CN112258069B - Agricultural product safety evaluation method and system based on risk entropy - Google Patents
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Abstract
The application provides a method and a system for evaluating agricultural product safety based on risk entropy, wherein the method comprises the following steps: constructing a monitoring matrix and preprocessing the monitoring matrix to obtain a sample matrix; constructing a decision matrix, and converting the sample matrix into a risk matrix based on the decision matrix; dividing the risk matrix into a plurality of sub-matrices according to varieties, converting each sub-matrix into risk entropy vectors of each variety according to a risk entropy value method, and longitudinally splicing the risk entropy vectors of all the varieties to obtain a risk entropy matrix; calculating the weight of each safety index based on the risk entropy matrix; the safety evaluation indexes of the varieties are calculated based on the risk entropy vectors of the varieties and the weights of the safety indexes, so that the safety evaluation of the agricultural products is carried out based on the safety evaluation indexes of the varieties of the agricultural products, and the key technical problems of agricultural product safety risk evaluation of multi-source risk monitoring data fusion, high-sparsity monitoring data conversion utilization, macroscopic quantitative evaluation and risk sequencing can be effectively solved.
Description
Technical Field
The application relates to the technical field of agricultural product quality safety risk assessment, in particular to an agricultural product safety evaluation method and system based on risk entropy.
Background
The safety evaluation is one of important technical means for guaranteeing the edible safety of agricultural products, and the evaluation result is an important scientific basis for developing risk supervision and decision. For a long time, the method is limited by the problems of non-uniform monitoring data structures, high sparsity of monitoring data and the like obtained by different monitoring works, the fusion utilization rate of multi-source data is low, the mining and utilization rate of effective information of large-scale monitoring data is not high, the safety evaluation work depends on qualitative evaluation methods such as qualification rate, standard exceeding rate and the like for a long time, and the work of risk evaluation and related risk sequencing, risk decision and the like lacks effective quantitative evaluation technology.
On the basis of risk monitoring data fusion, effective information contained in highly sparse monitoring data is fully mined and utilized to construct quantifiable evaluation indexes, and the method is the key for realizing quantitative evaluation of agricultural product safety and macroscopic decision support. At present, no quantitative evaluation-based agricultural product safety macroscopic evaluation and risk ranking method exists.
Disclosure of Invention
In view of this, the present application aims to provide a risk entropy-based agricultural product safety evaluation method and system, which can effectively solve the key technical problems of agricultural product safety risk assessment of multi-source risk monitoring data fusion, high sparsity monitoring data conversion and utilization, macroscopic quantitative evaluation and risk ranking thereof.
In a first aspect, an embodiment of the present application provides an agricultural product safety evaluation method based on risk entropy, including:
extracting monitoring data obtained by various risk monitoring works from a risk monitoring database, and performing data fusion by taking the varieties of various samples in the monitoring data as rows, safety indexes as columns and detection results of the safety indexes as elements to construct a monitoring matrix;
sequencing each row of the monitoring matrix according to a variety catalog, replacing undetected values in the monitoring matrix according to a preset rule and/or classifying varieties with sample size smaller than a reference value into other varieties to obtain a sample matrix;
constructing a judgment matrix by taking a safety threshold value corresponding to a combination of a variety and a safety index as an element, and dividing a detection result corresponding to each combination of the variety and the safety index in the sample matrix by a safety threshold value corresponding to the combination of the variety and the safety index in the judgment matrix to convert the element in the sample matrix into a risk value so as to convert the sample matrix into a risk matrix;
dividing the risk matrix into a plurality of sub-matrices according to varieties, converting each sub-matrix into a risk entropy vector of each variety according to a risk entropy value method, and longitudinally splicing the risk entropy vectors of all the varieties to obtain a risk entropy matrix;
calculating the weight of each safety index based on the risk entropy matrix;
and calculating the safety evaluation index of each variety based on the risk entropy vector of each variety and the weight of each safety index, so as to perform safety evaluation on the agricultural products based on the safety evaluation indexes of each variety of the agricultural products.
In a possible implementation manner, extracting monitoring data obtained by each risk monitoring job from a risk monitoring database, and performing data fusion by using the variety of each sample in the monitoring data as a row, the safety index as a column, and the detection result of the safety index as an element to construct a monitoring matrix, includes:
extracting monitoring data obtained by various risk monitoring works from a risk monitoring database;
taking the number of all samples in the monitoring data as a row number and the number of all related safety indexes as a column number, and creating a null matrix;
setting all related safety indexes as column names of the empty matrix, and setting varieties of each sample as row names of the empty matrix;
and filling the matrix position which is not filled with the detection result with an undetected value to construct a monitoring matrix.
In a possible embodiment, replacing the undetected values in the monitoring matrix according to a preset rule includes:
when the proportion of the undetected values in the monitoring matrix is greater than or equal to a preset value, replacing the undetected values with 1/2 detection limits;
and when the proportion of the undetected values in the monitoring matrix is smaller than a preset value, replacing the undetected values with detection limits.
In a possible embodiment, the reference value is set to 500 for the development of an agricultural product safety assessment on a national level;
setting the reference value to 100 for conducting agricultural product safety evaluation at a provincial level;
setting the reference value to 30 for conducting agricultural product safety evaluation on the market level or below;
the safety threshold is a food safety limit standard value officially issued by the state or a safety judgment value temporarily set by the state agricultural product quality safety risk assessment expert committee.
In one possible embodiment, the risk entropy method is used to convert risk values in the risk matrix into risk entropy values using the following expression:
wherein h isijRepresenting the risk entropy, n, of the jth safety index of the ith varietyiThe sample size, x, of the ith varietyijlThe risk value of the ith sample under the combination of the ith variety and the jth safety index is 1,2, …, ni。
In a possible embodiment, calculating the weight of each safety index based on the risk entropy matrix includes:
calculating the entropy value of the jth safety index based on the risk entropy matrix:
wherein e isjExpressing the entropy value of the jth safety index, and n expressing the number of varieties;
calculating the weight of the jth safety index based on the entropy of the jth safety index:
wherein, wjRepresents the weight of the jth security index, and m represents the number of security indexes.
In one possible embodiment, the safety evaluation index of each variety is calculated based on the risk entropy vector of each variety and the weight of each safety index:
wherein, FiThe safety evaluation index of the ith variety is shown.
In a second aspect, an embodiment of the present application further provides an agricultural product safety evaluation system based on risk entropy, including:
the monitoring matrix construction module is used for extracting monitoring data obtained by various risk monitoring works from a risk monitoring database, and performing data fusion by taking the varieties of various samples in the monitoring data as rows, safety indexes as columns and detection results of the safety indexes as elements to construct a monitoring matrix;
the sample matrix obtaining module is used for sequencing each row of the monitoring matrix according to a variety catalog, replacing an undetected value in the monitoring matrix according to a preset rule and/or classifying varieties with sample amount smaller than a reference value into other varieties to obtain a sample matrix;
the risk matrix conversion module is used for constructing a judgment matrix by taking a safety threshold value corresponding to a combination of a variety and a safety index as an element, and dividing a detection result corresponding to each combination of the variety and the safety index in the sample matrix by a safety threshold value corresponding to the combination of the variety and the safety index in the judgment matrix so as to convert the element in the sample matrix into a risk value and convert the sample matrix into a risk matrix;
the risk entropy matrix obtaining module is used for dividing the risk matrix into a plurality of sub-matrices according to varieties, converting each sub-matrix into a risk entropy vector of each variety according to a risk entropy value method, and longitudinally splicing the risk entropy vectors of all the varieties to obtain a risk entropy matrix;
the weight calculation module is used for calculating the weight of each safety index based on the risk entropy matrix;
and the safety evaluation index calculation module is used for calculating the safety evaluation indexes of all the varieties based on the risk entropy vectors of all the varieties and the weights of all the safety indexes, so that the safety evaluation of the agricultural products is carried out based on the safety evaluation indexes of all the varieties of the agricultural products.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the machine-readable instructions when executed by the processor performing the steps of the first aspect described above, or any possible implementation of the first aspect.
In a fourth aspect, this application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps in the first aspect or any one of the possible implementation manners of the first aspect.
According to the agricultural product safety evaluation method based on the risk entropy, firstly, monitoring data obtained by various risk monitoring works are extracted from a risk monitoring database, and data fusion is carried out by taking varieties of various samples in the monitoring data as rows, safety indexes as columns and detection results of the safety indexes as elements to construct a monitoring matrix; sequencing each row of the monitoring matrix according to a variety catalog, replacing undetected values in the monitoring matrix according to a preset rule and/or classifying varieties with sample size smaller than a reference value into other varieties to obtain a sample matrix, and solving the problems of non-uniform monitoring data structures, high sparsity of monitoring data and the like obtained by different monitoring works. Then, a judgment matrix is constructed by taking a safety threshold value corresponding to the combination of the variety and the safety index as an element, and a detection result corresponding to each combination of the variety and the safety index in the sample matrix is divided by a safety threshold value corresponding to the combination of the variety and the safety index in the judgment matrix, so that the element in the sample matrix is converted into a risk value, and the sample matrix is converted into a risk matrix; the risk matrix is divided into a plurality of sub-matrices according to varieties, each sub-matrix is converted into a risk entropy vector of each variety according to a risk entropy value method, the risk entropy vectors of all the varieties are longitudinally spliced to obtain a risk entropy matrix, a relatively complex monitoring matrix can be converted into a risk entropy matrix easy to analyze, and the mining and utilization rate of effective information of large-scale monitoring data is improved. Finally, calculating the weight of each safety index based on the risk entropy matrix; and calculating the safety evaluation index of each variety based on the risk entropy vector of each variety and the weight of each safety index, so that the safety evaluation of the agricultural products is performed based on the safety evaluation indexes of each variety of the agricultural products, and the safety risk of the agricultural products can be evaluated macroscopically and quantitatively. Therefore, the agricultural product safety risk assessment method and device can effectively solve the key technical problems of multi-source risk monitoring data fusion, high-sparsity monitoring data conversion and utilization, macroscopic quantitative evaluation and risk sequencing of the agricultural product safety risk assessment.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 is a flowchart illustrating a method for evaluating agricultural product safety based on risk entropy according to an embodiment of the present application;
FIG. 2 is a schematic diagram showing the results of a ranked comparison of annual vegetable monitoring results;
FIG. 3 shows a schematic of ranked comparison results of another year vegetable monitoring results;
FIG. 4 is a schematic structural diagram illustrating an agricultural product safety evaluation system based on risk entropy according to an embodiment of the present application;
fig. 5 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
Considering that the safety evaluation in the prior art is one of the important technical means for guaranteeing the edible safety of agricultural products, the evaluation result is an important scientific basis for developing risk supervision and decision-making. For a long time, the method is limited by the problems of non-uniform monitoring data structures, high sparsity of monitoring data and the like obtained by different monitoring works, the fusion utilization rate of multi-source data is low, the mining and utilization rate of effective information of large-scale monitoring data is not high, the safety evaluation work depends on qualitative evaluation methods such as qualification rate, standard exceeding rate and the like for a long time, and the work of risk evaluation and related risk sequencing, risk decision and the like lacks effective quantitative evaluation technology. On the basis of risk monitoring data fusion, effective information contained in highly sparse monitoring data is fully mined and utilized to construct quantifiable evaluation indexes, and the method is the key for realizing quantitative evaluation of agricultural product safety and macroscopic decision support. At present, no agricultural product safety macro risk assessment and risk ranking method based on quantitative evaluation exists. Based on this, the embodiment of the application provides an agricultural product safety evaluation method and system based on risk entropy, which are described below through an embodiment.
In order to facilitate understanding of the present embodiment, first, a method for evaluating safety of agricultural products based on risk entropy disclosed in the embodiments of the present application is described in detail.
Referring to fig. 1, fig. 1 is a flowchart of an agricultural product safety evaluation method based on risk entropy according to an embodiment of the present application. As shown in fig. 1, the method may include the steps of:
s101, extracting monitoring data obtained by various risk monitoring works from a risk monitoring database, and performing data fusion by taking the varieties of various samples in the monitoring data as rows, safety indexes as columns and detection results of the safety indexes as elements to construct a monitoring matrix;
s102, sequencing rows of the monitoring matrix according to a variety catalog, replacing undetected values in the monitoring matrix according to a preset rule and/or classifying varieties with sample size smaller than a reference value into other varieties to obtain a sample matrix;
s103, constructing a judgment matrix by taking a safety threshold value corresponding to a combination of a variety and a safety index as an element, and dividing a detection result corresponding to each combination of the variety and the safety index in the sample matrix by a safety threshold value corresponding to the combination of the variety and the safety index in the judgment matrix to convert the element in the sample matrix into a risk value so as to convert the sample matrix into a risk matrix;
s104, dividing the risk matrix into a plurality of sub-matrices according to varieties, converting each sub-matrix into risk entropy vectors of each variety according to a risk entropy value method, and longitudinally splicing the risk entropy vectors of all the varieties to obtain a risk entropy matrix;
s105, calculating the weight of each safety index based on the risk entropy matrix;
and S106, calculating the safety evaluation index of each variety based on the risk entropy vector of each variety and the weight of each safety index, and thus carrying out safety evaluation on the agricultural products based on the safety evaluation index of each variety of the agricultural products.
In step S101, monitoring data obtained by each risk monitoring job is extracted from a risk monitoring database; taking the number of all samples in the monitoring data as a row number and the number of all related safety indexes as a column number, and creating a null matrix; setting all related safety indexes as column names of the empty matrix, and setting varieties of each sample as row names of the empty matrix; and filling the matrix position which is not filled with the detection result with an undetected value to construct a monitoring matrix.
In this embodiment, the constructed monitoring matrix is represented by X:
wherein x isi,jThe detection result of the jth safety index of the ith variety is shown, m represents the number of all samples, and n represents the number of all related safety indexes.
In step S102, for the matrix sparsity problem, when the ratio of undetected values in the monitoring matrix is greater than or equal to a preset value, replacing 1/2 detection limits for the undetected values; and when the proportion of the undetected values in the monitoring matrix is smaller than a preset value, replacing the undetected values with detection limits.
And classifying the varieties with the sample size smaller than the reference value into other varieties to obtain a sample matrix aiming at the problem of small sampling amount. Setting the reference value to 500 for developing agricultural product safety evaluations on a national level; setting the reference value to 100 for conducting agricultural product safety evaluation at a provincial level; the reference value is set to 30 for conducting agricultural product safety evaluations at the market level or below. It should be noted that, in the specific implementation, the reference value may be set to other values.
In a possible implementation, each data in the monitoring matrix is divided by the corresponding expected value to update the monitoring matrix, so that the problem of standardization is solved in the statistical theory, and the processed index has practical significance.
In step S103, the safety threshold is a standard value of food safety limit officially issued by the country or a safety judgment value temporarily set by the national committee for quality and safety assessment of agricultural products.
In this embodiment, the constructed decision matrix is represented by EL:
wherein, ELi,jAnd the safety threshold value represents the jth safety index of the ith variety.
Performing the following processing on the sample matrix based on the decision matrix to convert elements in the sample matrix into risk values, so as to convert the sample matrix into a risk matrix:
wherein, yi,jRepresenting the risk value of the jth safety index of the ith variety;
in this example, the risk matrix of conversion completion is represented by Y:
in step S104, the risk entropy, which is an index defined between 0 and 1, evaluates useful information that the data can provide. When the difference between the detection values is larger, the risk entropy values are smaller and the data provides more useful information. For large-scale monitoring data, the risk entropy may reflect the overall level of a certain detected value of the sample. Thus, in this embodiment, the greater the risk entropy, i.e., closer to 1, the lower the overall level; conversely, the smaller the risk entropy, i.e. closer to 0, the higher the corresponding overall level.
The risk entropy method is used to convert risk values in the risk matrix into risk entropy values using the following expression:
wherein h isijRepresenting the risk entropy, n, of the jth safety index of the ith varietyiThe sample size, x, of the ith varietyijlThe risk value of the ith sample under the combination of the ith variety and the jth safety index is 1,2, …, ni。
In this embodiment, the risk entropy matrix is represented by H:
in step S105, calculating a weight of each safety index based on the risk entropy matrix, including:
calculating the entropy value of the jth safety index based on the risk entropy matrix:
wherein e isjExpressing the entropy value of the jth safety index, and n expressing the number of varieties;
calculating the weight of the jth safety index based on the entropy of the jth safety index:
wherein, wjRepresents the weight of the jth security index, and m represents the number of security indexes.
In step S106, a safety evaluation index for each item is calculated based on the risk entropy vector for each item and the weight of each safety index:
wherein, FiThe safety evaluation index of the ith variety is shown.
Through the steps from S101 to S106, the safety evaluation index of each variety is calculated, and therefore the safety evaluation of the agricultural products is carried out based on the safety evaluation index of each variety of the agricultural products.
Taking a vegetable monitoring result of a certain two years as an example, the traditional agricultural product safety evaluation method based on the qualification rate is compared with the agricultural product safety evaluation method based on the risk entropy, and the beneficial effect of the agricultural product safety evaluation method based on the risk entropy is illustrated.
In a traditional agricultural product safety evaluation method based on yield, the yield is often used for reflecting the pesticide residue condition of certain vegetables. Specifically, for a certain vegetable sample, if one of all pesticides to be detected exceeds the standard, the vegetable sample is judged to be unqualified. And judging whether all the detection samples of a certain vegetable in the year are qualified or not in such a way, and then recording the ratio of the qualified number to the total number of the samples as the qualified rate. In the traditional agricultural product safety evaluation method based on the qualification rate, the higher the qualification rate is, the safer the vegetables are considered; the lower the yield, the more serious the pesticide residue of the vegetable is considered.
Next, a certain two-year vegetable monitoring result is applied, and the qualification rate in the traditional qualification rate-based agricultural product safety evaluation method and the result of the safety evaluation index in the risk entropy-based agricultural product safety evaluation method of the embodiment are ranked and compared, and the ranked and compared results of the certain two-year vegetable monitoring result are respectively shown in fig. 2 and fig. 3.
Wherein, one point in fig. 2 and 3 represents a vegetable variety; the horizontal axis represents the ranking of the safety evaluation indexes of pesticide residues of vegetable varieties, and the closer to the origin, the more: under the agricultural product safety evaluation method based on risk entropy, the more serious the pesticide residue of the vegetables is; the vertical axis represents the percent of pass of the vegetable variety, the closer to the origin is the indication: under the traditional agricultural product safety evaluation method based on the qualification rate, the more serious the pesticide residue of the vegetables is.
As can be seen from fig. 2 and 3, most of the data points are located in the normal regions (i.e., the lower left region and the upper right region of fig. 2 and 3), that is, the yield in the conventional agricultural product security evaluation method based on the yield is mostly close to the detection effect of the security evaluation index in the agricultural product security evaluation method based on the risk entropy according to the embodiment.
However, in two years, one type of vegetable sample point is located in an abnormal region (i.e., the region marked by the dashed line frame in fig. 2 and 3, where Rank H is from 1 to 9 and the quality Ratio is from 95 to 100), and it can be seen that although the vegetable type has a high yield, the pesticide residue is serious. That is, a variety of pesticides are detected in the vegetable variety, but few are out of limits. In fig. 3 in particular, all samples were qualified, i.e. all samples did not exceed the maximum limit for various pesticide residues, but the vegetable variety clearly had mixed pesticide residue contamination. Therefore, the yield in the conventional yield-based agricultural product safety evaluation method does not reflect the risk. The safety evaluation index in the agricultural product safety evaluation method based on the risk entropy in the embodiment can reflect the risk.
Therefore, the function of the safety evaluation index in the agricultural product safety evaluation method based on risk entropy in the embodiment can replace the function of the qualification rate in the traditional agricultural product safety evaluation method based on the qualification rate on most vegetable varieties, but some risks which are difficult to identify by the traditional agricultural product safety evaluation method based on the qualification rate can be found. For example, the area marked by the dashed line in fig. 2 and 3 may be referred to as a "blind area" in the conventional yield-based agricultural product safety evaluation method. The agricultural products in the blind area have higher pesticide residue, and may be pesticide mixed pollution, but the qualification rate in the traditional agricultural product safety evaluation method based on the qualification rate is difficult to find. The agricultural products can be found through the safety evaluation index in the agricultural product safety evaluation method based on the risk entropy, so that the next step of effective treatment can be conveniently carried out. Therefore, the safety evaluation index in the agricultural product safety evaluation method based on the risk entropy can make up for the defects of the traditional agricultural product safety evaluation method based on the qualification rate in the agricultural product safety evaluation method, solve the problem of 'blind zones', timely find agricultural products contaminated by pesticide residues, and more objectively and comprehensively perform the safety evaluation of the pesticide residues of the agricultural products.
According to the agricultural product safety evaluation method based on the risk entropy, firstly, monitoring data obtained by various risk monitoring works are extracted from a risk monitoring database, and data fusion is carried out by taking varieties of various samples in the monitoring data as rows, safety indexes as columns and detection results of the safety indexes as elements to construct a monitoring matrix; sequencing each row of the monitoring matrix according to a variety catalog, replacing undetected values in the monitoring matrix according to a preset rule and/or classifying varieties with sample size smaller than a reference value into other varieties to obtain a sample matrix, and solving the problems of non-uniform monitoring data structures, high sparsity of monitoring data and the like obtained by different monitoring works. Then, a judgment matrix is constructed by taking a safety threshold value corresponding to the combination of the variety and the safety index as an element, and a detection result corresponding to each combination of the variety and the safety index in the sample matrix is divided by a safety threshold value corresponding to the combination of the variety and the safety index in the judgment matrix, so that the element in the sample matrix is converted into a risk value, and the sample matrix is converted into a risk matrix; the risk matrix is divided into a plurality of sub-matrices according to varieties, each sub-matrix is converted into a risk entropy vector of each variety according to a risk entropy value method, the risk entropy vectors of all the varieties are longitudinally spliced to obtain a risk entropy matrix, a relatively complex monitoring matrix can be converted into a risk entropy matrix easy to analyze, and the mining and utilization rate of effective information of large-scale monitoring data is improved. Finally, calculating the weight of each safety index based on the risk entropy matrix; and calculating the safety evaluation index of each variety based on the risk entropy vector of each variety and the weight of each safety index, so that the safety evaluation of the agricultural products is performed based on the safety evaluation indexes of each variety of the agricultural products, and the safety risk of the agricultural products can be evaluated macroscopically and quantitatively. Therefore, the agricultural product safety risk assessment method and device can effectively solve the key technical problems of multi-source risk monitoring data fusion, high-sparsity monitoring data conversion and utilization, macroscopic quantitative evaluation and risk sequencing of the agricultural product safety risk assessment.
Based on the same technical concept, the embodiment of the application further provides an agricultural product safety evaluation system based on risk entropy, an electronic device, a computer storage medium and the like, and the following embodiments can be specifically referred to.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an agricultural product safety evaluation system based on risk entropy according to an embodiment of the present application. As shown in fig. 4, the system may include:
the monitoring matrix construction module 10 is configured to extract monitoring data obtained by each risk monitoring job from a risk monitoring database, perform data fusion by using the varieties of each sample in the monitoring data as rows, the safety indexes as columns, and the detection results of the safety indexes as elements to construct a monitoring matrix;
a sample matrix obtaining module 20, configured to sort rows of the monitoring matrix according to a variety catalog, replace an undetected value in the monitoring matrix according to a preset rule, and/or classify a variety whose sample size is smaller than a reference value as another variety, so as to obtain a sample matrix;
a risk matrix transformation module 30, configured to construct a determination matrix by using a safety threshold corresponding to a combination of a variety and a safety index as an element, and divide a detection result corresponding to each combination of the variety and the safety index in the sample matrix by a safety threshold corresponding to a combination of the variety and the safety index in the determination matrix, so as to transform the element in the sample matrix into a risk value, thereby transforming the sample matrix into a risk matrix;
a risk entropy matrix obtaining module 40, configured to split the risk matrix into a plurality of sub-matrices according to varieties, convert each sub-matrix into a risk entropy vector of each variety according to a risk entropy value method, and longitudinally splice the risk entropy vectors of all the varieties to obtain a risk entropy matrix;
a weight calculation module 50, configured to calculate a weight of each safety index based on the risk entropy matrix;
and the safety evaluation index calculation module 60 is used for calculating the safety evaluation indexes of the varieties based on the risk entropy vectors of the varieties and the weights of the safety indexes, so that the safety evaluation of the agricultural products is performed based on the safety evaluation indexes of the varieties of the agricultural products.
An embodiment of the present application discloses an electronic device, as shown in fig. 5, including: a processor 501, a memory 502 and a bus 503, wherein the memory 502 stores machine-readable instructions executable by the processor 501, and when the electronic device is operated, the processor 501 and the memory 502 communicate with each other through the bus 503. The machine readable instructions, when executed by the processor 501, perform the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment, which is not described herein again.
The computer program product provided in the embodiment of the present application includes a computer-readable storage medium storing a nonvolatile program code executable by a processor, where instructions included in the program code may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment, and is not described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An agricultural product safety evaluation method based on risk entropy is characterized by comprising the following steps:
extracting monitoring data obtained by various risk monitoring works from a risk monitoring database, and performing data fusion by taking the varieties of various samples in the monitoring data as rows, safety indexes as columns and detection results of the safety indexes as elements to construct a monitoring matrix;
sequencing each row of the monitoring matrix according to a variety catalog, replacing undetected values in the monitoring matrix according to a preset rule and/or classifying varieties with sample size smaller than a reference value into other varieties to obtain a sample matrix;
constructing a judgment matrix by taking a safety threshold value corresponding to a combination of a variety and a safety index as an element, and dividing a detection result corresponding to each combination of the variety and the safety index in the sample matrix by a safety threshold value corresponding to the combination of the variety and the safety index in the judgment matrix to convert the element in the sample matrix into a risk value so as to convert the sample matrix into a risk matrix;
dividing the risk matrix into a plurality of sub-matrices according to varieties, converting each sub-matrix into a risk entropy vector of each variety according to a risk entropy value method, and longitudinally splicing the risk entropy vectors of all the varieties to obtain a risk entropy matrix;
calculating the weight of each safety index based on the risk entropy matrix;
and calculating the safety evaluation index of each variety based on the risk entropy vector of each variety and the weight of each safety index, so as to perform safety evaluation on the agricultural products based on the safety evaluation indexes of each variety of the agricultural products.
2. The method according to claim 1, wherein the step of extracting monitoring data obtained by each risk monitoring work from a risk monitoring database, and performing data fusion by using varieties of each sample in the monitoring data as rows, safety indexes as columns, and detection results of the safety indexes as elements to construct a monitoring matrix comprises the steps of:
extracting monitoring data obtained by various risk monitoring works from a risk monitoring database;
taking the number of all samples in the monitoring data as a row number and the number of all related safety indexes as a column number, and creating a null matrix;
setting all related safety indexes as column names of the empty matrix, and setting varieties of each sample as row names of the empty matrix;
and filling the matrix position which is not filled with the detection result with an undetected value to construct a monitoring matrix.
3. The method of claim 1, wherein replacing undetected values in the monitoring matrix according to a predetermined rule comprises:
when the proportion of the undetected values in the monitoring matrix is greater than or equal to a preset value, replacing the undetected values with 1/2 detection limits;
and when the proportion of the undetected values in the monitoring matrix is smaller than a preset value, replacing the undetected values with detection limits.
4. The method according to claim 1, characterized in that the reference value is set to 500 for carrying out agricultural product safety evaluations on a national level; setting the reference value to 100 for conducting agricultural product safety evaluation at a provincial level; setting the reference value to 30 for conducting agricultural product safety evaluation on the market level or below; the safety threshold is a food safety limit standard value officially issued by the state or a safety judgment value temporarily set by the state agricultural product quality safety risk assessment expert committee.
5. The method of claim 1, wherein the risk entropy method is used to convert risk values in the risk matrix into risk entropy values using the following expression:
wherein h isijRepresenting the risk entropy, n, of the jth safety index of the ith varietyiThe sample size, x, of the ith varietyijlThe risk value of the ith sample under the combination of the ith variety and the jth safety index is 1,2, …, ni。
6. The method of claim 5, wherein calculating the weight of each safety index based on the risk entropy matrix comprises:
calculating the entropy value of the jth safety index based on the risk entropy matrix:
wherein e isjExpressing the entropy value of the jth safety index, and n expressing the number of varieties;
calculating the weight of the jth safety index based on the entropy of the jth safety index:
wherein, wjRepresents the weight of the jth security index, and m represents the number of security indexes.
7. The method according to claim 6, wherein the safety evaluation index of each variety is calculated based on the risk entropy vector of each variety and the weight of each safety index:
wherein, FiThe safety evaluation index of the ith variety is shown.
8. An agricultural product safety evaluation system based on risk entropy is characterized by comprising:
the monitoring matrix construction module is used for extracting monitoring data obtained by various risk monitoring works from a risk monitoring database, and performing data fusion by taking the varieties of various samples in the monitoring data as rows, safety indexes as columns and detection results of the safety indexes as elements to construct a monitoring matrix;
the sample matrix obtaining module is used for sequencing each row of the monitoring matrix according to a variety catalog, replacing an undetected value in the monitoring matrix according to a preset rule and/or classifying varieties with sample amount smaller than a reference value into other varieties to obtain a sample matrix;
the risk matrix conversion module is used for constructing a judgment matrix by taking a safety threshold value corresponding to a combination of a variety and a safety index as an element, and dividing a detection result corresponding to each combination of the variety and the safety index in the sample matrix by a safety threshold value corresponding to the combination of the variety and the safety index in the judgment matrix so as to convert the element in the sample matrix into a risk value and convert the sample matrix into a risk matrix;
the risk entropy matrix obtaining module is used for dividing the risk matrix into a plurality of sub-matrices according to varieties, converting each sub-matrix into a risk entropy vector of each variety according to a risk entropy value method, and longitudinally splicing the risk entropy vectors of all the varieties to obtain a risk entropy matrix;
the weight calculation module is used for calculating the weight of each safety index based on the risk entropy matrix;
and the safety evaluation index calculation module is used for calculating the safety evaluation indexes of all the varieties based on the risk entropy vectors of all the varieties and the weights of all the safety indexes, so that the safety evaluation of the agricultural products is carried out based on the safety evaluation indexes of all the varieties of the agricultural products.
9. An electronic device, comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, when an electronic device runs, the processor and the storage medium communicate through the bus, and the processor executes the machine-readable instructions to execute the steps of the risk entropy-based agricultural product security evaluation method according to any one of claims 1 to 7.
10. A computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when executed by a processor, the computer program performs the steps of the risk entropy-based agricultural product security assessment method according to any one of claims 1 to 7.
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Effective date of registration: 20231206 Address after: 101206 Zhongguancun Science and Technology Park, Pinggu District, Beijing Pinggu Yuanyukou New Energy Industry Base, No. 38-23210 Yuyang Road (cluster registration) Patentee after: Beijing Youpin Zhilian Technology Co.,Ltd. Address before: 100000 No. 12 South Main Street, Haidian District, Beijing, Zhongguancun Patentee before: INSTITUTE OF QUALITY STANDARD AND TESTING TECHNOLOGY FOR AGRO-PRODUCTS, CHINESE ACADAMY OF AGRICULTURAL SCIENCES |