# Upgrading and Optimization of Ski Tourism Resort Using Data Mining and Data Fusion

Authors: Zhaoxin Liu; Ok-young Shin; Haeryong Park
Journal: Wireless Communications and Mobile Computing
 (2022)

---

## Abstract

After years of development, the ski industry has formed a prototype of a ski tourism industry cluster integrating skiing, skiing entertainment, skiing technology, and skiing culture. With the rapid development of global information, data mining and data fusion have attracted extensive attention as high-tech technologies for extensive data processing and useful information retrieval. The principles of these two data processing technologies are different, but they are functionally compatible with each other. This paper will use the methods of data mining and data fusion to research and analyze the upgrading and optimization of ski tourism destinations; eliminate unnecessary information; reduce rules; establish a fusion system according to the basic rules of rough sets; use industrial cluster theory, industrial structure and optimization theory, industrial economy, and sports tourism management theory to study the current situation and existing problems of ski tourism; and analyze the mechanism and theory of upgrading and optimization of ski tourism industry structure based on industrial clusters. The results show that the per capita ecological footprint of the ski resort has decreased by 0.06178 hm2 in the past five years, and the total ecological footprint of the whole region in 2020 is 535089.3 hm2. Compared with 2016, it increased by 0.97%, exceeding the ecological deficit carrying supply by 5 times. Compared with traditional algorithms, the performance of data mining and data fusion algorithms is improved by 48%. It is concluded that the development strategies of the ski tourism industry include the innovation of ice and snow sports products, the innovation of technology development paths, the optimization of the management mode and marketing mode of the ski tourism industry, and the innovation of industrial systems and systems.


---

## Body

## 1. Introduction

The ski tourism industry was formed with the rise of skiing. The ski tourism industry refers to an organic whole composed of multiple plates, which provides skiers with various tangible products and intangible services required for ski tourism activities. It is formed by the organic combination of the sports industry and the tourism industry. The scope of research includes ski resorts, ski equipment, and clothing, as well as training and ski touring services for professional ski touring personnel (tourism, transportation, catering, ski sporting events, ski clubs). With Beijing’s proposal to host the Winter Olympics and national policies to support the sports industry, the ski industry has begun to receive more attention, and its importance in the sports industry has become increasingly prominent. The ski tourism industry has always played an important role in tourism activities in various provinces. The scale and influence of the ski tourism industry has long been huge. The development of the ski tourism industry has formed the prototype of a multifaceted ski tourism industry cluster (such as skiing, skiing entertainment, skiing technology, and skiing culture), which has had a positive impact on attracting employment, stimulating related industries, revitalizing the regional economy, and optimizing the regional sports industry pattern. However, at present, the ski tourism market, as an emerging industry, still has big problems in the integration of resources and the rational operation of the market economy. As a result, the industrial layout and structure of ski tourism are unreasonable, the development of various regions is unbalanced, and there is a large gap in operation and income.At the same time, with the development of China’s ski tourism industry, especially Beijing proposed to host the Winter Olympics, the rise of the market-oriented ski industry has increased investment in the development of the ski industry. The special area has advantages in the ski industry, and it is a very challenging location. There is a huge gap in the training system of the ski tourism industry, and support for regional economy, maturity, and industrial facilities needs to be strengthened and adjusted. Industrial cluster is a major economic event in regional economic development and is recognized as the most effective form of regional economic organization in the world. Many countries are seeing economic and other benefits, and they are the most important tool for regional economic development. The introduction of the theory of industrial clusters has played a great role in promoting the upgrading and optimization of the ski tourism industry structure in Northeast China and the development of the ski tourism industry in Northeast China, which has strong practical significance.Through the research on data mining technology, fusion, and recommendation system, with the advancement of science and technology, more and more new sensors appear, and data sensors continue to expand. Although increasing the amount of information helps improve the accuracy of the analysis, it also brings many problems. For example, the amount of data needs to be adjusted to increase the amount of reads. Of course, relying only on data aggregation technology is not enough, and most data processing technologies still need to be introduced. Therefore, this paper combines data mining technology and data fusion technology to maximize the use of system integration, mining and analyzing the challenges of the travel recommendation system, providing corresponding solutions for these challenges, and finally improving the corresponding recommendation methods. By mining and analyzing the information data of ski tourist areas, the key factors that affect the choice of tourist attractions are obtained. In this way, the difficulties and solutions of the travel recommendation system are obtained because the travel recommendation system is more complex. At present, the commonly used collaborative filtering algorithms cannot well satisfy the real travel recommendation system. The commonly used collaborative filtering algorithm is optimized to better solve the difficulties of the recommendation system in ski tourism areas and make a good reference for the improvement of the travel recommendation system in the future.


## 2. Related Work

For the upgrading and optimization of ski resorts, domestic and foreign experts also have many research results. Nilashi et al. proposed a new recommendation method based on multicriteria CF. The prediction accuracy of recommendation systems in the tourism field is improved by clustering, dimensionality reduction, and prediction methods [1]. Fong et al. facilitated collaboration in tourism destination research by analyzing the dynamics of codevelopment between multistakeholder changing practice logic and the surrounding institutional environment [2]. Frolova et al. revealed that different social groups in the society have different views on cultural and educational potential and urban tourism services based on the development of the current cultural and educational tourism industry [3]. However, because the research method is too old and the data is not accurate, people use new methods of data mining and data fusion to conduct research.In terms of data mining and data fusion, Buczak and Guven describe a targeted literature survey on machine learning (ML) and data mining (DM) methods for network analysis to support intrusion detection [4]. Xu et al. took a broader perspective on privacy issues related to data mining and studied various methods that help protect sensitive information [5]. Kavakiotis et al. believed that the application of machine learning and data mining methods in biological sciences is now more important and indispensable than ever [6]. Liao et al. aimed at accurate land cover classification and sought innovations in thermal hyperspectral and color data fusion [7]. However, the results of the above experiments were not accepted by the public due to the imprecise experimental process.


## 3. Data Fusion and Data Mining Technology

In our research, we will use techniques of data mining and data fusion. Research on the upgrading and optimization of ski resorts, data mining, and data fusion can process massive data. And its processing efficiency and accuracy are high, and its processing speed is fast, which has unique advantages in researching the upgrading and optimization of ski resorts [8].


### 3.1. Basic Concepts of Data Fusion

Data acquisition is a multilevel, multidimensional, multilevel configuration that combines multiple sensor datasets generated in an object to create a new target. The implications here are broad [9]. With reference to different data retrieval systems and related databases, data fusion is the perfect algorithm for different data. The purpose of configuration is to confirm and identify the information received, and linking multiple sensor data in the same way can improve reliability and complexity [10]. Data fusion is actually an embedded system that uses computers to organize, manage, and make decisions on multiple data sources. The many functions of a data integration system include research, identification, and analysis. The system function model is shown in Figure 1 [11]. The connection system is divided into two phases: low-level configuration and high-level configuration, which are related to the discovery of information [12]. Estimating parameter states as well as low-level configuration is a numerical analysis. Giving numerical results at a higher level is a symbolic process, creating more natural results [13].Figure 1
Data fusion functional structure.Data tracking is the use of multiple sensors in a data fusion system to continuously scan and monitor targets to detect signal levels. The function of the data connection unit is to determine whether data comes from the same destination at different times and places [14]. Condition evaluation is the evaluation of target parameters based on sensor observations, and these estimates are used to predict the location of the next observed target [15]. Object detection is to generate a dimensional feature vector based on the characteristics of the object measured by different sensors, each dimension representing an independent part of the object. If a target is known to have the type and characteristics of multiple target types, the measurement vector can be compared to the known characteristic types to determine the target type. Predict the overall location and development based on information such as the location, route, and type of the destination [16]. In the data-level integration mode, the source data of sensors of similar size are directly integrated, and then feature extraction and recognition are evaluated based on the sensor data. To achieve this level of data integration, all sensors must be at the same or equal distance. Interaction with raw data determines whether the integrated data is relevant and unique to the target or organization, thereby identifying mixed sensor data and processing it accordingly [17], as shown in Figure 2.Figure 2
Data-level attribute fusion.The essence of the feature set fusion level is to identify patterns. Comparing sensor differences, many sensor systems can provide additional information about the target. Typical fusion methods include pattern recognition techniques [18]. Feature sets are one of the most well-established methods and have great opportunities to use and develop. However, the difficulty of pattern recognition technology itself limits R&D participation to the behavioral level. Different types of sensor data will be processed with the same purpose to integrate decision quality levels. This includes achieving, identifying, or determining quality [19]. Results will be collected directly by pooling and combining with the higher-level decision-making process, as shown in Figure 3.Figure 3
Decision-level attribute fusion.Option-level functional integration provides great convenience for data processing. And the bandwidth requirements of the system are low, which can effectively integrate different types of data around the target. However, the goal is difficult to achieve over time due to past knowledge, the basis of common sense, and the strength of the environment. Joint progress has been made in the various stages, but the selection has not been completed [20]. The comparison of the three methods is shown in Table 1. In general, correct data mixing is the most efficient, and the selection level of ensemble is the lowest [21], but the fusion of the data layer requires the same type of sensors. And the real-time operation is affected by the number of datasets. All three of these approaches allow for hybrid structured data fusion systems. In practice, the size of data fusion is usually selected and adjusted according to the actual situation.Table 1
Comparison of the three methods.
Fusion frameworkInformation lossReal-timePrecisionFault toleranceWorkloadFusion levelData level14.3612.2511.2513.259.3610.58Feature level15.6515.4515.6915.6514.9816.25Decision level23.5222.2125.6524.3221.9822.34


### 3.2. Application of Rough Set Theory in Data Fusion

This is a mathematical tool for analyzing uncertain data. The new theory differs from the old in that it considers knowledge as the basis for the classification of objects. The object can use this information to divide the data. Data can be divided into corresponding values and relationships between values. So, the data can be understood by dividing the gaps in the same way, and the data is the result of a two-dimensional distribution. If the synthetic equivalent distribution is clean, then due to limited access to the information, inseparable relationships arise, resulting in some information concepts not being accurately represented. In order to describe the accuracy of the information, the concept of estimation set and the concepts of high and low estimation are introduced, which can be used to describe negligence and ambiguity. Approximate set theory, as a new method to deal with fuzzy, ambiguous or incomplete information, its main idea is to keep the inseparable relationship of the information system unchanged. Relying on the two equivalence relationships in the discourse world, the compatible information is removed. And to understand the potentially valuable rules, for each subset and fuzzy relationship in a given information representation system, the set of low and high estimates is defined as follows:(1)P−X=∪xx∈U∧Xp⊆X,P−X=∪xx∈U∧Xp∩X≠Θ.Geometric BNp (X) becomes the P boundary of X.
(2)BNpX=P−X−P−X,POSpX=P−X,NEGPX=U−P−X.As a data mining method, rough set can also be applied to data fusion to obtain fusion rules. The principle of the fusion system is shown in Figure4, and the process is as follows:Figure 4
Schematic diagram of the fusion system.According to the data collected and accumulated at ordinary times, some factors appear ambiguous. Therefore, in the type identification, the factors that reflect the type prominently should be selected as the main identification factors. Through the rough set method, redundant information can be eliminated without any prior information, and the main identification factors of various types can be obtained. The sample data is shown in Table2, and the decision table is shown in Table 3.Table 2
Sample data.
SampleH (m)R (km)V (m/s)YEX1280002401500PromoteNoneX228000200300SmoothHaveX320000210800DeclineHaveX412018080SmoothHaveX52000220300DeclineNoneX6300002202200PromoteNoneX73000200100SmoothHaveX8250020080SmoothHaveX98020080DeclineHaveX108000200800DeclineNoneTable 3
Decision table.
Ua1A2A3A4A5dX1280002401500101X228000200300212X32000210800314X412018080213X52000220300405X6300002202200101X73000200400512X8250020080213X98020080414X108000200800405The rough set method is for discrete data; so, the attribute data should be discretized before processing. This paper adopts the discretization method based on attribute importance, and the specific steps are as follows: The first step calculates the importance of conditional attributes. Since only a1, a2, and a3 need to be discretized, only the importance of these three attributes is calculated here.(3)INDC=1,2,3,4,5,6,7,8,9,10,INDD=1,6,2,7,3,8,4,9,5,10,POSCD=U,POSC/a1D=U,POSC/a2D=U,POSC/a3D=U.It can be seen that the a5 attribute is redundant and should be removed. The reduction results are shown in Table4:Table 4
Reduced decision table.
UA2A3A4dx1, x62311x22222x32233x41124x5, x102245x72252x82123x92144Since the three conditional attributes have the same importance, the order of discretization should be determined by the number of breakpoints. The candidate breakpoint sets for the three attributes of the decision table are as follows:(4)Ea1=100,1060,2250,2500,2750,5500,14000,29000,Ea2=190,205,215,230,Ea3=190,350,600,1150,1850.When discretizing attribute data, some redundant attributes have been reduced. Through the attribute reduction algorithm, redundant attributes can be completely removed:(5)POSC/a2D=1,2,3,5,6,7,9,10POSC/a3D=1,3,4,5,6,7,9,10,POSC/a4D=1,4,5,6,10,POSC/a5D=U.Using the same principle to process other samples, a total of 4 rules are obtained:(6)a4,1⟶d,1,a4,5⟶d,2,a3,2∧a4,2⟶d,2,a4,3⟶d,3.


### 3.3. Application of Neural Network in Data Fusion

Since neural network is widely used in pattern recognition and classification, it can also be used in identity fusion to achieve target recognition-level data fusion. Before using neural network for identity fusion, it is sometimes necessary to perform feature extraction or other data preprocessing on sensor data. The research mainly discusses the application of neural network method in fusion; so, the processing of input data is omitted, and the input end of the network is considered. Each input node (x1, x2, …, xn in Figure5) represents a feature element, and the output y represents the target type. The output y can be a one-dimensional or multidimensional vector. If it is one-dimensional, 1, 2, 3, …, m can be used to represent m target types, if the output is multidimensional, y can be m-dimensional. [100…0]T, [010…0]T, [0010…0]T, …, [000…01]T, respectively, represent the target types. The system using neural network for identity fusion needs to build a knowledge base containing typical data. The sample data in the knowledge base includes various characteristic information of the target and the type of the target. Using these sample data to train the neural network, after the output error meets the requirements, the neural network can be used for identity fusion.Figure 5
Schematic diagram of neural network for identity fusion.


### 3.4. Algorithms and Simulation

Based on the superiority of the fuzzy neural network, in this paper, fuzzy neural network is used for simulation. Train the network with sample data, the learning algorithm adopts the negative gradient descent method, the network learning rate is 0.01, and the whole simulation process is carried out in the environment. After 135 times of training, the error of the network meets the requirements. After network learning, the membership function of each input variable is shown in Figure6, and the error curve of network learning is shown in Figure 7. The learned fuzzy neural network constitutes an information fusion system. When the fusion is performed, the feature data is input into the fuzzy neural network, the output is obtained through the network operation, and the output is the result of the feature fusion.Figure 6
Initial membership function for area and membership function for area after training.Figure 7
Initial membership functions for velocity and membership functions after training.


## 3.1. Basic Concepts of Data Fusion

Data acquisition is a multilevel, multidimensional, multilevel configuration that combines multiple sensor datasets generated in an object to create a new target. The implications here are broad [9]. With reference to different data retrieval systems and related databases, data fusion is the perfect algorithm for different data. The purpose of configuration is to confirm and identify the information received, and linking multiple sensor data in the same way can improve reliability and complexity [10]. Data fusion is actually an embedded system that uses computers to organize, manage, and make decisions on multiple data sources. The many functions of a data integration system include research, identification, and analysis. The system function model is shown in Figure 1 [11]. The connection system is divided into two phases: low-level configuration and high-level configuration, which are related to the discovery of information [12]. Estimating parameter states as well as low-level configuration is a numerical analysis. Giving numerical results at a higher level is a symbolic process, creating more natural results [13].Figure 1
Data fusion functional structure.Data tracking is the use of multiple sensors in a data fusion system to continuously scan and monitor targets to detect signal levels. The function of the data connection unit is to determine whether data comes from the same destination at different times and places [14]. Condition evaluation is the evaluation of target parameters based on sensor observations, and these estimates are used to predict the location of the next observed target [15]. Object detection is to generate a dimensional feature vector based on the characteristics of the object measured by different sensors, each dimension representing an independent part of the object. If a target is known to have the type and characteristics of multiple target types, the measurement vector can be compared to the known characteristic types to determine the target type. Predict the overall location and development based on information such as the location, route, and type of the destination [16]. In the data-level integration mode, the source data of sensors of similar size are directly integrated, and then feature extraction and recognition are evaluated based on the sensor data. To achieve this level of data integration, all sensors must be at the same or equal distance. Interaction with raw data determines whether the integrated data is relevant and unique to the target or organization, thereby identifying mixed sensor data and processing it accordingly [17], as shown in Figure 2.Figure 2
Data-level attribute fusion.The essence of the feature set fusion level is to identify patterns. Comparing sensor differences, many sensor systems can provide additional information about the target. Typical fusion methods include pattern recognition techniques [18]. Feature sets are one of the most well-established methods and have great opportunities to use and develop. However, the difficulty of pattern recognition technology itself limits R&D participation to the behavioral level. Different types of sensor data will be processed with the same purpose to integrate decision quality levels. This includes achieving, identifying, or determining quality [19]. Results will be collected directly by pooling and combining with the higher-level decision-making process, as shown in Figure 3.Figure 3
Decision-level attribute fusion.Option-level functional integration provides great convenience for data processing. And the bandwidth requirements of the system are low, which can effectively integrate different types of data around the target. However, the goal is difficult to achieve over time due to past knowledge, the basis of common sense, and the strength of the environment. Joint progress has been made in the various stages, but the selection has not been completed [20]. The comparison of the three methods is shown in Table 1. In general, correct data mixing is the most efficient, and the selection level of ensemble is the lowest [21], but the fusion of the data layer requires the same type of sensors. And the real-time operation is affected by the number of datasets. All three of these approaches allow for hybrid structured data fusion systems. In practice, the size of data fusion is usually selected and adjusted according to the actual situation.Table 1
Comparison of the three methods.
Fusion frameworkInformation lossReal-timePrecisionFault toleranceWorkloadFusion levelData level14.3612.2511.2513.259.3610.58Feature level15.6515.4515.6915.6514.9816.25Decision level23.5222.2125.6524.3221.9822.34


## 3.2. Application of Rough Set Theory in Data Fusion

This is a mathematical tool for analyzing uncertain data. The new theory differs from the old in that it considers knowledge as the basis for the classification of objects. The object can use this information to divide the data. Data can be divided into corresponding values and relationships between values. So, the data can be understood by dividing the gaps in the same way, and the data is the result of a two-dimensional distribution. If the synthetic equivalent distribution is clean, then due to limited access to the information, inseparable relationships arise, resulting in some information concepts not being accurately represented. In order to describe the accuracy of the information, the concept of estimation set and the concepts of high and low estimation are introduced, which can be used to describe negligence and ambiguity. Approximate set theory, as a new method to deal with fuzzy, ambiguous or incomplete information, its main idea is to keep the inseparable relationship of the information system unchanged. Relying on the two equivalence relationships in the discourse world, the compatible information is removed. And to understand the potentially valuable rules, for each subset and fuzzy relationship in a given information representation system, the set of low and high estimates is defined as follows:(1)P−X=∪xx∈U∧Xp⊆X,P−X=∪xx∈U∧Xp∩X≠Θ.Geometric BNp (X) becomes the P boundary of X.
(2)BNpX=P−X−P−X,POSpX=P−X,NEGPX=U−P−X.As a data mining method, rough set can also be applied to data fusion to obtain fusion rules. The principle of the fusion system is shown in Figure4, and the process is as follows:Figure 4
Schematic diagram of the fusion system.According to the data collected and accumulated at ordinary times, some factors appear ambiguous. Therefore, in the type identification, the factors that reflect the type prominently should be selected as the main identification factors. Through the rough set method, redundant information can be eliminated without any prior information, and the main identification factors of various types can be obtained. The sample data is shown in Table2, and the decision table is shown in Table 3.Table 2
Sample data.
SampleH (m)R (km)V (m/s)YEX1280002401500PromoteNoneX228000200300SmoothHaveX320000210800DeclineHaveX412018080SmoothHaveX52000220300DeclineNoneX6300002202200PromoteNoneX73000200100SmoothHaveX8250020080SmoothHaveX98020080DeclineHaveX108000200800DeclineNoneTable 3
Decision table.
Ua1A2A3A4A5dX1280002401500101X228000200300212X32000210800314X412018080213X52000220300405X6300002202200101X73000200400512X8250020080213X98020080414X108000200800405The rough set method is for discrete data; so, the attribute data should be discretized before processing. This paper adopts the discretization method based on attribute importance, and the specific steps are as follows: The first step calculates the importance of conditional attributes. Since only a1, a2, and a3 need to be discretized, only the importance of these three attributes is calculated here.(3)INDC=1,2,3,4,5,6,7,8,9,10,INDD=1,6,2,7,3,8,4,9,5,10,POSCD=U,POSC/a1D=U,POSC/a2D=U,POSC/a3D=U.It can be seen that the a5 attribute is redundant and should be removed. The reduction results are shown in Table4:Table 4
Reduced decision table.
UA2A3A4dx1, x62311x22222x32233x41124x5, x102245x72252x82123x92144Since the three conditional attributes have the same importance, the order of discretization should be determined by the number of breakpoints. The candidate breakpoint sets for the three attributes of the decision table are as follows:(4)Ea1=100,1060,2250,2500,2750,5500,14000,29000,Ea2=190,205,215,230,Ea3=190,350,600,1150,1850.When discretizing attribute data, some redundant attributes have been reduced. Through the attribute reduction algorithm, redundant attributes can be completely removed:(5)POSC/a2D=1,2,3,5,6,7,9,10POSC/a3D=1,3,4,5,6,7,9,10,POSC/a4D=1,4,5,6,10,POSC/a5D=U.Using the same principle to process other samples, a total of 4 rules are obtained:(6)a4,1⟶d,1,a4,5⟶d,2,a3,2∧a4,2⟶d,2,a4,3⟶d,3.


## 3.3. Application of Neural Network in Data Fusion

Since neural network is widely used in pattern recognition and classification, it can also be used in identity fusion to achieve target recognition-level data fusion. Before using neural network for identity fusion, it is sometimes necessary to perform feature extraction or other data preprocessing on sensor data. The research mainly discusses the application of neural network method in fusion; so, the processing of input data is omitted, and the input end of the network is considered. Each input node (x1, x2, …, xn in Figure5) represents a feature element, and the output y represents the target type. The output y can be a one-dimensional or multidimensional vector. If it is one-dimensional, 1, 2, 3, …, m can be used to represent m target types, if the output is multidimensional, y can be m-dimensional. [100…0]T, [010…0]T, [0010…0]T, …, [000…01]T, respectively, represent the target types. The system using neural network for identity fusion needs to build a knowledge base containing typical data. The sample data in the knowledge base includes various characteristic information of the target and the type of the target. Using these sample data to train the neural network, after the output error meets the requirements, the neural network can be used for identity fusion.Figure 5
Schematic diagram of neural network for identity fusion.


## 3.4. Algorithms and Simulation

Based on the superiority of the fuzzy neural network, in this paper, fuzzy neural network is used for simulation. Train the network with sample data, the learning algorithm adopts the negative gradient descent method, the network learning rate is 0.01, and the whole simulation process is carried out in the environment. After 135 times of training, the error of the network meets the requirements. After network learning, the membership function of each input variable is shown in Figure6, and the error curve of network learning is shown in Figure 7. The learned fuzzy neural network constitutes an information fusion system. When the fusion is performed, the feature data is input into the fuzzy neural network, the output is obtained through the network operation, and the output is the result of the feature fusion.Figure 6
Initial membership function for area and membership function for area after training.Figure 7
Initial membership functions for velocity and membership functions after training.


## 4. Calculation of Ecological Footprint of Tourism Traffic in Ski Resorts

This paper takes the structure of ski tourism industry in Northeast China as the research object. Through the research on the current situation of the ski tourism industry structure in Northeast China, and using the theory of industrial clusters to analyze the ski tourism industry structure in Northeast China, suggestions for optimizing the ski tourism industry structure in Northeast China are put forward to provide a theoretical basis for the optimization of the ski tourism industry structure. The basic situation of the development of the ski tourism industry in the representative Northeast region will be investigated, and relevant experts will be interviewed to obtain the theoretical basis and practical basis for the upgrading and optimization of the ski tourism industry structure.


### 4.1. Composition of Tourism Ecological Footprint of Ski Resorts

Tourism transportation has important academic significance. This means that we should look at it from two perspectives: tourism traffic in a narrow sense aims to plan and organize tourism routes; tourism traffic in a broad sense refers to traffic from various places to tourist destinations, which is often referred to as “big traffic.” These include railways, medium and long-distance roads, air and waterways. Because there are hundreds of kilometers, thousands of kilometers, or even tens of thousands of kilometers from the traveler’s departure point to the tourist destination, passenger transportation must be long-distance transportation. Often, one mode of transportation cannot work, two or more modes of transportation have to be used in combination, and multiple modes of transportation have to be configured with the idea of comprehensive transportation to achieve the purpose of economy, comfort, and safety. Generally speaking, there are many ways to plan a car and train; that is, take a car from home to the local train station, get on the car, and then take the car to the tourist destination. After arriving at the destination railway station, the “door-to-door” scheme of the car is carried out; that is, one is to take a private car from the door to the tourist destination. The other is to take a social vehicle from the house to the long-distance bus distribution center and then take the long-distance bus to the tourist destination. Air-car program is as follows; that is, take a car from home to the local airport, take a plane to the tourist airport, and then take a car to the tourist destination. Of course, the above forms are not absolute, and other forms of transportation can be inserted in the middle. For example, the airport is still far away from the tourist destination, and transportation in the form of trains or ships can be used. The second is the transportation from the accommodation hotel to the scenic spots after the traveler arrives at the tourist destination. The traffic in the scenic spot mainly includes ordinary roads for foreign vehicles to pass and park, there are also trails for tourists to go sightseeing and walking, and there are roads that have both functions. There are also cable cars for mountain climbing and battery cars for sightseeing. The code of conduct for tourism traffic refers to the rules for tourists to choose means of transport during the travel process. The technicality and economy of the means of transportation greatly affect the best choice of means of transportation for tourists. Tourists have different choices of means of transportation from the starting point to the destination. At this time, the choice of transportation for tourists depends on personal characteristics and traffic conditions. When choosing a travel mode, two issues are usually considered, namely, the characteristics of the traffic demand that underpin the travel. In the process of travel, tourists’ demand for transportation is mainly reflected in six aspects: economy, speed, convenience, comfort, safety, and entertainment.As shown in Figure8, relying on the unique natural conditions, a ski tourism resource with unique local characteristics has been formed, and it has become a representative ski tourism area. Ice and snow art, ice and snow culture, and ice and snow style have developed unique types of tourism products. Today’s developed ski tourism products include the following: scenic ski tourism, sports ski tourism and entertainment, ski tourism activity products, etc. About 130 ski resorts in the region have a high comprehensive score for ski tourism resources in the Chinese ski tourism resource evaluation system. Table 5 is a comparison of the conditions of the ski resort.Figure 8
The composition of the source market of ski resorts.Table 5
Comparison of development conditions of ski tourism in the region.
Average temperature in JanuarySnow seasonMountain conditionsProvince H-30°C–-18°C120More than 100 peaks suitable for skiing, with a slope of 30-45°Province H-20°C–-14°C100Transition zone between mountains and plainsProvince H-17°C–-4°C70Wide range of mountains and hillsIn recent years, the accommodation facilities in ski resorts have been greatly improved. The accommodation beds are divided into high, medium, and low grades as shown in Figure9.Figure 9
Overview of accommodation facilities in ski tourism resorts.Obviously, the influence of tourists on the consumption of tourism resources and the ecological environment will change with time, technological changes, and changes in resource management methods. This change is not only manifested in quantity but also more profoundly in the change of the proportion of resource utilization. Table1 shows the changes in the proportion of various types of land areas in the total tourism ecological footprint of ski resorts from 2016 to 2020. The calculation results in Figure 10 show that in the total demand for tourism ecological footprint in ski resorts, each type of demand has different degrees of change.Figure 10
The proportion of various types of biologically productive land in the tourism ecological footprint demand of ski resorts.


### 4.2. Technical Structure of Ski Tourism Industry

From the perspective of the development pattern of the ski tourism industry in the world, science and technology have always played an important role in the development of the industry. Advanced snowmaking systems once laid the groundwork for breaking through the regional constraints of the ski tourism industry. In the process of developing ski tourism, the improvement of the lift has greatly increased the capacity of the ski area. Expanding the operational capabilities of resorts and ski resorts, it can be seen that the development of science and technology is conducive to the rapid improvement and transformation of the ski tourism industry, especially with the development of the Internet, advanced intelligent systems, easy-to-use ropeway systems, O2O-based storage systems, and smart devices that can be worn in ice and snow games, such as smart ski equipment and implantable helmet equipment. Insert a data acquisition chip in the sky, record the slide track, slide position and other data throughout the process, control the slide technology, and record the skier’s sliding experience in real time. The policy framework for the ski tourism industry focuses on the internal resource allocation mechanism and macropolicies of the ski tourism sector to promote equitable and enhanced development of the ski tourism sector. The ski tourism policy framework places great emphasis on the growth of the industry and can compensate for market failures, effectively utilize and allocate the available resources of the ski tourism industry, promote fairness, and enhance the enjoyment of ski tourism. Each region has formulated a development plan for the ski tourism industry. The ski tourism industry is the highlight of the tourism industry and an important part of the development of the tourism industry. This is an important aspect of the economic structure of the overall growth of the industry, including investment in transport, accommodation, food, trade, and skiing in the investment structure of the ski tourism sector. This is an important way to improve the business structure of the ski tourism industry by reducing investment in low-tech industries and transforming high-tech industries. Because ski tourism is a large investment sector, in the early stage of the development of China’s ski tourism sector, China’s investment system was dominated by structural investment themes, especially the investment in the public economy was less. The ski tourism industry is financed by financial investment, ignoring the role of money market and financial market, which will significantly reduce the efficiency of cash use. In recent years, the proportion of public investment in the ski tourism sector has continued to decline, while the proportion of community capital investment has continued to increase.The demand of the ski tourism industry is related to the utilization of the ski tourism industry. The comprehensive ratio of the development of efficient procurement relationships in various industries can be said that demand determines reasonable support. If demand is disproportionate, it indicates that the agency may be introducing contradictory industrialization disproportionately to economic demand. In unique economic events, the demand for ski touring business is affected by the cost, profit level, advantages of ski touring itself, advantageous prices related to ski sales, market size, and number of consumers.


### 4.3. Existing Problems

In terms of investment, although a lot of money and manpower are invested in the development of ice and snow projects every year, and we are constantly striving to bring forth new ideas, compared with other regions, people still cannot get out of the skiing circle. Skiing projects have many traditional features, but the project development form is relatively monotonous, and the development of special projects is relatively small, especially with some cultural connections, which will promote tourism. Product life cycle analysis is as follows: today’s ski products are still in the development stage. Ice and snow tourism products are a common product concept. There are six points to consider: food, accommodation, transportation, travel, shopping, and entertainment. They are the six basic aspects of meeting tourism consumption needs. In these respects, the Northeast region is too extensive and has not yet reached the level of refined management.


## 4.1. Composition of Tourism Ecological Footprint of Ski Resorts

Tourism transportation has important academic significance. This means that we should look at it from two perspectives: tourism traffic in a narrow sense aims to plan and organize tourism routes; tourism traffic in a broad sense refers to traffic from various places to tourist destinations, which is often referred to as “big traffic.” These include railways, medium and long-distance roads, air and waterways. Because there are hundreds of kilometers, thousands of kilometers, or even tens of thousands of kilometers from the traveler’s departure point to the tourist destination, passenger transportation must be long-distance transportation. Often, one mode of transportation cannot work, two or more modes of transportation have to be used in combination, and multiple modes of transportation have to be configured with the idea of comprehensive transportation to achieve the purpose of economy, comfort, and safety. Generally speaking, there are many ways to plan a car and train; that is, take a car from home to the local train station, get on the car, and then take the car to the tourist destination. After arriving at the destination railway station, the “door-to-door” scheme of the car is carried out; that is, one is to take a private car from the door to the tourist destination. The other is to take a social vehicle from the house to the long-distance bus distribution center and then take the long-distance bus to the tourist destination. Air-car program is as follows; that is, take a car from home to the local airport, take a plane to the tourist airport, and then take a car to the tourist destination. Of course, the above forms are not absolute, and other forms of transportation can be inserted in the middle. For example, the airport is still far away from the tourist destination, and transportation in the form of trains or ships can be used. The second is the transportation from the accommodation hotel to the scenic spots after the traveler arrives at the tourist destination. The traffic in the scenic spot mainly includes ordinary roads for foreign vehicles to pass and park, there are also trails for tourists to go sightseeing and walking, and there are roads that have both functions. There are also cable cars for mountain climbing and battery cars for sightseeing. The code of conduct for tourism traffic refers to the rules for tourists to choose means of transport during the travel process. The technicality and economy of the means of transportation greatly affect the best choice of means of transportation for tourists. Tourists have different choices of means of transportation from the starting point to the destination. At this time, the choice of transportation for tourists depends on personal characteristics and traffic conditions. When choosing a travel mode, two issues are usually considered, namely, the characteristics of the traffic demand that underpin the travel. In the process of travel, tourists’ demand for transportation is mainly reflected in six aspects: economy, speed, convenience, comfort, safety, and entertainment.As shown in Figure8, relying on the unique natural conditions, a ski tourism resource with unique local characteristics has been formed, and it has become a representative ski tourism area. Ice and snow art, ice and snow culture, and ice and snow style have developed unique types of tourism products. Today’s developed ski tourism products include the following: scenic ski tourism, sports ski tourism and entertainment, ski tourism activity products, etc. About 130 ski resorts in the region have a high comprehensive score for ski tourism resources in the Chinese ski tourism resource evaluation system. Table 5 is a comparison of the conditions of the ski resort.Figure 8
The composition of the source market of ski resorts.Table 5
Comparison of development conditions of ski tourism in the region.
Average temperature in JanuarySnow seasonMountain conditionsProvince H-30°C–-18°C120More than 100 peaks suitable for skiing, with a slope of 30-45°Province H-20°C–-14°C100Transition zone between mountains and plainsProvince H-17°C–-4°C70Wide range of mountains and hillsIn recent years, the accommodation facilities in ski resorts have been greatly improved. The accommodation beds are divided into high, medium, and low grades as shown in Figure9.Figure 9
Overview of accommodation facilities in ski tourism resorts.Obviously, the influence of tourists on the consumption of tourism resources and the ecological environment will change with time, technological changes, and changes in resource management methods. This change is not only manifested in quantity but also more profoundly in the change of the proportion of resource utilization. Table1 shows the changes in the proportion of various types of land areas in the total tourism ecological footprint of ski resorts from 2016 to 2020. The calculation results in Figure 10 show that in the total demand for tourism ecological footprint in ski resorts, each type of demand has different degrees of change.Figure 10
The proportion of various types of biologically productive land in the tourism ecological footprint demand of ski resorts.


## 4.2. Technical Structure of Ski Tourism Industry

From the perspective of the development pattern of the ski tourism industry in the world, science and technology have always played an important role in the development of the industry. Advanced snowmaking systems once laid the groundwork for breaking through the regional constraints of the ski tourism industry. In the process of developing ski tourism, the improvement of the lift has greatly increased the capacity of the ski area. Expanding the operational capabilities of resorts and ski resorts, it can be seen that the development of science and technology is conducive to the rapid improvement and transformation of the ski tourism industry, especially with the development of the Internet, advanced intelligent systems, easy-to-use ropeway systems, O2O-based storage systems, and smart devices that can be worn in ice and snow games, such as smart ski equipment and implantable helmet equipment. Insert a data acquisition chip in the sky, record the slide track, slide position and other data throughout the process, control the slide technology, and record the skier’s sliding experience in real time. The policy framework for the ski tourism industry focuses on the internal resource allocation mechanism and macropolicies of the ski tourism sector to promote equitable and enhanced development of the ski tourism sector. The ski tourism policy framework places great emphasis on the growth of the industry and can compensate for market failures, effectively utilize and allocate the available resources of the ski tourism industry, promote fairness, and enhance the enjoyment of ski tourism. Each region has formulated a development plan for the ski tourism industry. The ski tourism industry is the highlight of the tourism industry and an important part of the development of the tourism industry. This is an important aspect of the economic structure of the overall growth of the industry, including investment in transport, accommodation, food, trade, and skiing in the investment structure of the ski tourism sector. This is an important way to improve the business structure of the ski tourism industry by reducing investment in low-tech industries and transforming high-tech industries. Because ski tourism is a large investment sector, in the early stage of the development of China’s ski tourism sector, China’s investment system was dominated by structural investment themes, especially the investment in the public economy was less. The ski tourism industry is financed by financial investment, ignoring the role of money market and financial market, which will significantly reduce the efficiency of cash use. In recent years, the proportion of public investment in the ski tourism sector has continued to decline, while the proportion of community capital investment has continued to increase.The demand of the ski tourism industry is related to the utilization of the ski tourism industry. The comprehensive ratio of the development of efficient procurement relationships in various industries can be said that demand determines reasonable support. If demand is disproportionate, it indicates that the agency may be introducing contradictory industrialization disproportionately to economic demand. In unique economic events, the demand for ski touring business is affected by the cost, profit level, advantages of ski touring itself, advantageous prices related to ski sales, market size, and number of consumers.


## 4.3. Existing Problems

In terms of investment, although a lot of money and manpower are invested in the development of ice and snow projects every year, and we are constantly striving to bring forth new ideas, compared with other regions, people still cannot get out of the skiing circle. Skiing projects have many traditional features, but the project development form is relatively monotonous, and the development of special projects is relatively small, especially with some cultural connections, which will promote tourism. Product life cycle analysis is as follows: today’s ski products are still in the development stage. Ice and snow tourism products are a common product concept. There are six points to consider: food, accommodation, transportation, travel, shopping, and entertainment. They are the six basic aspects of meeting tourism consumption needs. In these respects, the Northeast region is too extensive and has not yet reached the level of refined management.


## 5. Discussion

The upgrading and development of ski tourism industry are a process of structural optimization and adjustment, which has the characteristics of system and network. In the whole process of structural optimization, technological innovation is the most important aspect and the source of strength. Institutional innovation is an important guarantee of conditions, and conceptual change is the road to change. The ultimate goal of the transformation of the ski tourism industry is to enhance the competitiveness and profitability of the ski tourism industry in the entire region. On the basis of building a ski tourism industry cluster, the ski tourism industry should use the inherent advantages of the industry cluster to establish several large ski tourism industry clusters and strengthen infrastructure construction, so as to promote the ski tourism industry restructuring strategy integrating technological innovation, institutional innovation, and conceptual innovation.In the ski tourism industry development area with the establishment of industrial clusters, actively introduce advantageous core enterprises, support the development of key ski tourism projects, increase investment promotion, and actively create advantageous ski tourism products. In product innovation, it is necessary to give full play to the advantages of industrial clusters in the region and develop ski tourism products with distinctive features based on the region’s own characteristics and advantages, for example, abandoning the one-day tour mode of single skiing in the past and developing special ski tourism products such as family parent-child tours, leisure vacation tours, and student ski winter camps; develop ancillary products related to ski tourism, such as food and souvenirs with regional characteristics; extend the chain of the ski tourism industry; and establish a development brand of the ski tourism industry, so as to realize the transformation and upgrading of the ski tourism industry structure. The innovation of ski tourism products is inseparable from the establishment of a ski tourism industry chain with reasonable structure and complete functions. From a structural analysis, the ski tourism industry is centered on the ski fitness market, the ski competitive market, and the ski related industry market. Therefore, it should be possible to choose to focus on skiing and vigorously develop the ski service industry, including catering, accommodation, entertainment, and communications. According to convention, every increase of 1 yuan in ski tourism income can drive social income by 7-8 yuan. Therefore, while innovating ski tourism products, priority should be given to the development of the ski tourism service industry within the cluster to increase the added value of the service industry.As the lifeline of the development of ski tourism, ski tourism infrastructure is of great significance in the process of tourism industry structure adjustment, upgrading and optimization. How to build and improve it is particularly important. As a key sector of tourism, it needs to expand infrastructure construction, increase investment in training, and strengthen the position of regional groups in the global tourism industry. And it is required to organically combine human resources and policies to obtain the necessary updates to promote the upgrading of the ski tourism industry cluster. Within the industrial cluster, the infrastructure of the ski tourism industry is an important bottleneck for the development of the ski tourism industry. In addition to traditional transportation, restaurants, etc., ski tourism informatization and ski tourism planning also play a decisive role in the construction of tourism infrastructure.


## 6. Conclusions

The article covers manufacturing clusters, industrial planning and optimization, regional economy, industrial economy, tourism management system, and the current situation and problems of regional ski tourism, as well as the improvement and promotion of ski tourism planning, relying on the industrial cluster system. Based on the above analysis, the prevention of the development of the ski tourism industry precedes the problems of data mining, data connection, and fusion data. Compare the functions of data mining technology and data fusion technology and improve the information acquisition process and the accuracy of system integration by combining these two technologies. The results show that the problems and development of the ski tourism industry mainly include short-term projects, insufficient supporting facilities, lack of an entity cooperation system, and insufficient supporting support. Through the thinking process of the ski tourism industry, it was found that the market demand and changes were not good. At the same time, because the ski tourism industry did not grow in scale, it did not see the impact on related companies. This paper discusses the key elements for cluster products to promote the promotion and optimization of ski tourism enterprise clusters and then analyzes the ski tourism industry. The relationship between the ski touring business and its stakeholders is being studied. Therefore, there are obstacles to the development of the ski tourism industry. The optimization strategies of ski tourism industry structure based on industrial clusters mainly include ice and snow sports product innovation, technology development path innovation, and optimization of ski tourism industry management mode and marketing mode.



---

*Source: 5358440-2022-06-29.xml*