# Research on Integrated Management of Logistics Economy Based on Internet of Things

Authors: Jiang Zhu
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

In order to study the management and control ability of the government affairs 4.0 system on the logistics economic park and improve the data prediction and data early warning sensitivity of the economic management information system in the logistics economic park, this study recombs the basic model of the logistics economic park and puts forward the development model of the inward logistics economic park. Based on this model, a logistics economic big data system is built, and a data early warning model based on machine learning is designed. In the simulation verification, it is found that the original data demand of machine learning data early warning mode is smaller, the data sensitivity early warning cycle is longer, and the data early warning sensitivity is higher under the same conditions. Finally, it is considered that the machine learning linear programming algorithm model has high applicability in the government economic management process of inward logistics economic park.


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## Body

## 1. Introduction

Internet of Things (IOT) technology is an information big data technology based on the interconnection of all things. Nonelectronic devices are built into network nodes by giving traceable bar codes to nonelectronic devices, and embedded devices or auxiliary devices are used to collect their physical location, asset value, operation status, responsible person, and other data into the Internet technology architecture in real time [1]. Logistics economic system (LES) is an economic driving model based on the logistics industry, extending the transaction and delivery of bulk materials, material consumption processing enterprises, logistics management, transportation, and other industries [2]. Make use of lower freight rates and more systematic and efficient transportation and storage system to promote the realization of large-scale processing industry added value (GDP in 2nd layer) in regional economy. Obviously, logistics enterprises are an important part of the national economy, which is closely related to people’s daily work and life. In recent years, logistics enterprises have developed rapidly, and many technologies have been applied to logistics enterprises, and the Internet of Things technology is the most important one. Through the application of the Internet of Things technology, a complete logistics management system can be built to facilitate the query of logistics information.In relevant studies, the logistics economic management information system (LEMS) is defined as an information system that systematically manages the logistics market (including intercity transportation, local transportation and storage, and loading and unloading market), bulk material trading market, financial reports of processing enterprises, etc., under the government affairs 4.0 system [3]. The core development task of the early management information system is the SQL-related operation of the database, while the current development task of the management information system is more inclined to data curve estimation, data early warning, and so on.At present, there are three main problems in the field of logistics. First, the enterprise system needs to be improved. Many enterprises will face many problems before integration, especially in logistics management; there is a large amount of unaudited data. Second, the logistics management system needs to be updated. In the process of logistics information management, if computer technology is used unreasonably, various logistics management information is still sorted manually, which will cause serious adverse effects on the management quality and efficiency of logistics information. Third, the quality of management personnel needs to be improved. At present, the overall quality of the staff engaged in the logistics industry in China needs to be improved, and the computing skills of the staff are limited, which will have an impact on the development of the national logistics industry.The application of the Internet of Things technology in the logistics information management system is also reflected in the application of RFID. Through the application of RFID technology, the effective collection of information sources of different items can be realized, the information reading function can be automated, and the accuracy of various information data can be ensured. During logistics information management, RFID technology can accurately transmit the logistics information obtained in the process to every link involved in the logistics network to ensure the timeliness of logistics information.On October 5, 2021 (Beijing time), the Royal Swedish Academy of Sciences announced that the 2021 Nobel Prize in physics was awarded to Japanese American scientist Syukuro Manabe, German scientist Klaus Hasselmann, and Italian scientist Giorgio Parisi “A pioneering contribution to our understanding of complex physical systems.” After the introduction of the subject of operations research in the field of physics won the prize, the field of economics also began to carry out more active research on the subject of linear programming operation research of complex systems in economic description [4]. The LEMS system designed in this research takes the linear programming of operations research and complex system control as the core technical path, focuses on the application mode of relevant algorithms and analyzes its application effect.


## 2. Mathematical Model Expression of Logistics Economy

The logistics economy relies on the logistics system, but the main economies are processing enterprises and logistics service enterprises. The service roles include financial, insurance, and consulting enterprises. Assuming that there are only three roles in the economic zone, namely, production-oriented enterprises, logistics service enterprises, and financial service enterprises [5], the basic model of the logistics economic zone is shown in Figure 1.Figure 1
General model of logistics economic zone from the perspective of regional economy.In Figure1, there are three major roles in the logistics economic park: ① the role of logistics enterprises providing logistics services and forming a bulk material delivery system, which includes transportation, handling, loading and unloading, warehousing, and other service industries in the logistics industry, as well as bulk material delivery, trading, commerce, and trade and other industries; ②processing enterprises that use bulk materials for processing and production and want to provide deep-processing bulk materials or batch terminal commodities to the market; and ③ financial institutions that provide supply chain financial services for the logistics process of bulk delivery transactions, commercial loans for enterprises in the park, property insurance and personal safety insurance for enterprises in the park, financial leasing of relevant vehicle equipment and steel frame structure facilities for enterprises in the park, or other financial services. The three roles are analyzed as follows:


## 3. Logistics Enterprise Plate

Logistics enterprises have formed the core competitiveness of logistics economic park, which belongs to the basic service plate of regional economic park. Among them, transportation, handling, loading and unloading, and warehousing enterprises provide basic services in the logistics enterprise sector, provide greater bulk material transportation support capacity for the regional economy, and provide lower freight rates for the regional economy, so as to greatly reduce the raw material cost of processing enterprises. Taking steel-making enterprises and chemical enterprises as examples, the logistics cost accounts for 35~55% of their raw material cost. If the logistics cost decreases by 50%, the raw material cost will decrease by 17.5~27.5%, and the enterprises will have strong competitiveness in the global market [6].Liangshan Industrial Park in Jining, Shandong Province (coal washing and coal chemical industry), Baowu Industrial Park in Wuhan, Hubei Province (steelmaking and ironmaking), Yanghe Industrial Park in Suqian, Jiangsu Province (wine making and food processing), and other regional logistics economic industrial parks have established a logistics system integrating railways, expressways, inland rivers, and air transportation. Compared with the basic freight rate of 0.12~0.18 yuan per ton km in general areas in China, the basic freight rate of industrial enterprises is reduced to 0.02~0.04 yuan per ton km, saving 4~6 times the freight rate for local enterprises [7]. That is, the essence of logistics economic park is to solve the shortage of local bulk material resources and make the supply and marketing capacity of local bulk materials meet the production needs of local industrial enterprises. The logistics industry belongs to the service industry. The main source of productivity of the logistics economic park is the industrial processing enterprises rooted in the logistics industrial park.


## 4. Processing Enterprise Plate

For example, according to the previous analysis, simply building an export-oriented logistics industrial park, that is, only centralized storage facilities and loading, unloading and transshipment facilities are arranged in the industrial park. By providing services to the surrounding areas, on the one hand, the local transportation lines are extended and the logistics advantages of the large-scale logistics industrial park are lost. On the other hand, it cannot directly promote the industrial added value of the local regional economy. It can only promote industrial development in nearby areas. That is, compared with the inward logistics industrial park, the outward logistics industrial park has the disadvantage of getting twice the result with half the effort [8]. The logistics hub is a core facility in the logistics industrial park, but it is necessary for the logistics industrial park to form an inward oriented economic model, as shown in Figure 2.Figure 2
General model of inward logistics economic park.In Figure2, the traditional export-oriented logistics economic park only includes logistics hub and warehouse functions, that is, it provides logistics services to the surrounding areas by building a logistics service system, and its core part is generally a railway station, airport, or wharf. In the inward logistics economic park, the external logistics service function of warehouse is weakened, but large processing enterprises are directly integrated into the logistics system of the economic park, so that railway platforms, docks, and berths can directly go deep into the enterprise production system, greatly reducing the logistics cost of processing enterprises. Taking Yanghe Industrial Park as an example, there are hundreds of food processing enterprises and wine making enterprises above designated size in the park. The hundreds of thousands or even millions of tons of grain materials they need every year are directly unloaded from the wharf berth in the enterprise production system, and the processed products are directly loaded on the wharf berth in the enterprise production system. The system serves local production enterprises and directly drives the local industrial added value, rather than only providing the added value of service industry. Local regional economic indicators and fiscal revenue have been effectively promoted.


## 5. Financial Institutions Plate

Financial institutions and logistics enterprises are basic service enterprises in the park. Logistics enterprises provide supply and marketing services for bulk materials, and financial institutions provide funds for the operation of logistics economic park. Because its supporting effect on economy is similar to that of logistics enterprises, it is not discussed here.


## 6. Government Administration Task and Management Information System Design of Logistics Economic Park

### 6.1. Data Acquisition and Data Warehouse Management Mode

The government management information system of the logistics economic park has three core management objectives [9]: ① Control statistics of the total tax-related flow amount of logistics enterprises, production enterprises, and financial service institutions in the park, in which the input flow of logistics enterprises and financial service institutions belongs to GDP in 3rd layer and the input flow of production enterprises belongs to GDP in 2nd layer. ② Ensure the continuous investment attraction in the park, increase the collection and payment capacity of land use tax, ensure the operating profits of settled enterprises, and increase the collection and payment capacity of enterprise income tax. The increase of operating profits of settled enterprises will also stimulate the investment attraction. ③ Ensure the control of freight and increase the management of price management and other market order, so as to give full play to the advantages of the logistics economic park.In order to achieve the above management objectives, the system needs to build a series of data interfaces and a local data warehouse system to provide data resources for subsequent data analysis. The data interface structure is shown in Figure3.Figure 3
LEMS system interface logic architecture.In Figure3, at least 8 dynamic program interfaces (APIs) are used to realize the data import of the system data warehouse. The data driver selects the logical database dBASE software system. Because all data comes from the synchronous interface of the third-party system, there is no permanent data sequential backup system in the system. The core function of the system is to integrate the synchronous data of multiple external systems for data analysis. Data synchronization is a one-way process and does not write back data to relevant systems, so the API interface is a one-way synchronization interface [10]. The following will focus on the analysis of data analysis algorithms.


### 6.2. Linear Programming and Index Factor Control Algorithm

If the objective function of linear regression isy=bx+a, there is the following expression for factor a and factor b [11], as shown in formula (1).
(1)b=∑i=1nxi−x¯yi−y¯∑i=1nxi−x¯2,a=y¯−bx¯,wherexi and yi are the X and Y coordinates of the ith scatter point to be regressed; x¯,y¯ is the mean of all x values and all y values; b is the slope of regression function; a is the intercept of regression function; n is the number of scatter points to be regressed.The mean valuex¯,y¯ in the formula is the arithmetic mean value [12], as shown in formula (3).
(2)x¯=1n∑i=1nxi,y¯=1n∑i=1nyi.Among (2), the meaning of mathematical symbols is the same as formula (1).Coefficient of determination (R2) is an important statistic reflecting the goodness of fit of the model, which is the ratio of regression square difference to total square difference, as shown in formula (3).
(3)R2=1−∑i=1nxi−xi~2yi−yi~2∑i=1nxi−xi¯2yi−yi¯2,whereR2 is the coefficient of determination (R2), x¯,y¯ is the mean value of X or Y, and x~,y~ is the mean value of regression results corresponding to X or Y. The above two mean values are calculated by the arithmetic mean method, with reference to formula (2) above [13] Other mathematical symbols have the same meaning as formula (1) above.When linear regression is applied to linear programming, slopeb and control factor R2 are key factors, especially when multiple linear regression result functions are superimposed into state space functions, when b is positive, it is an upward trend, when b is negative, it is a downward trend, when R2>0.900, it is a strong data correlation, and when R2>0.700, it is a credible correlation. The system judges the relevance of multiple linear regression result functions through machine learning. Intercept a, slope b, and control factor R2 shall be introduced as input variables, weighted, and imported into the machine learning judgment module, and the output risk estimation value shall be used as the control variable of data early warning. This method is more reliable than the early state space method. The algorithm logic is shown in Figure 4.Figure 4
Architecture of machine learning algorithm for linear programming.In Figure4, the key technologies are weighting algorithm module and neural network module. The technical details are discussed below:
(1)
Weighting algorithm moduleThe weighting module is divided into two calculation steps. Firstly, all data are normalized to adjust the range of falling point value range to the [0,1] interval. AlthoughR2 value itself is in the [0,1] interval, the actual results are generally concentrated in [0.6,1] interval, so it is also necessary to use the normalization formula for linear reprojection sorting. For the a value, b value, and R2 value of all lines, calculate the maximum and minimum values, respectively, calculate the difference M between the maximum and minimum values, and calculate the difference N between each specific value and the minimum value. Then, calculate the ratio of N/M, which is the result value of linear reprojection [14], as shown in formula (4).
(4)yi=xi−minxmaxx−minx,wherex is the set of all input values. When the input value is the set of a values, it is the set of all a values. Similarly, calculate the value of b or R2; yi is the re projection result value; xi is the ith input value; minx is the minimum value of X set; maxx is the maximum value of X set.After completing the above normalization calculation, the falling point ranges ofa value, b value, and R2 value corresponding to each line are equally distributed in the [0,1] interval. According to the data importance, multiply the output results of all b values by 10 to adjust the falling point range of their value range to the [0,10] interval, multiply the output results of all a values by 1 to keep them in the [0,1] interval, and multiply the output results of all R2 values by 6. Adjust the falling point range of its value range to [0,6]. At this time, the weighting calculation process is completed.
(2)
Neural network moduleThe statistical significance of fuzzy convolution neural network is to fuse multiple columns of data into one output data. The output data is in double precision format. The output data reflects all the information of the input data with the support of depth iterative regression algorithm and purposefully amplifies the data information to be displayed. In this study, in order to judge the convergence degree of neural network in data training, all output data landing points are required to focus on the [0,1] interval. When the data is close to 1.000, it is considered that there is data risk; when the data is close to 0.000, it is considered that there is no data risk. In relevant references [15], polynomial depth iterative regression function is generally used as the node basis function of neural network, and its function expression is shown in formula (5).
(5)y=∑i=1n∑j=15Ajxij,wherey is the node output value, xi is the input value transmitted from the ith node of the previous layer to the node, Aj is the coefficient to be regressed of the jth-order polynomial, j is polynomial order, and N is the number of nodes of the previous neural network.


## 6.1. Data Acquisition and Data Warehouse Management Mode

The government management information system of the logistics economic park has three core management objectives [9]: ① Control statistics of the total tax-related flow amount of logistics enterprises, production enterprises, and financial service institutions in the park, in which the input flow of logistics enterprises and financial service institutions belongs to GDP in 3rd layer and the input flow of production enterprises belongs to GDP in 2nd layer. ② Ensure the continuous investment attraction in the park, increase the collection and payment capacity of land use tax, ensure the operating profits of settled enterprises, and increase the collection and payment capacity of enterprise income tax. The increase of operating profits of settled enterprises will also stimulate the investment attraction. ③ Ensure the control of freight and increase the management of price management and other market order, so as to give full play to the advantages of the logistics economic park.In order to achieve the above management objectives, the system needs to build a series of data interfaces and a local data warehouse system to provide data resources for subsequent data analysis. The data interface structure is shown in Figure3.Figure 3
LEMS system interface logic architecture.In Figure3, at least 8 dynamic program interfaces (APIs) are used to realize the data import of the system data warehouse. The data driver selects the logical database dBASE software system. Because all data comes from the synchronous interface of the third-party system, there is no permanent data sequential backup system in the system. The core function of the system is to integrate the synchronous data of multiple external systems for data analysis. Data synchronization is a one-way process and does not write back data to relevant systems, so the API interface is a one-way synchronization interface [10]. The following will focus on the analysis of data analysis algorithms.


## 6.2. Linear Programming and Index Factor Control Algorithm

If the objective function of linear regression isy=bx+a, there is the following expression for factor a and factor b [11], as shown in formula (1).
(1)b=∑i=1nxi−x¯yi−y¯∑i=1nxi−x¯2,a=y¯−bx¯,wherexi and yi are the X and Y coordinates of the ith scatter point to be regressed; x¯,y¯ is the mean of all x values and all y values; b is the slope of regression function; a is the intercept of regression function; n is the number of scatter points to be regressed.The mean valuex¯,y¯ in the formula is the arithmetic mean value [12], as shown in formula (3).
(2)x¯=1n∑i=1nxi,y¯=1n∑i=1nyi.Among (2), the meaning of mathematical symbols is the same as formula (1).Coefficient of determination (R2) is an important statistic reflecting the goodness of fit of the model, which is the ratio of regression square difference to total square difference, as shown in formula (3).
(3)R2=1−∑i=1nxi−xi~2yi−yi~2∑i=1nxi−xi¯2yi−yi¯2,whereR2 is the coefficient of determination (R2), x¯,y¯ is the mean value of X or Y, and x~,y~ is the mean value of regression results corresponding to X or Y. The above two mean values are calculated by the arithmetic mean method, with reference to formula (2) above [13] Other mathematical symbols have the same meaning as formula (1) above.When linear regression is applied to linear programming, slopeb and control factor R2 are key factors, especially when multiple linear regression result functions are superimposed into state space functions, when b is positive, it is an upward trend, when b is negative, it is a downward trend, when R2>0.900, it is a strong data correlation, and when R2>0.700, it is a credible correlation. The system judges the relevance of multiple linear regression result functions through machine learning. Intercept a, slope b, and control factor R2 shall be introduced as input variables, weighted, and imported into the machine learning judgment module, and the output risk estimation value shall be used as the control variable of data early warning. This method is more reliable than the early state space method. The algorithm logic is shown in Figure 4.Figure 4
Architecture of machine learning algorithm for linear programming.In Figure4, the key technologies are weighting algorithm module and neural network module. The technical details are discussed below:
(1)
Weighting algorithm moduleThe weighting module is divided into two calculation steps. Firstly, all data are normalized to adjust the range of falling point value range to the [0,1] interval. AlthoughR2 value itself is in the [0,1] interval, the actual results are generally concentrated in [0.6,1] interval, so it is also necessary to use the normalization formula for linear reprojection sorting. For the a value, b value, and R2 value of all lines, calculate the maximum and minimum values, respectively, calculate the difference M between the maximum and minimum values, and calculate the difference N between each specific value and the minimum value. Then, calculate the ratio of N/M, which is the result value of linear reprojection [14], as shown in formula (4).
(4)yi=xi−minxmaxx−minx,wherex is the set of all input values. When the input value is the set of a values, it is the set of all a values. Similarly, calculate the value of b or R2; yi is the re projection result value; xi is the ith input value; minx is the minimum value of X set; maxx is the maximum value of X set.After completing the above normalization calculation, the falling point ranges ofa value, b value, and R2 value corresponding to each line are equally distributed in the [0,1] interval. According to the data importance, multiply the output results of all b values by 10 to adjust the falling point range of their value range to the [0,10] interval, multiply the output results of all a values by 1 to keep them in the [0,1] interval, and multiply the output results of all R2 values by 6. Adjust the falling point range of its value range to [0,6]. At this time, the weighting calculation process is completed.
(2)
Neural network moduleThe statistical significance of fuzzy convolution neural network is to fuse multiple columns of data into one output data. The output data is in double precision format. The output data reflects all the information of the input data with the support of depth iterative regression algorithm and purposefully amplifies the data information to be displayed. In this study, in order to judge the convergence degree of neural network in data training, all output data landing points are required to focus on the [0,1] interval. When the data is close to 1.000, it is considered that there is data risk; when the data is close to 0.000, it is considered that there is no data risk. In relevant references [15], polynomial depth iterative regression function is generally used as the node basis function of neural network, and its function expression is shown in formula (5).
(5)y=∑i=1n∑j=15Ajxij,wherey is the node output value, xi is the input value transmitted from the ith node of the previous layer to the node, Aj is the coefficient to be regressed of the jth-order polynomial, j is polynomial order, and N is the number of nodes of the previous neural network.


## 7. Simulation Verification of Machine Learning Algorithm for Linear Programming

The basic purpose of the linear programming algorithm designed above is to predict and warn the enterprise loss, freight rate rise, credit events, etc., that is, the output value of the fuzzy convolution neural network finally output by formula (5) above is within different value ranges, and the occurrence probability of the above three early warning results needs to be verified. The comparison group uses the traditional prediction results based on state space (simple line function superposition). The simulation environment is built in Python big data analysis software, and the data comes from the actual operation data of Jiangsu Yanghe logistics park, Shandong Liangshan logistics park, and Hubei Baowu Industrial Park from 2019 to 2020 [16]. See Table 1 for analysis results.Table 1
Comparison of model early warning sensitivity.
Algorithm typeEarly warning resultsEnterprise lossFreight rate riseCredit eventMachine learning>0.90076.384.595.2>0.70069.872.983.6State space>0.90071.579.291.8>0.70061.264.776.6In Table1, under the three early warning targets, the algorithm structure is the same, but the data sources and neural network training data use different data structures, that is, the three early warning algorithms adopt different early warning systems. Due to space constraints, the similarities and differences of the above three early warning algorithms are not discussed here, but the comparison results directly reflect the data sensitivity gap between linear programming using machine learning and linear programming using traditional state space. Whether the early warning result is >0.900 or >0.700, the machine learning algorithm shows great advantages. It is proved that the algorithm efficiency of using linear programming algorithm to manage inward logistics economic park in this study has stronger applicability.The biggest disadvantage of linear programming is to predict the data of one cycle, which requires more front-end data. Simple linear programming is generally 1: 10 and complex linear programming is generally 1: 3~1: 5. In order to further verify the difference between the two algorithms, the amount of predata required by the two algorithms to predict 3 cycles of data on the premise ofsensitivity>85% and the maximum amount of data cycles that can be predicted on the premise of 50 lines of predata are analyzed. The statistical results are shown in Table 2.Table 2
Comparison of simulation results of data demand and data premeasurement (sensitivity>85%).
Algorithm typeForecast 3-cycle data demand50 predictable period of raw dataMachine learning5.827.3State space33.64.8In Table2, the data support required by the machine learning algorithm under the same prediction cycle demand is far less than that of the traditional state space algorithm, and the number of prediction cycles can be given far more than that of the state space algorithm under the same original data support conditions. The efficiency of machine learning algorithm is 5.79 times and 5.69 times higher than that of state space algorithm. No matter what application scenario, machine learning algorithm has the advantage of small demand for original data, and machine learning algorithm can realize high sensitivity data prediction with the support of incomplete data.


## 8. Summary

The data statistics cycle of the government system is long, generally taking January (30 days) as the statistical cycle, so the amount of data is small. When using the linear programming algorithm, it is easy to lead to low sensitivity of data prediction and data early warning due to the lack of front data support. Therefore, in the linear programming, the machine learning algorithm is used to replace the traditional state space algorithm. It can effectively improve the early warning ability of the system supported by incomplete data. The economic basic model of the algorithm research in this paper is the inward logistics economic park, that is, to build large processing enterprises with high demand for bulk materials in the logistics park and introduce financial institutions to provide financial support. The follow-up study will have a more in-depth discussion on the regional economic organization model.



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