Agricultural product processing quality and safety management method and system based on regional comprehensive characteristics

Abstract

The present invention discloses a method and system for managing the quality and safety of agricultural product processing based on regional comprehensive characteristics, and relates to the field of data analysis technology, including: establishing a standardized production database for agricultural products, generating the optimal operating rules for the corresponding agricultural product growth stage, and evaluating the professional production knowledge of producers to obtain the producer's knowledge mastery; determining a number of growth requirements within the agricultural product growth stage, analyzing the probability of agricultural product risk occurrence, and based on the producer's knowledge mastery and the probability of agricultural product risk occurrence, evaluating the producer's ability to cope with the risk of agricultural product quality decline, judging whether the risk response ability is at a safety threshold, and if not, judging that it affects the quality of agricultural products, then generating agricultural product knowledge exercises to be learned based on the standardized production database and the producer's knowledge mastery, forming learning tasks, and updating the producer's knowledge mastery. The advantages of the present invention are: improving the processing quality of agricultural products as a whole and enhancing market competitiveness.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to a method and system for managing the quality and safety of agricultural product processing based on comprehensive regional characteristics. Background Art

[0002] Agricultural product processing quality and safety management refers to the process of agricultural product processing, through a series of standardized control measures, strict monitoring system and continuous improvement mechanism to ensure that the processed agricultural products meet food safety standards, protect consumer health, maintain product quality, and comply with relevant laws and regulations, thereby improving market competitiveness and promoting the sustainable development of agriculture.

[0003] The quality of existing agricultural product processing is plagued by lax control of raw material sources, which leads to inconsistent product quality and potential contamination risks such as pesticide residues and heavy metal pollution. In addition, insufficient attention is paid to producers' ability to cope with risks during crop production, which further affects the safety and quality of the final agricultural products. Summary of the invention

[0004] In order to solve the above technical problems, a method and system for managing the quality and safety of agricultural product processing based on regional comprehensive characteristics is provided. This technical solution solves the above-mentioned existing agricultural product processing quality being troubled by the lax control of raw material sources, which leads to inconsistency in product quality and potential pollution risks, such as pesticide residues and heavy metal pollution. In addition, insufficient attention is paid to the ability of producers to cope with risks during the crop production process, which further affects the safety and quality of the final agricultural products.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] Agricultural product processing quality and safety management methods based on comprehensive regional characteristics include:

[0007] Establish a standardized production database for agricultural products based on professional production knowledge of agricultural products;

[0008] Based on the standardized production database of agricultural products, the optimal operation rules for the corresponding agricultural product growth stages are generated, and the producers' professional production knowledge is evaluated to obtain the producers' knowledge mastery;

[0009] Determine several growth requirements within the growth stage of agricultural products and analyze the probability of agricultural product risks;

[0010] Based on the knowledge mastery of producers and the probability of agricultural product risks, assess the ability of producers to cope with the risk of agricultural product quality decline;

[0011] Determine whether the producer's ability to cope with the risk of quality decline under the growth characteristics of agricultural products is within the safety threshold. If so, it is determined that it does not affect the quality of agricultural products. If not, it is determined that it affects the quality of agricultural products.

[0012] If it is determined that the quality of agricultural products is affected, agricultural product knowledge exercises to be learned are generated based on the standardized production database and the producer's knowledge mastery, forming learning tasks and updating the producer's knowledge mastery.

[0013] Preferably, based on the agricultural product standardized production database, the corresponding optimal operation rules for the agricultural product growth stage are generated, and the producer's professional production knowledge is evaluated to obtain the producer's knowledge mastery, which specifically includes:

[0014] Based on the agricultural product standardized production database, generate standardized operation specification exercises of different importance in the growth stages of agricultural products;

[0015] Weighting the test questions based on the different importance of the growth stages of agricultural products to obtain the weights of the test questions;

[0016] Conduct standardized operation practice test for producers and obtain test practice scores;

[0017] Based on the test question weights and test question scores, the producer's knowledge mastery is calculated using the knowledge mastery formula;

[0018] The specific formula of knowledge mastery is:

[0019]

[0020] In the formula, For producers’ knowledge mastery, For the Test question scores, For the The weight of each test question.

[0021] Preferably, determining several growth requirements within the growth stage of agricultural products and analyzing the probability of occurrence of agricultural product risks specifically include:

[0022] Based on several factors that affect crop production, collect data on changes in external factors affecting crops and record them as future environmental change data;

[0023] Determine several growth requirements of agricultural products during their growth stages;

[0024] Based on several growth requirements of agricultural products at the growth stage, future environmental change data related to several growth requirements of agricultural products at the growth stage are collected and combined into growth requirement characteristic data L. ,in, For the The future environmental change data of the jth agricultural product growth demand in the growth stage of unit time, is the total number of growth stages per unit time, The total number of data on changes in the environment required for the growth of agricultural products;

[0025] Based on the logistic regression model, a prediction model for the occurrence probability of agricultural product risks is established, with the future environmental change data of the growth demand of agricultural products in the growth demand characteristic data as input and the growth risk of agricultural products in the growth stage as output;

[0026] Among them, the prediction model expression of agricultural product risk probability is:

[0027]

[0028] In the formula, For a given input Under the conditions of The first unit of time growth stage The growth demand of agricultural products is facing the impact of future environmental change data. The probability of a risk, For the The first unit of time growth stage The growth demand of agricultural products is facing the impact of future environmental change data. A risk-related coefficient vector, is an exponential function.

[0029] Preferably, based on the producer's knowledge and the probability of agricultural product risk occurrence, the assessment of the producer's ability to cope with the risk of agricultural product quality decline specifically includes:

[0030] Based on the probability of agricultural product risk occurrence, the knowledge fields corresponding to several risk types are determined, and weights are assigned according to the magnitude of the probability of occurrence to obtain the risk probability weights and risk knowledge field weights;

[0031] According to the probability of agricultural product risk occurrence, the weight of risk probability occurrence, the producer's knowledge mastery and the weight of risk knowledge field, the producer's ability to cope with the risk of agricultural product quality decline is calculated through the risk response ability formula;

[0032] The risk response capability formula is:

[0033]

[0034] Where D is the producer's ability to cope with the risk of agricultural product quality decline. is the probability weight of the k-th risk, is the probability of occurrence of the kth agricultural product risk, is the weight of the zth risk knowledge area, is the producer's knowledge mastery of the zth risk knowledge area, is the total number of risk types, is the total number of knowledge areas.

[0035] Preferably, if it is determined that the quality of agricultural products is affected, agricultural product knowledge exercises to be learned are generated according to the standardized production database and the producer's knowledge mastery, forming a learning task, and updating the producer's knowledge mastery specifically includes:

[0036] Screen out the knowledge areas that affect the quality of agricultural products by the producer's ability to cope with the risk of agricultural product quality decline index;

[0037] According to the agricultural product standardization production database, the knowledge area of ​​producers' ability to cope with the risk of agricultural product quality decline index affecting agricultural product quality is generated to learn standardized operation specification exercises;

[0038] Establish learning tasks based on the standardized operating specifications to be learned, conduct learning for producers, conduct producer knowledge assessment, and update producer knowledge mastery.

[0039] Furthermore, a method system for managing the quality and safety of agricultural product processing based on comprehensive regional characteristics is proposed, which is used to implement the method for managing the quality and safety of agricultural product processing based on comprehensive regional characteristics as described above, including:

[0040] Database, which is used to build a standardized production database of agricultural products based on professional production knowledge of agricultural products;

[0041] The mastery evaluation module is electrically connected to the database and is used to generate the optimal operation rules for the corresponding agricultural product growth stages based on the agricultural product standardized production database, and to evaluate the producer's professional production knowledge to obtain the producer's knowledge mastery;

[0042] The risk occurrence probability module is used to determine several growth requirements within the growth stage of agricultural products and analyze the probability of agricultural product risk occurrence;

[0043] A risk response capability module, the risk response capability module is electrically connected to the mastery evaluation module and the risk occurrence probability module, and the risk response capability module is used to evaluate the producer's ability to respond to the risk of declining agricultural product quality based on the producer's knowledge mastery and the probability of agricultural product risk occurrence;

[0044] A response capability judgment module, the response capability judgment module is electrically connected to the risk response capability module, and the response capability judgment module is used to judge whether the producer's response capability to the risk of quality decline under the growth characteristics of agricultural products is within a safety threshold, if so, it is determined that it does not affect the quality of agricultural products, if not, it is determined that it affects the quality of agricultural products;

[0045] The knowledge mastery update module is electrically connected to the response capability judgment module. The knowledge mastery update module is used to generate agricultural product knowledge exercises to be learned based on the standardized production database and the producer's knowledge mastery, form learning tasks, and update the producer's knowledge mastery if it is determined that the quality of the agricultural products is affected.

[0046] Optionally, the mastery assessment module includes:

[0047] The standardization unit generates standardized operation specification exercises of different importance at the growth stages of agricultural products based on the standardized production database of agricultural products;

[0048] An exercise weight unit is used to weight the test exercises based on the different importance of the growth stages of agricultural products to obtain the test exercise weights;

[0049] The test unit conducts standardized operation practice tests on producers and obtains test practice scores;

[0050] The mastery calculation unit calculates the producer's knowledge mastery through the knowledge mastery formula based on the test exercise weights and the test exercise scores.

[0051] Optionally, the risk occurrence probability module includes:

[0052] The influencing factor data unit collects the change data of external influencing factors of crops based on several factors affecting crop production, and records them as future environmental change data;

[0053] Growth demand unit, which determines several growth demands of agricultural products at the growth stage;

[0054] A growth demand characteristic unit collects future environmental change data related to several growth demands of agricultural products at the growth stage based on several growth demands of agricultural products at the growth stage, and combines them into growth demand characteristic data;

[0055] The growth risk prediction unit establishes a probability prediction model for agricultural product risk based on the logistic regression model, taking the growth demand environment change data of agricultural products in the growth stage in the growth demand characteristic data as input and the growth risk of agricultural products in the growth stage as output.

[0056] Optionally, the coping capability judgment module includes:

[0057] The risk weighting unit determines the knowledge fields corresponding to several risk types based on the probability of occurrence of agricultural product risks, and assigns weights according to the magnitude of the probability of occurrence to obtain the risk probability weight and the risk knowledge field weight;

[0058] The response capability calculation unit calculates the producer's response capability index for the risk of declining agricultural product quality based on the probability of agricultural product risk occurrence, the weight of the risk probability occurrence, the producer's knowledge mastery and the weight of the risk knowledge field, using the risk response capability formula.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] The present invention proposes a quality and safety management scheme for agricultural product processing based on regional comprehensive characteristics. By building a standardized production database for agricultural products, it provides producers with optimal operation guidance based on professional production knowledge, evaluates their knowledge mastery, and analyzes agricultural product growth requirements and risk probability to evaluate producers' ability to cope with quality risks. For producers who fail to meet the safety threshold, the scheme helps them improve their knowledge level by generating targeted learning plans, thereby improving the overall processing quality of agricultural products and enhancing market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flow chart of the agricultural product processing quality and safety management method based on regional comprehensive characteristics;

[0062] Figure 2 A flow chart of the method to obtain producer knowledge mastery;

[0063] Figure 3 A flow chart of the method for analyzing the probability of agricultural product risk;

[0064] Figure 4 A flow chart of the methodology for assessing producers’ ability to cope with the risk of agricultural product quality degradation;

[0065] Figure 5 In order to generate agricultural product knowledge exercises to be learned, a flow chart of learning task methods is formed;

[0066] Figure 6 This is a framework diagram of the agricultural product processing quality and safety management system based on comprehensive regional characteristics. DETAILED DESCRIPTION

[0067] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0068] Reference Figure 1As shown in the figure, the agricultural product processing quality and safety management methods based on regional comprehensive characteristics include:

[0069] Establish a standardized production database for agricultural products based on professional production knowledge of agricultural products;

[0070] Based on the standardized production database of agricultural products, the optimal operation rules for the corresponding agricultural product growth stages are generated, and the producers' professional production knowledge is evaluated to obtain the producers' knowledge mastery;

[0071] Determine several growth requirements within the growth stage of agricultural products and analyze the probability of agricultural product risks;

[0072] Based on the knowledge mastery of producers and the probability of agricultural product risks, assess the ability of producers to cope with the risk of agricultural product quality decline;

[0073] Determine whether the producer's ability to cope with the risk of quality decline under the growth characteristics of agricultural products is within the safety threshold. If so, it is determined that it does not affect the quality of agricultural products. If not, it is determined that it affects the quality of agricultural products.

[0074] If it is determined that the quality of agricultural products is affected, agricultural product knowledge exercises to be learned are generated based on the standardized production database and the producer's knowledge mastery, forming learning tasks and updating the producer's knowledge mastery.

[0075] This solution establishes a standardized production database for agricultural products, evaluates and improves the knowledge level of producers, and quantifies risks based on growth characteristics to ensure the quality of agricultural products. For producers whose assessment results affect the quality of agricultural products, the system will generate personalized learning tasks based on their knowledge mastery and standardized production requirements to help them improve their professional skills, thereby updating and improving producers' knowledge mastery and ensuring continuous improvement and optimization of agricultural product quality and safety management.

[0076] It should be noted that the safety threshold represents the minimum level of knowledge and response capabilities that producers need to achieve when managing the growth and quality risks of agricultural products. This threshold is set based on the best operating practices and industry best practices in the agricultural product standardized production database. Only when the producer's actual knowledge and risk response capabilities reach or exceed this safety threshold can the growth environment of agricultural products and the quality of the final product meet the expected safety and quality standards, thereby avoiding the risk of quality degradation.

[0077] Reference Figure 2 As shown in the figure, based on the standardized production database of agricultural products, the corresponding optimal operation rules for agricultural product growth stages are generated, and the professional production knowledge of producers is evaluated to obtain the producer's knowledge mastery, which specifically includes:

[0078] Based on the agricultural product standardized production database, generate standardized operation specification exercises of different importance in the growth stages of agricultural products;

[0079] Weighting the test questions based on the different importance of the growth stages of agricultural products to obtain the weights of the test questions;

[0080] Conduct standardized operation practice test for producers and obtain test practice scores;

[0081] Based on the test question weights and test question scores, the producer's knowledge mastery is calculated using the knowledge mastery formula;

[0082] The specific formula of knowledge mastery is:

[0083]

[0084] In the formula, For producers’ knowledge mastery, For the Test question scores, For the The weight of each test question.

[0085] The purpose of this step is to ensure that producers have the ability to perform standardized production operations for agricultural products, thereby improving the overall quality and consistency of agricultural products. By evaluating and improving producers' understanding and application of operating specifications at key growth stages, quality problems caused by insufficient knowledge or improper operations can be reduced.

[0086] Reference Figure 3 As shown in the figure, several growth requirements within the growth stage of agricultural products are determined, and the probability of agricultural product risk occurrence is analyzed, including:

[0087] Based on several factors that affect crop production, collect data on changes in external factors affecting crops and record them as future environmental change data; the future environmental change data include but are not limited to: climate change, pest growth, and nutrient levels;

[0088] Determine several growth requirements of agricultural products during their growth stages;

[0089] Based on several growth requirements of agricultural products at the growth stage, future environmental change data related to several growth requirements of agricultural products at the growth stage are collected and combined into growth requirement characteristic data L. ,in, For the The future environmental change data of the jth agricultural product growth demand in the growth stage of unit time, is the total number of growth stages per unit time, The total number of data on changes in the environment required for the growth of agricultural products;

[0090] Based on the logistic regression model, a prediction model for the occurrence probability of agricultural product risks is established, with the future environmental change data of the growth demand of agricultural products in the growth demand characteristic data as input and the growth risk of agricultural products in the growth stage as output;

[0091] Among them, the prediction model expression of agricultural product risk probability is:

[0092]

[0093] In the formula, For a given input Under the conditions of The first unit of time growth stage The growth demand of agricultural products is facing the impact of future environmental change data. The probability of a risk, For the The first unit of time growth stage The growth demand of agricultural products is facing the impact of future environmental change data. A risk-related coefficient vector, is an exponential function.

[0094] The program aims to combine meteorological data and agricultural product growth requirements, and use the Logistic regression model to predict the risk probability faced at different growth stages, thereby providing scientific risk management strategies and decision-making support for agricultural production.

[0095] Reference Figure 4 As shown in the figure, based on the producer's knowledge and the probability of agricultural product risk, the assessment of the producer's ability to cope with the risk of agricultural product quality decline specifically includes:

[0096] Based on the probability of agricultural product risk occurrence, the knowledge fields corresponding to several risk types are determined, and weights are assigned according to the magnitude of the probability of occurrence to obtain the risk probability weights and risk knowledge field weights;

[0097] According to the probability of agricultural product risk occurrence, the weight of risk probability occurrence, the producer's knowledge mastery and the weight of risk knowledge field, the producer's ability to cope with the risk of agricultural product quality decline is calculated through the risk response ability formula;

[0098] The risk response capability formula is:

[0099]

[0100] Where D is the producer's ability to cope with the risk of agricultural product quality decline. is the probability weight of the k-th risk, is the probability of occurrence of the kth agricultural product risk, is the weight of the zth risk knowledge area, is the producer's knowledge mastery of the zth risk knowledge area, is the total number of risk types, is the total number of knowledge areas.

[0101] This program quantifies the ability of producers to cope with the risk of agricultural product quality decline by combining the probability of agricultural product risk and its corresponding knowledge domain weight, as well as the producers' mastery of these knowledge domains. This identifies and emphasizes the knowledge domains that are most critical to risk management, and provides producers with targeted improvement suggestions, thereby improving their risk response capabilities and ensuring the stability of agricultural product quality and the sustainability of agricultural production.

[0102] Reference Figure 5 As shown in the figure, if it is determined that the quality of agricultural products is affected, the agricultural product knowledge exercises to be learned are generated according to the standardized production database and the producer's knowledge mastery, forming a learning task, and updating the producer's knowledge mastery specifically includes:

[0103] Screen out the knowledge areas that affect the quality of agricultural products by the producer's ability to cope with the risk of agricultural product quality decline index;

[0104] According to the agricultural product standardization production database, the knowledge area of ​​producers' ability to cope with the risk of agricultural product quality decline index affecting agricultural product quality is generated to learn standardized operation specification exercises;

[0105] Establish learning tasks based on the standardized operating specifications to be learned, conduct learning for producers, conduct producer knowledge assessment, and update producer knowledge mastery.

[0106] Further, see Figure 6 As shown, the same invention concept of the agricultural product processing quality and safety management method based on regional comprehensive characteristics is proposed, and a method system for agricultural product processing quality and safety management based on regional comprehensive characteristics is proposed, including:

[0107] Database, which is used to build a standardized production database of agricultural products based on professional production knowledge of agricultural products;

[0108] The mastery evaluation module is electrically connected to the database and is used to generate the optimal operation rules for the corresponding agricultural product growth stages based on the agricultural product standardized production database, and to evaluate the producer's professional production knowledge to obtain the producer's knowledge mastery;

[0109] The risk occurrence probability module is used to determine several growth requirements within the growth stage of agricultural products and analyze the probability of agricultural product risk occurrence;

[0110] A risk response capability module, the risk response capability module is electrically connected to the mastery evaluation module and the risk occurrence probability module, and the risk response capability module is used to evaluate the producer's ability to respond to the risk of declining agricultural product quality based on the producer's knowledge mastery and the probability of agricultural product risk occurrence;

[0111] A response capability judgment module, the response capability judgment module is electrically connected to the risk response capability module, and the response capability judgment module is used to judge whether the producer's response capability to the risk of quality decline under the growth characteristics of agricultural products is within a safety threshold, if so, it is determined that it does not affect the quality of agricultural products, if not, it is determined that it affects the quality of agricultural products;

[0112] The knowledge mastery update module is electrically connected to the response capability judgment module. The knowledge mastery update module is used to generate agricultural product knowledge exercises to be learned based on the standardized production database and the producer's knowledge mastery, form learning tasks, and update the producer's knowledge mastery if it is determined that the quality of the agricultural products is affected.

[0113] The mastery assessment module includes:

[0114] The standardization unit generates standardized operation specification exercises of different importance at the growth stages of agricultural products based on the standardized production database of agricultural products;

[0115] An exercise weight unit is used to weight the test exercises based on the different importance of the growth stages of agricultural products to obtain the test exercise weights;

[0116] The test unit conducts standardized operation practice tests on producers and obtains test practice scores;

[0117] The mastery calculation unit calculates the producer's knowledge mastery through the knowledge mastery formula based on the test exercise weights and the test exercise scores.

[0118] The risk probability module includes:

[0119] The influencing factor data unit collects the change data of external influencing factors of crops based on several factors affecting crop production, and records them as future environmental change data;

[0120] Growth demand unit, which determines several growth demands of agricultural products at the growth stage;

[0121] A growth demand characteristic unit collects future environmental change data related to several growth demands of agricultural products at the growth stage based on several growth demands of agricultural products at the growth stage, and combines them into growth demand characteristic data;

[0122] The growth risk prediction unit establishes a probability prediction model for agricultural product risk based on the logistic regression model, taking the growth demand environment change data of agricultural products in the growth stage in the growth demand characteristic data as input and the growth risk of agricultural products in the growth stage as output.

[0123] The coping ability judgment module includes:

[0124] The risk weighting unit determines the knowledge fields corresponding to several risk types based on the probability of occurrence of agricultural product risks, and assigns weights according to the magnitude of the probability of occurrence to obtain the risk probability weight and the risk knowledge field weight;

[0125] The response capability calculation unit calculates the producer's response capability index for the risk of declining agricultural product quality based on the probability of agricultural product risk occurrence, the weight of the risk probability occurrence, the producer's knowledge mastery and the weight of the risk knowledge field, using the risk response capability formula.

[0126] The use process of the agricultural product processing quality and safety management method system based on regional comprehensive characteristics is as follows:

[0127] Step 1: Establish a standardized production database of agricultural products based on professional production knowledge of agricultural products;

[0128] Step 2: Based on the agricultural product standardized production database, generate standardized operation specification exercises of different importance at the growth stages of agricultural products;

[0129] Step 3: Weight the test questions based on the different importance of the agricultural product growth stages to obtain the test question weights;

[0130] Step 4: Conduct standardized operation practice test on producers and obtain test practice scores;

[0131] Step 5: Based on the test question weights and test question scores, calculate the producer's knowledge mastery through the knowledge mastery formula;

[0132] Step 6: Based on several factors that affect crop production, collect data on changes in external factors affecting crops and record them as future environmental change data;

[0133] Step 7: Determine several growth requirements of agricultural products during their growth stages;

[0134] Step 8: Based on several growth requirements of the agricultural product growth stage, collect future environmental change data related to several growth requirements of the agricultural product growth stage, and combine them into growth requirement characteristic data;

[0135] Step 9: Based on the logistic regression model, a prediction model for the occurrence probability of agricultural product risks is established, with the data on changes in the growth demand environment of agricultural products during their growth stages in the growth demand characteristic data as input and the growth risk of agricultural products during their growth stages as output;

[0136] Step 10: Based on the probability of agricultural product risk occurrence, determine the knowledge fields corresponding to several risk types, and assign weights according to the size of the probability of occurrence to obtain the risk probability weight and risk knowledge field weight;

[0137] Step 11: Based on the probability of agricultural product risk occurrence, the weight of the risk probability, the producer's knowledge mastery and the weight of the risk knowledge field, the risk response capability formula is used to calculate the producer's response capability index for the risk of agricultural product quality decline;

[0138] Step 12: Determine whether the producer's ability to cope with the risk of quality decline under the growth characteristics of agricultural products is at a safety threshold. If so, it is determined that it does not affect the quality of agricultural products. If not, it is determined that it affects the quality of agricultural products.

[0139] Step 13: If it is determined that the quality of agricultural products is affected, the agricultural product knowledge exercises to be learned are generated according to the standardized production database and the producer's knowledge mastery, forming a learning task and updating the producer's knowledge mastery.

[0140] In summary, the advantages of the present invention are: by building a standardized production database for agricultural products, it provides producers with optimal operation guidance based on professional production knowledge, evaluates their knowledge mastery, and analyzes agricultural product growth requirements and risk probability to evaluate producers' ability to cope with quality risks. For those producers who fail to meet the safety threshold, the solution helps them improve their knowledge level by generating targeted learning plans, thereby improving the overall processing quality of agricultural products and enhancing market competitiveness.

[0141] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for managing agricultural product processing quality and safety based on comprehensive regional characteristics, characterized in that: include: Establish a standardized production database for agricultural products based on professional production knowledge of agricultural products; Based on the standardized production database of agricultural products, the optimal operation rules for the corresponding agricultural product growth stages are generated, and the producers' professional production knowledge is evaluated to obtain the producers' knowledge mastery; Determine several growth requirements within the growth stage of agricultural products and analyze the probability of agricultural product risks; Based on the knowledge mastery of producers and the probability of agricultural product risks, assess the ability of producers to cope with the risk of agricultural product quality decline; Determine whether the producer's ability to cope with the risk of quality decline under the growth characteristics of agricultural products is within the safety threshold. If so, it is determined that it does not affect the quality of agricultural products. If not, it is determined that it affects the quality of agricultural products. If it is determined that the quality of agricultural products is affected, the agricultural product knowledge exercises to be learned are generated according to the standardized production database and the producer's knowledge mastery, forming a learning task and updating the producer's knowledge mastery; Among them, based on the producer's knowledge and the probability of agricultural product risk, the assessment of the producer's ability to cope with the risk of agricultural product quality decline specifically includes: Based on the probability of agricultural product risk occurrence, the knowledge fields corresponding to several risk types are determined, and weights are assigned according to the magnitude of the probability of occurrence to obtain the risk probability weights and risk knowledge field weights; According to the probability of agricultural product risk occurrence, the weight of risk probability occurrence, the producer's knowledge mastery and the weight of risk knowledge field, the producer's ability to cope with the risk of agricultural product quality decline is calculated through the risk response ability formula; The risk response capability formula is: Where D is the producer's ability to cope with the risk of agricultural product quality decline. is the probability weight of the k-th risk, is the probability of occurrence of the kth agricultural product risk, is the weight of the zth risk knowledge area, is the producer's knowledge mastery of the zth risk knowledge area, is the total number of risk types, is the total number of knowledge areas.

2. According to the method for managing agricultural product processing quality and safety based on regional comprehensive characteristics as described in claim 1, based on the standardized production database of agricultural products, the optimal operating rules for the corresponding agricultural product growth stages are generated, and the professional production knowledge of producers is evaluated to obtain the producer's knowledge mastery, which specifically includes: Based on the agricultural product standardized production database, generate standardized operation specification exercises of different importance at the growth stages of agricultural products; Weighting the test questions based on the different importance of the growth stages of agricultural products to obtain the weights of the test questions; Conduct standardized operation practice test for producers and obtain test practice scores; Based on the test question weights and test question scores, the producer's knowledge mastery is calculated using the knowledge mastery formula; The specific formula of knowledge mastery is: In the formula, For producers’ knowledge mastery, For the Test question scores, For the The weight of each test question.

3. According to the method for managing agricultural product processing quality and safety based on regional comprehensive characteristics as claimed in claim 2, determining a number of growth requirements within the growth stage of agricultural products and analyzing the probability of occurrence of agricultural product risks specifically include: Based on several factors that affect crop production, collect data on changes in external factors affecting crops and record them as future environmental change data; Determine several growth requirements of agricultural products during their growth stages; Based on several growth requirements of agricultural products at the growth stage, future environmental change data related to several growth requirements of agricultural products at the growth stage are collected and combined into growth requirement characteristic data L. ,in, For the The future environmental change data of the jth agricultural product growth demand in the growth stage of unit time, is the total number of growth stages per unit time, The total number of data on changes in the environment required for the growth of agricultural products; Based on the logistic regression model, a prediction model for the occurrence probability of agricultural product risks is established, with the future environmental change data of the growth demand of agricultural products in the growth demand characteristic data as input and the growth risk of agricultural products in the growth stage as output; Among them, the prediction model expression of agricultural product risk probability is: In the formula, For a given input Under the conditions of The first unit of time growth stage The growth demand of agricultural products is facing the impact of future environmental change data. The probability of a risk, For the The first unit of time growth stage The growth demand of agricultural products is facing the impact of future environmental change data. A risk-related coefficient vector, is an exponential function.

4. According to the method for managing agricultural product processing quality and safety based on regional comprehensive characteristics of claim 3, if it is determined that the quality of agricultural products is affected, agricultural product knowledge exercises to be learned are generated according to the standardized production database and the producer's knowledge mastery, forming a learning task, and updating the producer's knowledge mastery specifically includes: Screen out the knowledge areas that affect the quality of agricultural products by the producer's ability to cope with the risk of agricultural product quality decline index; According to the agricultural product standardization production database, the knowledge area of ​​producers' ability to cope with the risk of agricultural product quality decline index affecting agricultural product quality is generated to learn standardized operation specification exercises; Establish learning tasks based on the standardized operating specifications to be learned, conduct learning for producers, conduct producer knowledge assessment, and update producer knowledge mastery.

5. The agricultural product processing quality and safety management system based on regional comprehensive characteristics is characterized by: A method for managing agricultural product processing quality and safety based on comprehensive regional characteristics as claimed in any one of claims 1 to 4, comprising: Database, which is used to build a standardized production database of agricultural products based on professional production knowledge of agricultural products; The mastery evaluation module is electrically connected to the database and is used to generate the optimal operation rules for the corresponding agricultural product growth stages based on the agricultural product standardized production database, and to evaluate the producer's professional production knowledge to obtain the producer's knowledge mastery; The risk occurrence probability module is used to determine several growth requirements within the growth stage of agricultural products and analyze the probability of agricultural product risk occurrence; A risk response capability module, the risk response capability module is electrically connected to the mastery evaluation module and the risk occurrence probability module, and the risk response capability module is used to evaluate the producer's ability to respond to the risk of declining agricultural product quality based on the producer's knowledge mastery and the probability of agricultural product risk occurrence; A response capability judgment module, the response capability judgment module is electrically connected to the risk response capability module, and the response capability judgment module is used to judge whether the producer's response capability to the risk of quality decline under the growth characteristics of agricultural products is within a safety threshold, if so, it is determined that it does not affect the quality of agricultural products, if not, it is determined that it affects the quality of agricultural products; The knowledge mastery update module is electrically connected to the response capability judgment module. The knowledge mastery update module is used to generate agricultural product knowledge exercises to be learned based on the standardized production database and the producer's knowledge mastery, form learning tasks, and update the producer's knowledge mastery if it is determined that the quality of the agricultural products is affected.

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