Pesticide residue detection data analysis method and system for food engineering

Abstract

The invention discloses a pesticide residue detection data analysis method and system for food engineering, and relates to the field of food engineering.The system comprises a main control unit, the main control unit comprises a retrieval unit, the retrieval unit is electrically connected with a search module, and the search module is electrically connected with a food big data storage unit; the retrieval unit is electrically connected with an abnormal value detection and analysis module, the abnormal value detection and analysis module is electrically connected with a detection unit and an error value analysis unit, the detection unit is electrically connected with a detection information collection module, and the error value analysis unit is electrically connected with a danger label generation module. The danger label generation module is electrically connected with an updating module, a residual threshold setting module and a label marking module. According to the pesticide residue detection data analysis method and system for food engineering, the system can detect whether pesticide residues exist in adopted agricultural products or not and detect the residual quantity of the pesticide residues, and the risk of the agricultural products is judged based on a food big data storage unit.

Description

Technical Field

[0001] The present invention relates to the field of food engineering, and in particular to a method and system for analyzing pesticide residue detection data used in food engineering. Background Art

[0002] Pesticides can be used to kill insects, fungi and other organisms that harm the growth of crops. The earliest pesticides used could eliminate pests in large numbers and were stable. They could exist in the environment for a long time and continue to accumulate in animals, plants and humans. For this reason, they were eliminated. Later, organophosphorus pesticides, such as dichlorvos, were used to replace the original pesticides. However, these pesticides are too toxic and pose a great threat to humans and animals.

[0003] Agricultural products need to be sprayed with pesticides when they grow, but when the pesticide content exceeds the standard, it will cause harm to human health. When testing for pesticide residues, it is impossible to quickly determine whether the detected data exceeds the standard, and it is impossible to record the specific values ​​of the sampling, which will also affect the results of the analysis.

[0004] Therefore, it is necessary to propose a pesticide residue detection data analysis method and system for food engineering to solve the above problems. Summary of the invention

[0005] The main purpose of the present invention is to provide a method and system for analyzing pesticide residue detection data for food engineering, which can effectively solve the problems in the background technology.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for analyzing pesticide residue detection data for food engineering includes the following analysis steps:

[0008] S1: Sample food and upload the sampling information, including sampling time, sampling quantity, sampling area and sample type;

[0009] S2: Test the samples collected, use special pesticide testing instruments to test and process the samples, and analyze the test data after receiving them to confirm the amount and type of pesticide residues;

[0010] S3: Detect and analyze the abnormal values ​​of pesticides, access the food big data storage unit to search the data of pesticides and samples, and conduct comparative analysis based on the database of agricultural product types and pesticide types. Compare and classify the pesticide residue values ​​detected in step S2 according to the numerical standards of the pesticide residues in the corresponding agricultural products, and classify them into three values: high, medium and low. Determine whether the pesticide residue exceeds the standard. If the value exceeds the standard, mark the agricultural product and generate a label indicating that the residue exceeds the standard.

[0011] S4: Collect the information after the test again and analyze the error value. According to the data of sampling time, sampling quantity, sampling area and sample type, analyze whether the data will affect the test information. If it does, re-test the agricultural products. If it does not, confirm the results.

[0012] S5: After confirming the structure, produce hazard labels for the tested agricultural products and update the food big data based on the label data.

[0013] A pesticide residue detection data analysis system for food engineering, comprising a main control unit, the main control unit comprising a retrieval unit, the retrieval unit being electrically connected to a search module, the search module being electrically connected to a food big data storage unit, the retrieval unit being electrically connected to an abnormal value detection and analysis module, the abnormal value detection and analysis module being electrically connected to a detection unit and an error value analysis unit, the detection unit being electrically connected to a detection information collection module, the error value analysis unit being electrically connected to a danger label generation module, the danger label generation module being electrically connected to an update module, a residue threshold setting module and a label marking module.

[0014] Preferably, the output end of the retrieval unit is electrically connected to the input end of the search module, the output end of the search module is electrically connected to the input end of the food big data storage unit, the input end of the food big data storage unit is electrically connected to the output end of the update module, and the input end of the update module is electrically connected to the output end of the hazard label generation module.

[0015] Preferably, the input end of the retrieval unit is electrically connected to the output end of the outlier detection and analysis module, the output end of the detection unit is electrically connected to the input end of the outlier detection and analysis module, the output end of the detection unit is electrically connected to the input end of the detection information collection module, the output end of the detection information collection module is electrically connected to the input end of the error value analysis unit, the output end of the outlier detection and analysis module is electrically connected to the input end of the error value analysis unit, the output end of the error value analysis unit is electrically connected to the input end of the danger label generation module, and the output end of the danger label generation module is electrically connected to the input end of the label marking module.

[0016] Preferably, the food big data storage unit includes an agricultural product type storage module and a pesticide type storage module, the pesticide type storage module includes storage for different food pesticide types, and the stored information includes toxicity, composition and efficacy, the output ends of the agricultural product type storage module and the pesticide type storage module are electrically connected to the input end of the pesticide residue data access module, the output end of the pesticide residue data access module is electrically connected to the input end of the numerical classification module, the numerical classification module classifies the numerical values ​​into high, medium and low according to the numerical information, the output end of the numerical classification module is electrically connected to the input end of the marking module, and the output end of the marking module is electrically connected to the input end of the label generation module.

[0017] Preferably, the detection unit includes a detection data receiving module, the input end of the detection data receiving module is electrically connected to the detection data of the detection instrument, the detection instrument is a pesticide residue detection instrument, the number of the detection instruments is determined according to the number of pesticide types, the output end of the detection data receiving module is electrically connected to the input end of the analysis unit, the output end of the analysis unit is electrically connected to the input ends of the pesticide residue confirmation module and the pesticide type confirmation module, and the output end of the pesticide residue confirmation module is electrically connected to the input end of the residue amount confirmation module.

[0018] Preferably, the error value analysis unit includes a sample information module, and the sample information module is divided into sample information module 1, sample information module 2 and sample information module N. The sample information module 1, sample information module 2 and sample information module N all include information on the sampling time, sampling quantity, sample area and sample type of food.

[0019] Preferably, the output ends of the sample information module 1, the sample information module 2 and the sample information module N are electrically connected to a numerical comparison module, and the numerical comparison module is divided into a result confirmation and a detection unit according to the size of the numerical error. When the error is large, the numerical comparison module is electrically connected to the detection unit.

[0020] Beneficial Effects

[0021] Compared with the prior art, the present invention provides a method and system for analyzing pesticide residue detection data for food engineering, which has the following beneficial effects:

[0022] 1. The pesticide residue detection data analysis method and system for food engineering, through the food big data storage unit set up, can search for the types of various agricultural products and pesticides based on the data stored in the food big data storage unit when conducting pesticide residue detection, so that the detected data can be compared with the stored data to determine whether the pesticide residue exceeds the standard.

[0023] 2. The pesticide residue detection data analysis method and system for food engineering can analyze whether pesticides are residual, the amount of pesticide residues and the type of pesticides based on the data detected by the detection instrument through the detection data receiving module set up, so as to facilitate the subsequent generation of labels for agricultural products.

[0024] 3. The pesticide residue detection data analysis method and system for food engineering can analyze the errors of agricultural products based on sample information, including sampling time, sampling quantity, sample area and sample type, through the error value analysis unit set up, so as to take into account the impact of unexpected factors on agricultural products, and re-test them later. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a system diagram of the main control unit of the present invention;

[0026] Figure 2 is a system diagram of the food big data storage unit of the present invention;

[0027] Figure 3 is a system diagram of the detection unit of the present invention;

[0028] Figure 4 It is a system diagram of the error value analysis unit of the present invention. DETAILED DESCRIPTION

[0029] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0030] like Figure 1-4 As shown, a method for analyzing pesticide residue detection data for food engineering includes the following analysis steps:

[0031] S1: Sample the food and upload the sampling information, including the sampling time, sampling quantity, sampling area and sample type.

[0032] S2: Test the samples collected using specialized pesticide testing equipment, and analyze the test data after receiving it to confirm the amount and type of pesticide residues.

[0033] S3: Detect and analyze abnormal values ​​of pesticides, access the food big data storage unit to search the data of pesticides and samples, and conduct comparative analysis based on the database of agricultural product types and pesticide types. Compare and classify them according to the numerical standards of pesticide residues in corresponding agricultural products and the pesticide residue values ​​detected in step S2, and divide them into three values: high, medium and low. Determine whether the pesticide residue exceeds the standard. When the value exceeds the standard, mark the agricultural product and generate a label indicating that the residue exceeds the standard.

[0034] S4: Collect the information after the test again and analyze the error value. According to the data of sampling time, sampling quantity, sampling area and sample type, analyze whether the data will affect the test information. If it does affect, re-test the agricultural products. If it does not affect, confirm the results.

[0035] S5: After confirming the structure, produce hazard labels for the tested agricultural products and update the food big data based on the label data.

[0036] like Figure 1-4As shown, a pesticide residue detection data analysis system for food engineering includes a main control unit, the main control unit includes a retrieval unit, the retrieval unit is electrically connected to a search module, the search module is electrically connected to a food big data storage unit, the retrieval unit is electrically connected to an abnormal value detection and analysis module, the abnormal value detection and analysis module is electrically connected to a detection unit and an error value analysis unit, the detection unit is electrically connected to a detection information collection module, the error value analysis unit is electrically connected to a danger label generation module, the danger label generation module is electrically connected to an update module, a residue threshold setting module and a label marking module, the output end of the retrieval unit is electrically connected to the input end of the search module, the output end of the search module is electrically connected to the input end of the food big data storage unit, and the food big data The input end of the storage unit is electrically connected to the output end of the update module, the input end of the update module is electrically connected to the output end of the danger label generation module, the input end of the retrieval unit is electrically connected to the output end of the abnormal value detection and analysis module, the output end of the detection unit is electrically connected to the input end of the abnormal value detection and analysis module, the output end of the detection unit is electrically connected to the input end of the detection information collection module, the output end of the detection information collection module is electrically connected to the input end of the error value analysis unit, the output end of the abnormal value detection and analysis module is electrically connected to the input end of the error value analysis unit, the output end of the error value analysis unit is electrically connected to the input end of the danger label generation module, and the output end of the danger label generation module is electrically connected to the input end of the label marking module. The food big data storage unit includes an agricultural product type storage module and a pesticide type storage module. The pesticide type storage module includes storage for different types of food pesticides, and the stored information includes toxicity, composition and efficacy. The output ends of the agricultural product type storage module and the pesticide type storage module are electrically connected to the input end of the pesticide residue data access module. The output end of the pesticide residue data access module is electrically connected to the input end of the numerical classification module. The numerical classification module classifies the numerical values ​​into high, medium and low according to the numerical information. The output end of the numerical classification module is electrically connected to the input end of the marking module. The output end of the marking module is electrically connected to the input end of the label generation module. The detection unit includes a detection data receiving module. The input end of the detection data receiving module The input end is electrically connected to the detection data of the detection instrument, the detection instrument is a pesticide residue detection instrument, and the number of detection instruments is determined according to the number of types of pesticides. The output end of the detection data receiving module is electrically connected to the input end of the analysis unit, the output end of the analysis unit is electrically connected to the input ends of the pesticide residue confirmation module and the pesticide type confirmation module, the output end of the pesticide residue confirmation module is electrically connected to the input end of the residue confirmation module, the error value analysis unit includes a sample information module, the sample information module is divided into a sample information module 1, a sample information module 2 and a sample information module N, the sample information module 1, the sample information module 2 and the sample information module N all include information on the sampling time, sampling quantity, sample area and sample type of food,The output ends of the sample information module 1, sample information module 2 and sample information module N are electrically connected to a numerical comparison module, which is divided into a result confirmation and a detection unit according to the size of the numerical error. When the error is large, the numerical comparison module is electrically connected to the detection unit.

[0037] Through the food big data storage unit that is set up, when conducting pesticide residue detection, various agricultural products and pesticide types can be retrieved based on the data stored in the food big data storage unit, so that the detected data can be compared with the stored data to determine whether the pesticide residue exceeds the standard. Through the detection data receiving module that is set up, whether there are pesticide residues, the amount of pesticide residues and the type of pesticides can be analyzed based on the data detected by the detection instrument, so as to facilitate the subsequent generation of labels for agricultural products. Through the error value analysis unit that is set up, errors of agricultural products can be analyzed based on sample information, including sampling time, sampling quantity, sample area and sample type, so as to take into account the impact of unexpected factors on agricultural products, and they can be re-tested later.

[0038] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for analyzing pesticide residue detection data for food engineering, characterized in that: The analysis steps include: S1: Sample food and upload the sampling information, including sampling time, sampling quantity, sampling area and sample type; S2: Test the samples collected, use special pesticide testing instruments to test and process the samples, and analyze the test data after receiving them to confirm the amount and type of pesticide residues; S3: Detect and analyze the abnormal values ​​of pesticides, access the food big data storage unit to search the data of pesticides and samples, and conduct comparative analysis based on the database of agricultural product types and pesticide types. Compare and classify the pesticide residue values ​​detected in step S2 according to the numerical standards of the pesticide residues in the corresponding agricultural products, and classify them into three values: high, medium and low. Determine whether the pesticide residue exceeds the standard. If the value exceeds the standard, mark the agricultural product and generate a label indicating that the residue exceeds the standard. S4: Collect the information after the test again and analyze the error value. According to the data of sampling time, sampling quantity, sampling area and sample type, analyze whether the data will affect the test information. If it does, re-test the agricultural products. If it does not, confirm the results. S5: After confirming the structure, produce hazard labels for the tested agricultural products and update the food big data based on the label data.

2. A pesticide residue detection data analysis system for food engineering according to claim 1, comprising a main control unit, characterized in that: The main control unit includes a retrieval unit, the retrieval unit is electrically connected to a search module, the search module is electrically connected to a food big data storage unit, the retrieval unit is electrically connected to an abnormal value detection and analysis module, the abnormal value detection and analysis module is electrically connected to a detection unit and an error value analysis unit, the detection unit is electrically connected to a detection information collection module, the error value analysis unit is electrically connected to a danger label generation module, and the danger label generation module is electrically connected to an update module, a residual threshold setting module and a label marking module.

3. The pesticide residue detection data analysis system for food engineering according to claim 1, characterized in that: The output end of the retrieval unit is electrically connected to the input end of the search module, the output end of the search module is electrically connected to the input end of the food big data storage unit, the input end of the food big data storage unit is electrically connected to the output end of the update module, and the input end of the update module is electrically connected to the output end of the hazard label generation module.

4. The pesticide residue detection data analysis system for food engineering according to claim 1, characterized in that: The input end of the retrieval unit is electrically connected to the output end of the outlier detection and analysis module, the output end of the detection unit is electrically connected to the input end of the outlier detection and analysis module, the output end of the detection unit is electrically connected to the input end of the detection information collection module, the output end of the detection information collection module is electrically connected to the input end of the error value analysis unit, the output end of the outlier detection and analysis module is electrically connected to the input end of the error value analysis unit, the output end of the error value analysis unit is electrically connected to the input end of the danger label generation module, and the output end of the danger label generation module is electrically connected to the input end of the label marking module.

5. The pesticide residue detection data analysis system for food engineering according to claim 1, characterized in that: The food big data storage unit includes an agricultural product type storage module and a pesticide type storage module. The pesticide type storage module includes storage for different types of food pesticides, and the stored information includes toxicity, composition and efficacy. The output ends of the agricultural product type storage module and the pesticide type storage module are electrically connected to the input end of the pesticide residue data access module. The output end of the pesticide residue data access module is electrically connected to the input end of the numerical classification module. The numerical classification module classifies numerical values ​​into high, medium and low according to numerical information. The output end of the numerical classification module is electrically connected to the input end of the marking module, and the output end of the marking module is electrically connected to the input end of the label generation module.

6. The pesticide residue detection data analysis system for food engineering according to claim 1, characterized in that: The detection unit includes a detection data receiving module, the input end of the detection data receiving module is electrically connected to the detection data of the detection instrument, the detection instrument is a pesticide residue detection instrument, and the number of the detection instruments is determined according to the number of pesticide types. The output end of the detection data receiving module is electrically connected to the input end of the analysis unit, the output end of the analysis unit is electrically connected to the input ends of the pesticide residue confirmation module and the pesticide type confirmation module, and the output end of the pesticide residue confirmation module is electrically connected to the input end of the residue confirmation module.

7. The pesticide residue detection data analysis system for food engineering according to claim 1, characterized in that: The error value analysis unit includes a sample information module, which is divided into sample information module 1, sample information module 2 and sample information module N. The sample information module 1, sample information module 2 and sample information module N all include information on the sampling time, sampling quantity, sample area and sample type of food.

8. The pesticide residue detection data analysis system for food engineering according to claim 7, characterized in that: The output ends of the sample information module 1, the sample information module 2 and the sample information module N are electrically connected to a numerical comparison module, and the numerical comparison module is divided into a result confirmation and a detection unit according to the size of the numerical error. When the error is large, the numerical comparison module is electrically connected to the detection unit.

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