Multidimensional analysis model of agricultural product supply chain competition based on mean fuzzy

Abstract

Efficient and reliable fresh agricultural products supply chain is the key to meet the demand of consumers for fresh agricultural products, and also the guarantee for suppliers to realize their economic benefits. Therefore, a multi-dimensional analysis model of agricultural products supply chain competition based on fuzzy mean value is proposed. Firstly, the information distribution model of multi-dimensional analysis of agricultural product supply chain competition is proposed. On this basis, the multi-dimensional analysis information scheduling fusion of agricultural product supply chain competition is processed. Then, the application of mean value fuzzy in agricultural product supply chain is analyzed. According to the identification module of agricultural product information code, the fuzzy comprehensive evaluation model of supply chain and the mean fuzzy analytic hierarchy process, the competition of agricultural product supply chain is established Dimension analysis model. The experimental results show that the performance score of agricultural product supply chain is higher, the accuracy of supply chain information diagnosis is higher, and the clustering of agricultural product supply chain information diagnosis is better.

Keywords

Mean fuzzy agricultural product supply chain competition multidimensional analysis model mean fuzzy analytic hierarchy process

1 Introduction

A series of safety incidents of agricultural products make people pay more attention to the safety and health of agricultural products. With the development of economic globalization and international food trade, food safety has become an important and global public health problem [19]. Agricultural products are mainly produced by scattered farmers, lack of modern planting and breeding technology, difficult to obtain scale effect, low income and cost, and lack of integrated organization of production, supply and marketing, which is difficult to meet the needs of the market. In the circulation link, the logistics level of agricultural products is low and the loss is huge, which limits the realization of the value of agricultural products and the development of rural economy. Agricultural product safety accidents will not only bring serious economic losses to the whole food supply chain, but also threaten people’s life safety [2, 11]. In view of the loss of consumers’ confidence in food safety, countries are formulating the technology, regulations and standards related to food safety management, and using the mean fuzzy technology to establish and improve the food safety supervision and management system.

Reference [13] proposed the distribution system analysis of synthetic grain protectant supplied to small-scale corn farmers in Zimbabwe, and the impact on the distribution of sealed grain storage bags. For small farmers, non pesticide sealed bag grain storage is a promising alternative to synthetic pesticide treatment and polypropylene bag storage, but it is not convenient for rural customers to use sealed bags. Therefore, the factors influencing the distribution channel design of food synthetic insecticides were studied, and the experience of airtight bag manufacturers was considered. Interviews with key informants of pesticide producers and / or distributors and agricultural traders from the state to the grassroots level in the supply chain, supplemented by farmers’ panel discussions. The supply chain of pesticide agricultural products was analyzed in terms of channel length and respiratory rate. Literature [5] proposed the fresh degree incentive mechanism of fresh agricultural products supply chain based on consumer utility. The improvement of the overall freshness of fresh agricultural products supply chain is of great significance to reduce waste, improve the overall profit level of the supply chain and social welfare. Based on the theory of consumer utility, this paper studies the supply chain system composed of two suppliers and a single retailer, and constructs the freshness incentive model of fresh agricultural products in a single cycle. The model takes the supplier as the leader and adopts the Stackelberg game method to solve the model. The optimal pricing strategy of the supplier and retailer under the equilibrium state and the supplier freshness effort selection are obtained. Manufacturers and the government should work together to guide consumers’ consumption concept, reduce the moral hazard caused by information asymmetry as much as possible. Under the condition that the market price substitution rate remains unchanged or even decreases, the replacement rate of freshness should be increased, so that consumers’ willingness to pay the price will rise, and their bargaining power can be improved to obtain more profits.

Although the above research has made some progress, but the outbreak of agricultural product quality and safety incidents makes consumers put forward new requirements for agricultural product safety, and urgently need new management mode and technology to ensure the quality and safety of agricultural products and people’s health. Therefore, a multi-dimensional analysis model of agricultural product supply chain competition based on fuzzy mean value is proposed. By using the fusion correlation detection method, the information of agricultural product supply chain is reconstructed, and the statistical characteristics of agricultural product supply chain information are extracted to realize the reliability diagnosis of agricultural product supply chain information. It is imperative to improve the competitiveness of agricultural products in the face of fierce competition in the international food supply chain. Referring to the existing agricultural development experience, improving the level of informatization is an important means to improve the competitiveness of agricultural products supply chain. As an important information collection and transmission technology, mean based fuzzy method has been widely used in supply chain management of various industries, and is an effective method to enhance the competitiveness of supply chain.

1.1 Novelty

By using the fusion correlation detection method, the information of agricultural product supply chain is reconstructed, and the statistical characteristics of agricultural product supply chain information are extracted to realize the reliability diagnosis of agricultural product supply chain information. In order to optimize the scheduling and adaptive diagnosis of multi-dimensional analysis information of agricultural product supply chain competition, a multi-dimensional analysis information model for reliability diagnosis of agricultural product supply chain competition information is established. The reliability of multi-dimensional analysis information of agricultural product supply chain competition is diagnosed and tested by using distributed hierarchical clustering analysis method, and the support vector machine model is constructed. The information distribution model of agricultural product supply chain based on XML / Web service is designed by using multi feature distributed detection method. Combined with the equilibrium control method, the dynamic transmission model of agricultural product supply chain information is constructed. On the basis of constructing the multi-dimensional analysis information distribution model of agricultural product supply chain competition, the statistical characteristic sequence analysis method based on XML / Web service is adopted, and the multi-dimensional analysis information scheduling of agricultural product supply chain competition is integrated. The single variable time series of multi-dimensional analysis information scheduling of agricultural product supply chain competition is regarded as the association rule set of agricultural product supply chain information [0,1], Combined with the similarity distribution of agricultural product supply chain information, the fuzzy evolutionary characteristics are analyzed to improve the competitiveness of agricultural product supply chain.

1.2 Motivation

Efficient and reliable supply chain of fresh agricultural products is the key to meet the demand of consumers for fresh agricultural products, and also the guarantee for suppliers to realize their economic benefits. Therefore, a multi-dimensional analysis model of agricultural product supply chain competitiveness based on fuzzy mean is proposed. The outbreak of the quality and safety incidents of agricultural products makes consumers put forward new requirements for the safety of agricultural products. New management modes and technologies are urgently needed to ensure the quality and safety of agricultural products and the health of the people.

2 Multi dimensional analysis information distribution model and information fusion scheduling of agricultural product supply chain competition

Through the establishment of agricultural product supply chain information reliability detection model, using the fusion correlation detection method, the agricultural product supply chain information distributed reconstruction, extraction of agricultural product supply chain information statistical characteristics, the realization of agricultural product supply chain information reliability diagnosis, the establishment of agricultural product supply chain integrated management mode optimization selection model, combined with innovative agricultural supply chain drive Dynamic construction [21].

2.1 Multi dimensional analysis information distribution model of agricultural product supply chain competition

In order to realize the reliability diagnosis of multi-dimensional analysis information of agricultural product supply chain competition, make the distribution and management of agricultural product supply chain meet the needs of customers, and improve the supply quality of agricultural product supply chain, it is necessary to optimize scheduling and self-adaptive diagnosis of multi-dimensional analysis information of agricultural product supply chain competition combined with XML/Web service technology, and establish multidimensional analysis information of agricultural product supply chain competition Information reliability diagnosis model [20]. According to the participation degree of each enterprise node in the supply chain, the business scheduling and adaptive adjustment are carried out. The distributed hierarchical clustering analysis method is used to diagnose and detect the information reliability of multi-dimensional analysis of agricultural product supply chain competition, and the support vector machine model is constructed In the process of dynamic transmission of multi-dimensional analysis information of agricultural product supply chain competition, the dynamic learning function of multidimensional analysis information of agricultural product supply chain competition is obtained as follows: $P (e_{i}) = A + S_{j}$ (1)

In the formula, A represents the basic information value of multidimensional analysis of agricultural product supply chain competition, and S_j represents the information antece elements of supply chain. Based on the dynamic transmission of multi-dimensional analysis information of agricultural product supply chain competition, the reliability of multi-dimensional analysis information of agricultural product supply chain competition is optimized, and the self-adaptive matching model is constructed. Fuzzy clustering analysis method is used to obtain the clustering analysis formula of agricultural product supply chain reliability diagnosis: $C (n_{j}) = (G_{l} + G_{d}) P (e_{i}) + E_{Tx (l, d_{j})}$ (2)

In the formula, G_l is the quality reliability of the agricultural product supply chain, G_d is the product qualification of the agricultural product supply chain, and E_{Tx(l,d_j)} is the on-time delivery rate of the agricultural product supply chain. The information distribution model of agricultural product supply chain is designed based on XML/Web services by using the distributed detection method of multiple characteristics. The dynamic transmission of agricultural product supply chain information is constructed by combining the balanced control method. The integrated management and data fitting of agricultural product supply chain are carried out with regression analysis method. The expression is as follows: $H (t) = C (n_{j}) - \min (G_{l} + G_{d}) + {wk}_{μ} (t)$ (3)

In the formula, A1 is the relative weight; k_μ (t) is the characteristic quantity of agricultural product supply chain information impulse response at t time; combined with the limited steady-state conditions, the integrated management and information distributed construction of agricultural product supply chain are carried out to improve the ability of distributed detection and diagnosis of agricultural product supply chain information.

2.2 Multi dimensional analysis of agricultural product supply chain competition information scheduling fusion processing

Onthe basis of constructing the information distribution model of multi-dimensional analysis of agricultural supply chain competition, the XML / Web service based statistical feature sequence analysis method is used to fuse the multi-dimensional analysis information scheduling of agricultural product supply chain competition, and the single variable time series of statistical big data of agricultural product supply chain competition multidimensional analysis information scheduling is {x_n}, u _j (t + 1) is used as the association rule set of agricultural product supply chain information within the scope of [0,1], and the fuzzy evolution feature analysis is carried out combining with the similarity distribution of agricultural product supply chain information [3]. mbest (t + 1) is used to represent the correlation characteristics of agricultural product supply chain information, and the fusion center of agricultural product supply chain information diagnosis is obtained, mbest (t + 1) is used to represent the statistical characteristic quantity of agricultural product supply chain information in the p_j (t + 1) cluster center. Under the integrated management mode of agricultural product supply chain, the standard vector quantization characteristic distribution sets a₁ and a₂ are constructed. In the M dimensional random distribution model, the standard characteristic quantity p_g (t) of agricultural product supply chain information reliability diagnosis is defined as: $\begin{matrix} p_{g} (t) = mbest (t + 1) + \\ arg min {f (p_{j} (t)), j = 1, 2, \dots, n} \end{matrix}$ (4)

In the formula, f (p_j (t)) is the fuzzy clustering center for integrated management and information reliability diagnosis of agricultural product supply chain, and j is used as the node for information search. In the optimal position, the optimal position of information fusion of agricultural product supply chain after t generation search is obtained. Combined with big data fuzzy clustering control method, the information fusion of agricultural product supply chain is carried out, and the feature vector of information fusion is obtained a₁ and a₂ are determined by the following formula: $\begin{matrix} a_{1} = c_{1} r_{1} \\ a_{2} = c_{2} r_{2} \end{matrix}$ (5)

In the formula, r₁ and r₂ are the random vectors of M dimension agricultural product supply chain information fusion; c₁ is the statistical characteristic sequence under the integrated management mode of agricultural product supply chain; c₂ is the characteristic quantity of interference components. According to the above analysis, the integrated management and information fuzzy scheduling of agricultural product supply chain are realized, and the ability of feature extraction and quantitative regression analysis of statistical big data of agricultural product supply chain integrated management mode is improved [12]. On this basis, the information reliability feature detection model of agricultural product supply chain is constructed as {x (t₀ + iΔt)}, i = 0, 1, ⋯ , N - 1. The reliability diagnosis of agricultural product supply chain information is carried out, and the update rules of information diagnosis of agricultural product supply chain are analyzed. The expression of information scheduling fusion processing for multi-dimensional analysis of supply chain competition is as follows: $λ = \frac{1}{1 + α (\frac{\partial S}{\partial t})^{2}} + k_{μ}^{\land} (t) + Q (t + 1)$ (6)

In the formula, $\frac{\partial S}{\partial t}$ is the fuzzy parameter distribution set of diagnosis information of agricultural product supply chain extracted at time t, α is the residual characteristic quantity of agricultural product supply chain information, and $k_{μ}^{\land} (t)$ is the random probability density distribution. Q is a process factor representing the information diagnosis of agricultural product supply chain. According to the above analysis, the multi-dimensional analysis information scheduling fusion processing of agricultural product supply chain competition is constructed, and the optimization design of agricultural product supply chain information reliability diagnosis is carried out. On the basis of the above information scheduling fusion processing of agricultural supply chain, the reliability diagnosis of agricultural product supply chain information is carried out, and the feature analysis model of agricultural product supply chain information chain is established. The extracted diagnosis data of agricultural product supply chain information are processed by adaptive fusion clustering with adaptive optimization method, and the information of agricultural product supply chain is analyzed by cumulative variance analysis Integrated management and scheduling, time-frequency feature transformation of agricultural products supply chain information, A1 is obtained, and principal component analysis method is used to obtain the principal component characteristics of big data information flow: $C_{or 3} = \frac{(x_{n} - \bar{x}) (x_{n - d} - \bar{x}) (x_{n - D} - \bar{x})}{{(x_{n} - \bar{x})}^{3}}$ (7)

In the formula, x_n represents the discrete feature sequence of agricultural product supply chain information. The integrated information management model of agricultural product supply chain is constructed, and the average distribution of statistical characteristics of supply chain information is obtained as follows: $\bar{x} = \frac{1}{N} \sum_{i = 1}^{N} | x_{i} |$ (8)

In the discrete subspace, integrated scheduling of agricultural product supply chain is carried out, and the characteristic distribution subspace of integrated information diagnosis of agricultural product supply chain is w, which is an n×m-dimensional characteristic distribution set of integrated information diagnosis of agricultural product supply chain [7, 18], The statistical characteristic p_q is obtained, and the probability distribution function is constructed as P (n_i) = {p_k|pr_kj = 1, k = 1, 2, ⋯ , m}. The ontology regularization scheduling of agricultural product supply chain is carried out, and the fuzzy association rule set v_i of agricultural product supply chain is established: $\bar{W_{i}} = \frac{1}{m} \sum_{q = 1}^{m} W (v_{i}, p_{q})$ (9)

C is used to represent the task set of agricultural product supply scheduling, and C (v_i, v_j) is the fuzzy index distribution set of integrated information scheduling of agricultural product supply chain. The characteristic quantities v_i and v_j of association rules are obtained. According to the above analysis, the regression analysis model of agricultural product supply chain information reliability diagnosis is constructed as follows: $x (t) = \sum_{i = 0}^{p} a (θ_{i}) s_{i} (t) + n (t)$ (10)

In the formula, p is the conditional probability density of information reliability diagnosis of agricultural product supply chain, n (t) is the big data scheduling interference item of integrated management of agricultural product supply chain, and s_i (t) is the statistical characteristic quantity of big data of integrated management of agricultural product supply chain, s_i (t) is the principal component characteristic quantity of integrated scheduling of agricultural product supply chain. By using fuzzy clustering analysis method, the information fuzzy adaptive scheduling of agricultural product supply chain reliability diagnosis is constructed: $R_{s}^{(0)} = \sum_{n = 0}^{k} (R_{s}^{(n)}, d_{γ n}) d_{γ n} + R_{s}^{(k + 1)}$ (11)

In the formula, $R_{s}^{(n)}$ is the implementation cost of the supply chain, d_γn is the information reconstruction dimension of the agricultural supply chain, and $R_{s}^{(k + 1)}$ is the limited data set of the agricultural supply chain.

3 Research on the application of fuzzy mean value in agricultural product supply chain

3.1 Multi dimensional analysis process of agricultural products supply chain competition

The application of mean fuzzy is based on RFID technology and sensor technology. The application research of mean fuzzy in supply chain is mostly based on the application of RFID in supply chain.

The application of mean fuzzy can collect the manufacturer’s product sales information in real time and conveniently, so as to reflect the manufacturer’s market demand in time, and transmit the market demand information to the suppliers in time, so that the suppliers have the conditions to truly reflect, thoroughly understand and scientifically predict the manufacturer’s supply demand, so as to promote collaborative forecasting, collaborative planning, forecasting and replenishment planning and just in time system Production makes the relationship between suppliers and manufacturers more closely, and it can also achieve good implementation effect in the implementation of VML. The application of mean fuzzy in agricultural product supply chain is mainly from the qualitative and quantitative analysis of the implementation income of mean fuzzy, the application of mean fuzzy in all aspects of the supply chain, the influence of mean fuzzy on the overall efficiency of the supply chain, the application of mean fuzzy in various fields of supply chain and the application of mean fuzzy in agricultural product information traceability This paper explores the application prospect and economic benefits of fuzzy mean value in supply chain. The agricultural product information management system based on RFID technology and public key realizes the information traceability of agricultural products in the supply chain. The application of RFID technology in animal traceability and health monitoring, precision agriculture and circulation and its impact on the quality of agricultural products, the application of RFID technology in the production, processing, circulation, sales and consumption process of safe food supply chain, and the practical application research of agricultural products safety traceability supply chain based on RFID and the fuzzy mean value of agricultural products were carried out in cooperation with an agricultural company The theoretical exploration and research. The flow chart of multidimensional analysis of agricultural product supply chain competition is shown in Fig. 1.

Fig. 1

Flow chart of multidimensional analysis of agricultural product supply chain competition.

The specific analysis steps are as follows:

(1) Analysis of agricultural products supply chain system

The supply chain of agricultural products is a series of processes, which take agricultural products as the object, around the management and control of the logistics, capital flow and information flow, coordinate the interests of agricultural means of production suppliers, farmers, agricultural product operators and consumers, starting from the purchase of agricultural materials, and completing the production, purchase, transportation and market distribution of agricultural products. It is a complex network system with multi-level, multi type and overall function.

(2) Establish a multi-dimensional analysis model of agricultural products supply chain competition

The node enterprises in the network structure of agricultural product supply chain are represented by operators in the multidimensional analysis model diagram, and the agricultural product logistics is transformed into the signal flow in the multidimensional analysis model diagram. According to the operation characteristics of the agricultural product supply chain network system, the multi-dimensional analysis model diagram is used to simulate the network system of agricultural product supply chain.

(3) Carry out multidimensional analysis model operation

The state probability of the output signal is obtained by quantitative operation and analysis of the input signal state and unit state of each operator, and the state probability of the output signal representing the system is calculated step by step. The reliability data of the unit is put into the multidimensional analysis model diagram to perform the multidimensional analysis model operation.

(4) System reliability evaluation

The reliability of the system is obtained after the operation of multidimensional analysis model, and the system is evaluated according to its functions and requirements.

3.2 Multi dimensional analysis model of agricultural product supply chain

This paper selects a representative agricultural product supply chain structure diagram as the research object. In this structure diagram, two farmer production groups and one production base are selected as the suppliers of agricultural products. The reason for this treatment is that small-scale production is mainly carried out in rural areas or households with farmers as the unit. As the main achievement of new rural construction, the production base is in the pilot process and has not been popularized. Therefore, the above-mentioned treatment in the proportion of agricultural product suppliers is in line with the actual situation. The situation is consistent, which makes the research process closer to the reality.

3.2.1 Agricultural product information code identification module

The information code of agricultural products is marked, and the information of the previous link of the supply chain is constantly updated in each link, so as to ensure that the supply chain links affecting the quality of agricultural products can be found in time when the quality problems of agricultural products occur. In the control system designed in this paper, each agricultural product has a unique barcode, and the barcode related information is input into the database of the control system for storage. When agricultural products are transferred to the next link in the supply chain, the identification module of the control system identifies them [16]. The information codes of agricultural products are as follows:

Prompt the user for the coefficients a, b, and c.

Read a, b, and c discriminant←b∧2-4*a*cif discriminat>0x 1←(-b+sqrt(discriminant)) / (2**a)x 1←(-b- sqrt(discriminant)) / (2 a)

Write msg that equation has two distinct real roots.

Write out the two roots.elseif discriminant==0x1←-b/ (2*a)

Write msg that equation has two identical real roots.

Write out the repeated roots.elsere al_p art←-b/ (2*a)imag_p art←sqrt(ab s(di s criminant)) / (2*a)

Write msg that equation has two complex roots. Prompt the user for the coefficients a,b,and c.

Read a, b, and c discriminant←b∧2-4*a*cif discriminat>0x 1←(-b+sqrt(discriminant)) / (2**a)x 1←(-b- sqrt(discriminant)) / (2 a)

Write msg that equation has two distinct real roots.

Write out the two roots.elseif discriminant==0x1←-b/ (2*a)

Write msg that equation has two identical real roots.

Write out the repeated roots.elsere al_p art←-b/ (2*a)imag_p art←sqrt(ab s(di s criminant)) / (2*a)

Write msg that equation has two complex roots.

This paper uses RFID technology to design agricultural product information code identification module, and chooses mfrc52202hn1 RF chip. The working frequency of the RFID chip is 13.56 MHz, and the working temperature is –25°C∼85°C. It can identify the agricultural product identification code in non-contact condition. When the agricultural product identification module works, the receiving pin receives the agricultural product identification code information and judges it as the agricultural product in the supply chain. Under the control of FPGA chip, the RF chip transmits the comparison results to the microprocessor module of risk control system. The microprocessor processes the received information, and then transmits it to the RF chip according to the corresponding pin working mode of FPGA chip to complete the identification of agricultural product information. FPGA the working mode of chip pin is shown in Table 1.

Table 1
Pin working mode of FPGA chip

Signal Direction Explain

CLOCK Input Clock signal

RES Input Restart transmission / reset

P0∼P3 Input Corresponding pin request

P4∼P6 Input Special input pin

P7∼P9 Input Low level input

UPDOWN[1:0] Input Configuration control

GOTO[1:0] Output Output signal

Signal	Direction	Explain
CLOCK	Input	Clock signal
RES	Input	Restart transmission / reset
P0∼P3	Input	Corresponding pin request
P4∼P6	Input	Special input pin
P7∼P9	Input	Low level input
UPDOWN[1:0]	Input	Configuration control
GOTO[1:0]	Output	Output signal

The RF chip transmits the updated agricultural product information to the database of the control system to update the information in the database.

The RFID chip inputs the identified agricultural product information code into the AD converter chip ADC0809CCN connected with the microprocessor. The AD converter converts the digital signal of information code into analog signal, and the analog signal is input into micro processing, which drives the microprocessor for preliminary data processing [15]. The microprocessor outputs the processed signal and transmits it to the computer through the HDMI communication port. In the computer, the data processed by microprocessor is further analyzed and processed to control the quality and safety risk of agricultural products supply chain. After the initial processing signal is transmitted to the computer by the microprocessor, the clock signal of the internal register of the microprocessor becomes low level, the driver chip resets and the processing program is closed. When the AD converter inputs a new signal, the clock signal is converted to a high level and the above process is repeated.

3.2.2 Fuzzy comprehensive evaluation model of supply chain

The analytic hierarchy process is used to determine the index weight. The analytic hierarchy process (Analytic Hierarch Process, abbreviated as AHP) is a multi criteria decision-making method which combines qualitative and quantitative analysis proposed by the famous American operational research scientist t.l.satty in the 1970 s. It is a decision-making method that decomposes the relevant elements of decision-making problems into objectives, criteria, schemes and other levels, and carries out qualitative and quantitative analysis on this basis [1]. It hierarchizes and quantifies human thinking process, and provides quantitative basis for analysis, decision-making, prediction, or control with mathematical methods [4]. When using AHP to determine the weight of indicators, the problem should be divided into different components according to the nature of the problem and the total goal achieved. At each level, the judgment matrix can be determined by comparing the factors of this layer according to a factor of the upper layer. The weight of the factors on the criterion can be obtained through matrix calculation, and finally the factor pair can be calculated According to the combination weight of the overall objective, the weight of different factors is obtained, as shown in Table 2.

Table 2
Weight of different factors

Scale Meaning

1 It means that two factors have the same importance compared with each other

3 Indicates that one factor is slightly more important than the other

5 It means that one factor is more important than the other

7 It means that one factor is more important than the other

9 It means that one factor is more important than the other

2, 4, 6, 8 Represents the median value of two adjacent judgments

Scale	Meaning
1	It means that two factors have the same importance compared with each other
3	Indicates that one factor is slightly more important than the other
5	It means that one factor is more important than the other
7	It means that one factor is more important than the other
9	It means that one factor is more important than the other
2, 4, 6, 8	Represents the median value of two adjacent judgments

3.2.3 Mean fuzzy analytic hierarchy process

The basic idea of fuzzy comprehensive evaluation is: many factors are divided into several categories according to their nature, and each category contains several factors [14, 17]. First, the comprehensive evaluation is carried out according to the category, and then the comprehensive evaluation is carried out for all categories. The specific steps are as follows:

(1) Set up evaluation factor set

The evaluation factor set is the set of evaluation indexes of supply chain competitiveness, which is divided into two levels. According to the evaluation index system in Table 1, X can be divided into $X = \sum_{i = 1}^{n} X_{i}$ and X_i ∩ X_j = ∅ (i ≠ j), which is called the first level index set. Each first level index is X ={ X_i } further divided, and X_i = { X_ij } (j = 1, …, m) is called the second level index set.

(2) Determine evaluation set

Evaluation set is a set of comments used to measure the performance of the evaluated object on the index. Let the evaluation set of this model be Y ={ Y1, Y2, Y3, Y4, Y5 }.

(3) Determine index weight set

Determine the weight of each index, the first level index A = { a_i } (i = 1, …, n), meet $\sum_{i = 1}^{n} a_{i} = 1$ ; the second level index weight A_i = { a_ij } (j = 1, …, m), meet $\sum_{ij = 1}^{n} a_{ij} = 1$ .

(4) Establishing single factor fuzzy evaluation matrix

First, single factor evaluation is carried out on the secondary index X_ij to determine the probability distribution of the index factor X_ij of the supply chain on each comment in the evaluation set. The fuzzy mapping: f : X_i → Y is established, and the single factor evaluation matrix of the secondary index set X_i ={ X_ij } is obtained: $R_{i} = [\begin{matrix} r_{i 1}^{1} & r_{i 1}^{2} & r_{i 1}^{3} & r_{i 1}^{4} & r_{i 1}^{5} \\ r_{i 2}^{1} & r_{i 2}^{2} & r_{i 2}^{3} & r_{i 2}^{4} & r_{i 2}^{5} \\ r_{i 3}^{1} & r_{i 3}^{2} & r_{i 3}^{3} & r_{i 3}^{4} & r_{i 3}^{5} \\ r_{i 4}^{1} & r_{i 4}^{2} & r_{i 4}^{3} & r_{i 4}^{4} & r_{i 4}^{5} \end{matrix}]$ (12)

In the formula, $r_{ij}^{k}$ represents the allocation rate of the indicator element X_ij of the supply chain in the comment k. These are the normalized values, namely $\sum_{k} r_{ij}^{k}$ .

(5) Carry on the fuzzy comprehensive evaluation

The single element evaluation matrix R_i and weight vector A_i of the secondary index set X_i ={ X_ij } are used for fuzzy comprehensive evaluation [9, 8]. The fuzzy comprehensive evaluation vector of the secondary index is obtained and normalized: $\begin{matrix} B_{i} = A_{i} {oR}_{i} : (b_{i}^{1}, b_{i}^{2}, b_{i}^{3}, b_{i}^{4}, b_{i}^{5}); \\ i = 1, \dots, m \end{matrix}$ (13)

In the formula, $b_{i}^{k}$ is the value of the membership function of the comprehensive evaluation B_i at the k comment, and o represents the fuzzy operation. According to the above formula, a multidimensional analysis model of agricultural product supply chain competition is obtained, as shown in Fig. 2.

Fig. 2

Multi dimensional analysis model of agricultural product supply chain competition.

According to the multi-dimensional analysis model of agricultural product supply chain competition in Fig. 2, the reliability Diagnosis design of agricultural product supply chain is carried out by using two parallel input output control ports [6, 10], and the information dynamic transmission and reliability diagnosis of agricultural product supply chain are carried out in VME bus.

4 Experimental analysis

This paper designs a multi-dimensional analysis model of agricultural product supply chain competition based on mean fuzzy. In order to verify the effect and performance of the model, we will test it. The specific analysis content is as follows.

4.1 Experimental content

In order to scientifically test the performance of the multi-dimensional analysis model of agricultural product supply chain competition, the data source is the agricultural product information data in the database of China Customs Data intelligence network. This experiment will analyze the effectiveness and feasibility of the multidimensional analysis model of agricultural product supply chain competition designed in this paper by comparing the performance of literature [13] and literature [5]. The comparison index of the control experiment is the performance score of the supply chain in the experimental cycle. Supply chain performance is the evaluation of the overall operation performance of the whole supply chain, the supply chain node enterprises and the cooperative relationship between the node enterprises in the supply chain. This experiment evaluates the performance score of the supply chain by scoring each link of the supply chain.

4.2 Parameter setting

Two agricultural product supply chains with parameter error less than±15% were selected as the experimental objects in this experiment. They were labeled as control group and experimental group respectively, corresponding to the risk control system of experimental group and two control groups. The specific parameters of three agricultural product supply chains are shown in Table 3.

Table 3
Experimental supply chain parameters

Parameter The model of this paper Literature [13] model Literature [5] model

Types of agricultural products 31 30 25

Number of supply chain links 8 7 6

Number of suppliers 138 121 100

Number of manufacturers 194 185 167

Number of distributors 356 346 304

Number of distribution logistics routes 24 33 28

Agricultural products storage node 3 4 5

Parameter	The model of this paper	Literature [13] model	Literature [5] model
Types of agricultural products	31	30	25
Number of supply chain links	8	7	6
Number of suppliers	138	121	100
Number of manufacturers	194	185	167
Number of distributors	356	346	304
Number of distribution logistics routes	24	33	28
Agricultural products storage node	3	4	5

Without the application of any risk control system, the three supply chains operate according to the original operation mode, and the performance scores of the three agricultural product supply chains have little difference. The supply chain performance score is compared with the initial performance score after applying the three risk control systems. The experimental data were recorded. Analyze the experimental data and draw the experimental conclusion.

4.3 Experimental results and analysis

The experimental results are shown in Fig. 3. The information in Fig. 3 is analyzed to draw the conclusion of this experiment.

Fig. 3

Comparative experimental results of risk control of different agricultural products supply chain competition multidimensional analysis model.

It can be seen from Fig. 3 that the performance scores of agricultural products supply chain in this paper are higher than those in references [13] and [5] after applying three groups of risk control models. It shows that the application of the experimental group risk control model can effectively control the supply chain risk and improve the supply chain performance score. In this paper, the multi-dimensional analysis of agricultural products supply chain competition based on fuzzy mean value can improve the performance of the supply chain, and the performance is better. Using this method and literature [13] and literature [5] method comparative analysis of agricultural product supply chain competition multidimensional analysis information diagnostic accuracy, get its diagnostic accuracy comparison results are shown in Table 4.

Table 4

Comparison of diagnostic accuracy of multidimensional analysis of agricultural product supply chain competition

Number of iterations	The method of this paper	Literature [13]	Literature [5]
100	0.894	0.724	0.645
200	0.934	0.856	0.756
300	0.956	0.676	0.674
400	0.985	0.701	0.790
500	0.991	0.681	0.812

Analysis of Table 4 shows that the accuracy of this method for agricultural supply chain information diagnosis is higher than that of literature [13] and literature [5], indicating that the reliability of this method for agricultural product supply chain information diagnosis is better. On this basis, the distribution scatter characteristics of supply chain information diagnosis are counted, and the output statistical scatter diagram is shown in Fig. 4.

Fig. 4

Statistical distribution scatter diagram of agricultural product supply chain information diagnosis.

Analysis of Fig. 4 shows that the clustering of agricultural products supply chain information diagnosis using this method is better, the ability of integrated management is improved, and the competitiveness of agricultural product supply chain based on fuzzy mean value is significantly improved.

5 Conclusion and prospect

5.1 Conclusion

In view of the problems existing in the traditional supply chain of agricultural products in China, considering the various demands of consumers for agricultural products, this paper analyzes the application of fuzzy mean value in the supply chain of agricultural products. This paper discusses the application of fuzzy mean value in the production, processing, transportation, storage and sales of agricultural products and its future application trend.

The multi-dimensional analysis model designed in this paper can improve the performance of the supply chain and has advantages.

The results show that this method has good reliability and strong adaptability for fresh agricultural products supply chain information diagnosis, which improves the optimal scheduling ability of fresh agricultural products supply chain, the clustering of information diagnosis analysis is good, and the integrated management ability is significantly improved.

In the future research, through the introduction of digital supply chain technology and cloud platform, and the improvement method is applied to the actual agricultural product supply chain, in order to improve the competitiveness of agricultural product supply chain.

5.2 Prospect

Due to the limitation of data acquisition, the practical application of this model may be limited. In order to enhance the operability of the model, an example is analyzed, but no case study is carried out. In the future research work, it is necessary to improve the methods of data collection and establish the data sharing mechanism of multidimensional analysis model of agricultural product supply chain competition, so as to obtain comprehensive and effective data and improve the effectiveness and practicability of the model.

In the multi-dimensional analysis of agricultural product supply chain competition, there is subjectivity in the setting of grading standards, which may lead to unreasonable grade evaluation. In the future, more scientific and reasonable methods should be used to determine the threshold of each level, so that the classification of agricultural products supply chain is more in line with the competitive characteristics of agricultural products supply chain.

In the calculation model of supply chain competitiveness, the mean value fuzzy analysis method is used to solve the evaluation problem of qualitative and quantitative indicators in the supply chain of agricultural products. However, the calculation process is very complicated due to too many evaluation levels, which should be further optimized.

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Multidimensional analysis model of agricultural product supply chain competition based on mean fuzzy

Abstract

Keywords

1 Introduction

1.1 Novelty

1.2 Motivation

2 Multi dimensional analysis information distribution model and information fusion scheduling of agricultural product supply chain competition

2.1 Multi dimensional analysis information distribution model of agricultural product supply chain competition

3.1 Multi dimensional analysis process of agricultural products supply chain competition

3.2.1 Agricultural product information code identification module

Table 1 Pin working mode of FPGA chip Signal Direction Explain CLOCK Input Clock signal RES Input Restart transmission / reset P0∼P3 Input Corresponding pin request P4∼P6 Input Special input pin P7∼P9 Input Low level input UPDOWN[1:0] Input Configuration control GOTO[1:0] Output Output signal

4.1 Experimental content

4.2 Parameter setting

5.1 Conclusion

5.2 Prospect

References