CN117192015A - Risk assessment method and system for pesticide residues in medlar - Google Patents
Risk assessment method and system for pesticide residues in medlar Download PDFInfo
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- 239000000447 pesticide residue Substances 0.000 title claims abstract description 63
- 238000012502 risk assessment Methods 0.000 title claims abstract description 58
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- 240000002624 Mespilus germanica Species 0.000 title abstract 11
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
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Abstract
The application discloses a risk assessment method and a system for pesticide residues in medlar, relates to the field of agriculture, and solves the problem that the risk of pesticide residues in medlar products cannot be assessed and known in daily purchase, wherein the risk assessment method comprises the following steps: the data acquisition module acquires real-time sample data of the medlar samples in the same batch; the data analysis module performs data analysis on the medlar samples in different batches to obtain product risk values or sample abnormal signals of the medlar samples in different batches; the operation monitoring module monitors pesticide operation conditions of the medlar samples in different batches to obtain operation risk values of the medlar samples in different batches; the intelligent evaluation module evaluates the risk level of pesticide residues in medlar samples in different batches; the user terminal scans the batch code on the medlar packaging bag, and the information comparison module compares the identification information of the medlar packaging bag.
Description
Technical Field
The application belongs to the field of agriculture, relates to a risk assessment technology, and in particular relates to a risk assessment method and a risk assessment system for pesticide residues in medlar.
Background
Lycium barbarum, a perennial woody plant of Lycium of Solanaceae, has weak branches, and is bent or sagged in an arcuate shape, and is light gray; the blade is oval; flowers grow singly or doubly on long branches and are grown on leaf axils; the fruit is oval berry red; the flowering period is 6-7 months; the fruit period is 8-10 months. The medlar needs to be sprayed with pesticide in the daily growth process, so that the residual pesticide dosage in the medlar needs to be detected in the picking and subsequent use processes of the medlar so as to ensure that the medlar can be safely eaten.
When buying medlar products in daily life, although the medlar products put on shelf have been subjected to food safety quality inspection, a part of medlar products still have trace pesticide dosage, and the resistances to pesticides are different due to different ages of medlar audience groups, so how to evaluate and know the pesticide residue risk of the medlar products is important during buying;
therefore, we propose a risk assessment method and system for pesticide residues in medlar.
Disclosure of Invention
The application aims at: in order to solve the above problems, a risk assessment method and a risk assessment system for pesticide residues in wolfberry are provided to solve the problems in the prior art.
In order to achieve the above purpose, the present application adopts the following technical scheme:
a risk assessment method for pesticide residues in medlar is specifically as follows:
step S100, a data acquisition module acquires real-time sample data of medlar samples in different batches, the real-time sample data is sent to a server, and the server sends the real-time sample data to a data analysis module;
step S200, the data analysis module performs data analysis on the medlar samples in different batches to obtain product risk values or sample abnormal signals of the medlar samples in different batches, and feeds back the product risk values or sample abnormal signals to the server, if the server receives the sample abnormal signals, no operation is performed, and if the server receives the product risk values of the medlar samples in different batches, the product risk values are sent to the intelligent evaluation module;
step S300, the storage module sends stored pesticide operation data of different batches of medlar samples to the operation monitoring module, the operation monitoring module monitors pesticide operation conditions of the different batches of medlar samples, operation risk values of the different batches of medlar samples are obtained and fed back to the server, and the server sends the operation risk values of the different batches of medlar samples to the intelligent evaluation module;
step S400, the intelligent evaluation module evaluates the risk levels of pesticide residues in the medlar samples in different batches, the risk levels of the pesticide residues in the medlar samples in different batches are obtained through evaluation and fed back to the server, the server sends the risk levels of the pesticide residues in the medlar samples in different batches to the storage module, and the storage module stores the risk levels of the pesticide residues in the medlar samples in different batches;
step S500, the user terminal scans the batch code on the medlar packaging bag and uploads the batch code to the server, the server sends the batch code to the information comparison module, the information comparison module compares the identification information of the medlar packaging bag to obtain risk grade feedback of the medlar packaging bag to the server, the server sends the risk grade of the medlar packaging bag to the user terminal, and the user terminal checks the risk grade of the medlar packaging bag.
Further, the real-time sample data are slurry real-time weight values of medlar samples in different batches and real-time doses of each residual pesticide.
Further, the data analysis process of the data analysis module is specifically as follows:
acquiring a slurry real-time weight value of any medlar sample in the same batch and a real-time dose of each residual pesticide, and summing and comparing the residual pesticide real-time dose of each residual pesticide with the slurry real-time weight value to obtain a residual pesticide dose ratio of any medlar sample in the same batch;
if the residual pesticide dosage ratio is greater than or equal to the second preset pesticide dosage ratio, generating a sample abnormal signal;
if the residual pesticide dosage ratio is smaller than the second preset pesticide dosage ratio, carrying out the subsequent steps;
putting another wolfberry sample in the same batch into the soaking liquid for soaking, recording the beginning soaking time of the wolfberry sample, then obtaining the real-time dose of each residual pesticide in the soaked wolfberry sample, recording the ending soaking time of the wolfberry sample, and obtaining the soaking pesticide dose ratio of the soaked wolfberry sample by adding and summing the real-time dose of each residual pesticide in the soaked wolfberry sample and comparing the real-time weight value of the slurry; wherein the soaking liquid is salt water, clear water and black tea water;
if the soaking pesticide dosage ratio is smaller than the first preset pesticide dosage ratio, subtracting the soaking starting time from the soaking ending time to obtain the residual pesticide removing duration of the soaked medlar sample; wherein the value of the first preset pesticide dosage ratio is smaller than the value of the second preset pesticide dosage ratio;
and calculating product risk values of different batches of medlar samples.
Further, the pesticide operation data are the pesticide spraying times of the medlar samples in different batches, and the spraying amount and the spraying time of each pesticide spraying.
Further, the monitoring process of the operation monitoring module is specifically as follows:
acquiring pesticide spraying times of medlar samples in the same batch;
then, the spraying quantity of the medlar samples in the same batch is obtained when each pesticide is sprayed, and the spraying quantity of the medlar samples in the same batch is obtained by adding and summing the spraying quantity of each pesticide spraying;
finally, the spraying time of each pesticide spraying of the same batch of medlar samples is obtained, the time intervals of adjacent spraying time are calculated to obtain a plurality of groups of pesticide spraying interval duration, and the pesticide spraying interval duration of the plurality of groups of pesticide spraying interval duration is added and summed to obtain an average value to obtain the pesticide spraying interval average duration of the same batch of medlar samples;
and calculating the operation risk values of the medlar samples in different batches.
Further, the evaluation process of the intelligent evaluation module is specifically as follows;
obtaining product risk values and operation risk values of medlar samples in different batches;
calculating risk assessment values of medlar samples in different batches;
if the risk assessment value is greater than or equal to the second risk assessment threshold, the risk level of pesticide residues in the medlar samples in different batches is the first risk level;
if the risk assessment value is smaller than the second risk assessment threshold value and larger than or equal to the first risk assessment threshold value, the risk level of pesticide residues in the medlar samples in different batches is the second risk level;
and if the risk assessment value is smaller than the first risk assessment threshold, the risk level of pesticide residues in the medlar samples in different batches is a third risk level.
Further, the first risk rating threshold is less than the first risk rating threshold, the degree of risk of the first risk level is greater than the degree of risk of the second risk level, and the degree of risk of the second risk level is greater than the degree of risk of the third risk level.
Further, the comparison process of the information comparison module is specifically as follows:
comparing the batch code on the medlar packaging bag with the batch numbers of medlar samples in different batches of the storage module;
if the batch code is matched with the batch number, acquiring a risk level corresponding to the medlar sample according to the batch number;
and (5) marking the risk level corresponding to the medlar sample as the risk level of the medlar packaging bag.
A risk assessment system for pesticide residues in medlar comprises a data acquisition module, a data analysis module, an intelligent evaluation module, an operation monitoring module, an information comparison module, a storage module, a user terminal and a server;
the data acquisition module is used for acquiring real-time sample data of medlar samples in different batches and sending the real-time sample data to the server, and the server sends the real-time sample data to the data analysis module;
the data analysis module is used for carrying out data analysis on medlar samples in different batches to obtain product risk values of medlar samples in different batches or generating sample abnormal signals and feeding the sample abnormal signals back to the server, if the server receives the sample abnormal signals, no operation is carried out, and if the server receives the product risk values of medlar samples in different batches, the product risk values of medlar samples in different batches are sent to the intelligent evaluation module;
the storage module is used for storing pesticide operation data of medlar samples in different batches and sending the pesticide operation data to the operation monitoring module;
the operation monitoring module is used for monitoring pesticide operation conditions of different batches of medlar samples, obtaining operation risk values of the different batches of medlar samples and feeding back the operation risk values to the server, and the server sends the operation risk values of the different batches of medlar samples to the intelligent evaluation module;
the intelligent evaluation module is used for evaluating the risk levels of pesticide residues in the medlar samples in different batches, obtaining the risk levels of the pesticide residues in the medlar samples in different batches, feeding back the risk levels of the pesticide residues in the medlar samples in different batches to the server, sending the risk levels of the pesticide residues in the medlar samples in different batches to the storage module by the server, and storing the risk levels of the pesticide residues in the medlar samples in different batches by the storage module;
the user terminal is used for scanning the batch code on the medlar packaging bag and uploading the batch code to the server, and the server sends the batch code to the information comparison module;
the information comparison module is used for comparing the identification information of the medlar packaging bags, the risk level of the medlar packaging bags is obtained through comparison and fed back to the server, and the server sends the risk level of the medlar packaging bags to the user terminal;
the user terminal is used for checking the risk level of the medlar packaging bag.
Compared with the prior art, the application has the beneficial effects that:
according to the application, firstly, data analysis is carried out on medlar samples of different batches through a data analysis module to obtain product risk values or sample abnormal signals of the medlar samples of different batches, if the product risk values of the medlar samples of different batches are obtained, the product risk values are sent to an intelligent evaluation module, meanwhile, the pesticide operation conditions of the medlar samples of different batches are monitored by utilizing an operation monitoring module, the operation risk values of the medlar samples of different batches are obtained and sent to the intelligent evaluation module through a server, the intelligent evaluation module evaluates the risk grades of pesticide residues in the medlar samples of different batches, a storage module stores the risk grades of pesticide residues in the medlar samples of different batches, and when the risk grades are actually determined, a user terminal scans the batch codes on medlar packaging bags and sends to an information comparison module, and the information comparison module compares the identification information of the medlar packaging bags to obtain the risk grades of the medlar packaging bags.
Drawings
The present application is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is a flow chart of the method of the present application;
fig. 2 is an overall system block diagram of the present application.
Detailed Description
The technical solutions of the present application will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
Referring to fig. 1, the present application provides a risk assessment method for pesticide residues in wolfberry, which specifically comprises the following steps:
step S100, a data acquisition module acquires real-time sample data of medlar samples in different batches, the real-time sample data is sent to a server, and the server sends the real-time sample data to a data analysis module;
the real-time sample data are slurry real-time weight values of medlar samples in different batches and real-time doses of each residual pesticide; in actual collection, the medlar samples are required to be ensured to be in the same batch, so that the real-time weight value of the slurry of the medlar samples and the real-time dosage of the residual pesticides are ensured to be on the same starting line, the residual pesticides comprise imidacloprid, acetamiprid, carbendazim, chlorpyrifos, cypermethrin, cyhalothrin, difenoconazole and pyridaben, but are not limited to the same, in practice, 20mL of acetonitrile and crushed medlar can be added into a test tube for centrifugation, then 300 mu L of the centrifuged medlar upper layer is filtered into a 1.5mL of small bottle through a 0.45 mu m polyvinylidene fluoride (PVDF) injector filter, and finally the liquid in the small bottle is detected and analyzed through an ACQUITY ultra-high performance liquid chromatography system and a QT 5500 triple quadrupole mass spectrometer, so that the real-time dosage of the residual pesticides in the medlar samples is obtained;
step S200, the data analysis module performs data analysis on the medlar samples in different batches to obtain product risk values or sample abnormal signals of the medlar samples in different batches, and feeds back the product risk values or sample abnormal signals to the server, if the server receives the sample abnormal signals, no operation is performed, and if the server receives the product risk values of the medlar samples in different batches, the product risk values are sent to the intelligent evaluation module;
in step S200, the data analysis process of the data analysis module specifically includes the following steps:
step S201, obtaining the real-time weight value of the slurry of any medlar sample in the same batch and the real-time dose of each residual pesticide, and obtaining the residual pesticide dose ratio CNi of any medlar sample in the same batch by summing and comparing the real-time weight values of the residual pesticides of each residual pesticide, wherein i is the batch number of medlar samples, the batch number can be composed of pure numbers, can be composed of English, and the like;
step S202, if the residual pesticide dose ratio is greater than or equal to a second preset pesticide dose ratio, generating a sample abnormal signal;
step S203, if the residual pesticide dose ratio is smaller than the second preset pesticide dose ratio, carrying out the subsequent steps;
step S204, another medlar sample in the same batch is put into the soaking liquid for soaking, the beginning soaking time of the medlar sample is recorded, then the real-time dose of each residual pesticide in the soaked medlar sample is obtained, the ending soaking time of the medlar sample is recorded, and the real-time dose of each residual pesticide in the soaked medlar sample is added, summed and compared with the real-time weight value of the slurry to obtain the soaking pesticide dose ratio of the soaked medlar sample; wherein the soaking liquid can be salt water, clear water, black tea water, etc.;
step S205, if the soaked pesticide dose ratio is smaller than the first preset pesticide dose ratio, the soaked time is subtracted from the soaking ending time to obtain the residual pesticide removal duration CTi of the soaked medlar sample; wherein the value of the first preset pesticide dosage ratio is smaller than the value of the second preset pesticide dosage ratio;
step S206, calculating the product risk value CFi of the medlar samples of different batches according to the formula CFi= (CNi×a1+CTi×a2)/(a1+a2); wherein a1 and a2 are proportionality coefficients with fixed values, and the values of a1 and a2 are larger than zero;
the daily removal method of the pesticide residue in the medlar comprises the following steps:
(1) The salt water soaking method is also a simple method. The salt is used for soaking the medlar to remove most pesticide residues. Adding a small spoon of salt into cold water, soaking fructus Lycii in salt water for 5-10 min, and cleaning with clear water; (2) the cold bubble method is a relatively common method. Soaking fructus Lycii in clear water for 30 min, and washing with flowing water for several times, wherein soap or detergent is not suitable for cleaning, which is easy to remain in fructus Lycii; (3) Black tea infusion is also a good method for removing pesticides. The medlar is soaked in the black tea for about 5 minutes, so that most pesticide residues in the medlar can be removed. If green tea or other tea is used for soaking the medlar, other flavors are left on the medlar, and eating is not recommended;
step S300, the storage module sends stored pesticide operation data of different batches of medlar samples to the operation monitoring module, the operation monitoring module monitors pesticide operation conditions of the different batches of medlar samples, operation risk values of the different batches of medlar samples are obtained and fed back to the server, and the server sends the operation risk values of the different batches of medlar samples to the intelligent evaluation module;
the pesticide operation data is the pesticide spraying times of medlar samples in different batches, and the spraying quantity and the spraying time of each pesticide spraying, and the pesticide operation data is related data in the period from planting to picking of medlar;
in step S300, the monitoring process of the job monitoring module specifically includes the following steps:
step S301, acquiring the pesticide spraying times of the medlar samples in the same batch, and marking the pesticide spraying times as PCi;
step S302, the spraying quantity of the same batch of medlar samples when each pesticide is sprayed is obtained, and the spraying quantity of each pesticide is added and summed to obtain the pesticide spraying quantity PLi of the same batch of medlar samples;
step S303, finally, the spraying time of the same batch of medlar samples when each pesticide is sprayed is obtained, the time intervals of adjacent spraying time are calculated to obtain a plurality of groups of pesticide spraying interval duration, and the pesticide spraying interval duration of the plurality of groups of pesticide spraying interval duration are added, summed and averaged to obtain the pesticide spraying interval average duration PJTi of the same batch of medlar samples;
step S305, calculating the operation risk value ZFi of the medlar samples of different batches according to the formula ZFi = (pci+pli)/PJTi;
step S400, the intelligent evaluation module evaluates the risk levels of pesticide residues in the medlar samples in different batches, the risk levels of the pesticide residues in the medlar samples in different batches are obtained through evaluation and fed back to the server, the server sends the risk levels of the pesticide residues in the medlar samples in different batches to the storage module, and the storage module stores the risk levels of the pesticide residues in the medlar samples in different batches;
in this embodiment, the evaluation process of the intelligent evaluation module in the step S400 is specifically as follows;
step S401, obtaining the product risk values CFi and the operation risk values ZFi of the medlar samples in different batches obtained by the calculation;
step S402, calculating risk assessment values FPi for different batches of medlar samples using the formula FPi =cfi×α+ ZFi ×β; wherein, alpha and beta are weight coefficients with fixed values, and alpha+beta=1;
step S403, if the risk assessment value is greater than or equal to the second risk assessment threshold, the risk level of the pesticide residue in the medlar samples of different batches is the first risk level, if the risk assessment value is less than the second risk assessment threshold and greater than or equal to the first risk assessment threshold, the risk level of the pesticide residue in the medlar samples of different batches is the second risk level, and if the risk assessment value is less than the first risk assessment threshold, the risk level of the pesticide residue in the medlar samples of different batches is the third risk level;
the first risk assessment threshold is smaller than the first risk assessment threshold, the risk degree of the first risk level is larger than the risk degree of the second risk level, and the risk degree of the second risk level is larger than the risk degree of the third risk level;
step S500, the user terminal scans the batch code on the medlar packaging bag and uploads the batch code to the server, the server sends the batch code to the information comparison module, the information comparison module compares the identification information of the medlar packaging bag to obtain risk level feedback of the medlar packaging bag to the server, the server sends the risk level of the medlar packaging bag to the user terminal, and the user terminal checks the risk level of the medlar packaging bag;
specifically, the user terminal is configured to input personal information by using a person, and send the personal information to the server, where the personal information is a name, a mobile phone number, etc. of the person;
in step S500, the comparison process of the information comparison module specifically includes the following steps:
step S501, comparing the batch code on the medlar packaging bag with the batch numbers of medlar samples in different batches of the storage module, wherein the comparison method can be used for comparing keywords or contours;
step S502, if the batch code is matched with the batch number, acquiring a risk level corresponding to the medlar sample according to the batch number;
step S503, the risk level corresponding to the medlar sample is marked as the risk level of the medlar packaging bag.
Example two
Based on another concept of the same application, referring to fig. 2, a risk assessment system for pesticide residues in medlar is now provided, which comprises a data acquisition module, a data analysis module, an intelligent assessment module, an operation monitoring module, an information comparison module, a storage module, a user terminal and a server;
in this embodiment, the data acquisition module is configured to acquire real-time sample data of medlar samples in different batches, and send the real-time sample data to the server, where the server sends the real-time sample data to the data analysis module;
the real-time sample data are slurry real-time weight values of medlar samples in different batches and real-time doses of each residual pesticide; in actual collection, the medlar samples are required to be ensured to be in the same batch, so that the real-time weight value of the slurry of the medlar samples and the real-time dosage of the residual pesticides are ensured to be on the same starting line, the residual pesticides comprise imidacloprid, acetamiprid, carbendazim, chlorpyrifos, cypermethrin, cyhalothrin, difenoconazole and pyridaben, but are not limited to the same, in practice, 20mL of acetonitrile and crushed medlar can be added into a test tube for centrifugation, then 300 mu L of the centrifuged medlar upper layer is filtered into a 1.5mL of small bottle through a 0.45 mu m polyvinylidene fluoride (PVDF) injector filter, and finally the liquid in the small bottle is detected and analyzed through an ACQUITY ultra-high performance liquid chromatography system and a QT 5500 triple quadrupole mass spectrometer, so that the real-time dosage of the residual pesticides in the medlar samples is obtained;
the data analysis module is used for carrying out data analysis on medlar samples in different batches, and the data analysis process is specifically as follows:
acquiring a slurry real-time weight value of any medlar sample in the same batch and a real-time dose of each residual pesticide, adding and comparing the real-time doses of each residual pesticide to the slurry real-time weight value to obtain a residual pesticide dose ratio CNi of any medlar sample in the same batch, wherein i is a batch number of medlar samples, and the batch number can be composed of pure numbers, english, and the like;
if the residual pesticide dosage ratio is greater than or equal to the second preset pesticide dosage ratio, generating a sample abnormal signal;
if the residual pesticide dosage ratio is smaller than the second preset pesticide dosage ratio, carrying out the subsequent steps;
putting another wolfberry sample in the same batch into the soaking liquid for soaking, recording the beginning soaking time of the wolfberry sample, then obtaining the real-time dose of each residual pesticide in the soaked wolfberry sample, recording the ending soaking time of the wolfberry sample, and obtaining the soaking pesticide dose ratio of the soaked wolfberry sample by adding and summing the real-time dose of each residual pesticide in the soaked wolfberry sample and comparing the real-time weight value of the slurry; wherein the soaking liquid can be salt water, clear water, black tea water, etc.;
if the soaking pesticide dosage ratio is smaller than the first preset pesticide dosage ratio, subtracting the soaking starting time from the soaking ending time to obtain the residual pesticide removal duration CTi of the soaked medlar sample; wherein the value of the first preset pesticide dosage ratio is smaller than the value of the second preset pesticide dosage ratio;
calculating to obtain product risk values CFi of different batches of medlar samples through a formula CFi= (CNi×a1+CTi×a2)/(a1+a2); wherein a1 and a2 are proportionality coefficients with fixed values, and the values of a1 and a2 are larger than zero;
the daily removal method of the residual pesticide in the medlar comprises the following steps:
(1) The salt water soaking method is also a simple method. The salt is used for soaking the medlar to remove most pesticide residues. Adding a small spoon of salt into cold water, soaking fructus Lycii in salt water for 5-10 min, and cleaning with clear water; (2) the cold bubble method is a relatively common method. Soaking fructus Lycii in clear water for 30 min, and washing with flowing water for several times, wherein soap or detergent is not suitable for cleaning, which is easy to remain in fructus Lycii; (3) Black tea infusion is also a good method for removing pesticides. The medlar is soaked in the black tea for about 5 minutes, so that most pesticide residues in the medlar can be removed. If green tea or other tea is used for soaking the medlar, other flavors are left on the medlar, and eating is not recommended;
the data analysis module feeds back product risk values or sample abnormal signals of the medlar samples in different batches to the server, if the server receives the sample abnormal signals, no operation is performed, and if the server receives the product risk values of the medlar samples in different batches, the product risk values are sent to the intelligent evaluation module;
in this embodiment, the server is connected with a storage module, and the storage module is used for storing pesticide operation data of medlar samples in different batches and sending the pesticide operation data to the operation monitoring module;
the pesticide operation data is the pesticide spraying times of medlar samples in different batches, and the spraying quantity and the spraying time of each pesticide spraying, and the pesticide operation data is related data in the period from planting to picking of medlar;
the operation monitoring module is used for monitoring pesticide operation conditions of medlar samples in different batches, and the monitoring process is specifically as follows:
acquiring pesticide spraying times of the medlar samples in the same batch, and marking the pesticide spraying times as PCi;
then, the spraying quantity of the medlar samples in the same batch is obtained when each pesticide is sprayed, and the spraying quantity of each pesticide is added and summed to obtain the pesticide spraying quantity PLi of the medlar samples in the same batch;
finally, the spraying time of each pesticide spraying of the same batch of medlar samples is obtained, the time intervals of adjacent spraying time are calculated to obtain a plurality of groups of pesticide spraying interval duration, and the pesticide spraying interval duration of the plurality of groups of pesticide spraying interval duration is added and summed to obtain an average value to obtain the pesticide spraying interval average duration PJTi of the same batch of medlar samples;
calculating to obtain operation risk values ZFi of medlar samples in different batches through a formula ZFi = (PCi+PLi)/PJTi;
the operation monitoring module feeds back operation risk values of the medlar samples in different batches to the server, and the server sends the operation risk values of the medlar samples in different batches to the intelligent evaluation module;
the intelligent evaluation module is used for evaluating the risk level of pesticide residues in medlar samples in different batches, and the evaluation process is specifically as follows;
obtaining the product risk values CFi and the operation risk values ZFi of the medlar samples in different batches through calculation;
calculating risk scores FPi for different batches of medlar samples using the formula FPi =cfi×α+ ZFi ×β; wherein, alpha and beta are weight coefficients with fixed values, and alpha+beta=1;
if the risk assessment value is greater than or equal to the second risk assessment threshold, the risk level of pesticide residues in the medlar samples in different batches is the first risk level;
if the risk assessment value is smaller than the second risk assessment threshold value and larger than or equal to the first risk assessment threshold value, the risk level of pesticide residues in the medlar samples in different batches is the second risk level;
if the risk assessment value is smaller than the first risk assessment threshold, the risk level of pesticide residues in the medlar samples in different batches is a third risk level; the first risk assessment threshold is smaller than the first risk assessment threshold, the risk degree of the first risk level is larger than the risk degree of the second risk level, and the risk degree of the second risk level is larger than the risk degree of the third risk level;
the intelligent evaluation module feeds back the risk levels of pesticide residues in the medlar samples in different batches to the server, the server sends the risk levels of the pesticide residues in the medlar samples in different batches to the storage module, and the storage module is used for storing the risk levels of the pesticide residues in the medlar samples in different batches;
specifically, the user terminal is configured to input personal information by using a person, and send the personal information to the server, where the personal information is a name, a mobile phone number, etc. of the person;
in a specific implementation, the user terminal is used for scanning the batch code on the medlar packaging bag and uploading the batch code to the server, the server sends the batch code to the information comparison module, and the information comparison module is used for comparing the identification information of the medlar packaging bag, wherein the comparison process is specifically as follows:
comparing the batch code on the medlar packaging bag with the batch numbers of medlar samples in different batches of the storage module, wherein the comparison method can be used for comparing keywords or contours;
if the batch code is matched with the batch number, acquiring a risk level corresponding to the medlar sample according to the batch number;
the risk level corresponding to the medlar sample is marked as the risk level of the medlar packaging bag;
the information comparison module feeds back the risk level of the medlar packaging bag to the server, the server sends the risk level of the medlar packaging bag to the user terminal, and the user terminal is used for checking the risk level of the medlar packaging bag;
in the application, if a corresponding calculation formula appears, the calculation formulas are all dimensionality-removed and numerical calculation, and the weight coefficient, the proportion coefficient and other coefficients in the formulas are set to be a result value obtained by quantizing each parameter, so long as the proportion relation between the parameter and the result value is not influenced.
The preferred embodiments of the application disclosed above are intended only to assist in the explanation of the application. The preferred embodiments are not intended to be exhaustive or to limit the application to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the application and the practical application, to thereby enable others skilled in the art to best understand and utilize the application. The application is limited only by the claims and the full scope and equivalents thereof.
Claims (9)
1. The risk assessment method for pesticide residues in medlar is characterized by comprising the following steps of:
step S100, a data acquisition module acquires real-time sample data of medlar samples in the same batch, and sends the real-time sample data to a data analysis module through a server;
step S200, the data analysis module performs data analysis on the medlar samples in different batches to obtain product risk values or sample abnormal signals of the medlar samples in different batches, if the sample abnormal signals are generated, no operation is performed, and if the product risk values of the medlar samples in different batches are obtained, the product risk values are sent to the intelligent evaluation module;
step S300, the storage module sends stored pesticide operation data of different batches of medlar samples to the operation monitoring module, the operation monitoring module monitors pesticide operation conditions of the different batches of medlar samples, and operation risk values of the different batches of medlar samples are obtained and sent to the intelligent evaluation module through the server;
step S400, the intelligent evaluation module evaluates the risk levels of pesticide residues in the medlar samples in different batches and sends the risk levels to the storage module through the server, and the storage module stores the risk levels of pesticide residues in the medlar samples in different batches;
step S500, the user terminal scans the batch code on the medlar packaging bag and sends the batch code to the information comparison module through the server, the information comparison module compares the identification information of the medlar packaging bag, the obtained risk level of the medlar packaging bag is sent to the user terminal through the server, and the user terminal checks the risk level of the medlar packaging bag.
2. The method for risk assessment of pesticide residues in wolfberry according to claim 1, wherein the real-time sample data is a real-time weight value of slurry of wolfberry samples of different batches, and a real-time dose of each residual pesticide.
3. The risk assessment method for pesticide residues in medlar according to claim 1, wherein the data analysis process of the data analysis module is specifically as follows:
acquiring a slurry real-time weight value of any medlar sample in the same batch and a real-time dose of each residual pesticide, and summing and comparing the residual pesticide real-time dose of each residual pesticide with the slurry real-time weight value to obtain a residual pesticide dose ratio of any medlar sample in the same batch;
if the residual pesticide dosage ratio is greater than or equal to the second preset pesticide dosage ratio, generating a sample abnormal signal;
if the residual pesticide dosage ratio is smaller than the second preset pesticide dosage ratio, carrying out the subsequent steps;
putting another wolfberry sample in the same batch into the soaking liquid for soaking, recording the beginning soaking time of the wolfberry sample, then obtaining the real-time dose of each residual pesticide in the soaked wolfberry sample, recording the ending soaking time of the wolfberry sample, and obtaining the soaking pesticide dose ratio of the soaked wolfberry sample by adding and summing the real-time dose of each residual pesticide in the soaked wolfberry sample and comparing the real-time weight value of the slurry; wherein the soaking liquid is salt water, clear water and black tea water;
if the soaking pesticide dosage ratio is smaller than the first preset pesticide dosage ratio, subtracting the soaking starting time from the soaking ending time to obtain the residual pesticide removing duration of the soaked medlar sample; wherein the value of the first preset pesticide dosage ratio is smaller than the value of the second preset pesticide dosage ratio;
and calculating product risk values of different batches of medlar samples.
4. The method for risk assessment of pesticide residues in wolfberry according to claim 1, wherein the pesticide operation data is the number of pesticide sprays of different batches of wolfberry samples, and the amount and time of each pesticide spray.
5. The method for risk assessment of pesticide residues in wolfberry as set forth in claim 4, wherein the monitoring process of the operation monitoring module is specifically as follows:
acquiring pesticide spraying times of medlar samples in the same batch;
then, the spraying quantity of the medlar samples in the same batch is obtained when each pesticide is sprayed, and the spraying quantity of the medlar samples in the same batch is obtained by adding and summing the spraying quantity of each pesticide spraying;
finally, the spraying time of each pesticide spraying of the same batch of medlar samples is obtained, the time intervals of adjacent spraying time are calculated to obtain a plurality of groups of pesticide spraying interval duration, and the pesticide spraying interval duration of the plurality of groups of pesticide spraying interval duration is added and summed to obtain an average value to obtain the pesticide spraying interval average duration of the same batch of medlar samples;
and calculating the operation risk values of the medlar samples in different batches.
6. The risk assessment method for pesticide residues in medlar according to claim 1, wherein the assessment process of the intelligent assessment module is specifically as follows;
obtaining product risk values and operation risk values of medlar samples in different batches;
calculating risk assessment values of medlar samples in different batches;
if the risk assessment value is greater than or equal to the second risk assessment threshold, the risk level of pesticide residues in the medlar samples in different batches is the first risk level;
if the risk assessment value is smaller than the second risk assessment threshold value and larger than or equal to the first risk assessment threshold value, the risk level of pesticide residues in the medlar samples in different batches is the second risk level;
and if the risk assessment value is smaller than the first risk assessment threshold, the risk level of pesticide residues in the medlar samples in different batches is a third risk level.
7. The method of claim 6, wherein the first risk rating threshold is less than the first risk rating threshold, the first risk level is greater than the second risk level, and the second risk level is greater than the third risk level.
8. The risk assessment method for pesticide residues in medlar according to claim 1, wherein the comparison process of the information comparison module is specifically as follows:
comparing the batch code on the medlar packaging bag with the batch numbers of medlar samples in different batches of the storage module;
if the batch code is matched with the batch number, acquiring a risk level corresponding to the medlar sample according to the batch number;
and (5) marking the risk level corresponding to the medlar sample as the risk level of the medlar packaging bag.
9. The risk assessment system for pesticide residues in medlar is characterized by being suitable for the risk assessment method for pesticide residues in medlar according to any one of claims 1-8, and comprises a data acquisition module, a data analysis module, an intelligent assessment module, a job monitoring module, an information comparison module, a storage module, a user terminal and a server;
the data acquisition module is used for acquiring real-time sample data of medlar samples in different batches and sending the real-time sample data to the data analysis module through the server;
the data analysis module is used for carrying out data analysis on medlar samples in different batches to obtain product risk values of medlar samples in different batches or generating sample abnormal signals and feeding the sample abnormal signals back to the server, if the server receives the sample abnormal signals, no operation is carried out, and if the server receives the product risk values of medlar samples in different batches, the product risk values of medlar samples in different batches are sent to the intelligent evaluation module;
the storage module is used for storing pesticide operation data of medlar samples in different batches and sending the pesticide operation data to the operation monitoring module;
the operation monitoring module is used for monitoring pesticide operation conditions of the medlar samples in different batches, obtaining operation risk values of the medlar samples in different batches and sending the operation risk values to the intelligent evaluation module through the server;
the intelligent evaluation module is used for evaluating the risk grades of pesticide residues in the medlar samples in different batches, the obtained risk grades of the pesticide residues in the medlar samples in different batches are sent to the storage module through the server, and the storage module is used for storing the risk grades of the pesticide residues in the medlar samples in different batches;
the user terminal is used for scanning the batch code on the medlar packaging bag and sending the batch code to the information comparison module through the server;
the information comparison module is used for comparing the identification information of the medlar packaging bags, and the risk level of the medlar packaging bags obtained by comparison is sent to the user terminal through the server;
the user terminal is used for checking the risk level of the medlar packaging bag.
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