Agricultural product traceability management method and system based on block chain technology

Abstract

The invention provides an agricultural product traceability management method and system based on a block chain technology, and the method comprises the steps: collecting key information of a current link in an agricultural product supply chain, determining risk factors, carrying out the quantitative evaluation of each evaluation index of each risk factor through fuzzy comprehensive evaluation, determining the initial risk level of each risk factor through a risk matrix, and carrying out the traceability management of the agricultural product. And determining the correlation degree among the risk factors through grey correlation analysis, correcting the initial risk level according to the correlation degree among the risk factors to obtain a final risk level, and performing real-time uplink storage on the key information and the risk levels of the risk factors through a block chain. According to the method, the risk level of the risk factor is determined by using fuzzy comprehensive evaluation and the risk matrix, and then the risk level is corrected in combination with grey correlation analysis, so that accurate risk management is realized, and meanwhile, a digital guarantee is provided for risk and traceability management of agricultural products through real-time chaining storage of the block chain.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural product traceability management, and in particular to an agricultural product traceability management method and system based on blockchain technology. Background Art

[0002] The entire supply chain of agricultural products, from planting / breeding to final sales, involves multiple links, each of which may have an impact on the quality and safety of agricultural products. Therefore, it is necessary to conduct traceability management of the agricultural product supply chain to ensure the traceability of agricultural products.

[0003] Traditional agricultural product traceability management methods often only focus on the source and flow of agricultural products, but lack a comprehensive assessment and management of risk factors in each link of the supply chain. This makes it impossible to accurately identify potential supply chain risks, making it difficult for regulatory authorities to promptly discover and respond to potential safety risks, and consumers are unable to make wise purchasing decisions based on traceability information. At the same time, the phenomenon of information islands makes it impossible to effectively transmit risk information, increasing the possibility of food safety accidents. Summary of the invention

[0004] Based on this, the purpose of the present invention is to propose a method and system for agricultural product traceability management based on blockchain technology to solve the above-mentioned problems.

[0005] According to a blockchain-based agricultural product traceability management method proposed by the present invention, the method comprises:

[0006] Collect key information on the current links in the agricultural product supply chain, including planting / breeding, packaging, processing, storage, transportation or sales;

[0007] Identify risk factors at current stages of the agricultural product supply chain, including pesticide residues, veterinary drug residues, excessive additives, inappropriate storage conditions, and transport contamination;

[0008] Based on the key information related to the risk factors, the evaluation indicators of each risk factor are quantitatively evaluated through fuzzy comprehensive evaluation, and the evaluation indicators include the possibility of occurrence and the potential impact degree;

[0009] According to the probability of occurrence and potential impact of risk factors, the initial risk level of risk factors is determined through the risk matrix;

[0010] The correlation between risk factors was determined through grey correlation analysis;

[0011] According to the correlation between the risk factors, the initial risk level of each risk factor is revised to obtain the final risk level of each risk factor to reflect the actual risk status of each risk factor in the supply chain;

[0012] Through blockchain, key information of each link in the agricultural product supply chain and the risk levels of its risk factors are stored on the chain in real time, and a unique blockchain identifier is generated for each agricultural product.

[0013] Furthermore, based on the key information related to the risk factors, the evaluation indicators of each risk factor are quantitatively evaluated through fuzzy comprehensive evaluation, including:

[0014] For each risk factor, an evaluation index system is established, and the evaluation indexes include the possibility of occurrence and the potential impact;

[0015] Based on the key information related to the risk factor assessment indicators, each risk factor is initially scored for its likelihood of occurrence and potential impact;

[0016] Through the membership function, the scores of the occurrence possibility and potential impact of each risk factor are converted into fuzzy numbers, and the fuzzy numbers of the occurrence possibility and impact degree of each risk factor are obtained.

[0017] Furthermore, the score of the occurrence possibility and potential impact degree of each risk factor is converted into a fuzzy number through the membership function, and the fuzzy number of the occurrence possibility and the fuzzy number of the impact degree of each risk factor is obtained, including:

[0018] Choose an appropriate membership function;

[0019] Determine the parameters of the membership function according to the range of the comprehensive score and the form of the membership function;

[0020] Substitute the scores of the occurrence possibility and potential impact of each risk factor into the membership function, calculate the membership of the corresponding fuzzy sets, and obtain the fuzzy number of the occurrence possibility and the fuzzy number of the potential impact of each risk factor, where the fuzzy number is expressed as a combination of different fuzzy sets and their memberships.

[0021] Furthermore, the initial risk level of the risk factor is determined through a risk matrix according to the probability of occurrence and the potential impact of the risk factor, including:

[0022] Classify the probability of occurrence and potential impact of risk factors and construct a risk matrix;

[0023] For each risk factor, the fuzzy number of its occurrence possibility and the fuzzy number of its impact degree are combined through fuzzy operation to obtain a combined fuzzy number, which is used to represent the overall risk level of the risk factor after comprehensively considering the occurrence possibility and potential impact degree;

[0024] According to the membership distribution of the combined fuzzy numbers and the level classification standard of the risk matrix, the initial risk level of each risk factor is determined;

[0025] Determine the initial risk level of the risk factor based on its initial position in the risk matrix.

[0026] Furthermore, the initial risk level of each risk factor is determined according to the membership distribution of the combined fuzzy number and the level classification standard of the risk matrix, including:

[0027] The fuzzy number of occurrence possibility is defined as: E = (e1, e2, ..., e i ,…,e m ), where e i Indicates the membership degree of the risk factor to the i-th occurrence possibility fuzzy set;

[0028] The potential impact degree fuzzy number is defined as: F = (f1, f2, ..., f j , …, f n ), where f j Indicates the membership degree of the risk factor to the jth potential impact degree fuzzy set;

[0029] Introducing the weight vector W E =(w e1 , w e2 ,…,w ei ,…,w em ) and W F =(w f1 , w f2 ,…,w fj ,…,w fn ), and according to the fuzzy operation rules, the fuzzy number of the possibility of occurrence and the fuzzy number of the degree of influence are combined to obtain the combined fuzzy number G = (g1, g2, ..., g k , …, g p ), where g k It indicates the membership degree of the risk factor to the kth combined risk fuzzy set after considering the probability of occurrence and the potential impact. For each g k , the calculation formula is:

[0030]

[0031] Among them, w ei and w fj are the weights of the fuzzy number of the possibility of occurrence and the fuzzy number of the degree of influence, which are used to adjust the membership degree e i and f j The relative importance in the combination, the correlation factor (i, j, k) is used to convert the membership degree e i and f jThe combination of is mapped to the kth combined risk fuzzy set, reflecting the contribution of the corresponding combination to the overall risk.

[0032] Furthermore, the initial risk level of each risk factor is determined according to the membership distribution of the combined fuzzy number and the level classification standard of the risk matrix, including:

[0033] For each risk factor, find the maximum membership degree from the combined fuzzy numbers;

[0034] The maximum membership degree is located at the corresponding cell in the risk matrix, and the risk level mapped by the corresponding cell is used as the initial risk level of the risk factor.

[0035] Furthermore, the grey correlation analysis is used to determine the correlation between the risk factors, including:

[0036] Select one or more data series that can comprehensively reflect the risk level of agricultural product supply chains as reference series;

[0037] The evaluation results of each risk factor are used as a comparison series;

[0038] For each moment, calculate the absolute difference between the comparison sequence and the reference sequence;

[0039] Find the maximum and minimum among all absolute differences;

[0040] The grey correlation coefficient formula is used to calculate the correlation coefficient between each risk factor and other risk factors at each time. The calculation formula is:

[0041]

[0042] Among them, Δ is the absolute difference at time t, Δmin is the minimum absolute difference, Δmax is the maximum absolute difference, and ρ is the resolution coefficient;

[0043] The correlation coefficients between each risk factor and other risk factors were averaged to obtain the correlation degree, which reflects the correlation between the two risk factors.

[0044] Furthermore, the initial risk level of each risk factor is modified according to the correlation between each risk factor to obtain the final risk level of each risk factor, including:

[0045] According to the correlation between risk factors, set reasonable risk level adjustment rules;

[0046] Apply the risk level adjustment rules to revise the initial risk level to obtain the final risk level.

[0047] The present invention also proposes an agricultural product traceability management system based on blockchain technology, which is used to implement the above-mentioned agricultural product traceability management method based on blockchain technology. The system includes:

[0048] Collection module: used to collect key information of the current link in the agricultural product supply chain, including planting / breeding, packaging, processing, storage, transportation or sales;

[0049] Risk Factor Module: used to determine the risk factors of the current link in the agricultural product supply chain, including pesticide residues, veterinary drug residues, excessive additives, improper storage conditions and transportation contamination;

[0050] Evaluation module: used to quantitatively evaluate the evaluation indicators of each risk factor through fuzzy comprehensive evaluation based on the key information related to the risk factors. The evaluation indicators include the possibility of occurrence and the potential impact degree;

[0051] Risk level module: used to determine the initial risk level of risk factors through the risk matrix according to the probability of occurrence and potential impact of risk factors;

[0052] Correlation module: used to determine the correlation between risk factors through grey correlation analysis;

[0053] Level correction module: used to correct the initial risk level of each risk factor according to the correlation between the risk factors, and obtain the final risk level of each risk factor to reflect the actual risk status of each risk factor in the supply chain;

[0054] On-chain storage module: It is used to store the key information of each link in the agricultural product supply chain and the risk level of its risk factors in real time on the chain through blockchain, and generate a unique blockchain identifier for each agricultural product for consumers to query.

[0055] In summary, the agricultural product traceability management method based on blockchain technology of the present invention collects key information of each link in the supply chain, and determines the corresponding risk factors, provides basic data for risk management, and quantifies the possibility and potential impact of risk factors through fuzzy comprehensive evaluation, so as to deal with the uncertainty and ambiguity of each risk factor in the agricultural product supply chain, and determines the risk level of risk factors through risk matrix according to the quantified fuzzy number, so that risk management is more accurate and scientific, and further uses gray correlation analysis to consider the correlation between risk factors, and corrects the risk level to obtain a more practical final risk level. At the same time, with the help of blockchain technology, the real-time chain storage of key information and risk level of risk factors in each link of the agricultural product supply chain is realized, ensuring the immutability and traceability of data, and each agricultural product has a unique blockchain identifier, and consumers and agricultural product supervision and management agencies can easily query and obtain the complete supply chain information and risk assessment information of agricultural products, providing digital protection for the risk and traceability management of agricultural products.

[0056] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description or will be learned through embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0058] Figure 1 This is a flowchart of a method for tracing agricultural products based on blockchain technology according to the first embodiment of the present invention;

[0059] Figure 2 This is a system block diagram of an agricultural product traceability management system based on blockchain technology according to Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0060] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0061] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0063] Embodiment 1

[0064] See also Figure 1 The present invention proposes a method for agricultural product traceability management based on blockchain technology, which includes steps S101 to S107:

[0065] S101, collect key information of the current link in the agricultural product supply chain, including planting / breeding, packaging, processing, storage, transportation or sales.

[0066] It should be noted that the agricultural product supply chain includes multiple links, such as planting / breeding, packaging, processing, storage, transportation or sales, etc. First of all, it is necessary to comprehensively collect key information of each link in the agricultural product supply chain, such as input use records in the planting / breeding process (such as the amount, time and method of use of pesticides, veterinary drugs, fertilizers, etc.), production environment parameters (such as temperature, humidity, soil quality, etc.), processing parameters (such as processing temperature, time, use of additives, etc.), storage conditions (such as storage conditions, storage time, etc.), transportation conditions (such as transportation routes, time, temperature control, etc.) and sales conditions (such as sales channels, sales volume, sales time, etc.).

[0067] By comprehensively and accurately collecting key information of the current links in the agricultural product supply chain, a sound data foundation is provided for subsequent risk assessment, risk level determination and traceability management. It is also necessary to use blockchain technology to store the collected key information in real time on the chain for real-time query by consumers or supervisory and management agencies, thereby improving the transparency and credibility of agricultural product data traceability.

[0068] S102, determine the risk factors of the current links in the agricultural product supply chain, including pesticide residues, veterinary drug residues, excessive additives, improper storage conditions and transportation pollution;

[0069] It should be noted that there are various risk factors in the agricultural product supply chain, such as pesticide residue risk, veterinary drug residue risk, excessive additive risk, improper storage conditions risk or transportation contamination risk, etc. These risk factors are the main factors affecting the quality and output of agricultural products. By clarifying the risk factors in each link of the agricultural product supply chain, clear goals and directions are provided for subsequent risk assessment, risk level determination and risk management.

[0070] S103, based on the key information related to the risk factors, each evaluation indicator of each risk factor is quantitatively evaluated through fuzzy comprehensive evaluation, and the evaluation indicators include the possibility of occurrence and the potential impact degree.

[0071] It should be noted that since the risk factors in the agricultural product supply chain are often complex and changeable, it is difficult to describe them with an accurate mathematical model. The present invention evaluates each evaluation index of each risk factor through fuzzy comprehensive evaluation, which can well deal with the fuzziness and uncertainty of risk factors in the agricultural product supply chain, thereby quantifying evaluation indicators such as the possibility of occurrence and potential impact of risk factors, making the evaluation of risk factors more objective, scientific and accurate, and providing a direct basis for the subsequent confirmation of risk levels, so as to more accurately identify key risk factors in the agricultural product supply chain.

[0072] Further optionally, the quantitative evaluation of each evaluation index of each risk factor is performed through fuzzy comprehensive evaluation based on key information related to the risk factor, including:

[0073] For each risk factor, an evaluation index system is established, and the evaluation indexes include the possibility of occurrence and the potential impact;

[0074] Based on the key information related to the risk factor assessment indicators, each risk factor is initially scored for its likelihood of occurrence and potential impact. The scoring process can be carried out through expert scoring or historical data statistical analysis;

[0075] Through the membership function, the scores of the occurrence possibility and potential impact of each risk factor are converted into fuzzy numbers, and the fuzzy numbers of the occurrence possibility and impact degree of each risk factor are obtained.

[0076] Understandably, for each risk factor, its evaluation index is clearly defined, mainly including the possibility of occurrence and potential impact. The possibility of occurrence can take into account various factors such as historical data, expert experience, environmental conditions, etc., and the potential impact can take into account the possible impact on the quality, safety, market reputation and other aspects of agricultural products.

[0077] The evaluation dimensions for the possibility of occurrence and the potential impact can be further refined. For example, the evaluation dimensions for the possibility of occurrence can include frequency, probability, historical records and other information for preliminary scoring, and the dimensions for the potential impact can include severity, scope of impact, duration, etc.

[0078] Based on the evaluation indicators and detailed dimensions, a scoring standard is formulated, and then based on the scoring standard, a preliminary score is given to the possibility of occurrence and potential impact of each risk factor. The scoring process can be carried out in combination with expert scoring, statistical analysis of historical data and other methods.

[0079] Since the preliminary scoring is subjective to a certain extent, because it depends on the experience and judgment of the scorer, it also reflects the uncertainty and ambiguity in risk assessment. Fuzzy comprehensive evaluation is combined to deal with the ambiguity and uncertainty in risk assessment, that is, the clear score is converted into a fuzzy number through the membership function to express the ambiguity of the possibility of occurrence and potential impact of risk factors, so that the evaluation results are more in line with the actual situation.

[0080] Based on the key information related to the risk factors, the present invention quantifies each evaluation index of each risk factor through fuzzy comprehensive evaluation to deal with the uncertainty and ambiguity of each risk factor in the agricultural product supply chain, thereby improving the accuracy and reliability of the risk assessment of the agricultural product supply chain. This includes the possibility of occurrence and the potential impact. A preliminary score is first performed, and then the fuzzy numerical value of the possibility of occurrence and the fuzzy numerical value of the impact of each risk factor are obtained through the membership function.

[0081] Further optionally, the score of the occurrence possibility and potential impact degree of each risk factor is converted into a fuzzy number through a membership function to obtain a fuzzy number of the occurrence possibility and a fuzzy number of the impact degree of each risk factor, including:

[0082] Choose an appropriate membership function;

[0083] According to the range of the comprehensive score and the form of the membership function, the parameters of the membership function are determined. For example, for a triangle membership function, its vertices, left and right boundaries and other parameters need to be determined.

[0084] Substitute the scores of the occurrence possibility and potential impact of each risk factor into the membership function, calculate the membership of the corresponding fuzzy sets, and obtain the fuzzy number of the occurrence possibility and the fuzzy number of the potential impact of each risk factor, where the fuzzy number is expressed as a combination of different fuzzy sets and their memberships.

[0085] It is understandable that a triangular function, a trapezoidal membership function, a Gaussian membership function, etc. can be selected as the membership function. If the triangular membership function is selected as the membership function, the calculation formula of the triangular membership function is:

[0086]

[0087] Among them, a, b, and c are the parameters of the triangle membership function, b is the vertex of the triangle, indicating the maximum point of membership, and a and c are the left and right boundaries of the triangle respectively.

[0088] For example, set five fuzzy sets of "very low", "low", "medium", "high", and "very high". Assuming that the comprehensive score ranges from 0 to 100, based on this range, the parameters of the membership function can be set as:

[0089] For the "very low" fuzzy set: a = 0, b = 10, c = 20;

[0090] For the "low" fuzzy set: a = 10, b = 30, c = 50;

[0091] For the "medium" fuzzy set: a = 30, b = 60, c = 80;

[0092] For the "high" fuzzy set: a = 60, b = 80, c = 95;

[0093] For the "very high" fuzzy set: a=90, b=100, c=100.

[0094] The specific choice of these parameters can be adjusted according to the needs of the actual problem and the distribution of the data.

[0095] If the probability score of a risk factor is 70, then according to the definition of the triangular membership function, the membership degree of the corresponding fuzzy sets is calculated:

[0096] For the "very low" fuzzy set: 70 is not within the domain of the fuzzy set (0 to 20), so μ 极低 (70) = 0;

[0097] For the "low" fuzzy set: 70 is not within the domain of the fuzzy set (10 to 50), so μ 低 (70) = 0;

[0098] For the “medium” fuzzy set: 70∈[30, 80], μ 中等 (70) = (80 - 70) / (80 - 60) = 0.5;

[0099] For “high” fuzzy set: 70∈[60, 95], μ 高 (70) = (95 - 70) / (95 - 60) ≈ 0.714;

[0100] For the "very high" fuzzy set: 70 is not within the domain of the fuzzy set (90 to 100), so μ 极高 (70)=0.

[0101] According to the membership calculation results, the fuzzy number of the occurrence probability of the risk factor is determined to be (extremely low: 0, low: 0, medium: 0.5, high: 0.714, extremely high: 0). This means that the probability of the occurrence of the risk factor has a membership of 0.5 on the "medium" fuzzy set and a membership of 0.714 on the "high" fuzzy set.

[0102] Similarly, the impact score of each risk factor can also be calculated according to the above steps to obtain its impact fuzzy number.

[0103] S104, determining the initial risk level of the risk factor through a risk matrix according to the value of the probability of occurrence and the potential impact of the risk factor.

[0104] It should be noted that the risk matrix is ​​a tool that intuitively displays the risk status. After obtaining the quantitative assessment values ​​of the risk possibility and potential impact (i.e., fuzzy numbers) through fuzzy comprehensive assessment, the quantitative assessment values ​​of the risk possibility and potential impact can be combined and mapped into the matrix, so as to quickly and clearly see the risk level of each risk factor.

[0105] Further optionally, the initial risk level of the risk factor is determined by a risk matrix according to the value of the probability of occurrence and the potential impact of the risk factor, including:

[0106] Classify the probability of occurrence and potential impact of risk factors and construct a risk matrix;

[0107] For each risk factor, the fuzzy number of its occurrence possibility and the fuzzy number of its impact degree are combined through fuzzy operation to obtain a combined fuzzy number, which is used to represent the overall risk level of the risk factor after comprehensively considering the occurrence possibility and potential impact degree;

[0108] According to the membership distribution of combined fuzzy numbers and the grade classification standard of risk matrix, the initial risk level of each risk factor is determined.

[0109] Understandably, a risk matrix is ​​constructed, with the horizontal axis representing the level of probability of occurrence (such as very low, low, medium, high, very high), and the vertical axis representing the level of potential impact (such as slight, general, severe, very severe). The following Table 1 is an example of a risk matrix:

[0110] Possibility of occurrence Potential Impact Initial risk level Very low (0.1-0.2) Mild (0.1-0.2) Low risk Low (0.2-0.4) Normal (0.3-0.5) Medium risk Medium (0.4-0.6) Moderate / Severe (0.3-0.7) Medium to high risk High (0.6-0.8) Severe / very severe (0.5-0.9) High risk Very high (0.9-1.0) - Very high risk

[0111] Table 1

[0112] For each risk factor, the fuzzy number of the risk possibility and the fuzzy number of the potential impact degree are combined through fuzzy operations (such as fuzzy intersection, fuzzy union or weighted average, etc.) to obtain a combined fuzzy number G. The specific operation is further described in detail below.

[0113] Find the maximum membership in the combined fuzzy number, and find the corresponding position in the risk matrix according to the maximum membership. If the maximum membership is 0.6, in Table 1, it falls in the intersection area of ​​"medium" possibility of occurrence and "general / serious" potential impact, and the corresponding risk level of this area is "medium-high risk". Therefore, the initial risk level of this risk factor is determined to be "medium-high risk".

[0114] The present invention determines the initial risk level of risk factors through risk matrix and fuzzy logic. It not only takes into account the possibility of occurrence and potential impact of risk factors, but also quantifies the fuzziness of these factors through fuzzy operations and membership distribution, thereby improving the flexibility and accuracy of risk assessment. At the same time, through the intuitive representation of the risk matrix, it is easier to understand and compare the risk levels of different risk factors.

[0115] Further optionally, the determining of the initial risk level of each risk factor according to the membership distribution of the combined fuzzy number and the level classification standard of the risk matrix includes:

[0116] The fuzzy number of occurrence possibility is defined as: E = (e1, e2, ..., e i ,…,e m ), where e i Indicates the membership degree of the risk factor to the i-th occurrence possibility fuzzy set;

[0117] The potential impact degree fuzzy number is defined as: F = (f1, f2, ..., f j , …, f n ), where f j Indicates the membership degree of the risk factor to the jth potential impact degree fuzzy set;

[0118] Introducing the weight vector W E =(w e1 , w e2 ,…,w ei ,…,w em ) and W F =(w f1 , w f2 ,…,w fj ,…,w fn ), and according to the fuzzy operation rules, the fuzzy number of the possibility of occurrence and the fuzzy number of the degree of influence are combined to obtain the combined fuzzy number G = (g1, g2, ..., g k , …, g p), where g k It indicates the membership degree of the risk factor to the kth combined risk fuzzy set after considering the probability of occurrence and the potential impact. For each g k , the calculation formula is:

[0119]

[0120] Among them, w ei and w fj are the weights of the fuzzy number of the possibility of occurrence and the fuzzy number of the degree of influence, which are used to adjust the membership degree e i and f j The relative importance in the combination, the correlation factor (i, j, k) is used to convert the membership degree e i and f j The combination of is mapped to the kth combined risk fuzzy set, reflecting the contribution of the corresponding combination to the overall risk.

[0121] Further optionally, the determining of the initial risk level of each risk factor according to the membership distribution of the combined fuzzy number and the level classification standard of the risk matrix includes:

[0122] For each risk factor, find the maximum membership degree from the combined fuzzy numbers;

[0123] The maximum membership degree is located at the corresponding cell in the risk matrix, and the risk level mapped by the corresponding cell is used as the initial risk level of the risk factor.

[0124] S105, determining the correlation between the risk factors through grey correlation analysis.

[0125] It should be noted that the initial risk level of each risk factor is preliminarily determined through the risk matrix. However, since the risk factors in the agricultural product supply chain often do not exist in isolation, there may be complex interactions and associations between the risk factors, so that the actual risk status of some risk factors may be different from their initial risk level. Moreover, risk assessment in the agricultural product supply chain is a complex process involving multiple factors and uncertainties.

[0126] By determining the correlation between risk factors through grey correlation analysis, we can more comprehensively consider the mutual influence between risk factors, thereby making a more reasonable revision of the initial risk level, so that the revised risk level can more accurately and comprehensively reflect the actual risk status of the supply chain.

[0127] The results of grey correlation analysis can be directly used in the formulation of management decisions and the optimization of resource allocation. By obtaining the correlation between various risk factors, we can give priority to those factors that have a high correlation with other risk factors and have a greater impact on the overall risk of the supply chain, so as to reasonably allocate resources and improve the efficiency of risk management.

[0128] Further optionally, the determining the correlation between the risk factors by grey correlation analysis includes:

[0129] Select one or more data series that can comprehensively reflect the risk level of agricultural product supply chains as reference series;

[0130] The evaluation results of each risk factor are used as a comparison series;

[0131] For each moment, calculate the absolute difference between the comparison sequence and the reference sequence;

[0132] Find the maximum and minimum among all absolute differences;

[0133] The grey correlation coefficient formula is used to calculate the correlation coefficient between each risk factor and other risk factors at each time. The calculation formula is:

[0134]

[0135] Among them, Δ is the absolute difference at time t, Δmin is the minimum absolute difference, Δmax is the maximum absolute difference, and ρ is the resolution coefficient, which is usually 0.5;

[0136] The correlation coefficients between each risk factor and other risk factors were averaged to obtain the correlation degree, which reflects the correlation between the two risk factors.

[0137] Understandably, one or more data series that can comprehensively reflect the risk level of the agricultural product supply chain are selected as reference series. The unqualified rate of quality sampling of agricultural products over a period of time can be selected as a reference series. Assume that there is a time series data, X0 = [0.1, 0.2, 0.15, 0.05, 0.0], which represents the unqualified rate of sampling for five consecutive months, as a reference series.

[0138] The evaluation results of each risk factor (such as the possibility of occurrence, potential impact or the quantitative value converted from the initial risk level) are used as a comparison series. Assume that there are three risk factors X1 (pesticide residues), X2 (excessive additives), and X3 (improper storage conditions). Their evaluation results in each month (standardized or quantified) are X1 = [0.6, 0.7, 0.5, 0.4, 0.3], X2 = [0.5, 0.8, 0.6, 0.4, 0.2], and X3 = [0.4, 0.5, 0.7, 0.6, 0.5] (this is just an example, and the actual evaluation results should be determined according to the specific situation). These values ​​represent the risk assessment results of each risk factor in each month. The higher the value, the greater the risk.

[0139] For each moment (such as each month), the absolute difference between the comparison sequence and the reference sequence is calculated. For example, for the first month, the absolute difference between X1 and X0 is |0.6-0.1|=0.5, the absolute difference between X2 and X0 is |0.5-0.1|=0.4, the absolute difference between X3 and X0 is |0.4-0.1|=0.3, and so on for each month.

[0140] Find the maximum value Δmax = 0.6 and the minimum value Δmin = 0.2 in all absolute differences. Then use the grey correlation coefficient formula to calculate the correlation coefficient between each risk factor and other risk factors at each time. In the formula, ρ is the resolution coefficient, which is usually 0.5. The correlation coefficient between X1, X2, and X3 in each month can be calculated.

[0141] The correlation coefficients between each risk factor and other risk factors are averaged to obtain the correlation matrix, such as Each value in the correlation matrix reflects the degree of correlation between two risk factors. The closer the correlation is to 1, the stronger the correlation between the two risk factors.

[0142] The present invention can quantify the correlation between various risk factors in the agricultural product supply chain through grey correlation analysis, so as to more clearly understand the mutual influence and transmission mechanism between risks. Then, based on the correlation between risk factors, the initial risk level is revised to make the risk level more accurately and comprehensively reflect the actual risk status of the supply chain.

[0143] S106, according to the correlation between each risk factor, the initial risk level of each risk factor is modified to obtain the final risk level of each risk factor to reflect the actual risk status of each risk factor in the supply chain.

[0144] It should be noted that through grey correlation analysis, the degree of correlation between various risk factors in the agricultural product supply chain can be quantified, so as to more clearly understand the mutual influence and transmission mechanism between risks. Based on the correlation between risk factors, the initial risk level is revised to make the risk level more accurately and comprehensively reflect the actual risk status of the supply chain. From the perspective of supply chain risk management, supply chain risk management measures can be formulated more targeted, limited resources can be invested in the most critical risk management links, priority attention can be paid to risk factors with high correlation, and the efficiency and effectiveness of risk management can be improved.

[0145] Further optionally, the initial risk level of each risk factor is modified according to the correlation between each risk factor to obtain the final risk level of each risk factor, including:

[0146] According to the correlation between risk factors, set reasonable risk level adjustment rules;

[0147] Apply the risk level adjustment rules to revise the initial risk level to obtain the final risk level.

[0148] Understandably, according to the correlation between risk factors, reasonable risk level adjustment rules are set, and the risk level adjustment rules are applied to correct the initial risk level to obtain the final risk level. That is, if the correlation between two risk factors is very high, greater than a preset threshold (such as greater than 0.7), it means that the two risk factors may have some kind of internal connection or mutual influence. If the initial risk level of one of the risk factors is higher, the risk level of the other risk factor can be appropriately increased. The revised risk level can more accurately reflect the actual risk status of the risk factors in the supply chain.

[0149] For example, suppose the initial risk levels are: X1 (high), X2 (medium), X3 (low). If the correlation analysis results show that the correlation between X1 and X2 is very high (such as 0.9), and the risk level of X1 is high. Then according to the adjustment rules, the risk level of X2 is adjusted from medium to high. The revised risk levels are: X1 (high), X2 (high), X3 (low).

[0150] S107, through blockchain, the key information of each link in the agricultural product supply chain and the risk level of its risk factors are stored on the chain in real time, and a unique blockchain identifier is generated for each agricultural product.

[0151] It should be noted that blockchain is a distributed ledger technology that uses encryption algorithms and network consensus mechanisms to achieve data immutability and traceability. When uploading data to the blockchain, a unique blockchain identifier (such as a hash value or digital ID) needs to be generated for each agricultural product. This unique identifier can serve as the digital ID of the agricultural product, making it easier for consumers and regulators to query the complete traceability information of the product on the blockchain network.

[0152] The key information and risk levels of risk factors in each link of the agricultural product supply chain can be stored on the blockchain in real time. That is, once new information is generated, it can be immediately recorded on the blockchain to ensure the timeliness and accuracy of the information. The specific steps for chain storage are as follows:

[0153] The sorted data can be formatted, such as converted into standard formats such as JSON and XML, to meet the data storage requirements of the blockchain network;

[0154] Encode the data, such as using Base64 encoding or other suitable encoding methods;

[0155] Use a hash function (such as SHA-256) to perform a hash operation on the formatted data to generate a unique hash value as the blockchain identifier of the agricultural product;

[0156] The formatted and encoded data, as well as the generated unique identifier, are then packaged into a blockchain transaction, and the data storage location, access rights and other related information are clearly specified in the transaction;

[0157] Submit the constructed blockchain transaction to the nodes in the blockchain network, and the nodes will verify and confirm the transaction according to the network consensus mechanism;

[0158] Once a transaction is confirmed by enough nodes in the blockchain network, it is considered valid and permanently stored on the blockchain. In this way, the key information of agricultural products and the risk level of their risk factors, as well as the generated unique identifier, are securely stored on the blockchain for subsequent query and verification;

[0159] The system then sends a notification to relevant parties (such as agricultural product producers, sellers, regulatory authorities, etc.) to inform them that the agricultural product information has been successfully uploaded to the chain and provides a query method.

[0160] Through the above steps, key information in the agricultural product supply chain and the risk levels of its risk factors are stored on the chain in real time, and a unique blockchain identifier is generated for each agricultural product, achieving transparency and traceability of information.

[0161] After the data is stored on the chain and a unique identifier is generated, consumers can query the complete traceability information of the product on the blockchain network by scanning the QR code on the agricultural product packaging or entering the identification information, including the production environment, processing technology, logistics track, sales records, and risk level of risk factors. This transparent and digital traceability management can greatly enhance consumers' trust in agricultural products. At the same time, regulatory authorities can also use blockchain technology to monitor the production, processing, transportation and sales of agricultural products in real time, promptly identify potential risk factors and take corresponding measures to intervene. This will achieve real-time monitoring and rapid response supervision of the agricultural product supply chain to ensure the quality and safety of agricultural products.

[0162] In summary, the agricultural product traceability management method based on blockchain technology of the present invention collects key information of each link in the supply chain, and determines the corresponding risk factors, provides basic data for risk management, and quantifies the possibility and potential impact of risk factors through fuzzy comprehensive evaluation, so as to deal with the uncertainty and ambiguity of each risk factor in the agricultural product supply chain, and determines the risk level of risk factors through risk matrix according to the quantified fuzzy number, so that risk management is more accurate and scientific, and further uses gray correlation analysis to consider the correlation between risk factors, and corrects the risk level to obtain a more practical final risk level. At the same time, with the help of blockchain technology, the real-time chain storage of key information and risk level of risk factors in each link of the agricultural product supply chain is realized, ensuring the immutability and traceability of data, and each agricultural product has a unique blockchain identifier, and consumers and agricultural product supervision and management agencies can easily query and obtain the complete supply chain information and risk assessment information of agricultural products, providing digital protection for the risk and traceability management of agricultural products.

[0163] Embodiment 2

[0164] See also Figure 2 The present invention proposes an agricultural product traceability management system based on blockchain technology, the system comprising:

[0165] Collection module: used to collect key information of each link in the agricultural product supply chain, including planting / breeding, packaging, processing, storage and transportation. Key information includes input usage records, production environment parameters, processing technology parameters and logistics tracks in the planting / breeding process;

[0166] Risk Factor Module: used to determine the risk factors of the current link in the agricultural product supply chain, including pesticide residues, veterinary drug residues, excessive additives, improper storage conditions and transportation contamination;

[0167] Evaluation module: used to quantitatively evaluate the evaluation indicators of each risk factor through fuzzy comprehensive evaluation based on the key information related to the risk factors. The evaluation indicators include the possibility of occurrence and the potential impact degree;

[0168] Risk level module: used to determine the initial risk level of risk factors through the risk matrix according to the probability of occurrence and potential impact of risk factors;

[0169] Correlation module: used to determine the correlation between risk factors through grey correlation analysis;

[0170] Level correction module: used to correct the initial risk level of each risk factor according to the correlation between the risk factors, and obtain the final risk level of each risk factor to reflect the actual risk status of each risk factor in the supply chain;

[0171] On-chain storage module: It is used to store the key information of each link in the agricultural product supply chain and the risk level of its risk factors in real time on the chain through blockchain, and generate a unique blockchain identifier for each agricultural product for consumers to query.

[0172] Further optionally, the evaluation module is also used for:

[0173] For each risk factor, an evaluation index system is established, and the evaluation indexes include the possibility of occurrence and the potential impact;

[0174] Based on the key information related to the risk factor assessment indicators, each risk factor is initially scored for its likelihood of occurrence and potential impact;

[0175] Through the membership function, the scores of the occurrence possibility and potential impact of each risk factor are converted into fuzzy numbers, and the fuzzy numbers of the occurrence possibility and impact degree of each risk factor are obtained.

[0176] Further optionally, the evaluation module is also used for:

[0177] Choose an appropriate membership function;

[0178] Determine the parameters of the membership function according to the range of the comprehensive score and the form of the membership function;

[0179] Substitute the scores of the occurrence possibility and potential impact of each risk factor into the membership function, calculate the membership of the corresponding fuzzy sets, and obtain the fuzzy number of the occurrence possibility and the fuzzy number of the potential impact of each risk factor, where the fuzzy number is expressed as a combination of different fuzzy sets and their memberships.

[0180] Further optionally, the risk level module is also used for:

[0181] Classify the probability of occurrence and potential impact of risk factors and construct a risk matrix;

[0182] For each risk factor, the fuzzy number of its occurrence possibility and the fuzzy number of its impact degree are combined through fuzzy operation to obtain a combined fuzzy number, which is used to represent the overall risk level of the risk factor after comprehensively considering the occurrence possibility and potential impact degree;

[0183] According to the membership distribution of the combined fuzzy numbers and the level classification standard of the risk matrix, the initial risk level of each risk factor is determined;

[0184] Determine the initial risk level of the risk factor based on its initial position in the risk matrix.

[0185] Further optionally, the risk level module is also used for:

[0186] The fuzzy number of occurrence possibility is defined as: E = (e1, e2, ..., e i ,…,e m ), where e i Indicates the membership degree of the risk factor to the i-th occurrence possibility fuzzy set;

[0187] The potential impact degree fuzzy number is defined as: F = (f1, f2, ..., f j , …, f n ), where f j Indicates the membership degree of the risk factor to the jth potential impact degree fuzzy set;

[0188] Introducing the weight vector W E =(w e1 , w e2 ,…,w ei ,…,w em ) and W F =(w f1 , w f2 ,…,w fj ,…,w fn ), and according to the fuzzy operation rules, the fuzzy number of the possibility of occurrence and the fuzzy number of the degree of influence are combined to obtain the combined fuzzy number G = (g1, g2, ..., g k , …, g p ), where g k It indicates the membership degree of the risk factor to the kth combined risk fuzzy set after considering the probability of occurrence and the potential impact. For each g k , the calculation formula is:

[0189]

[0190] Among them, wei and w fj are the weights of the fuzzy number of the possibility of occurrence and the fuzzy number of the degree of influence, which are used to adjust the membership degree e i and f j The relative importance in the combination, the correlation factor (i, j, k) is used to convert the membership degree e i and f j The combination of is mapped to the kth combined risk fuzzy set, reflecting the contribution of the corresponding combination to the overall risk.

[0191] Further optionally, the risk level module is also used for:

[0192] For each risk factor, find the maximum membership degree from the combined fuzzy numbers;

[0193] The maximum membership degree is located at the corresponding cell in the risk matrix, and the risk level mapped by the corresponding cell is used as the initial risk level of the risk factor.

[0194] Further optionally, the association module is also used for:

[0195] Select one or more data series that can comprehensively reflect the risk level of agricultural product supply chains as reference series;

[0196] The evaluation results of each risk factor are used as a comparison series;

[0197] For each moment, calculate the absolute difference between the comparison sequence and the reference sequence;

[0198] Find the maximum and minimum among all absolute differences;

[0199] The grey correlation coefficient formula is used to calculate the correlation coefficient between each risk factor and other risk factors at each time. The calculation formula is:

[0200]

[0201] Among them, Δ is the absolute difference at time t, Δmin is the minimum absolute difference, Δmax is the maximum absolute difference, and ρ is the resolution coefficient;

[0202] The correlation coefficients between each risk factor and other risk factors were averaged to obtain the correlation degree, which reflects the correlation between the two risk factors.

[0203] Further optionally, the level correction module is further used for:

[0204] According to the correlation between risk factors, set reasonable risk level adjustment rules;

[0205] Apply the risk level adjustment rules to revise the initial risk level to obtain the final risk level.

[0206] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for agricultural product traceability management based on blockchain technology, characterized in that: The method comprises: Collect key information on the current links in the agricultural product supply chain, including planting / breeding, packaging, processing, storage, transportation or sales; Identify risk factors at current stages of the agricultural product supply chain, including pesticide residues, veterinary drug residues, excessive additives, inappropriate storage conditions, and transport contamination; Based on the key information related to the risk factors, the evaluation indicators of each risk factor are quantitatively evaluated through fuzzy comprehensive evaluation, and the evaluation indicators include the possibility of occurrence and the potential impact degree; According to the probability of occurrence and potential impact of risk factors, the initial risk level of risk factors is determined through the risk matrix; The correlation between risk factors was determined through grey correlation analysis; According to the correlation between the risk factors, the initial risk level of each risk factor is revised to obtain the final risk level of each risk factor to reflect the actual risk status of each risk factor in the supply chain; Through blockchain, key information of each link in the agricultural product supply chain and the risk levels of its risk factors are stored on the chain in real time, and a unique blockchain identifier is generated for each agricultural product.

2. The agricultural product traceability management method based on blockchain technology according to claim 1 is characterized in that: Based on the key information related to the risk factors, the evaluation indicators of each risk factor are quantitatively evaluated through fuzzy comprehensive evaluation, including: For each risk factor, an evaluation index system is established, and the evaluation indexes include the possibility of occurrence and the potential impact; Based on the key information related to the risk factor assessment indicators, each risk factor is initially scored for its likelihood of occurrence and potential impact; Through the membership function, the scores of the occurrence possibility and potential impact of each risk factor are converted into fuzzy numbers, and the fuzzy numbers of the occurrence possibility and impact degree of each risk factor are obtained.

3. The agricultural product traceability management method based on blockchain technology according to claim 2 is characterized in that: The membership function is used to convert the scores of the occurrence possibility and potential impact of each risk factor into fuzzy numbers, and the fuzzy numbers of the occurrence possibility and impact of each risk factor are obtained, including: Choose an appropriate membership function; Determine the parameters of the membership function according to the range of the comprehensive score and the form of the membership function; Substitute the scores of the occurrence possibility and potential impact of each risk factor into the membership function, calculate the membership of the corresponding fuzzy sets, and obtain the fuzzy number of the occurrence possibility and the fuzzy number of the potential impact of each risk factor, where the fuzzy number is expressed as a combination of different fuzzy sets and their memberships.

4. The agricultural product traceability management method based on blockchain technology according to claim 2 is characterized in that: The initial risk level of the risk factor is determined through the risk matrix according to the probability of occurrence and the potential impact of the risk factor, including: Classify the probability of occurrence and potential impact of risk factors and construct a risk matrix; For each risk factor, the fuzzy number of its occurrence possibility and the fuzzy number of its impact degree are combined through fuzzy operation to obtain a combined fuzzy number, which is used to represent the overall risk level of the risk factor after comprehensively considering the occurrence possibility and potential impact degree; According to the membership distribution of the combined fuzzy numbers and the level classification standard of the risk matrix, the initial risk level of each risk factor is determined; Determine the initial risk level of the risk factor based on its initial position in the risk matrix.

5. The agricultural product traceability management method based on blockchain technology according to claim 4 is characterized in that: The initial risk level of each risk factor is determined based on the membership distribution of the combined fuzzy number and the level classification standard of the risk matrix, including: The fuzzy number of occurrence possibility is defined as: E = (e1, e2, ..., e i ,…,e m ), where e i Indicates the membership degree of the risk factor to the i-th occurrence possibility fuzzy set; The potential impact degree fuzzy number is defined as: F = (f1, f2, ..., f j , …, f n ), where f j Indicates the membership degree of the risk factor to the jth potential impact degree fuzzy set; Introducing the weight vector W E =(w e1 , w e2 ,…,w ei ,…,w em ) and W F =(w f1 , w f2 ,…,w fj ,…,w fn ), and according to the fuzzy operation rules, the fuzzy number of the possibility of occurrence and the fuzzy number of the degree of influence are combined to obtain the combined fuzzy number G = (g1, g2, ..., g k , …, g p ), where g k It indicates the membership degree of the risk factor to the kth combined risk fuzzy set after considering the probability of occurrence and the potential impact. For each g k , the calculation formula is: Among them, w ei and w fj are the weights of the fuzzy number of the possibility of occurrence and the fuzzy number of the degree of influence, which are used to adjust the membership degree e i and f j The relative importance in the combination, the correlation factor (i, j, k) is used to convert the membership degree e i and f j The combination of is mapped to the kth combined risk fuzzy set, reflecting the contribution of the corresponding combination to the overall risk.

6. The agricultural product traceability management method based on blockchain technology according to claim 4 is characterized in that: The initial risk level of each risk factor is determined based on the membership distribution of the combined fuzzy number and the level classification standard of the risk matrix, including: For each risk factor, find the maximum membership degree from the combined fuzzy numbers; The maximum membership degree is located at the corresponding cell in the risk matrix, and the risk level mapped by the corresponding cell is used as the initial risk level of the risk factor.

7. The agricultural product traceability management method based on blockchain technology according to claim 1 is characterized in that: Determining the correlation between risk factors through grey correlation analysis includes: Select one or more data series that can comprehensively reflect the risk level of agricultural product supply chains as reference series; The evaluation results of each risk factor are used as a comparison series; For each moment, calculate the absolute difference between the comparison sequence and the reference sequence; Find the maximum and minimum among all absolute differences; The grey correlation coefficient formula is used to calculate the correlation coefficient between each risk factor and other risk factors at each time. The calculation formula is: Among them, Δ is the absolute difference at time t, Δmin is the minimum absolute difference, Δmax is the maximum absolute difference, and ρ is the resolution coefficient; The correlation coefficients between each risk factor and other risk factors were averaged to obtain the correlation degree, which reflects the correlation between the two risk factors.

8. The agricultural product traceability management method based on blockchain technology according to claim 1 is characterized in that: The initial risk level of each risk factor is modified according to the correlation between each risk factor to obtain the final risk level of each risk factor, including: According to the correlation between risk factors, set reasonable risk level adjustment rules; Apply the risk level adjustment rules to revise the initial risk level to obtain the final risk level.

9. An agricultural product traceability management system based on blockchain technology, used to implement the agricultural product traceability management method based on blockchain technology as described in any one of claims 1 to 8, characterized in that: The system comprises: Collection module: used to collect key information of the current link in the agricultural product supply chain, including planting / breeding, packaging, processing, storage, transportation or sales; Risk Factor Module: used to determine the risk factors of the current link in the agricultural product supply chain, including pesticide residues, veterinary drug residues, excessive additives, improper storage conditions and transportation contamination; Evaluation module: used to quantitatively evaluate the evaluation indicators of each risk factor through fuzzy comprehensive evaluation based on the key information related to the risk factors. The evaluation indicators include the possibility of occurrence and the potential impact degree; Risk level module: used to determine the initial risk level of risk factors through the risk matrix according to the probability of occurrence and potential impact of risk factors; Correlation module: used to determine the correlation between risk factors through grey correlation analysis; Level correction module: used to correct the initial risk level of each risk factor according to the correlation between the risk factors, and obtain the final risk level of each risk factor to reflect the actual risk status of each risk factor in the supply chain; On-chain storage module: used to store the key information of each link in the agricultural product supply chain and the risk level of its risk factors in real time on the chain through blockchain, and generate a unique blockchain identifier for each agricultural product.

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