# Selection and Optimization of Regional Economic Industrial Structure Based on Fuzzyk-Means Clustering Algorithm

Authors: Honglin Yuan; Bo Gao
Journal: Computational Intelligence and Neuroscience
 (2022)

---

## Abstract

Learning about the regional business model is essential for the sustainable development of the regional economy. From the perspective of urban renewable energy, city A is the product of energy development. This paper analyzes the current situation and existing problems of the industrial model of city A through fuzzyk-means clustering algorithm. The results show that although the optimization of industrial structure in city A has achieved some results, the more intuitive problems mainly include low labor productivity of the primary industry, strong resource dependence, insufficient extension of industrial chain, and slow development of technology intensive industries. This paper uses fuzzy k-means clustering algorithm to select the leading industries from the perspective of the current situation of leading industries, urban development pattern, and regional policies in city A. The results show that, as a renewable resource-based city, the leading industries suitable for the current development of city A include manufacturing, power, alkali gas and water production and supply, transportation, warehousing and postal industry, leasing, and business services. The results of fuzzy k-means clustering algorithm are quite excellent, and the accuracy rate is 93.3%. This paper uses the grey dynamic linear programming model to predict the future development of the Urban A business model and combines the selection of key functions to obtain the best business model: deep and efficient technical equipment as a good goal, achieved through regional logistics, transportation, new services, etc., to enhance the output value of the tertiary industry in city A and optimize the internal structure of the secondary industry in city A.


---

## Body

## 1. Introduction

Under the new economic normal, China's industrial structure adjustment and upgrading has also entered the “fast lane” of development [1]. In the new century, China's industrial structure adjustment and upgrading has achieved remarkable results, and the proportion among the three industries is becoming more and more reasonable. However, there are also some problems, such as large but not strong industry, relatively lagging development of service industry, slow internal upgrading of industrial structure, overcapacity of traditional industries, unbalanced regional industrial development, and weak industrial technological innovation. In terms of the regional distribution of industries, compared with the coastal provinces, the central and western provinces have a relatively slow pace of industrial structure adjustment and upgrading and a relatively low degree of industrial structure optimization, which is not conducive to the coordinated development of China's regional economy and the construction of an all-round well-off society and a harmonious society [2, 3].The rationalization and upgrading of industrial structure are gradually becoming the source of economic growth. Therefore, the research on industrial structure has become a hot issue in academic circles in recent years. At present, due to the single industrial structure, high proportion of heavy industry, and excessive dependence on resource-based industries in Northeast China and the reduced demand of relevant domestic downstream industries, the development of Northeast China has been greatly affected. In the new normal period, the whole country is facing severe pressure of economic development, the northeast economy is also in a significant decline, and the backlog of deeper structural contradictions are concentrated [4]. The research on the phenomenon of “recession” in the Northeast has changed from the “new phenomenon” in the northeast to the “unsustainable phenomenon” in the northeast. In view of this recession, a general consensus has been reached to accelerate economic transformation through structural adjustment, but there are relatively few systematic studies on the direction of structural adjustment and how to adjust, and most of them focus on qualitative analysis.


## 2. Literature Review

In view of this research problem, the industrial development model proposed by Dang and Tang from the supply level pays attention to the obvious impact of structural change on productivity improvement [5]. Fan et al. vividly describe economic growth as two processes: one is called mushroom effect, which refers to the growth, decline, and significant differences caused by the continuous flow of factors from low productivity industries to high productivity industries. The other is called yeast process, which means that all industries show a common development trend affected by the same macroeconomic fundamentals [6]. Xia et al. studied 39 countries as samples, but did not find that the change of manufacturing structure can promote productivity growth [7]. Nazeer et al. studied the change trend of China's industrial productivity since the founding of new China and decomposed TFP. The results show that TFP growth has a significant contribution to economic growth during the study period, reflecting that rapid industrialization has attracted employment and improved efficiency [8].For the organization and optimization methods of regional industrial structure, there are many studies on fuzzy mean clustering algorithm. Qu and Wang proposed a fuzzyk-means clustering algorithm. By introducing the penalty term into the objective function, the algorithm is no longer sensitive to the initial clustering center, and the clustering effect of the algorithm is improved [9]. Zhang et al. introduced the concept of point symmetry into the k-means clustering algorithm, making the k-means clustering algorithm successfully applied to face recognition [10]. Qu and Wang proposed a k-means clustering algorithm, which regards the whole data set as a class and then divides the data set according to the attributes in the class until the required number of classes is reached. The algorithm is better for clustering sparse high-dimensional data sets [9]. Wang et al. proposed a conditional space kernel fuzzy c-means clustering algorithm, which effectively reduces the noise sensitivity and intensity heterogeneity in magnetic resonance imaging data by introducing kernel induced distance measurement and local spatial information into the weighted membership function [11]. Lin and Xu proposed a new suppression possibility c-means clustering algorithm to solve the problem of coincidence clustering that has always existed in the possibility c-means and improved the relationship between classes by introducing the suppression competitive learning strategy into the probability statistical model [12]. Zhao and Zhang introduced a new regularization method, membership affinity lasso (MAL), and applied it to fuzzy clustering to make the similarity between membership degrees consistent with the similarity between original data. This method shows superior performance in processing complex distributed data [13]. Yang et al. worked out the mathematical expression of membership degree through variational optimization to quantify the uncertainty of variable value [14].This paper determines the current situation of the industrial structure of city a through the application of fuzzyk-means clustering algorithm and selects the appropriate development direction of the industrial structure according to the clustering results.


## 3. Research Methods

### 3.1. Fuzzy k-Means Clustering Algorithm

#### 3.1.1. Principle of Fuzzy Cluster Analysis

LetX=x1,x2,…,xn be all samples to be clustered, each sample xkk=1,2,…,n in X is represented by a finite number of values, each value represents a feature of xk, and the vector pxkxk1,xk2,…,xkm corresponding to all features of object xk is the feature vector, where xk1l=1,2,..,m is the value of the lth feature of xk. Thus, the characteristic index matrix of the sample can be obtained as follows:(1)X11X12…X1mX21X22…X2m…………Xn1Xn2…Xnm.The fuzzy clustering problem can be changed into a planning problem, and the objective function is as follows:(2)minJmU,V=∑i=1n∑j=1cuijm,where ∑j=1cuij=1,1≤i≤n,0≤uij,0<∑j=1cuij<n,1≤i≤n。Cluster analysis is to divide the samplex1,x2,…,xn into a series of subsets X1,X2,…,Xc according to the kinship between the samples and meet the conditions of the following:(3)X1∪X2∪…∪Xc=X,Xi⋂Xj≠ϕ,1≤I≠j≤c.The membership relationship between samplexk and subset Xi is expressed by the membership function of the following:(4)Uxixk=Uik=1,xk⊂Xi0,xk⊄Xi.


#### 3.1.2. Fuzzyk-Means Clustering Model

Dividen vectors xii=1,2,…,n into c clusters Gii=1,2,…,c, and obtain the cluster center of each cluster through the following formula, so as to minimize the sum of variance in the cluster:(5)Ju,v=∑k=1n∑i=1cuikmxk−vi,where(6)∑i=1cuik,uik⊂0,1.Iterative process is as follows.Given data setU=u1,u2,…,unuj=xj1,xj2,…,xjm∈Rm, set c=2,3,…,n−1andr⊂1,∞ and initialize by the following:(7)A0⊂Mfc.The initial clustering center is given.N is the number of iterations, the maximum number of iterations is T, and the threshold is the following:(8)V0=V10,V20,…,Vc0.When the number of iterations isL=0,1,2,……, calculate the cluster center vector aijNr through the following:(9)ViN=∑j=1naijNruj∑j=1naijNr,1≤i≤c.UpdateuikN+1 with the following formula:(10)uikN+1=1,若i=argminxk−viN0.UpdateviN+1 with the following formula:(11)viN+1=∑k=1nuikN+1xk∑k=1nuikN+1.Ifmax=viN+1−viN<ε, or N>T, stop; otherwiseN=N+1, turn to (2).


#### 3.1.3. Steps of Fuzzyk-Means Clustering Analysis Algorithm

The specific steps of fuzzyk-means clustering algorithm are shown in Figure 1.Figure 1
Steps of fuzzy cluster analysis algorithm.


### 3.2. Analysis on the Current Situation and Existing Problems of Industrial Structure in City A

#### 3.2.1. Analysis on the Development Status of Industrial Structure in City A

City A is a typical petrochemical city. It is “born and prospered because of oil.” Before 2016, the economic development of city A basically depended on the secondary industry, and the industrial structure showed typical dual structure characteristics: large in the middle and small at both ends, as shown in Figure2. After 2016, the proportion of the added value of the secondary industry in city A began to narrow, the proportion of the added value of the tertiary industry increased, and the proportion gap between the secondary industry and the tertiary industry gradually narrowed. In 2020, the proportion of tertiary industrial structure in city A has changed significantly, and the proportion of added value of tertiary industry has increased significantly, almost the same as that of secondary industry. However, after 2020, the proportion of secondary industry and tertiary industry will show a slight rebound, and the proportion of secondary industry will increase [15, 16].Figure 2
Structure of added value of three industries in city A from 2009 to 2018.Table 1
Internal structure of primary industry in city A from 2009 to 2018 unit: %.
Industry2009201020112012201320142015201620172018Planting45.944.542.841.941.541.642.143.141.741Forestry0.50.60.60.60.60.60.50.30.20.4Animal husbandry19.921.924.125.426.626.926.526.525.425.3Aquaculture31.530.830.530.129.328.828.627.730.230.9Agriculture, forestry, animal husbandry, and fishery services2.22.22.02.02.02.12.32.42.52.4


#### 3.2.2. Analysis on the Internal Structure of Three Industries

(1)
Analysis of the internal structure of the original production: as can be seen from Table1, the internal structure of the primary industry is generally reflected in planting, aquaculture, and animal husbandry, supplemented by forestry, agriculture, forestry, animal husbandry, and fishery services. From the perspective of trend change, the proportion of planting industry decreased year by year, from 45.9% in 2009 to 41% in 2018. The proportion of aquaculture industry has increased since 2017, with a large increase. The proportion of animal husbandry has been increasing until 2015 and has declined since 2015. During the Tenth Five-Year Plan period, city A put forward the structural adjustment direction of “strengthening livestock.” Although the overall increment of animal husbandry has been increasing, which reflects the implementation effect of city A's “strengthening livestock” policy, the development of aquaculture is better, and the total economic volume and proportion in the primary industry have the upper hand. Since 2009, the proportion of aquaculture has been in the forefront. The development of forestry has not improved much, which is mainly due to the geographical location of city A [17]. At present, aquaculture has gradually developed into a department with a large proportion of the primary industry in city A and representative products such as river crab A.(2)
Analysis of the structure of the second industry refers to the integration of oil production, coking and nuclear fuel processing, and oil and gas applications, as well as the production of chemical equipment in the field. Cost of second-generation goods in city A: while it is always in our top three, the share of other industries in the total product development is small and the differences are uncertain.From 2009 to 2015, the proportion of the added value of the oil and gas exploitation industry in city A has been declining. After a slight increase from 2016 to 2017, there is a downward trend. As of 2018, the added value of oil and gas exploitation industry in city A accounted for about 12%. The petroleum processing, coking, and nuclear fuel processing industries showed a step-by-step leap. In 2009, their proportion exceeded that of the oil and gas exploitation industry for the first time and increased significantly from 2009 to 2011, indicating that city A has entered the stage of stable resource development and has changed from a growing resource-based city to a mature resource-based city. From 2011 to 2015, with the exploitation of resources, the resources of city A were nearly exhausted, social problems were gradually revealed, the proportion of output value of petroleum processing, coking, and nuclear fuel processing industries showed a downward trend, and the urban development entered the recession period of resource-based cities. Since 2016, the proportion of petroleum processing, coking, and nuclear fuel processing industries has increased significantly, which is closely related to the economic transformation of city A and the adjustment of the internal structure of the secondary industry from resource rough processing to fine processing. City A has reduced its dependence on resources and gradually stepped into a benign track of economic development. So far, city A has changed from a declining resource-based city to a renewable resource-based city. As of 2018, the added value of petroleum processing, coking, and nuclear fuel processing industries in city A accounted for more than 70% and still showed an upward trend in the future.(3)
Analysis of the internal structure of the tertiary industry: in 2005, the municipal government of city A made great efforts to improve the urban infrastructure, which laid the foundation for the good development of the tertiary industry. By the end of 2018, the contribution rate of the tertiary industry in city A to the regional GDP had reached 41.4%, becoming an important force driving the urban economic development, showing the gratifying situation of multi-industry development in city A.As shown in Table2, it can be seen that, from 2009 to 2018, the wholesale and retail industry increased significantly and rapidly, thanks to the commodity circulation market and new circulation formats in city A. In addition, the financial industry and real estate industry of city A go hand in hand, which is inseparable from the increase of regional construction in city A. With the regional advantages of port A and the policy support of city A for the development of transportation and logistics, the transportation, warehousing, and postal industries of city A show an obvious increasing trend. As other service industries cover more subdivided industries, the proportion of industrial added value has fluctuated sharply. Among them, technology intensive industries such as information technology service industry and scientific research industry have developed slowly, which needs attention. In the tertiary industry of city A, some traditional service industries are developing steadily, but the development of technological service industries and emerging service industries is slow [18, 19].Table 2
Internal structure of primary industry in city A from 2009 to 2018 unit: %.
Industry2009201020112012201320142015201620172018Wholesale and retail20.319.620.117.116.621.320.427.726.827.2Transportation, storage, and postal services5.45.15.16.86.98.88.69.59.39.2Accommodation and catering4.44.34.44.64.45.65.45.35.15.3Finance7.37.29.88.28.210.611.112.812.212.1Real estate23.124.321.714.915.019.217.210.910.910.0Other services39.539.538.948.448.934.537.333.835.736.2Before 2009, city A was in the early stage of resource development, with huge resource development potential and strong resource dependence. It was a growing resource-based city; from 2009 to 2011, the petroleum processing industry developed rapidly, the economic development basically depended on the secondary industry, and the industrial structure also showed typical characteristics of dual structure. City A changed from a growing city to a mature resource-based city. With the depletion of resources, since 2012, the contribution of the crude oil processing industry to the output value has decreased, the tertiary industry has developed rapidly, the gap between the added value of the secondary industry and the tertiary industry has gradually narrowed, and city A has entered the ranks of declining resource-based cities. In 2016, with the economic transformation of city A, the petroleum processing industry changed from rough processing to fine processing, the output value increased and the dependence on resources weakened. City A changed from a declining resource-based city to a renewable resource-based city, and the road of industrial structure adjustment of city A still has a long way to go [20, 21].


### 3.3. Analysis on the Selection of Leading Industries in City A

The development of regional economy is limited by the development of leading industries. Therefore, clarifying the evolution law between leading industries and other industries is of great strategic significance to promote regional economic development. Industrial structure also affects the development of leading industries to a certain extent. Therefore, the formulation of industrial policies should be fully combined with the characteristics of leading industries in order to achieve the ultimate goal of industrial structure optimization. Regional characteristics at different stages of development are also different.The construction principle of the index system of the leading industry is the benchmark of the index construction, but the index weight in the construction process is different. Therefore, it is necessary to reasonably determine the weight of each index. In the process of multi-index comprehensive evaluation, it is mainly divided into subjective and objective index weight weighting methods. The disadvantage of subjective weighting method is obvious; that is, subjective factors are considered in the weighting process. Therefore, this paper uses the entropy weight method in the objective weighting method to calculate the weight [22, 23].The basic principle of entropy weight method is to analyze the gap between index values by forming the index data to be evaluated into the original data matrix. The greater the difference between the index values, the greater the role of the index in the comprehensive evaluation. If the index values of a certain sample are all equal, the index will not play a role in the comprehensive evaluation. The calculation process is as follows.First, quantify each index with the same degree, and calculate the proportion of theith object index value under the yth index. The calculation formula is as follows:(12)pij=xij∑i=1mxij.Calculate the entropy of indexj according to the following:(13)ej=−k∑i=1mpij∗lnpij,where k=1/lnm.Then calculate the difference coefficient of thejth index. For a given j, the smaller the difference of xij, the greater the entropy.The difference coefficient is defined as follows:(14)gj=1−ej.Finally, the weight is defined as follows:(15)wj=gj∑j=1ngj.For regions at different stages of development, we should comprehensively consider the actual situation of the region. Since city A is a prefecture level city and has no input-output table, it is difficult to consider the correlation between industries. Combined with the actual situation and data acquisition of city A, the leading industry selection index system of city A is constructed, as shown in Table3, in which ii=1,2,…,n represents the observed industrial object, and the research sample range is from 2017 to 2018.Table 3
Selection index system of leading industries in city A.
Primary indexSecondary indexSignificanceOverall industrial situationx1Income elasticity of demandxi1Measuring industrial growth potentialIndustrial value added ratioxi2Measure industrial scaleIndustry leadingx2Labor productivityxi3Measure the level of industrial productionRatio of investment to output valuexi4Measure industrial contributionIndustrial dynamicsx3Labor employment absorption ratexi5Measure the function of industrial employmentGrowth rate of labor productivityxi6Measure the state of industrial technology


## 3.1. Fuzzy k-Means Clustering Algorithm

### 3.1.1. Principle of Fuzzy Cluster Analysis

LetX=x1,x2,…,xn be all samples to be clustered, each sample xkk=1,2,…,n in X is represented by a finite number of values, each value represents a feature of xk, and the vector pxkxk1,xk2,…,xkm corresponding to all features of object xk is the feature vector, where xk1l=1,2,..,m is the value of the lth feature of xk. Thus, the characteristic index matrix of the sample can be obtained as follows:(1)X11X12…X1mX21X22…X2m…………Xn1Xn2…Xnm.The fuzzy clustering problem can be changed into a planning problem, and the objective function is as follows:(2)minJmU,V=∑i=1n∑j=1cuijm,where ∑j=1cuij=1,1≤i≤n,0≤uij,0<∑j=1cuij<n,1≤i≤n。Cluster analysis is to divide the samplex1,x2,…,xn into a series of subsets X1,X2,…,Xc according to the kinship between the samples and meet the conditions of the following:(3)X1∪X2∪…∪Xc=X,Xi⋂Xj≠ϕ,1≤I≠j≤c.The membership relationship between samplexk and subset Xi is expressed by the membership function of the following:(4)Uxixk=Uik=1,xk⊂Xi0,xk⊄Xi.


### 3.1.2. Fuzzyk-Means Clustering Model

Dividen vectors xii=1,2,…,n into c clusters Gii=1,2,…,c, and obtain the cluster center of each cluster through the following formula, so as to minimize the sum of variance in the cluster:(5)Ju,v=∑k=1n∑i=1cuikmxk−vi,where(6)∑i=1cuik,uik⊂0,1.Iterative process is as follows.Given data setU=u1,u2,…,unuj=xj1,xj2,…,xjm∈Rm, set c=2,3,…,n−1andr⊂1,∞ and initialize by the following:(7)A0⊂Mfc.The initial clustering center is given.N is the number of iterations, the maximum number of iterations is T, and the threshold is the following:(8)V0=V10,V20,…,Vc0.When the number of iterations isL=0,1,2,……, calculate the cluster center vector aijNr through the following:(9)ViN=∑j=1naijNruj∑j=1naijNr,1≤i≤c.UpdateuikN+1 with the following formula:(10)uikN+1=1,若i=argminxk−viN0.UpdateviN+1 with the following formula:(11)viN+1=∑k=1nuikN+1xk∑k=1nuikN+1.Ifmax=viN+1−viN<ε, or N>T, stop; otherwiseN=N+1, turn to (2).


### 3.1.3. Steps of Fuzzyk-Means Clustering Analysis Algorithm

The specific steps of fuzzyk-means clustering algorithm are shown in Figure 1.Figure 1
Steps of fuzzy cluster analysis algorithm.


## 3.1.1. Principle of Fuzzy Cluster Analysis

LetX=x1,x2,…,xn be all samples to be clustered, each sample xkk=1,2,…,n in X is represented by a finite number of values, each value represents a feature of xk, and the vector pxkxk1,xk2,…,xkm corresponding to all features of object xk is the feature vector, where xk1l=1,2,..,m is the value of the lth feature of xk. Thus, the characteristic index matrix of the sample can be obtained as follows:(1)X11X12…X1mX21X22…X2m…………Xn1Xn2…Xnm.The fuzzy clustering problem can be changed into a planning problem, and the objective function is as follows:(2)minJmU,V=∑i=1n∑j=1cuijm,where ∑j=1cuij=1,1≤i≤n,0≤uij,0<∑j=1cuij<n,1≤i≤n。Cluster analysis is to divide the samplex1,x2,…,xn into a series of subsets X1,X2,…,Xc according to the kinship between the samples and meet the conditions of the following:(3)X1∪X2∪…∪Xc=X,Xi⋂Xj≠ϕ,1≤I≠j≤c.The membership relationship between samplexk and subset Xi is expressed by the membership function of the following:(4)Uxixk=Uik=1,xk⊂Xi0,xk⊄Xi.


## 3.1.2. Fuzzyk-Means Clustering Model

Dividen vectors xii=1,2,…,n into c clusters Gii=1,2,…,c, and obtain the cluster center of each cluster through the following formula, so as to minimize the sum of variance in the cluster:(5)Ju,v=∑k=1n∑i=1cuikmxk−vi,where(6)∑i=1cuik,uik⊂0,1.Iterative process is as follows.Given data setU=u1,u2,…,unuj=xj1,xj2,…,xjm∈Rm, set c=2,3,…,n−1andr⊂1,∞ and initialize by the following:(7)A0⊂Mfc.The initial clustering center is given.N is the number of iterations, the maximum number of iterations is T, and the threshold is the following:(8)V0=V10,V20,…,Vc0.When the number of iterations isL=0,1,2,……, calculate the cluster center vector aijNr through the following:(9)ViN=∑j=1naijNruj∑j=1naijNr,1≤i≤c.UpdateuikN+1 with the following formula:(10)uikN+1=1,若i=argminxk−viN0.UpdateviN+1 with the following formula:(11)viN+1=∑k=1nuikN+1xk∑k=1nuikN+1.Ifmax=viN+1−viN<ε, or N>T, stop; otherwiseN=N+1, turn to (2).


## 3.1.3. Steps of Fuzzyk-Means Clustering Analysis Algorithm

The specific steps of fuzzyk-means clustering algorithm are shown in Figure 1.Figure 1
Steps of fuzzy cluster analysis algorithm.


## 3.2. Analysis on the Current Situation and Existing Problems of Industrial Structure in City A

### 3.2.1. Analysis on the Development Status of Industrial Structure in City A

City A is a typical petrochemical city. It is “born and prospered because of oil.” Before 2016, the economic development of city A basically depended on the secondary industry, and the industrial structure showed typical dual structure characteristics: large in the middle and small at both ends, as shown in Figure2. After 2016, the proportion of the added value of the secondary industry in city A began to narrow, the proportion of the added value of the tertiary industry increased, and the proportion gap between the secondary industry and the tertiary industry gradually narrowed. In 2020, the proportion of tertiary industrial structure in city A has changed significantly, and the proportion of added value of tertiary industry has increased significantly, almost the same as that of secondary industry. However, after 2020, the proportion of secondary industry and tertiary industry will show a slight rebound, and the proportion of secondary industry will increase [15, 16].Figure 2
Structure of added value of three industries in city A from 2009 to 2018.Table 1
Internal structure of primary industry in city A from 2009 to 2018 unit: %.
Industry2009201020112012201320142015201620172018Planting45.944.542.841.941.541.642.143.141.741Forestry0.50.60.60.60.60.60.50.30.20.4Animal husbandry19.921.924.125.426.626.926.526.525.425.3Aquaculture31.530.830.530.129.328.828.627.730.230.9Agriculture, forestry, animal husbandry, and fishery services2.22.22.02.02.02.12.32.42.52.4


### 3.2.2. Analysis on the Internal Structure of Three Industries

(1)
Analysis of the internal structure of the original production: as can be seen from Table1, the internal structure of the primary industry is generally reflected in planting, aquaculture, and animal husbandry, supplemented by forestry, agriculture, forestry, animal husbandry, and fishery services. From the perspective of trend change, the proportion of planting industry decreased year by year, from 45.9% in 2009 to 41% in 2018. The proportion of aquaculture industry has increased since 2017, with a large increase. The proportion of animal husbandry has been increasing until 2015 and has declined since 2015. During the Tenth Five-Year Plan period, city A put forward the structural adjustment direction of “strengthening livestock.” Although the overall increment of animal husbandry has been increasing, which reflects the implementation effect of city A's “strengthening livestock” policy, the development of aquaculture is better, and the total economic volume and proportion in the primary industry have the upper hand. Since 2009, the proportion of aquaculture has been in the forefront. The development of forestry has not improved much, which is mainly due to the geographical location of city A [17]. At present, aquaculture has gradually developed into a department with a large proportion of the primary industry in city A and representative products such as river crab A.(2)
Analysis of the structure of the second industry refers to the integration of oil production, coking and nuclear fuel processing, and oil and gas applications, as well as the production of chemical equipment in the field. Cost of second-generation goods in city A: while it is always in our top three, the share of other industries in the total product development is small and the differences are uncertain.From 2009 to 2015, the proportion of the added value of the oil and gas exploitation industry in city A has been declining. After a slight increase from 2016 to 2017, there is a downward trend. As of 2018, the added value of oil and gas exploitation industry in city A accounted for about 12%. The petroleum processing, coking, and nuclear fuel processing industries showed a step-by-step leap. In 2009, their proportion exceeded that of the oil and gas exploitation industry for the first time and increased significantly from 2009 to 2011, indicating that city A has entered the stage of stable resource development and has changed from a growing resource-based city to a mature resource-based city. From 2011 to 2015, with the exploitation of resources, the resources of city A were nearly exhausted, social problems were gradually revealed, the proportion of output value of petroleum processing, coking, and nuclear fuel processing industries showed a downward trend, and the urban development entered the recession period of resource-based cities. Since 2016, the proportion of petroleum processing, coking, and nuclear fuel processing industries has increased significantly, which is closely related to the economic transformation of city A and the adjustment of the internal structure of the secondary industry from resource rough processing to fine processing. City A has reduced its dependence on resources and gradually stepped into a benign track of economic development. So far, city A has changed from a declining resource-based city to a renewable resource-based city. As of 2018, the added value of petroleum processing, coking, and nuclear fuel processing industries in city A accounted for more than 70% and still showed an upward trend in the future.(3)
Analysis of the internal structure of the tertiary industry: in 2005, the municipal government of city A made great efforts to improve the urban infrastructure, which laid the foundation for the good development of the tertiary industry. By the end of 2018, the contribution rate of the tertiary industry in city A to the regional GDP had reached 41.4%, becoming an important force driving the urban economic development, showing the gratifying situation of multi-industry development in city A.As shown in Table2, it can be seen that, from 2009 to 2018, the wholesale and retail industry increased significantly and rapidly, thanks to the commodity circulation market and new circulation formats in city A. In addition, the financial industry and real estate industry of city A go hand in hand, which is inseparable from the increase of regional construction in city A. With the regional advantages of port A and the policy support of city A for the development of transportation and logistics, the transportation, warehousing, and postal industries of city A show an obvious increasing trend. As other service industries cover more subdivided industries, the proportion of industrial added value has fluctuated sharply. Among them, technology intensive industries such as information technology service industry and scientific research industry have developed slowly, which needs attention. In the tertiary industry of city A, some traditional service industries are developing steadily, but the development of technological service industries and emerging service industries is slow [18, 19].Table 2
Internal structure of primary industry in city A from 2009 to 2018 unit: %.
Industry2009201020112012201320142015201620172018Wholesale and retail20.319.620.117.116.621.320.427.726.827.2Transportation, storage, and postal services5.45.15.16.86.98.88.69.59.39.2Accommodation and catering4.44.34.44.64.45.65.45.35.15.3Finance7.37.29.88.28.210.611.112.812.212.1Real estate23.124.321.714.915.019.217.210.910.910.0Other services39.539.538.948.448.934.537.333.835.736.2Before 2009, city A was in the early stage of resource development, with huge resource development potential and strong resource dependence. It was a growing resource-based city; from 2009 to 2011, the petroleum processing industry developed rapidly, the economic development basically depended on the secondary industry, and the industrial structure also showed typical characteristics of dual structure. City A changed from a growing city to a mature resource-based city. With the depletion of resources, since 2012, the contribution of the crude oil processing industry to the output value has decreased, the tertiary industry has developed rapidly, the gap between the added value of the secondary industry and the tertiary industry has gradually narrowed, and city A has entered the ranks of declining resource-based cities. In 2016, with the economic transformation of city A, the petroleum processing industry changed from rough processing to fine processing, the output value increased and the dependence on resources weakened. City A changed from a declining resource-based city to a renewable resource-based city, and the road of industrial structure adjustment of city A still has a long way to go [20, 21].


## 3.2.1. Analysis on the Development Status of Industrial Structure in City A

City A is a typical petrochemical city. It is “born and prospered because of oil.” Before 2016, the economic development of city A basically depended on the secondary industry, and the industrial structure showed typical dual structure characteristics: large in the middle and small at both ends, as shown in Figure2. After 2016, the proportion of the added value of the secondary industry in city A began to narrow, the proportion of the added value of the tertiary industry increased, and the proportion gap between the secondary industry and the tertiary industry gradually narrowed. In 2020, the proportion of tertiary industrial structure in city A has changed significantly, and the proportion of added value of tertiary industry has increased significantly, almost the same as that of secondary industry. However, after 2020, the proportion of secondary industry and tertiary industry will show a slight rebound, and the proportion of secondary industry will increase [15, 16].Figure 2
Structure of added value of three industries in city A from 2009 to 2018.Table 1
Internal structure of primary industry in city A from 2009 to 2018 unit: %.
Industry2009201020112012201320142015201620172018Planting45.944.542.841.941.541.642.143.141.741Forestry0.50.60.60.60.60.60.50.30.20.4Animal husbandry19.921.924.125.426.626.926.526.525.425.3Aquaculture31.530.830.530.129.328.828.627.730.230.9Agriculture, forestry, animal husbandry, and fishery services2.22.22.02.02.02.12.32.42.52.4


## 3.2.2. Analysis on the Internal Structure of Three Industries

(1)
Analysis of the internal structure of the original production: as can be seen from Table1, the internal structure of the primary industry is generally reflected in planting, aquaculture, and animal husbandry, supplemented by forestry, agriculture, forestry, animal husbandry, and fishery services. From the perspective of trend change, the proportion of planting industry decreased year by year, from 45.9% in 2009 to 41% in 2018. The proportion of aquaculture industry has increased since 2017, with a large increase. The proportion of animal husbandry has been increasing until 2015 and has declined since 2015. During the Tenth Five-Year Plan period, city A put forward the structural adjustment direction of “strengthening livestock.” Although the overall increment of animal husbandry has been increasing, which reflects the implementation effect of city A's “strengthening livestock” policy, the development of aquaculture is better, and the total economic volume and proportion in the primary industry have the upper hand. Since 2009, the proportion of aquaculture has been in the forefront. The development of forestry has not improved much, which is mainly due to the geographical location of city A [17]. At present, aquaculture has gradually developed into a department with a large proportion of the primary industry in city A and representative products such as river crab A.(2)
Analysis of the structure of the second industry refers to the integration of oil production, coking and nuclear fuel processing, and oil and gas applications, as well as the production of chemical equipment in the field. Cost of second-generation goods in city A: while it is always in our top three, the share of other industries in the total product development is small and the differences are uncertain.From 2009 to 2015, the proportion of the added value of the oil and gas exploitation industry in city A has been declining. After a slight increase from 2016 to 2017, there is a downward trend. As of 2018, the added value of oil and gas exploitation industry in city A accounted for about 12%. The petroleum processing, coking, and nuclear fuel processing industries showed a step-by-step leap. In 2009, their proportion exceeded that of the oil and gas exploitation industry for the first time and increased significantly from 2009 to 2011, indicating that city A has entered the stage of stable resource development and has changed from a growing resource-based city to a mature resource-based city. From 2011 to 2015, with the exploitation of resources, the resources of city A were nearly exhausted, social problems were gradually revealed, the proportion of output value of petroleum processing, coking, and nuclear fuel processing industries showed a downward trend, and the urban development entered the recession period of resource-based cities. Since 2016, the proportion of petroleum processing, coking, and nuclear fuel processing industries has increased significantly, which is closely related to the economic transformation of city A and the adjustment of the internal structure of the secondary industry from resource rough processing to fine processing. City A has reduced its dependence on resources and gradually stepped into a benign track of economic development. So far, city A has changed from a declining resource-based city to a renewable resource-based city. As of 2018, the added value of petroleum processing, coking, and nuclear fuel processing industries in city A accounted for more than 70% and still showed an upward trend in the future.(3)
Analysis of the internal structure of the tertiary industry: in 2005, the municipal government of city A made great efforts to improve the urban infrastructure, which laid the foundation for the good development of the tertiary industry. By the end of 2018, the contribution rate of the tertiary industry in city A to the regional GDP had reached 41.4%, becoming an important force driving the urban economic development, showing the gratifying situation of multi-industry development in city A.As shown in Table2, it can be seen that, from 2009 to 2018, the wholesale and retail industry increased significantly and rapidly, thanks to the commodity circulation market and new circulation formats in city A. In addition, the financial industry and real estate industry of city A go hand in hand, which is inseparable from the increase of regional construction in city A. With the regional advantages of port A and the policy support of city A for the development of transportation and logistics, the transportation, warehousing, and postal industries of city A show an obvious increasing trend. As other service industries cover more subdivided industries, the proportion of industrial added value has fluctuated sharply. Among them, technology intensive industries such as information technology service industry and scientific research industry have developed slowly, which needs attention. In the tertiary industry of city A, some traditional service industries are developing steadily, but the development of technological service industries and emerging service industries is slow [18, 19].Table 2
Internal structure of primary industry in city A from 2009 to 2018 unit: %.
Industry2009201020112012201320142015201620172018Wholesale and retail20.319.620.117.116.621.320.427.726.827.2Transportation, storage, and postal services5.45.15.16.86.98.88.69.59.39.2Accommodation and catering4.44.34.44.64.45.65.45.35.15.3Finance7.37.29.88.28.210.611.112.812.212.1Real estate23.124.321.714.915.019.217.210.910.910.0Other services39.539.538.948.448.934.537.333.835.736.2Before 2009, city A was in the early stage of resource development, with huge resource development potential and strong resource dependence. It was a growing resource-based city; from 2009 to 2011, the petroleum processing industry developed rapidly, the economic development basically depended on the secondary industry, and the industrial structure also showed typical characteristics of dual structure. City A changed from a growing city to a mature resource-based city. With the depletion of resources, since 2012, the contribution of the crude oil processing industry to the output value has decreased, the tertiary industry has developed rapidly, the gap between the added value of the secondary industry and the tertiary industry has gradually narrowed, and city A has entered the ranks of declining resource-based cities. In 2016, with the economic transformation of city A, the petroleum processing industry changed from rough processing to fine processing, the output value increased and the dependence on resources weakened. City A changed from a declining resource-based city to a renewable resource-based city, and the road of industrial structure adjustment of city A still has a long way to go [20, 21].


## 3.3. Analysis on the Selection of Leading Industries in City A

The development of regional economy is limited by the development of leading industries. Therefore, clarifying the evolution law between leading industries and other industries is of great strategic significance to promote regional economic development. Industrial structure also affects the development of leading industries to a certain extent. Therefore, the formulation of industrial policies should be fully combined with the characteristics of leading industries in order to achieve the ultimate goal of industrial structure optimization. Regional characteristics at different stages of development are also different.The construction principle of the index system of the leading industry is the benchmark of the index construction, but the index weight in the construction process is different. Therefore, it is necessary to reasonably determine the weight of each index. In the process of multi-index comprehensive evaluation, it is mainly divided into subjective and objective index weight weighting methods. The disadvantage of subjective weighting method is obvious; that is, subjective factors are considered in the weighting process. Therefore, this paper uses the entropy weight method in the objective weighting method to calculate the weight [22, 23].The basic principle of entropy weight method is to analyze the gap between index values by forming the index data to be evaluated into the original data matrix. The greater the difference between the index values, the greater the role of the index in the comprehensive evaluation. If the index values of a certain sample are all equal, the index will not play a role in the comprehensive evaluation. The calculation process is as follows.First, quantify each index with the same degree, and calculate the proportion of theith object index value under the yth index. The calculation formula is as follows:(12)pij=xij∑i=1mxij.Calculate the entropy of indexj according to the following:(13)ej=−k∑i=1mpij∗lnpij,where k=1/lnm.Then calculate the difference coefficient of thejth index. For a given j, the smaller the difference of xij, the greater the entropy.The difference coefficient is defined as follows:(14)gj=1−ej.Finally, the weight is defined as follows:(15)wj=gj∑j=1ngj.For regions at different stages of development, we should comprehensively consider the actual situation of the region. Since city A is a prefecture level city and has no input-output table, it is difficult to consider the correlation between industries. Combined with the actual situation and data acquisition of city A, the leading industry selection index system of city A is constructed, as shown in Table3, in which ii=1,2,…,n represents the observed industrial object, and the research sample range is from 2017 to 2018.Table 3
Selection index system of leading industries in city A.
Primary indexSecondary indexSignificanceOverall industrial situationx1Income elasticity of demandxi1Measuring industrial growth potentialIndustrial value added ratioxi2Measure industrial scaleIndustry leadingx2Labor productivityxi3Measure the level of industrial productionRatio of investment to output valuexi4Measure industrial contributionIndustrial dynamicsx3Labor employment absorption ratexi5Measure the function of industrial employmentGrowth rate of labor productivityxi6Measure the state of industrial technology


## 4. Result Analysis

### 4.1. Clustering Results

The significance and size of each evaluation index are different. In order to eliminate the influence caused by dimension, the data of each evaluation index are averaged. The formula is as follows:(16)xij∗=xijxji=1,2,…,19;j=1,2,…,6.Determine the value range of indicatorjj=1,2,…,6 as shown in Table 4.Table 4
Index value range.
IndexValue rangeIncome elasticity of demand[0, 11.301]Industrial value added ratio[0, 5.501]Labor productivity[0, 3.007]Ratio of investment to output value[0, 9.421]Employment absorption rate[0, 10.762]Growth rate of labor productivity[0, 4.883]The entropy weight method is used to determine the clustering weightwjj=1,2,…,6 of the indicators. The clustering weight of each indicator is shown in Table 5.Table 5
Clustering weight of each index.
IndexWeightIncome elasticity of demand0.32Industrial value added ratio0.12Labor productivity0.06Ratio of investment to output value0.19Employment absorption rate0.23Growth rate of labor productivity0.08According to the clustering results, we can sort out the industry categories of city A, as shown in Table6, so as to provide reference for establishing the final leading industry.Table 6
Industrial classification of city A.
Industry categoryIndustry namePrime mover industryAgriculture, forestry, animal husbandry and fishery, power, gas and water production and supply, transportation, warehousing and postal industry, real estate industry, water conservancy environment and public facilities management, leasing and business services, public management, and social security industryAuxiliary industryMining, manufacturing, construction, wholesale and retail, finance, culture, sports and entertainment, residential services, repair, and other servicesGeneral industryAccommodation and catering, information transmission and information technology services, scientific research and technology services, education, health, and social work


### 4.2. Experimental Analysis of Clustering Effect

In order to test the clustering effect ofk-means algorithm, the internationally recognized iris data set is used as the standard test data. Experiments are carried out on iris data sets with k-means algorithm. The number of iterations is t = 1000, the threshold is ε = 0.001, and the number of clusters is c = 3. The experimental results are shown in Table 7.Table 7
Performance comparison of cluster analysis.
AlgorithmObjective function valueNumber of iterationsCorrect rate (%)k-means6269100093.3Because thek-means adopts the initial clustering center, it is not easy for the algorithm to select the noise data. In addition, the weight is introduced into the algorithm, which makes the accuracy of each iteration of the algorithm relatively high. Therefore, the clustering effect of k-means algorithm is good.In order to analyze the antinoise performance ofk-means algorithm, add 10%, 20%, 30%, and 40% noise data to the original iris data set, and then conduct experiments on the data set, respectively. The experimental results are shown in Figure 3.Figure 3
Antinoise performance ofk-means algorithm.


### 4.3. Research on Optimization Path and Scheme of Industrial Structure in City A

First, the improvement of labor productivity can promote the optimization of industrial structure. In the process of improving labor productivity, we should not only take the improvement of output value as the measurement standard, but also pay attention to the internal changes of labor resources. The employees of city A are mainly distributed in the agricultural and petrochemical industries. This situation is closely related to the attribute of the oil-based city of city A. Due to the strong demand for labor in the resource-based industry, if the factor of labor is invested in other industries under the condition of full employment, the industry will also develop to a certain extent. Therefore, the influence of labor needs to be considered in the optimization of industrial structure.Second, for a typical oil resource-based city A, the petrochemical industry is the main driving force of economic growth in city A. With its industry scale, Petrochemical Group has made important contributions to the stable operation of the city's economy. City A has always paid enough attention to petrochemical related industries, with a high amount of investment in such industries, and the change of investment will change the industrial structure to a certain extent. This is because investing in the original industries will promote the rapid development of these industries, and investing in new industries will change the original industrial structure. Therefore, the government will adjust the investment structure to achieve the goal of industrial structure optimization.But also constrained by power resources, this is because power plays an important role in the national economy and people's life. The planning of power must follow the scientific development and reasonable planning. The power consumption of the city is also one of the important factors affecting the industrial structure.Grey system GM (1, 1) model is a prediction and analysis method in grey system theory. It is mainly used to predict a certain element in the system and reveal the change law and future development trend of the element. The basic idea is that the accumulation method is used to generate a time series with obvious change trend. After considering the influence of grey factors, the model is established according to the trend for prediction. Finally, the time series is restored by the subtraction method and the predicted value is obtained. The specific steps are as follows.The first step is to construct the original form of GM (1, 1) model. Lety0=y01,y02,…,y0n be the original sequence and let y1=y11,y12,…,y1n be the cumulative sequence, and then the expression of the original sequence is as follows:(17)y0k+αy1k=β,where y is the prediction parameter and α and β are the development coefficient and grey action quantity in the model, respectively.In the second step, the whitening equation is constructed, and the expression is as follows:(18)dy1dt+αx1=β.The third step is to solve the whitening equation and obtain the time series expression as follows:(19)y^k+1=y01−βαe−αk+βα.The fourth step is the progressive reduction, and the predicted value of the parameter is obtained as follows:(20)y^k+1=1−e−αy01−βαe−αk.The fifth step is to check the error of the predicted value. If the predicted value is highly consistent with the original value, i.e., the relative error valueγ<0.01, it indicates that the error is small, which is a good level. If the fitting degree between the predicted value and the original value is medium, that is, the relative error 0.01<γ<0.05, it indicates that the error is generally second-class good. If the fitting degree between the predicted value and the original value is low, that is, the relative error 0.05<γ<0.10, indicating that the error is large, it is qualified at level 3. If γ>0.2, the inspection fails.The industrial structure of city A is restricted by resources, population, policy, technology, and other aspects. It can be regarded as a grey system containing known information and unknown information, and GM (1, 1) model can be established. Combined with the analysis of the influencing factors of industrial structure optimization and based on the acquisition of data, the number of employees, investment in fixed assets, power consumption and the number of employees, investment in fixed assets and power consumption required per 100 million yuan of industrial added value of the three industries are finally selected as the prediction parameters, in order to facilitate the consistency of the measurement units of all parameters in the process of linear programming calculation.The data of a city from 2009 to 2018 are selected as the sample interval. According to the employment, fixed asset investment, and power consumption of the three industries in a city, the employment required for each 100 million yuan of industrial added value of the three industries in a city, the fixed asset investment required for each 100 million yuan of industrial added value of the three industries and the power required for each 100 million yuan of industrial added value of the three industries are calculated. Take 2022 to 2025 as the prediction interval. The interval data of all parameter samples are shown in Table8.Table 8
Employment, investment in fixed assets and power consumption in city A from 2009 to 2018.
Particular yearEmployment (10000)Investment in fixed assets (10000 yuan)Electric power (10000 KWH)200982.9505430306440197201085.5906924951568000201187.6307943686643701201287.8009822209671893201395.18011376933738048201494.92011574017752345201594.5009830605763518201695.8706014213798159201792.9506038742885537201894.7604800800965772The grey system modeling software is used to import the data in Table8 to obtain the test value of GM (1, 1) model effect, as shown in Table 9.Table 9
Model effect test values.
Development coefficientαGrey action quantityβSimulation errorγEmployment−0.01286.4900.023Investment in fixed assets0.0391000.4470.098Electricity consumption0.05954.6930.026From the simulation values of the parameters in Table9, the simulation effect of employment and power consumption is good at level 2, and the simulation effect of fixed assets investment is qualified at Level 3, indicating that GM (1, 1) model can be used to predict the future.By establishing the GM (1, 1) model of the above parameters, the predicted values of the parameters of city A from 2022 to 2025 can be obtained. The specific prediction results are shown in Table10.Table 10
Forecast value of employment, fixed assets investment, and power consumption in city A from 2022 to 2025.
Particular yearEmployment (10000)Investment in fixed assets (10000 yuan)Electric power (10000 KWH)2022101.306652920310939602023101.507603938211911612024102.722581020112630222025105.19655897211339211By examining the choice of key industries as a way to improve the industrial model of city A, we can get ideas to optimize the industrial model of city A, for example, by modifying the internal structure of the secondary industry. The production process and costs of the tertiary industry will be improved, the efficiency of the interconnection industry will be established, and the secondary and tertiary industry will be driven by two wheels in the direction of market economy.


## 4.1. Clustering Results

The significance and size of each evaluation index are different. In order to eliminate the influence caused by dimension, the data of each evaluation index are averaged. The formula is as follows:(16)xij∗=xijxji=1,2,…,19;j=1,2,…,6.Determine the value range of indicatorjj=1,2,…,6 as shown in Table 4.Table 4
Index value range.
IndexValue rangeIncome elasticity of demand[0, 11.301]Industrial value added ratio[0, 5.501]Labor productivity[0, 3.007]Ratio of investment to output value[0, 9.421]Employment absorption rate[0, 10.762]Growth rate of labor productivity[0, 4.883]The entropy weight method is used to determine the clustering weightwjj=1,2,…,6 of the indicators. The clustering weight of each indicator is shown in Table 5.Table 5
Clustering weight of each index.
IndexWeightIncome elasticity of demand0.32Industrial value added ratio0.12Labor productivity0.06Ratio of investment to output value0.19Employment absorption rate0.23Growth rate of labor productivity0.08According to the clustering results, we can sort out the industry categories of city A, as shown in Table6, so as to provide reference for establishing the final leading industry.Table 6
Industrial classification of city A.
Industry categoryIndustry namePrime mover industryAgriculture, forestry, animal husbandry and fishery, power, gas and water production and supply, transportation, warehousing and postal industry, real estate industry, water conservancy environment and public facilities management, leasing and business services, public management, and social security industryAuxiliary industryMining, manufacturing, construction, wholesale and retail, finance, culture, sports and entertainment, residential services, repair, and other servicesGeneral industryAccommodation and catering, information transmission and information technology services, scientific research and technology services, education, health, and social work


## 4.2. Experimental Analysis of Clustering Effect

In order to test the clustering effect ofk-means algorithm, the internationally recognized iris data set is used as the standard test data. Experiments are carried out on iris data sets with k-means algorithm. The number of iterations is t = 1000, the threshold is ε = 0.001, and the number of clusters is c = 3. The experimental results are shown in Table 7.Table 7
Performance comparison of cluster analysis.
AlgorithmObjective function valueNumber of iterationsCorrect rate (%)k-means6269100093.3Because thek-means adopts the initial clustering center, it is not easy for the algorithm to select the noise data. In addition, the weight is introduced into the algorithm, which makes the accuracy of each iteration of the algorithm relatively high. Therefore, the clustering effect of k-means algorithm is good.In order to analyze the antinoise performance ofk-means algorithm, add 10%, 20%, 30%, and 40% noise data to the original iris data set, and then conduct experiments on the data set, respectively. The experimental results are shown in Figure 3.Figure 3
Antinoise performance ofk-means algorithm.


## 4.3. Research on Optimization Path and Scheme of Industrial Structure in City A

First, the improvement of labor productivity can promote the optimization of industrial structure. In the process of improving labor productivity, we should not only take the improvement of output value as the measurement standard, but also pay attention to the internal changes of labor resources. The employees of city A are mainly distributed in the agricultural and petrochemical industries. This situation is closely related to the attribute of the oil-based city of city A. Due to the strong demand for labor in the resource-based industry, if the factor of labor is invested in other industries under the condition of full employment, the industry will also develop to a certain extent. Therefore, the influence of labor needs to be considered in the optimization of industrial structure.Second, for a typical oil resource-based city A, the petrochemical industry is the main driving force of economic growth in city A. With its industry scale, Petrochemical Group has made important contributions to the stable operation of the city's economy. City A has always paid enough attention to petrochemical related industries, with a high amount of investment in such industries, and the change of investment will change the industrial structure to a certain extent. This is because investing in the original industries will promote the rapid development of these industries, and investing in new industries will change the original industrial structure. Therefore, the government will adjust the investment structure to achieve the goal of industrial structure optimization.But also constrained by power resources, this is because power plays an important role in the national economy and people's life. The planning of power must follow the scientific development and reasonable planning. The power consumption of the city is also one of the important factors affecting the industrial structure.Grey system GM (1, 1) model is a prediction and analysis method in grey system theory. It is mainly used to predict a certain element in the system and reveal the change law and future development trend of the element. The basic idea is that the accumulation method is used to generate a time series with obvious change trend. After considering the influence of grey factors, the model is established according to the trend for prediction. Finally, the time series is restored by the subtraction method and the predicted value is obtained. The specific steps are as follows.The first step is to construct the original form of GM (1, 1) model. Lety0=y01,y02,…,y0n be the original sequence and let y1=y11,y12,…,y1n be the cumulative sequence, and then the expression of the original sequence is as follows:(17)y0k+αy1k=β,where y is the prediction parameter and α and β are the development coefficient and grey action quantity in the model, respectively.In the second step, the whitening equation is constructed, and the expression is as follows:(18)dy1dt+αx1=β.The third step is to solve the whitening equation and obtain the time series expression as follows:(19)y^k+1=y01−βαe−αk+βα.The fourth step is the progressive reduction, and the predicted value of the parameter is obtained as follows:(20)y^k+1=1−e−αy01−βαe−αk.The fifth step is to check the error of the predicted value. If the predicted value is highly consistent with the original value, i.e., the relative error valueγ<0.01, it indicates that the error is small, which is a good level. If the fitting degree between the predicted value and the original value is medium, that is, the relative error 0.01<γ<0.05, it indicates that the error is generally second-class good. If the fitting degree between the predicted value and the original value is low, that is, the relative error 0.05<γ<0.10, indicating that the error is large, it is qualified at level 3. If γ>0.2, the inspection fails.The industrial structure of city A is restricted by resources, population, policy, technology, and other aspects. It can be regarded as a grey system containing known information and unknown information, and GM (1, 1) model can be established. Combined with the analysis of the influencing factors of industrial structure optimization and based on the acquisition of data, the number of employees, investment in fixed assets, power consumption and the number of employees, investment in fixed assets and power consumption required per 100 million yuan of industrial added value of the three industries are finally selected as the prediction parameters, in order to facilitate the consistency of the measurement units of all parameters in the process of linear programming calculation.The data of a city from 2009 to 2018 are selected as the sample interval. According to the employment, fixed asset investment, and power consumption of the three industries in a city, the employment required for each 100 million yuan of industrial added value of the three industries in a city, the fixed asset investment required for each 100 million yuan of industrial added value of the three industries and the power required for each 100 million yuan of industrial added value of the three industries are calculated. Take 2022 to 2025 as the prediction interval. The interval data of all parameter samples are shown in Table8.Table 8
Employment, investment in fixed assets and power consumption in city A from 2009 to 2018.
Particular yearEmployment (10000)Investment in fixed assets (10000 yuan)Electric power (10000 KWH)200982.9505430306440197201085.5906924951568000201187.6307943686643701201287.8009822209671893201395.18011376933738048201494.92011574017752345201594.5009830605763518201695.8706014213798159201792.9506038742885537201894.7604800800965772The grey system modeling software is used to import the data in Table8 to obtain the test value of GM (1, 1) model effect, as shown in Table 9.Table 9
Model effect test values.
Development coefficientαGrey action quantityβSimulation errorγEmployment−0.01286.4900.023Investment in fixed assets0.0391000.4470.098Electricity consumption0.05954.6930.026From the simulation values of the parameters in Table9, the simulation effect of employment and power consumption is good at level 2, and the simulation effect of fixed assets investment is qualified at Level 3, indicating that GM (1, 1) model can be used to predict the future.By establishing the GM (1, 1) model of the above parameters, the predicted values of the parameters of city A from 2022 to 2025 can be obtained. The specific prediction results are shown in Table10.Table 10
Forecast value of employment, fixed assets investment, and power consumption in city A from 2022 to 2025.
Particular yearEmployment (10000)Investment in fixed assets (10000 yuan)Electric power (10000 KWH)2022101.306652920310939602023101.507603938211911612024102.722581020112630222025105.19655897211339211By examining the choice of key industries as a way to improve the industrial model of city A, we can get ideas to optimize the industrial model of city A, for example, by modifying the internal structure of the secondary industry. The production process and costs of the tertiary industry will be improved, the efficiency of the interconnection industry will be established, and the secondary and tertiary industry will be driven by two wheels in the direction of market economy.


## 5. Conclusion

Firstly, based on the existing research and practical experience, this paper deeply analyzes the development status and existing problems of the industrial structure of renewable resource-based city A. Second, the leading industry is selected based on fuzzyk-means clustering model. The aquaculture industry has developed rapidly, but the proportion of employees is too high. The development trend of petroleum processing industry is good and has gradually shifted from resource exploitation to deep processing of resources. However, the fine processing industry of petrochemical products is relatively in a weak stage, the proportion of light and heavy industries in the secondary industry is seriously unbalanced, and the extension of the upstream and downstream of the industrial chain is not wide enough. The traditional service industry has developed steadily, but the technical service industry has developed slowly. The selection of leading industries shows that the leading industries in city A are manufacturing, power, gas and water production and supply, transportation, warehousing and postal services, leasing, and business services. The prediction results of industrial structure of city A show that, from 2022 to 2025, the tertiary industry of city A develops rapidly, and the proportion of added value of secondary industry and tertiary industry tends to be average. Combined with the selection of leading industries, the optimization scheme of industrial structure of city A can be obtained.



---

*Source: 4278524-2022-09-07.xml*