Method and system for efficiently detecting pesticide residues in food

Abstract

The invention relates to the technical field of pesticide residue detection, in particular to a high-efficiency food pesticide residue detection method and system, and the method comprises the steps: obtaining the abnormal expression degree of pesticide residues according to the actual pesticide residue value of each sampling inspection sample of a current food source under each detection item; in the historical attention period, according to the number of the abnormal pesticide residue samples of the target batch of sampling inspection samples of the current food source under the target detection item and the number of the abnormal pesticide residue samples under each detection item, obtaining a continuous influence trend degree; calculating the pesticide application proximity based on the latest pesticide application interval and the historical latest pesticide application interval; and obtaining a single pesticide residue tendency degree based on the pesticide residue abnormal expression degree, the continuous influence tendency degree and the pesticide application proximity, and when the maximum value of the single pesticide residue tendency degree of a detection item is not less than a comparison threshold value, only detecting the detection item for the current food source. According to the invention, whether the current food source can only be subjected to single drug residue detection or not can be judged.

Description

Technical Field

[0001] The present invention relates to the technical field of pesticide residue detection, and particularly to a high-efficiency detection method and system for food pesticide residues. 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 pesticide residue detection is increasing continuously. At present, the food pesticide residue detection industry is showing a rapid development trend in terms of technology research and development, 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 areas (for example, the dominant residual in production area A is organophosphorus pesticides, while that in production area B 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, greatly increasing the detection time and cost. 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 food pesticide residues, and the specific technical solutions adopted are as follows: Obtain the pesticide residue degree, abnormal pesticide residue samples, and 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 obtain the abnormal manifestation degree of pesticide residues; Under the historical attention period, take the ratio of the number of the abnormal pesticide residue samples of the target batch of 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 of sampling inspection samples of the current food source under each detection item as the pesticide residue significance of the target batch of sampling inspection samples of 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; 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; Obtain the single pesticide residue tendency degree based on the pesticide residue abnormality manifestation degree, the continuous influence trend degree, and the pesticide application proximity degree. When the maximum value of the single pesticide residue tendency degree of the detection item is greater than or equal to the preset comparison threshold, only perform the detection of the detection item on the current food source; otherwise, perform the detection of all detection items.

[0005] Further, the process of obtaining the pesticide residue degree includes: 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.

[0006] Further, the process of obtaining the abnormal pesticide residue sample includes: 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.

[0007] Further, the process of obtaining the sampling abnormality degree includes: 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 abnormality degree of the current food source under the j-th detection item.

[0008] Further, the process of obtaining the pesticide residue abnormality manifestation degree includes: 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 abnormality degree to obtain the pesticide residue abnormality manifestation degree.

[0009] Further, the process of obtaining the historical nearest pesticide application interval includes: The average value of the time intervals from the most recent pesticide application time to the detection time for each batch of foods collected under the historical attention period of the current food source is used as the historical nearest pesticide application interval.

[0010] Further, the process of obtaining the pesticide application proximity includes: 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.

[0011] Further, the process of obtaining the single pesticide residue tendency degree includes: Under the j-th detection item of the current food source, the value obtained by multiplying the application proximity by the abnormal manifestation degree of pesticide residue and then adding the continuous influence trend degree is subtracted from the value obtained by multiplying the application proximity by the continuous influence trend degree to obtain the single pesticide residue tendency degree.

[0012] Further, the comparison threshold is 0.65.

[0013] 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.

[0014] The present invention has the following beneficial effects: 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 abnormal manifestation degree of pesticide residue is obtained. The higher the abnormal manifestation degree of pesticide residue, the more serious the pesticide residue situation of the sampling sample.

[0015] Second, under the historical attention period, 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, the pesticide residue significance degree is obtained, and the average value of the pesticide residue significance degree 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.

[0016] Furthermore, based on the most recent application interval of the current food source and the historical most recent application interval, the application proximity is calculated. 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.

[0017] Finally, based on the abnormal manifestation degree of pesticide residue, 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 the 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, that is, single drug residue detection, or all detection items for the sampling sample.

[0018] The present invention can judge whether the current food source can only perform single drug residue detection. The judgment method is fast and scientific, and can avoid unnecessary tests on some sampling samples, thereby saving detection time and detection cost. Description of the Drawings

[0019] 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 drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0020] 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. Specific embodiments

[0021] 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 drawings and preferred embodiments, describe in detail a method and system for efficiently detecting the pesticide residue amount in food proposed according to the present invention, including its specific embodiments, structures, features, and effects. 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.

[0022] 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.

[0023] 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 drawings.

[0024] 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: S101. Obtain the pesticide residue degree, abnormal pesticide residue samples, and sampling abnormality degree based on the actual pesticide residue values of each sampling sample of the current food source under each detection item, and then obtain the abnormal manifestation degree of pesticide residues.

[0025] The currently to-be-detected food 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, sampling samples of each food source are screened through uniform sampling. For the sampling samples of each food source, relevant detection data are obtained through methods such as pesticide residue detectors.

[0026] The foods that can be detected by the pesticide residue detector include fruits, vegetables, grains, tea leaves, etc. The standards for relevant detection data are common knowledge.

[0027] When detecting pesticide residues in food, it is usually necessary to consider the types of pesticides used in the food for corresponding targeted detection. Due to differences in the spraying time, degradation rate, etc. of different types of pesticides, even the same type of food may have different degrees and types of pesticide residues due to different sources.

[0028] Generally, for foods from the same 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 test results of the sampled samples for each food source, the pesticide residue status of the entire food under each food source can be indirectly reflected.

[0029] For each pesticide residue detection item, the maximum residue limit value of the current food can be obtained correspondingly, denoted as , indicating the maximum residue limit value of the j-th detection item of the food.

[0030] Specifically, the process of obtaining the pesticide residue degree includes: 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.

[0031] The pesticide residue degree can be expressed by the formula: ; wherein, the represents the actual pesticide residue value of the i-th sampled 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 sampled sample under the j-th detection item.

[0032] Specifically, the process of obtaining the abnormal pesticide residue sample includes: 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.

[0033] The pesticide residue threshold can be set independently, and is preferably set to 1.

[0034] When the pesticide residue degree is greater than the pesticide residue threshold, the i-th sampled sample under the current food source is marked as the abnormal pesticide residue sample. At the same time, the greater the pesticide residue degree, the greater the degree of pesticide residue of the current sampled sample.

[0035] Under the current food source, determine whether each sampled sample under each detection item is the abnormal pesticide residue sample through the above method, and count the number of abnormal pesticide residue samples under each detection item under the current food source and the pesticide residue degree marked as the abnormal pesticide residue sample.

[0036] Specifically, the process of obtaining the sampling abnormality degree includes: 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 sampled samples under the j-th detection item of the current food source is used as the sampling abnormality degree under the j-th detection item of the current food source.

[0037] The sampling abnormality degree can be expressed by the formula: ; 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 sampled samples under the j-th detection item of the current food source, and the represents the sampling abnormality degree under the j-th detection item of the current food source.

[0038] Specifically, the process of obtaining the abnormal manifestation degree of pesticide residue includes: 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 abnormality degree to obtain the abnormal manifestation degree of pesticide residue.

[0039] The abnormal manifestation degree of pesticide residue can be expressed by the formula: ; Among them, the represents the average value of the pesticide residue degrees corresponding to each abnormal pesticide residue sample under the j-th detection item of the current food source, the represents the pesticide residue degree of the m-th sampled sample of the current food source under the j-th detection item, M represents the number of abnormal pesticide residue samples under the j-th detection item of the current food source, and the represents the sampling abnormality degree under the j-th detection item of the current food source, and the represents the abnormal manifestation degree of pesticide residue under the j-th detection item of the current food source.

[0040] Repeating the process of obtaining the abnormal manifestation degree of pesticide residue, the abnormal manifestation degree of pesticide residue under each detection item under the current food source can be obtained.

[0041] S102. Under the historical attention period, the ratio of the number of abnormal pesticide residue samples of the target batch of sampling samples of the current food source under the target detection item to the number of abnormal pesticide residue samples of the target batch of sampling samples of the current food source under each detection item is used as the pesticide residue significance of the target batch of sampling samples of the current food source under the target detection item, and the average value of the pesticide residue significance is used as the continuous influence trend degree of the historical same-source characteristics of the target detection item.

[0042] The quantity of the batches and the categories of the detection items can both be set independently. The target batch can be any batch, and the target detection item can be any detection item.

[0043] 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.

[0044] The historical attention period can be set independently, and preferably it is the most recent month from the current monitoring.

[0045] The pesticide residue significance can be expressed by the formula: ; Among them, the represents the number of abnormal pesticide residue samples of the k-th batch of sampling samples of the current food source under the j-th detection item, and the represents the number of abnormal pesticide residue samples of the k-th batch of sampling samples of the current food source under all detection items, and the represents the pesticide residue significance of the k-th batch of sampling samples of the current food source under the j-th detection item.

[0046] Repeating the process of obtaining the pesticide residue significance, the pesticide residue significance of each batch of sampling samples of the current food source under each detection item can be obtained.

[0047] When the pesticide residue significance of each detection 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 is more serious than in other tests. Therefore, the performance of the pesticide residue significance of the same-item tests of the foods collected in multiple batches under the historical attention period can be compared to reflect whether there is an impact of the same-source characteristics, specifically as follows: In this process, considering the high proportion of continuity of the same test item in different batches under the historical attention period, when the pesticide residue significance of the same test item remains continuously high in multiple batches, it indicates that there may be an impact of the same source characteristic in the food source area.

[0048] ; 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 the K represents the number of food batch collections of the current food source within the historical attention period. represents the pesticide residue significance of the k-th batch of sampling samples of the current food source under the j-th test item.

[0049] Repeating the process of obtaining the continuous influence trend degree can determine the continuous influence trend degree of the historical same source of each test item under the current food source.

[0050] S103. Calculate the application proximity based on the most recent application interval of the current food source and the historical most recent application interval.

[0051] The most recent application record of the food can reflect the proximity in time from the application time to the current detection time. Since the degradation process of pesticides is gradual, when pesticides are applied, the closer the time is to the current detection time, the more likely it is that some pesticides will remain on the crop surface for a long time or enter the plant body and not be fully degraded, resulting in excessive pesticide detection.

[0052] Here, not only the time from the most recent application of the current food source to the current detection can 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.

[0053] Here, obtain the time interval (in days) from the most recent application time of the current food source to the current detection time, denoted as the most recent application interval of the current food source, which can be represented by to represent.

[0054] Specifically, the process of obtaining the historical most recent application interval includes: Taking the average value of the time intervals from the most recent 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 application interval, which can be represented by to represent.

[0055] Specifically, the process of obtaining the application proximity includes: Normalize the square of the most recent application interval by dividing it by the value of the historical most recent application interval to obtain a normalized value, and subtract the normalized value from a preset second value to obtain the application proximity.

[0056] The application proximity can be expressed by the formula: ; where E represents the application proximity, norm represents the normalization function, represents the most recent application interval, and represents the historical most recent application interval.

[0057] S104. Obtain the single - type pesticide residue tendency based on the pesticide residue abnormal manifestation degree, the continuous influence trend degree, and the application proximity. When the maximum value of the single - type pesticide residue tendency of a detection item is greater than or equal to a preset comparison threshold, only perform the detection for the detection item on the current food source; otherwise, perform the detection for all detection items.

[0058] 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 currently and historically.

[0059] Since foods from the same source area are continuously affected by the environmental conditions, soil fertility, and pesticide accumulation effect in that area, this may cause the drug residues of a certain detection to be more obvious in the long - term when conducting food pesticide detection. Therefore, here, the application proximity of the current food source is used to balance the pesticide residue performance status of the current food source currently and historically. When the application proximity of the current food source is larger, more attention is paid to the pesticide residue abnormal manifestation degree of each detection item of the current - source food; when the application proximity 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.

[0060] Specifically, the process of obtaining the single - type pesticide residue tendency includes: Under the j - th detection item of the current food source, multiply the value of the application proximity by the pesticide residue abnormal manifestation degree, add the continuous influence trend degree, and then subtract the value obtained by multiplying the application proximity by the continuous influence trend degree to obtain the single - type pesticide residue tendency.

[0061] The single - type pesticide residue tendency can be expressed by the formula: ; where E represents the application proximity, represents the continuous influence trend degree of the historical same-source characteristics of the j-th test item of the current food source, and the represents the abnormal performance degree of the pesticide residues under the j-th test item of the current food source, and the represents the single-pesticide residue tendency degree under the j-th test item of the current food source.

[0062] By repeating the process of obtaining the single-pesticide residue tendency degree, the single-pesticide residue tendency degrees of each test item of the current food source can be determined, and the maximum value of the single-pesticide residue tendency degrees of the test items can be screened out therefrom.

[0063] The comparison threshold can be set independently, preferably 0.65. When the maximum value of the single-pesticide residue tendency degrees of the test items in the current food source is greater than or equal to the comparison threshold, it is considered that the pesticide residues in the current food source are more inclined to the test item corresponding to the maximum value of the single-pesticide residue tendency degree. Therefore, only this test item can be carried out for the current food source to complete the overall pesticide residue detection work. Otherwise, all test items will be carried out to complete the overall pesticide residue detection work.

[0064] The embodiment of the present invention also provides a high-efficiency detection system for the amount of pesticide residues in 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.

[0065] The present invention has the following beneficial effects: First, according to the actual pesticide residue values of each sampling sample of the current food source under each test item, the pesticide residue degree, abnormal pesticide residue samples, and sampling abnormality degree are obtained, and then the abnormal performance degree of pesticide residues is obtained. The higher the abnormal performance degree of pesticide residues indicates that the pesticide residue situation of the sampling samples is more serious.

[0066] Second, under the historical attention period, 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 test item to the total number of sampling samples in the corresponding batch and corresponding test item, the pesticide residue significance is obtained, and 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 test 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.

[0067] Third, based on the most recent pesticide application interval of the current food source and the historical most recent pesticide application interval, the pesticide application proximity is calculated. The pesticide application proximity is used to reflect the influence of the short time interval between the pesticide application time and the detection time on the excessive pesticide detection.

[0068] Finally, a single pesticide residue propensity is obtained 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 pesticide residue propensity of a detection item is greater than or equal to a preset first value, only the test of the detection item is performed on the current food source; otherwise, tests of all detection items are performed. The single pesticide residue propensity is used to determine whether to detect a certain detection item, i.e., single drug residue detection, or all detection items for the sampled sample.

[0069] The present invention can determine whether a single drug residue detection can be performed on the current food source. The determination method is fast and scientific, and can avoid unnecessary tests on some sampled samples, thereby saving detection time and detection costs.

[0070] 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 accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

Claims

1. A method for efficiently detecting pesticide residues in food, characterized in that: The method comprises: According to the actual pesticide residue values ​​of each sample sampled from the current food source under each test item, the pesticide residue degree, abnormal pesticide residue samples and sampling abnormality degree are obtained, and then the pesticide residue abnormality expression degree is obtained; Under the historical attention cycle, the ratio of the number of abnormal pesticide residue samples of the target batch sampled from the current food source under the target detection item to the number of abnormal pesticide residue samples of the target batch sampled from the current food source under each detection item is used as the pesticide residue significance of the target batch sampled from the current food source under the target detection item, and the average value of the pesticide residue significance is used as the continuous influence trend of the historical same source characteristics of the target detection item; Calculate the pesticide application proximity based on the most recent pesticide application interval of the current food source and the most recent pesticide application interval in history; The single pesticide residue tendency is obtained based on the abnormal expression degree of pesticide residue, the continuous impact trend degree and the proximity of pesticide application. When the maximum value of the single pesticide residue tendency of a detection item is greater than or equal to a preset comparison threshold, only the detection item is tested for the current food source, otherwise all detection items are tested.

2. The method for efficiently detecting pesticide residues in food according to claim 1, characterized in that: The process of obtaining the pesticide residue comprises: The actual pesticide residue value of the i-th sample sample of the current food source under the j-th test item is divided by the preset maximum residue limit value of the j-th test item to obtain the pesticide residue degree of the i-th sample sample under the j-th test item.

3. The method for efficiently detecting pesticide residues in food according to claim 1, characterized in that: The process of obtaining the abnormal pesticide residue sample includes: When the pesticide residue is greater than a preset pesticide residue threshold, the sample corresponding to the pesticide residue is used as the abnormal pesticide residue sample.

4. The method for efficiently detecting pesticide residues in food according to claim 1, characterized in that: The process of obtaining the sampling abnormality degree includes: The value of the number of abnormal pesticide residue samples of the current food source under the jth detection item divided by the number of random inspection samples of the current food source under the jth detection item is taken as the abnormality degree of the random inspection of the current food source under the jth detection item.

5. The method for efficiently detecting pesticide residues in food according to claim 1, characterized in that: The process of obtaining the abnormal expression of pesticide residues includes: Under the j-th detection item of the current food source, the average value of the pesticide residue corresponding to each of the abnormal pesticide residue samples is multiplied by the sampling abnormality to obtain the pesticide residue abnormality expression degree.

6. The method for efficiently detecting pesticide residues in food according to claim 1, characterized in that: The process of obtaining the most recent historical pesticide application interval includes: The average of the time intervals between the most recent drug application time and the detection time of each batch of food collected from the current food source during the historical focus period is used as the historical most recent drug application interval.

7. The method for efficiently detecting pesticide residues in food according to claim 1, characterized in that: The process of obtaining the spraying proximity includes: The square of the most recent medication application interval is divided by the value of the most recent historical medication application interval for normalization to obtain a normalized value, and the medication application proximity is obtained by subtracting the normalized value from a preset second value.

8. The method for efficiently detecting pesticide residues in food according to claim 1, characterized in that: The process of obtaining the single pesticide residue tendency degree includes: Under the jth detection item of the current food source, the pesticide application proximity multiplied by the value of the pesticide residue abnormality expression degree plus the continuous impact trend degree is subtracted from the value of the pesticide application proximity multiplied by the continuous impact trend degree to obtain the single pesticide residue tendency degree.

9. The method for efficiently detecting pesticide residues in food according to claim 1, characterized in that: The contrast threshold is 0.

65.

10. A highly 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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