An efficient detection method and system for pesticide residues in food

Abstract

The present invention relates to the technical field of pesticide residue detection, and specifically relates to a high-efficiency detection method and system for the pesticide residue content in food. The method includes obtaining the abnormal manifestation degree of pesticide residues by using the actual pesticide residue values of each sampling sample from the current food source under each detection item; under the historical attention period, obtaining the continuous influence trend degree by using the number of abnormal pesticide residue samples of the target batch sampling samples from the current food source under the target detection item and the number of abnormal pesticide residue samples under each detection item; calculating the application proximity based on the most recent application interval and the historical most recent application interval; obtaining the single pesticide residue tendency degree based on the abnormal manifestation degree of pesticide residues, the continuous influence trend degree, and the application proximity. When the maximum value of the single pesticide residue tendency degree of the detection item is not less than the comparison threshold, only this detection item is detected for the current food source. The present invention can determine whether only single pesticide residue detection can be performed on the current food source.

Description

Technical Field

[0001] The present invention relates to the technical field of pesticide residue detection, and particularly relates to a high-efficiency detection method and system for the pesticide residue amount in food. Background Art

[0002] Currently, food safety issues have become increasingly prominent, and the problem of pesticide residues has become the focus of attention of the public and regulatory authorities. Pesticides are widely used in agricultural production, resulting in pesticide residues in food, which not only endanger human health but may also induce various diseases. Therefore, the demand for the detection of pesticide residues is increasing continuously. At present, the food pesticide residue detection industry is showing a rapid development trend in terms of technology R & D, standard system construction, and application scenario expansion, but still faces the dual challenges of efficiency improvement and technological innovation.

[0003] In the actual food pesticide detection process, due to differences in factors such as pest types and pesticide application habits in different planting areas, the main types of residual pesticides in the same food show obvious regional characteristics in different regions (for example, the dominant residual in Area A is organophosphorus pesticides, while in Area B it is mainly pyrethroids). In addition, for different types of pesticide residues, diverse detection methods are often required. This means that when using traditional single detection methods for food pesticide detection, multiple experiments are needed to comprehensively cover all residual components, and the detection time and cost will increase significantly. Summary of the Invention

[0004] In order to solve the technical problems of the long detection time and high detection cost of the existing pesticide residue detection technology, the purpose of the present invention is to provide a high-efficiency detection method for the pesticide residue amount in food, and the specific technical solutions adopted are as follows:

[0005] Obtain the pesticide residue degree, abnormal pesticide residue samples, and sampling abnormality degree based on the actual pesticide residue values of each sampling sample from the current food source under each detection item, and then obtain the abnormal manifestation degree of pesticide residues;

[0006] Under the historical attention period, take the ratio of the number of the abnormal pesticide residue samples of the target batch of sampling samples from the current food source under the target detection item to the number of the abnormal pesticide residue samples of the target batch of sampling samples from the current food source under each detection item as the pesticide residue significance of the target batch of sampling samples from the current food source under the target detection item, and take the average value of the pesticide residue significance as the continuous influence trend degree of the historical same-source characteristics of the target detection item;

[0007] Calculate the pesticide application proximity based on the most recent pesticide application interval of the current food source and the historical most recent pesticide application interval;

[0008] Obtain the single - type pesticide residue tendency degree based on the abnormal manifestation degree of the pesticide residue, the continuous influence trend degree, and the pesticide application proximity degree. When the maximum value of the single - type pesticide residue tendency degree of a detection item is greater than or equal to a preset comparison threshold, only perform the detection of this detection item for the current food source; otherwise, perform the detection of all detection items.

[0009] Furthermore, the process of obtaining the pesticide residue degree includes:

[0010] The actual pesticide residue value of the i - th sampled sample of the current food source under the j - th detection item is divided by the preset maximum residue limit value of the j - th detection item to obtain the pesticide residue degree of the i - th sampled sample under the j - th detection item.

[0011] Furthermore, the process of obtaining the abnormal pesticide residue sample includes:

[0012] When the pesticide residue degree is greater than the preset pesticide residue threshold, the sampled sample corresponding to the pesticide residue degree is used as the abnormal pesticide residue sample.

[0013] Furthermore, the process of obtaining the sampling abnormal degree includes:

[0014] The value obtained by dividing the number of abnormal pesticide residue samples of the current food source under the j - th detection item by the number of sampled samples of the current food source under the j - th detection item is used as the sampling abnormal degree of the current food source under the j - th detection item.

[0015] Furthermore, the process of obtaining the abnormal manifestation degree of the pesticide residue includes:

[0016] Under the j - th detection item of the current food source, the average value of the pesticide residue degrees corresponding to each abnormal pesticide residue sample is multiplied by the sampling abnormal degree to obtain the abnormal manifestation degree of the pesticide residue.

[0017] Furthermore, the process of obtaining the historical nearest pesticide application interval includes:

[0018] The average value of the time intervals from the most recent pesticide application time of each batch of collected foods of the current food source during the historical attention period to the detection time is used as the historical nearest pesticide application interval.

[0019] Furthermore, the process of obtaining the pesticide application proximity includes:

[0020] The square of the nearest pesticide application interval is divided by the historical nearest pesticide application interval value for normalization to obtain a normalized value, and the preset second value minus the normalized value is used to obtain the pesticide application proximity.

[0021] Furthermore, the process of obtaining the single - type pesticide residue tendency degree includes:

[0022] Under the j-th detection item of the current food source, after multiplying the application proximity by the abnormal manifestation degree of pesticide residue and adding the continuous influence trend degree, subtract the value obtained by multiplying the application proximity by the continuous influence trend degree to obtain the single pesticide residue tendency degree.

[0023] Further, the comparison threshold is 0.65.

[0024] The embodiment of the present invention also provides a high-efficiency detection system for food pesticide residue. The system includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above method are implemented.

[0025] The present invention has the following beneficial effects:

[0026] First, according to the actual pesticide residue values of each sampling sample of the current food source under each detection item, obtain the pesticide residue degree, abnormal pesticide residue samples, and sampling abnormality degree, and then obtain the abnormal manifestation degree of pesticide residue. The higher the abnormal manifestation degree of pesticide residue, the more serious the pesticide residue situation of the sampling sample.

[0027] Secondly, under the historical attention period, obtain the pesticide residue significance degree according to the ratio of the number of the abnormal pesticide residue samples of each batch of sampling samples of the current food source under each detection item to the total number of sampling samples in the corresponding batch and corresponding detection item, and take the average value of the pesticide residue significance degree as the continuous influence trend degree of the historical same-source characteristics of the corresponding detection item. The higher the continuous influence trend degree, the more the foods in different periods under the same source show the influence of the same-source characteristics.

[0028] Furthermore, calculate the application proximity based on the most recent application interval of the current food source and the historical most recent application interval. The application proximity is used to reflect the influence of the short time interval between the application time and the detection time on the excessive pesticide detection.

[0029] Finally, obtain the single pesticide residue tendency degree based on the abnormal manifestation degree of pesticide residue, the continuous influence trend degree, and the application proximity. When the maximum value of the single pesticide residue tendency degree of a detection item is greater than or equal to a preset first value, only test the detection item for the current food source, otherwise test all detection items. Determine whether to detect a certain detection item, that is, single drug residue detection, or all detection items for the sampling sample according to the single pesticide residue tendency degree.

[0030] The present invention can determine whether the current food source can be subjected to only single-drug residue detection. The determination method is fast and scientific, which can avoid unnecessary tests on some randomly selected samples, thereby saving detection time and costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following-described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0032] Figure 1 It is a flowchart of a method for efficiently detecting the pesticide residue amount in food provided by the first embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, detail the specific implementation manners, structures, features, and effects of a method and system for efficiently detecting the pesticide residue amount in food proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0035] The following will specifically describe the specific solution of a method for efficiently detecting the pesticide residue amount in food provided by the present invention in conjunction with the accompanying drawings.

[0036] Please refer to Figure 1 , which shows a flowchart of a method for efficiently detecting the pesticide residue amount in food provided by the first embodiment of the present invention. The method includes:

[0037] S101. Obtain the pesticide residue degree, abnormal pesticide residue samples, and sampling abnormality degree based on the actual pesticide residue values of each randomly selected sample of the current food source under each detection item, and then obtain the abnormal manifestation degree of pesticide residues.

[0038] The current food to be detected is usually collected from different planting areas, and for each planting area, there is a corresponding food source. Therefore, for the current food under each food source, the sampling inspection samples of each food source are screened by means of uniform sampling. For the sampling inspection samples of each food source, detection is carried out by means of a pesticide residue detector and other methods to obtain relevant detection data.

[0039] The foods that can be detected by the pesticide residue detector include fruits, vegetables, grains, tea leaves, etc. The standards of relevant detection data are common knowledge.

[0040] When detecting the pesticide residues in foods, it is usually necessary to consider the types of pesticides used in the foods for corresponding targeted detection. Since there are differences in the spraying time, degradation speed, etc. of different types of pesticides, it will lead to differences in the degree and types of pesticide residues on the same food even due to different sources.

[0041] Generally, for foods of the same food source, since they are in the same planting environment and are relatively consistent in terms of being affected by pests and diseases and the spraying of pesticides, therefore, by sampling to determine the detection results of the sampling inspection samples under each food source, the pesticide residue status of the entire food under each food source can be indirectly reflected.

[0042] For each pesticide residue detection item, the maximum residue limit value of the current food can be correspondingly obtained, denoted as , indicating the maximum residue limit value of the j-th detection item of the food.

[0043] Specifically, the process of obtaining the pesticide residue degree includes:

[0044] The actual pesticide residue value of the i-th sampling inspection sample of the current food source under the j-th detection item is divided by the preset maximum residue limit value of the j-th detection item to obtain the pesticide residue degree of the i-th sampling inspection sample under the j-th detection item.

[0045] The pesticide residue degree can be expressed by the formula:

[0046] ;

[0047] Among them, the represents the actual pesticide residue value of the i-th sampling inspection sample of the current food source under the j-th detection item, the represents the preset maximum residue limit value of the j-th detection item, and the represents the pesticide residue degree of the i-th sampling inspection sample under the j-th detection item.

[0048] Specifically, the process of obtaining the abnormal pesticide residue sample includes:

[0049] When the pesticide residue level is greater than the preset pesticide residue threshold, the sampling inspection sample corresponding to the pesticide residue level is used as the abnormal pesticide residue sample.

[0050] The pesticide residue threshold can be set independently, and is preferably set to 1.

[0051] When the pesticide residue level is greater than the pesticide residue threshold, the i-th sampling inspection sample under the current food source is marked as the abnormal pesticide residue sample. At the same time, the greater the pesticide residue level, the greater the degree of pesticide residue in the current sampling inspection sample.

[0052] Under the current food source, the above method is used to determine whether each sampling inspection sample under each detection item is the abnormal pesticide residue sample, and the number of abnormal pesticide residue samples under each detection item under the current food source and the pesticide residue levels of the samples marked as the abnormal pesticide residue samples are counted.

[0053] Specifically, the process of obtaining the sampling inspection abnormality degree includes:

[0054] The value obtained by dividing the number of abnormal pesticide residue samples under the j-th detection item of the current food source by the number of sampling inspection samples under the j-th detection item of the current food source is used as the sampling inspection abnormality degree of the current food source under the j-th detection item.

[0055] The sampling inspection abnormality degree can be expressed by the formula:

[0056] ;

[0057] Among them, the represents the number of abnormal pesticide residue samples under the j-th detection item of the current food source, the represents the number of sampling inspection samples under the j-th detection item of the current food source, and the represents the sampling inspection abnormality degree of the current food source under the j-th detection item.

[0058] Specifically, the process of obtaining the pesticide residue abnormal manifestation degree includes:

[0059] Under the j-th detection item of the current food source, the average value of the pesticide residue levels corresponding to each abnormal pesticide residue sample is multiplied by the sampling inspection abnormality degree to obtain the pesticide residue abnormal manifestation degree.

[0060] The pesticide residue abnormal manifestation degree can be expressed by the formula:

[0061] ;

[0062] Among them, the represents the average of the pesticide residue degrees corresponding to each of the abnormal pesticide residue samples under the j-th test item of the current food source, and the represents the pesticide residue degree of the m-th sampled sample of the current food source under the j-th test item. M represents the number of abnormal pesticide residue samples under the j-th test item of the current food source, and the represents the sampling abnormality degree of the current food source under the j-th test item, and the represents the abnormal manifestation degree of pesticide residues under the j-th test item of the current food source.

[0063] Repeating the process of obtaining the abnormal manifestation degree of pesticide residues, the abnormal manifestation degree of pesticide residues under each test item of the current food source can be obtained.

[0064] S102. Under the historical attention period, take the ratio of the number of abnormal pesticide residue samples of the target batch of sampled samples of the current food source under the target test item to the number of abnormal pesticide residue samples of the target batch of sampled samples of the current food source under each test item as the pesticide residue significance of the target batch of sampled samples of the current food source under the target test item, and take the average value of the pesticide residue significance as the continuous influence trend degree of the historical same-source characteristics of the target test item.

[0065] The number of batches and the category of test items can both be set independently. The target batch can be any batch, and the target test item can be any test item.

[0066] For food, considering that the impacts brought by factors such as pest disasters, soil, and climate are usually long-term, therefore, for foods from the same source at different times, corresponding same-source characteristic impacts will be presented. Thus, the pesticide detection results of historical foods are used to reflect whether such same-source characteristic impacts will occur in the long term, thereby improving the confidence of subsequent analysis.

[0067] The historical attention period can be set independently, and preferably it is the most recent month from the current monitoring.

[0068] The pesticide residue significance can be expressed by the formula:

[0069] ;

[0070] wherein, the represents the number of abnormal pesticide residue samples of the k-th batch of sampled samples of the current food source under the j-th test item, and the represents the number of abnormal pesticide residue samples of the k-th batch of sampled samples of the current food source under all test items, and the It represents the pesticide residue significance of the k-th batch of sampled samples of the current food source under the j-th test item.

[0071] By repeating the process of obtaining the pesticide residue significance, the pesticide residue significance of the sampled samples of each batch of the current food source under each test item can be obtained.

[0072] When the pesticide residue significance of each test item of the foods collected in each batch of the current food source under the historical attention period is greater, it indicates that the situation of excessive pesticide residues in the foods collected in this batch in this test item is more serious than that in other tests. Therefore, it is possible to reflect whether there is an impact of the same source characteristic by comparing the pesticide residue significance performance of the same test item of the foods collected in multiple batches under the historical attention period, as follows:

[0073] In this process, considering the high continuity proportion of the same test item in different batches under the historical attention period, when the pesticide residue significance of the same test item maintains a continuous high proportion in multiple batches, it indicates that there may be an impact of the same source characteristic in this food source area.

[0074] ;

[0075] Among them, the represents the continuous influence trend degree of the historical same source characteristic of the j-th test item of the current food source, and K represents the number of food batch collections of the current food source within the historical attention period. It represents the pesticide residue significance of the k-th batch of sampled samples of the current food source under the j-th test item.

[0076] By repeating the process of obtaining the continuous influence trend degree, the continuous influence trend degree of the historical same source of each test item under the current food source can be determined.

[0077] S103. Calculate the application proximity based on the most recent application interval of the current food source and the historical most recent application interval.

[0078] The most recent application record of the food can reflect the proximity in time from the application to the current detection. Since the degradation process of pesticides is gradual, when pesticides are applied, the closer the time to the current detection, the more likely it is that some pesticides remain on the crop surface for a long time or enter the plant body and are not fully degraded, resulting in excessive pesticide detection.

[0079] Here, not only can the time from the most recent application of the current food source to the current detection be considered, but also the time from the historical most recent application of the current food source to the current detection needs to be combined to further reflect whether the current food source may have a more recent most recent application.

[0080] Here, obtain the time interval (in days) from the most recent pesticide application time of the current food source to the current detection time, denoted as the most recent pesticide application interval of the current food source, which can be represented by .

[0081] Specifically, the process of obtaining the historical most recent pesticide application interval includes:

[0082] Taking the mean of the time intervals from the most recent pesticide application time of the foods collected in each batch of the current food source under the historical attention period to the current detection time as the historical most recent pesticide application interval, which can be represented by .

[0083] Specifically, the process of obtaining the pesticide application proximity includes:

[0084] Normalize the value by dividing the square of the most recent pesticide application interval by the value of the historical most recent pesticide application interval to obtain a normalized value, and subtract the normalized value from a preset second value to obtain the pesticide application proximity.

[0085] The pesticide application proximity can be represented by the formula:

[0086] ;

[0087] where, E represents the pesticide application proximity, norm represents the normalization function, represents the most recent pesticide application interval, and represents the historical most recent pesticide application interval.

[0088] S104. Obtain the single - type pesticide residue tendency degree based on the pesticide residue abnormal manifestation degree, the continuous influence trend degree, and the pesticide application proximity. When the maximum value of the single - type pesticide residue tendency degree of a detection item is greater than or equal to a preset comparison threshold, only perform the detection of this detection item for the current food source; otherwise, perform the detection of all detection items.

[0089] Steps S101 and S102 analyze the pesticide residue abnormal manifestation degree and the continuous influence trend degree of each detection item of the current source food, which reflects the pesticide residue performance status of the current source food at present and historically.

[0090] Since foods from the same source are continuously affected by the environmental conditions, soil fertility, and pesticide accumulation effects in that region, this may cause the drug residues in the foods to show a more obvious situation in a certain detection during food pesticide detection. Therefore, the proximity of pesticide application of the current food source is used to weigh the current and historical pesticide residue performance of the current food source. When the proximity of pesticide application of the current food source is greater, more attention is paid to the abnormal performance degree of the pesticide residues of each detection item of the current source food. When the proximity of pesticide application of the current food source is smaller, more attention is paid to the continuous influence trend degree of the historical same-source characteristics of each detection item of the current food source.

[0091] Specifically, the process of obtaining the single-pesticide residue tendency degree includes:

[0092] Under the j-th detection item of the current food source, the value obtained by multiplying the pesticide application proximity by the abnormal performance degree of the pesticide residue and then adding the continuous influence trend degree is subtracted from the value obtained by multiplying the pesticide application proximity by the continuous influence trend degree to obtain the single-pesticide residue tendency degree.

[0093] The single-pesticide residue tendency degree can be expressed by the formula:

[0094] ;

[0095] where, the E represents the pesticide application proximity, the represents the continuous influence trend degree of the historical same-source characteristics of the j-th detection item of the current food source, the represents the abnormal performance degree of the pesticide residue under the j-th detection item of the current food source, and the represents the single-pesticide residue tendency degree of the j-th detection item of the current food source.

[0096] Repeating the process of obtaining the single-pesticide residue tendency degree can determine the single-pesticide residue tendency degrees of each detection item of the current food source, and the maximum value of the single-pesticide residue tendency degrees of the detection items is selected therefrom.

[0097] The comparison threshold can be set independently, preferably 0.65. When the maximum value of the single-pesticide residue tendency degree of one of the detection items of the current food source is greater than or equal to the comparison threshold, it is considered that the pesticide residue in the current food source is more inclined to the detection item corresponding to the maximum value of the single-pesticide residue tendency degree. Therefore, only this detection item can be carried out for the current food source to complete the overall pesticide residue detection work. Otherwise, all detection items will be carried out to complete the overall pesticide residue detection work.

[0098] An embodiment of the present invention also provides a high-efficiency detection system for the pesticide residue amount of food. The system includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above method are implemented.

[0099] The present invention has the following beneficial effects:

[0100] First, according to the actual pesticide residue values of each sampling sample of the current food source under each detection item, the pesticide residue degree, abnormal pesticide residue samples, and sampling abnormality degree are obtained, and then the pesticide residue abnormal manifestation degree is obtained. The higher the pesticide residue abnormal manifestation degree, the more serious the pesticide residue situation of the sampling sample.

[0101] Second, under the historical attention period, the pesticide residue significance is obtained according to the ratio of the number of abnormal pesticide residue samples of each batch of sampling samples of the current food source under each detection item to the total number of sampling samples in the corresponding detection item of the corresponding batch. The average value of the pesticide residue significance is used as the continuous influence trend degree of the historical same-source characteristics of the corresponding detection item. The higher the continuous influence trend degree, the more the foods in different periods under the same source show the influence of the same source characteristics.

[0102] Third, the application proximity is calculated based on the most recent application interval of the current food source and the historical most recent application interval. The application proximity is used to reflect the influence of the short time interval between the application time and the detection time on the excessive pesticide detection.

[0103] Finally, based on the pesticide residue abnormal manifestation degree, the continuous influence trend degree, and the application proximity, the single pesticide residue tendency degree is obtained. When the maximum value of the single pesticide residue tendency degree of a detection item is greater than or equal to a preset first value, only the detection item test is performed on the current food source; otherwise, all detection item tests are performed. According to the single pesticide residue tendency degree, it is determined whether to detect a certain detection item for the sampling sample, that is, single drug residue detection, or all detection items.

[0104] The present invention can judge whether a single drug residue detection can be performed on the current food source. The judgment method is fast and scientific, which can avoid unnecessary tests on some sampling samples, thereby saving detection time and detection cost.

[0105] It should be noted that: the above sequence of embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0106] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are mainly described in each embodiment.

Claims

1. An efficient detection method for pesticide residues in food, characterized in that, The method includes: Obtaining a pesticide residue degree, abnormal pesticide residue samples, and a sampling inspection abnormality degree based on the actual pesticide residue values of each sampling inspection sample of the current food source under each detection item, and further obtaining a pesticide residue abnormal manifestation degree; Under the historical attention period, taking the ratio of the number of the abnormal pesticide residue samples of the target batch sampling inspection samples of the current food source under the target detection item to the number of the abnormal pesticide residue samples of the target batch sampling inspection samples of the current food source under each detection item as the pesticide residue significance degree of the target batch sampling inspection samples of the current food source under the target detection item, and taking the average value of the pesticide residue significance degrees as the continuous influence trend degree of the historical same-source characteristics of the target detection item; Calculating a pesticide application proximity based on the most recent pesticide application interval of the current food source and the historical most recent pesticide application interval; Obtaining a single pesticide residue tendency degree based on the pesticide residue abnormal manifestation degree, the continuous influence trend degree, and the pesticide application proximity. When the maximum value of the single pesticide residue tendency degree of a detection item is greater than or equal to a preset comparison threshold, only the detection item is detected for the current food source, otherwise all detection items are detected.

2. The high-efficiency detection method for pesticide residues in food according to claim 1, characterized in that The process of obtaining the pesticide residue degree includes: Dividing the actual pesticide residue value of the i-th sampling inspection sample of the current food source under the j-th detection item by the preset maximum residue limit value of the j-th detection item to obtain the pesticide residue degree of the i-th sampling inspection sample of the current food source under the j-th detection item.

3. The high-efficiency detection method for pesticide residues in food according to claim 1, characterized in that, The process of obtaining the abnormal pesticide residue samples includes: When the pesticide residue degree is greater than a preset pesticide residue threshold, the sampling inspection sample corresponding to the pesticide residue degree is used as the abnormal pesticide residue sample.

4. The high-efficiency detection method for food pesticide residues according to claim 1, characterized in that, The process of obtaining the sampling inspection abnormality degree includes: Taking the value obtained by dividing the number of the abnormal pesticide residue samples of the current food source under the j-th detection item by the number of the sampling inspection samples of the current food source under the j-th detection item as the sampling inspection abnormality degree of the current food source under the j-th detection item.

5. The high-efficiency detection method for food pesticide residues according to claim 1, wherein, The process of obtaining the pesticide residue abnormal manifestation degree includes: Under the j-th detection item of the current food source, multiplying the average value of the pesticide residue degrees corresponding to each of the abnormal pesticide residue samples by the sampling inspection abnormality degree to obtain the pesticide residue abnormal manifestation degree.

6. The high-efficiency detection method for food pesticide residues according to claim 1, characterized in that The process of obtaining the historical most recent pesticide application interval includes: Taking the average value of the time intervals from the most recent pesticide application time of the foods collected in each batch of the current food source under the historical attention period to the detection time as the historical most recent pesticide application interval.

7. The high-efficiency detection method for pesticide residues in food according to claim 1, characterized in that, The process of obtaining the pesticide application proximity includes: Normalizing the value obtained by dividing the square of the most recent pesticide application interval by the historical most recent pesticide application interval to obtain a normalized value, and subtracting the normalized value from a preset second value to obtain the pesticide application proximity.

8. The high-efficiency detection method for food pesticide residues according to claim 1, characterized in that, The process of obtaining the single pesticide residue tendency degree includes: Under the j-th detection item of the current food source, subtracting the value obtained by multiplying the pesticide application proximity by the continuous influence trend degree from the value obtained by multiplying the pesticide application proximity by the pesticide residue abnormal manifestation degree and then adding the continuous influence trend degree to obtain the single pesticide residue tendency degree.

9. The high-efficiency detection method for pesticide residues in food according to claim 1, characterized in that, The comparison threshold is 0.

65.

10. An efficient detection system for pesticide residues in food, the system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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