RETRACTED ARTICLE: Application research of perception data fusion system of agricultural product supply chain based on Internet of things

The concept of logistics was introduced to China in the 1970s. At present, there are still many obstacles to the development of our logistics industry: the construction of logistics infrastructure is lagging behind, which is mainly manifested in the lack of hard technology construction and application, especially soft technology construction [9]. Affected by the planned economy in the past, restricted by the acceptance system and traditional concepts, our logistics theory lacks corresponding innovations and breakthroughs, let alone scientific, systematic, and technological logistics theory [10]. The managers of many logistics companies have insufficient market awareness in terms of ideology, market awareness and market awareness [11]. Without systematic logistics knowledge, modern logistics theory cannot be fully utilized to realize the committee's traditional concept of emphasizing material production and ignoring logistics. We have insufficient understanding of the importance of cultivating modern logistics theory research personnel [12]. Our logistics education level is relatively low, logistics talents are still in short supply, the integrated transportation system is imperfect, there is no seamless connection between different modes of transportation, the degree of specialization of the logistics industry is low, the degree of socialization is low, and the distribution of integrated logistics elements lacks coordination. The development of our logistics industry is also hindered by the lack of strong multinational companies [13]. Intelligent logistics is at the top of the times, and it is also the first in the development of logistics industry,and faces more problems than this [14]. However, the company urgently needs to develop intelligent logistics, which is not enough to ensure the healthy development of intelligent logistics [15]. Perceived logistics refers to some emerging forms of logistics, such as two-dimensional logistics barcodes. It has been used in the logistics field for some time, including barcodes, and also applicable to product numbers. However, there are only a few types of agricultural product barcode technology applications in the logistics industry, and the promotion and application of agricultural products has a long history. The logistic burden of this farm is [16]. The perceived logistics utilization rate of agricultural products can be calculated in Eqs. 1, 2, 3, and 4 according to the following formula.

CV refers to the annual penetration rate of agricultural product logistics, R(x) perceives the utilization rate of logistics, in formula 3C represents the overall coordination rate during the operation of the internet of things, and T in formula 4 represents the compatibility of the internet of things. In formula 1, n represents the penetration rate calculation factor, and D in formula 4 is a constant for adjusting the meter compatibility.

At the macro level, logistics is a cross regional and cross industry comprehensive system. The level of its standardization is directly related to whether the internal functions, elements and modules of the logistics system can be effectively connected and coordinated development, and to a certain extent, it determines the logistics efficiency of the whole society. At the micro level, logistics standards are the key supporting factors to ensure the coordination and unification of logistics activities and the close technical connection between logistics system and other systems [17]. Only when the logistics standardization is realized can the management efficiency of the logistics system be improved, the connection with other systems be strengthened, the economic and social benefits of the logistics system can be effectively improved, the competitiveness of the logistics industry can be enhanced, and the development of intelligent logistics can be promoted [18]. At present, China's logistics standardization work is relatively backward, resulting in poor compatibility of logistics facilities and equipment, low degree of convergence of logistics operations, and high efficiency of the overall operation of the logistics system [19].

It is generally believed that the entire Internet of Things can be divided into three basic levels: perception layer, transmission layer and receiving control layer. The perception layer mainly uses sensors to dynamically perceive the properties and changes of objects, and collects the perception status through radio frequency and other technologies. The transmission layer uses Internet technology to process the sensed data through a microprocessor to achieve long-distance transmission. The receiving control layer is the user side, which realizes the visualization of the object perception results and realizes the control of the perception objects and conditions [20]. Agricultural product supply chain management is still a relatively new management concept and method in China. Its core is to emphasize the use of integrated ideas and concepts to guide the management behavior of each node in the supply chain, that is, to guide the operation of the entire supply chain based on consumer demand and the entire supply chain is managed as a system to improve the operating efficiency and economic benefits of the entire supply chain [21]. Under this model, the node companies in the supply chain do not pursue their own profit maximization alone, but establish strategic partnerships, aiming at maximizing the interests of the entire supply chain, and use certain profit distribution mechanisms to make the economic efficiency of all trading partners in the supply chain has been improved. The impact of the Internet of things on the agricultural product supply chain is huge. The application of Internet of things technology can make the facilities, inventory, transportation, information and procurement involved in the agricultural product supply chain highly optimized; it can affect the production, transportation, and consumption of agricultural products. Real-time management of links can reduce supply chain costs and enable supply chain management to achieve a high degree of agility and complete integration [22]. The Internet of things based on radio frequency/electronic product code technology has been deeply integrated into all aspects of agricultural product supply chain management, and has had a profound impact on the optimization of agricultural product supply chain [23].

With the help of satellite communication system, the agriculture has established an agricultural information network center. On this basis, the provincial agricultural system website group and agricultural information professional website were established. The agricultural information sharing network has been established with the central government and local government to realize the agricultural information sharing with the central government and local government. Agricultural information platform integrates modern communication technology, computer network technology, information retrieval and push technology, and modern information management technology. It builds a comprehensive agricultural information service platform push, technology matching and e-commerce, which integrates hotline. SMS interaction, intelligent retrieval and informatization. With the development of the Internet of things, the internet of things technology is gradually used in the agricultural field, forming the agricultural Internet of things [24]. The multi-scale transmission of agricultural information is realized through wireless sensor network, telecommunication network and internet. The massive agricultural information obtained is fused and processed, and agricultural monitoring, scientific management and instant service are realized through intelligent operation terminal. Using summation formula and polynomial, we can calculate the new growth probability and the new development point of enterprises brought by the Internet of things [25]. Through the Internet of things, we can get the real popular and profitable product model of enterprises. We can use big data technology to accurately grasp the psychology of consumers and expand our own advantages. The commonly used formulas are 5,6,7.

Among them, F(a) represents the new growth rate of agricultural products sales brought by the internet of things, the loss is the total savings of agricultural resources. GH is the profit margin of the enterprise brought about by the interconnection of all things, and E(L) represents the internet of things to save agricultural products. The amount of wasted resources. In the formula, a represents the operating coefficient of the internet of things, and b represents the growth rate operator.

There are many links in the supply chain of agricultural products, including rice planting, agricultural products processing, finished products distribution, agricultural products consumption and other basic links, as well as warehousing, transportation, loading and unloading and other logistics activities, which run through the internal links of the supply chain and the upstream and downstream circulation links. According to the current situation of production, circulation and consumption of agricultural products in China, combined with field investigation, relevant data of agricultural products planting, harvesting, rice milling, processing, detection, distribution, transportation and sales are collected, and various data are recorded with organic RFID tags, which are uploaded to the system data center layer by layer, so as to realize the tracking of agricultural products supply chain nodes, including all links and references in the whole process of agricultural products. Traceability with the unit, as well as the key steps and key processes of each link of specific batches of agricultural products, to ensure the traceability management of agricultural products supply chain. According to the different factory numbers of agricultural products, we can calculate the transportation time, growth cycle and sales volume of agricultural products by formula 8 and 9.where V is the transportation time and shelf life of agricultural products. M is the minimum savings rate of agricultural products, and T(s) is the transportation time of agricultural products.

Where D is the transportation time and shelf life of agricultural products, min is the minimum saving rate of agricultural products, and t(s) is the transportation time of agricultural products. The distribution channels of fruits, vegetables and agricultural products are complex and diverse. As far as the main body of fruits and vegetables and agricultural products are concerned, one is farmers who organize production and operation activities as a family unit, and the other is a large-scale and specialized production base. Therefore, in order to prevent certain diseases or problems of agricultural products, it is necessary to trace the source of the disease [26]. The main tracking objects include farmers/production bases, wholesale companies at all levels, logistics supply companies and sales companies in the fruit and vegetable supply chain. The scope of corporate traceability is generally divided into internal traceability and external traceability [27]. Internal traceability emphasizes the traceability of corporate information, such as vegetable packaging, cleaning and segmentation, operator information, internal environmental information, external traceability is mainly to trace the circulation information of fruits and vegetables in the supply chain. When there is a problem in any link of the fruit and vegetable supply chain, the company can trace the origin and processing history information of the fruit and vegetable through the traceability system to analyze the cause of the quality problem [28]. For products that have already circulated to the next link or entered the market, the product range can be locked in time and customers can be recalled.

On the other hand, whether it is pilot projects, demonstration first, and supporting enterprises' gradual development model. It is still a system of division of labor, and the development model of all links going hand in hand, which provides ideas for the development of intelligent logistics and reduces the blindness of development. Enterprises and industries should focus on the innovation of smart logistics development concepts and actively explore. Through a large amount of relevant information, the prospects and existing problems of the agricultural product perception supply chain under the background of the internet of things are analyzed, and the problems that are conducive to the better development of the agricultural product perception supply chain are obtained. Data analysis is carried out on the investigation and research of the IoT-aware supply chain, and the data analysis adopts DCM technology. At the same time, through questionnaire surveys and model construction, relevant conclusions are drawn, through various data comparisons and analysis, through the presentation of data, to more intuitively understand the impact of the internet of things on the supply of agricultural products.

In all recognition systems, machine learning mainly solves the problem of classification, inferring a complex and reasonable mapping criterion based on the basic information of the recognized object and the category of the recognized object, so as to predict the type of the object. Common machine learning algorithms include: support vector machines, neural networks, correlation vector machines, and fuzzy recognition methods, etc. These methods all have good results in specific applications. This article mainly uses multi-source data to jointly identify agricultural products in the supply chain, and the collected agricultural product data samples are small. The Relevance Vector Machine (RVM) learning method adopts the Bayesian method, introducing the prior of weights, and the weights are assigned one by one by hyperparameters, and their values are calculated through repeated iterations of data.

RVM solves the weight of the correlation vector by maximizing the posterior probability (MAP). For a given training sample set:

\(x_{{ij}}\) represents the j-th feature of the i-th sample, a is the number of samples for training, and b is the number of sample features), the model function of RVM is:where \(u_{i}\) is the correlation weight, \(k(x,x_{i} )\) is the kernel function, assuming that the target has a noise \(\vartheta _{i}\) that obeys the expectation of 0 and the variance \(\sigma ^{2}\) is Gaussian distribution.

Therefore, for a given sample \(x_{i}\), the probability of belonging to \(t_{i}\) is:

Then the likelihood function of the training data is:among them:

\(o\) is an N*(N + 1)-dimensional high-dimensional structural matrix composed of multiple kernel functions, and its expression is:

Each element corresponds to:

The weight u in the Yees method is estimated according to the maximum likelihood function, but it is prone to over-learning. In order to avoid this problem, a conditional probability distribution function is set to constrain u, so the prior probability distribution function of w is as follows:

a is a hyperparameter vector, which controls the deviation degree of u. The likelihood distribution of the output can be obtained by integrating the weights, namely:

According to Bayes' criterion, the posterior probability expression of w is:

In order to maximize it, the derivative of Eq. (20) is obtained:

According to formula (20) and formula (21), iteratively update a and \(\sigma ^{2}\) until the preset convergence condition (parameter change range is small or reaches the set number of iterations) is satisfied.

The experimental data in this paper come from the big data of agricultural product survey in China, as well as foreign data obtained from literature, network and other authoritative institutions.Due to the limitation of grain storage capacity and technology, the loss of fresh agricultural products from the factory is generally 25–30%, while that of developed countries is only about 5%. At present, the degree of processing of agricultural products in developed countries has reached more than 80%, the processing power is less than 30%, and the processing value-added rate is also low. Cold chain logistics technology plays an important role in ensuring food safety and stabilizing food quality. In developed countries, the proportion of fresh agricultural products in the logistics link is generally around 5%, but our proportion is much higher. Our fresh agricultural products are generally transported by conventional methods at natural temperatures, and low-level packaging and low-level storage are common, which directly lead to serious corruption of fresh products in the logistics process, as shown in Table 1.

According to statistics, in the process of agricultural products circulation, the no-load rate of China's transportation has reached about 40%. The reason is that the market scale is small and the information circulation channel is not smooth, resulting in huge cost loss. Due to the information asymmetry, on the one hand, it will lead to blind production of farmers, leading to "buy cheap and sell expensive", and the prices of agricultural products will rise and fall sharply. On the other hand, there will be a high amount of intermediary fees. According to a company, intermediary fees are charged for logistics information provided by enterprises. Generally, the price per 10 tons is about 300–400 yuan. This will not only increase the cost of agricultural products logistics process, but also greatly damage the interests of consumers and farmers. The establishment of agricultural products green logistics information network system based on the Internet of things can achieve the flow of agricultural products through reducing costs. The specific data is shown in Table 2

China's agricultural products logistics is mainly based on normal temperature logistics or natural form logistics. The lack of refrigeration technology is the bottleneck of China's transportation industry, especially in the transportation of agricultural products. The survey shows that the loss rate of fruits, vegetables and other agricultural products in logistics links such as picking, transportation, storage and transportation is relatively high, especially in the transportation link, reaching 20–28%. Among them, the loss of agricultural products in transportation is as high as 75 billion yuan, which makes us deeply realize that improving the technology of transportation link is an important means to realize the value-added of transportation link. This data is obtained by formulas 24 and 25.where s(U) is the total loss and f(DX) is the transportation loss rate.

According to the formal definition of the data collection and modeling of agricultural product logistics quality perception Internet of things, combined with the use of the formal model of the Internet of things process structure, the node classification and relationship diagrams of the agricultural products logistics are consistent with the supply chain process structure based on the agricultural products. The supply chain process integrates the IoT perception data set D in the process of agricultural product supply chain perception, the Internet of things quality set Q and the basic static information set E of quality perception, and establishes the agricultural product supply chain perception dynamic data traceability set T, and finally forms the construction cold Chain logistics quality perception IoT data collection and modeling formal model, and then realize the real-time monitoring of the agricultural product supply chain, and improve the transparency, safety and full traceability of the agricultural product supply chain perception process. Through data perception, the establishment of databases, etc., this model is used to study the results of perception data application in the agricultural product supply chain of the Internet of Things. The static information based on the supply chain model design process is shown in Table 3.

According to the three experimental methods proposed above, the above table reflects the situation of agricultural products transportation and production of big data perception under the internet of things. Among them, under the condition of Internet of things, the efficiency of agricultural products transportation has been significantly improved, from 56.78% to 89.92%, the transportation time has been reduced from the original 34 h to 13 h, the damage rate of transport commodities has been sharply reduced from 45 to 23%, the utilization rate of agricultural products has been significantly improved, from 68.79 to 87.5%. The farm can get more accurate information about the sale and production of agricultural products. This has greatly reduced the waste of production capacity, and the waste of agricultural products has been reduced from 10 million tons to 4.5 million tons.

According to the data analysis of this paper, the backwardness of logistics technology is finally reflected in the logistics cost. In the cost of domestic finished products, the logistics cost accounts for about 34%, and the proportion of fresh agricultural products is even as high as 50%. However, the logistics cost in developed countries only accounts for about 10% of the cost of finished products. In addition, the third-party logistics of agricultural products has not yet formed a scale, agricultural products mainly rely on the self-supporting logistics mode of agricultural enterprises, and the cold chain logistics has not yet developed and popularized, which makes the loss of agricultural products in the whole circulation link is large, which also intensifies the operation cost of the whole agricultural product supply chain. Using simulation research to eliminate inventory inaccuracy can reduce the operation cost and shortage level of the whole chain. Through building a model to prove the total cost change problem under the condition of inaccurate inventory caused by different reasons, and study the radio frequency technology to eliminate the inaccurate inventory difference value part is quantified. This paper proves that the application of RF technology can reduce the inventory inaccuracy rate by 20–37%. It is helpful to get a clearer understanding of the internet of things environment and more accurate design and analysis of the supply chain management operation system. The specific data is shown in Fig. 1.

As can be seen from the data in Fig. 1, the logistics cost accounts for about 34% of the cost of domestic finished products, and the proportion of fresh agricultural products is even as high as 50%, while the logistics cost of developed countries only accounts for about 10% of the cost of finished products. This paper proves that the application of RF technology can reduce the inventory inaccuracy rate by 20–30%.

Investigation and research have found that when the loss rate of fresh agricultural products inventory is very small, as the replacement rate a of fresh agricultural products increases, the profit loss of the agricultural product supply chain decreases, and with the gradual increase of the agricultural product inventory loss rate, the agricultural product supply chain. The profit and loss of will change strongly with the change of the replacement rate a of agricultural products, which indicates that the replacement of agricultural products will reduce the shortage penalty cost caused by the loss of agricultural products inventory. This also proves that as the replacement rate an increase, the profit of the agricultural product supply chain will increase. Its effective demand rate and profit rate will also increase, as shown in Fig. 2.

The profit loss of the agricultural product supply chain decreases with the increase of the fresh agricultural product substitution ratio a, while the profit loss of the agricultural product supply chain will change strongly with the agricultural product replacement ratio. With the continuous upgrading of logistics technology, the waste of agricultural products will be reduced by 23%—40% every year, and the transportation time has changed from 34 to 13 h.

After the experiment, the start time, end time, life cycle and the actual number of data frames of each sensor node are counted in the database. Compared with the theoretical frame number calculated by using the life cycle and sampling interval, the packet loss rate of the node in the whole life cycle and the whole data link span is obtained. The test results show that the highest packet loss rate is 4.59% and the lowest is 1.40%. The average packet loss rate is about 3.58% and the variance is 1.15%. The communication link of system integration is relatively reliable. Under the condition of 1440mwh battery power supply, the life cycle of sensing node is less than 490,000 s, and the number of data frames is about 9000. Reducing the communication energy consumption and prolonging the system life is an important prerequisite for the practical application of the system, as shown in Fig. 3.

As can be seen from the data in Fig. 3, the highest packet loss rate is 4.59%, and the lowest is 1.40%. The average packet loss rate is about 3.58%, and the variance is 1.15%. The communication link of system integration is relatively reliable.

The development concepts such as "intelligent logistics is the inevitable trend of the development of the logistics industry", the logistics industry should further accelerate the development, only can be related to the "intelligent logistics", such as the Internet of things should be popularized and transformed into social cognition, so that the public can pay attention to the development of intelligent logistics, recognize the development of intelligent logistics, and then promote the development of intelligent logistics. According to our survey, 89.98% of the respondents agree with the concept of 10,000 household interconnection, and 15.98% think that there are hidden worries. In order to ensure the food safety of people's daily life, the traceability of agricultural products has become a very important topic. The use of internet of things technology can effectively achieve the traceability of agricultural products, including the production, processing, transportation, circulation and sales of agricultural products throughout the entire agricultural supply chain, as shown in Fig. 4.

As can be seen from the data in Fig. 4, according to our survey, 89.98% of the people who agree with the concept of 10,000 households are in agreement, and 15.98% of those who think there are hidden concerns. On the other hand, whether it is pilot projects, demonstration first, and supporting enterprises' gradual development model. It is still a system of division of labor, and the development model of all links going hand in hand, which provides ideas for the development of intelligent logistics and reduces the blindness of development. Enterprises and industries should pay attention to the innovation of intelligent logistics development concepts and actively explore. 78.42% of enterprises are connected to the internet of everything, 74.37% of enterprises believe that the perception of the internet of things is very important, and the tracking of information through bar code entry is the most basic operating.

The sensory data fusion algorithm is simulated. The simulation reflects the heterogeneity of the Internet of things environment. In order to analyze more clearly, we set four different types of nodes in the monitored environment, and the initial attribute values of each type of node as shown in Table 4:

It is assumed that the number of four types of nodes is 60, the system has 220 nodes in total, and the time length of one rotation of the system is 110 s. First use the ordinary LEACHA algorithm, and then use the sensory data fusion algorithm under the same environment to count the time between the data transmission from the node to the complete reception by the cluster head node. Statistics on the time slot time required for various nodes to transmit data when the sensory data fusion algorithm is applied, the results are shown in Fig. 5.

Analyzing the four types of nodes given in the figure, it can be concluded that type A nodes have the highest aging level, that is, the system has the highest real-time requirements, and type D nodes have the lowest failure level. Type B nodes have the same aging level as type C nodes, but The data sending rate of class nodes is less than that of class C nodes.

In addition, the improved fuzzy algorithm is simulated. The number of nodes selected in this paper is 2000. The original fuzzy algorithm and the improved fuzzy algorithm are respectively applied in the system, and the data is fused 50 times. The statistics of the two fusions are fused from the data for the first time. The data volume of each fusion after the start, the result obtained is shown in Fig. 6:

Analyzing the above figure, the above broken line represents the fuzzy algorithm before improvement. It can be seen that the fluctuation range of the broken line is not very large, which means that the fluctuation of the amount of data involved in data fusion is not very large, that is, the data transmitted by each sensor node is used for fusion. It can be concluded that when facing the massive data of the Internet of Things, the fusion node will also adopt the same method for fusion, which will cause a large amount of system energy consumption and the premature death of the node. The following broken line represents the improved fuzzy algorithm. It can be seen that the broken line has dropped significantly since the second time, which indicates that the amount of data involved in the fusion has been significantly reduced since the second time, which indicates that the threshold setting can be Effectively reduce the amount of data to be fused.