualified test threshold F3, and the fourth preset qualified test threshold F4 are preset, and F1 > F2 > F3 > F4 = Fx; the first preset quality and safety level M1, the second preset quality and safety level M2, the third preset quality and safety level M3, and the fourth preset quality and safety level M4 are preset, and M1 > M2 > M3 > M4; When F≥F1, the data analysis unit selects the first preset quality and safety level M1 as the quality and safety level of the pesticide test item; when F1>F≥F2, the data analysis unit selects the second preset quality and safety level M2 as the quality and safety level of the pesticide test item; when F2>F≥F3, the data analysis unit selects the third preset quality and safety level M3 as the quality and safety level of the pesticide test item; when F3>F≥F4, the data analysis unit selects the fourth preset quality and safety level M4 as the quality and safety level of the pesticide test item. After the data analysis unit selects the i-th preset quality and safety level Mi as the quality and safety level of the pesticide test item, i=1, 2, 3, 4, it also includes: the data analysis unit obtains all pesticide test items according to the pesticide residue test data, and sequentially determines the quality and safety level Mi corresponding to all the pesticide test items; After determining the quality and safety level Mi corresponding to all the pesticide testing items, the data analysis unit further includes: obtaining the number of pesticide testing items included when the quality and safety level Mi=1, denoted as N1; obtaining the number of pesticide testing items included when the quality and safety level Mi=2, denoted as N2; obtaining the number of pesticide testing items included when the quality and safety level Mi=3, denoted as N3; obtaining the number of pesticide testing items included when the quality and safety level Mi=4, denoted as N4; extracting the maximum value from N1, N2, N3 and N4, obtaining the corresponding quality and safety level Mi based on the maximum value, and using the quality and safety level Mi corresponding to the maximum value as the initial quality and safety level of the agricultural product; After obtaining the corresponding quality and safety level Mi from the maximum value as the initial quality and safety level of the agricultural product, the data analysis unit further includes: obtaining the total precipitation in the target area during the agricultural product's growth cycle based on the environmental monitoring data, denoted as C; pre-setting a minimum precipitation threshold Ca; when C > Ca, the data analysis unit adjusts the initial quality and safety level Mi of the agricultural product once; when C ≤ Ca, the data analysis unit does not adjust the initial quality and safety level Mi of the agricultural product once, and directly uses the initial quality and safety level Mi of the agricultural product as the final quality and safety level of the agricultural product. When C > Ca, the data analysis unit adjusts the initial quality and safety level Mi of the agricultural product once, including: presetting a first preset precipitation threshold C1 and a second preset precipitation threshold C2, where C1 > C2 and C2 = Ca; when C2 < C ≤ C1, the data analysis unit raises the initial quality and safety level Mi of the agricultural product by one level, not exceeding the first preset quality and safety level M1; when C1 < C, the data analysis unit raises the initial quality and safety level Mi of the agricultural product by two levels, not exceeding the first preset quality and safety level M1; and the data analysis unit uses the adjusted initial quality and safety level Mi of the agricultural product as the adjusted quality and safety level Ma of the agricultural product. After the data analysis unit obtains the adjusted quality and safety level Ma of the agricultural product, the method further includes: obtaining the average daily air humidity of the target area during the agricultural product growth cycle based on the environmental monitoring data, denoted as D; obtaining the average daily air temperature of the target area during the agricultural product growth cycle based on the environmental monitoring data, denoted as E; pre-setting a maximum threshold value for average daily air humidity Da and a maximum threshold value for average daily air temperature Ea; when either D > Da or E > Ea is satisfied, the data analysis unit performs a secondary adjustment to the initial quality and safety level Mi of the agricultural product; when neither D > Da nor E > Ea is satisfied, the data analysis unit does not perform a secondary adjustment to the initial quality and safety level Mi of the agricultural product, and uses the adjusted quality and safety level Ma of the agricultural product as the final quality and safety level of the agricultural product.

2. The agricultural product pesticide residue detection and analysis system based on big data according to claim 1, characterized in that, When the data analysis unit determines the final quality and safety level of agricultural products in the target area based on the environmental monitoring preprocessing data and pesticide residue detection preprocessing data, it includes: The total number of pesticide residue detection anomalies is obtained based on the pesticide residue detection data and denoted as Ab. When Ab=0, the data analysis unit determines that the agricultural product is qualified and makes a final determination of the quality and safety level of the agricultural product. When Ab≠0, the data analysis unit determines that the agricultural product is unqualified and does not determine the final quality and safety level of the agricultural product.

3. The agricultural product pesticide residue detection and analysis system based on big data according to claim 2, characterized in that, When the data analysis unit performs a secondary adjustment to the initial quality and safety level Mi of agricultural products, it includes: When only one of D > Da and E > Ea is satisfied, the data analysis unit will raise the quality and safety level Ma of the agricultural product by one level, not exceeding the first preset quality and safety level M1. When two of D > Da and E > Ea are satisfied, the data analysis unit will raise the quality and safety level Ma of the agricultural product by two levels, with the maximum not exceeding the first preset quality and safety level M1; Furthermore, the data analysis unit uses the initial quality and safety level Mi of the agricultural product after secondary adjustment as the final quality and safety level of the agricultural product.

4. A method for detecting and analyzing pesticide residues in agricultural products based on big data, characterized in that, The application of the big data-based pesticide residue detection and analysis system for agricultural products as described in any one of claims 1-3 includes: Collect environmental monitoring data of the target area during the complete growth cycle of agricultural products, and obtain pesticide residue detection data of the agricultural products from the cloud storage platform; The environmental monitoring data and pesticide residue detection data are preprocessed to obtain preprocessed environmental monitoring data and preprocessed pesticide residue detection data. The final quality and safety level of agricultural products in the target area is determined based on the environmental monitoring pretreatment data and pesticide residue detection pretreatment data. The final quality and safety level of agricultural products in the target region will be published and displayed.

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