The Methodology and Application of New Three-dimensional Guide System and Intelligent Service in Intelligent Scenic Spot: A Case Study of Qixing Scenic Spot in Guilin City, Guangxi, China

Nowadays, 3D navigation systems and intelligent services are costly to use, and the accuracy of intelligent navigation does not meet the needs of tourists, and the practicality is not strong. Based on the current situation of the construction of the smart scenic navigation system and the actual needs of tourists, this study determines the main principles and methods of the tour guide system and intelligent services. Taking a certain scenic spot as the specific research object, the A* algorithm is used to solve the path. By optimizing the online gracing service of the arc gis server network, the appropriate data interpolation method is used to improve the accuracy of the elevation data of the scenic spot, and the characteristics of the three-dimensional symbol system are combined. The 3D symbol design system is optimized, and a high-precision 3D navigation map model is established. Finally, the corresponding 3D tour guide system and scenic area intelligent service are designed based on this, and the principle and application flow of the intelligent scenic area navigation system construction are summarized. The three-dimensional map model and its navigation system designed by this research institute reduce the risk of tourists during the tour, improve the quality and efficiency of the tour, and provide a practical service optimization solution for the smart scenic three-dimensional navigation system. The construction and use cost of the high-precision three-dimensional navigation system in the scenic spot is reduced.


INTRODUCE
With the rapid development of the economy, the advancement of science and technology, the improvement of people's quality of life, and the continuous improvement of the national vacation system, tourism has become the main choice for people on holidays. Based on the background, the famous tourist attractions will have a huge flow of people during the holidays. Some of the attractions are likely to be crowd,which affects the tourist experience. The tourism industry is booming and the number of tourists is increasing. This brings more opportunities to the tourist attractions, but it also brings greater challenges .In essence, the main reasons for this situation are the problems which are within the scenic spot.Such as the management system is not rigorous,the defects of the service system, the scenic traffic facilities and the map navigation are not perfect enough to lead to the efficiency of the service visitors. Because the map navigation symbols are general, the map lacks terrain elements, and the three-dimensional map navigation system of the scenic spot has low precision, low practicability, and unclear identification of safety hazard areas, which is not conducive to tourists choosing efficient and reasonable navigation routes when visiting scenic spots. It can't help the scenic area to properly digest too crowded people in the case of huge human traffic, and to alleviate problems such as blockages. Establishing a systematic, networked and intelligent three-dimensional map navigation system for scenic spots has become an increasingly urgent need.

The Acquisition of scenic terrain data
Building a navigation service system in the relevant area and obtaining local topographic data is the most basic but necessary task. The current methods of obtaining basic geographic information fall into three categories: (1) Obtained from the map of the finished product. The scanner is used for scanning, and the scanned photos are registered according to the accurate coordinate points, and then the corresponding digital map is obtained by vectorization.
(2) Perform basic measurements using basic measuring instruments such as total stations and theodolites. The instrument directly measures the coordinates and elevation of the scenic spot, and finally the data processing and mapping on the computer. (

PATH SOLVING ALGORITHM
In the scenic terrain of complex terrain, the road conditions and terrain fluctuations are more complicated. Obstacle and hidden points are doped in the scenic area, so we find an algorithm that is computationally efficient and can take advantage of 3D path planning. Among the existing problems of 3D path planning, the main research algorithms are A* algorithm, genetic algorithm, ant colony algorithm, based on minimum energy function method, particle swarm optimization algorithm and artificial potential energy algorithm. The genetic algorithm is to study Darwin's biological evolution theory to simulate the natural evolution process and the biological evolution process related to genetics. By establishing a computational model to search for the optimal solution, the genetic algorithm can directly operate on structural objects. Without the limitation of derivative and function continuity, the process is simple, but the search speed of genetic algorithm is slow, and it takes more time to get accurate solution; ant colony algorithm is a self-organizing, essentially parallel algorithm. It is a positive feedback process and has strong robustness. The ant colony algorithm is a probabilistic algorithm used to find the optimal path in the graph. The group optimization law through the foraging behavior of ant colony The bionic optimization mathematical model is established, but the ant colony algorithm The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W10, 2020 International Conference on Geomatics in the Big Data Era (ICGBD), 15-17 November 2019, Guilin, Guangxi, China is easy to lose the optimal solution, which is caused by the process of positive feedback in the algorithm. The minimum energy function method is mainly applied to the 3D path planning of unmanned autonomous vehicles or ground mobile robots. The question is whether the criterion of a certain path satisfies the minimum elastic energy of the curve as the criterion for the optimal path; the A* algorithm is one Heuristic search algorithm, and in the field of path optimization, A* algorithm is playing an important role. For example, when people use electronic map navigation in scenic spots, they choose the starting point and the end point to get the navigation route, and the A* algorithm is research. The shortest path problem from point to point including the specific path. The following are three algorithms that are more suitable for 3D path planning.

Genetic algorithm
Genetic algorithm (GA) is a global optimization probability search algorithm that simulates the inheritance and survival of the inferior rule in Darwin's biological evolution theory. It is an algorithm that mimics the natural genetic mechanism and the rule of survival of the fittest. In the early and late stages of the genetic evolution process, the probability of individual selection in the population will vary to varying degrees. The idea of genetic algorithm is to first select the candidate solution, then test the fitness of the candidate solution according to the established adaptive index, leaving the solution with higher fitness, and then repeat the above operation to make new The candidate solution is looped until the optimal solution is found.Genetic algorithms have strong robustness and good ability to search global optimal solutions, especially when studying the optimal combination of optimal paths. Another feature of the genetic algorithm is that it has better flexibility and adaptability, so it can find the global optimal solution, and it is difficult to fall into the local optimal solution. However, in the process of searching, the genetic algorithm needs to use a large amount of historical information to genetic algorithm, so this causes the algorithm to be similar to premature, low computational efficiency, etc., and also determines that the genetic algorithm has strong limitation. Therefore, in the case of a complex landscape with complex environment and rugged terrain, the shortcomings of the genetic algorithm's computational efficiency and stability are not high, and the ability to search for new paths is poor. It may be necessary to obtain multiple calculations at times. Stable results, which will undoubtedly consume a lot of time and economic costs, limit the wide application of genetic algorithms in intelligent scenic spots.

Ant Colony Algorithm
Ant colony algorithm is a probabilistic algorithm, which is mainly used to find the optimal path in the graph. This algorithm is also called ant algorithm. The ant colony algorithm is produced by its creators by observing the natural ant's process of finding food and using artificial ants to mimic the natural ant foraging behavior, so this is a bio-bionic algorithm.
The core of this algorithm is because ants release a very important thing in the path during the foraging processpheromone. The behavior of the whole ant colony foraging will continue to affect the concentration of pheromones on each path. The more ants passing through the path, the higher the pheromone concentration, and vice versa, the lower the pheromone concentration, and the pheromone concentration.
The higher the path is selected by ants, the higher the probability is, the so-called positive feedback process, but how does this process ensure that the path with the highest concentration of pheromone is the shortest path? In fact, it can be understood that when a path makes the food closer to the ant nest than the other path, the time for the ant to return to the nest through this path will be shorter. When the number of ants going back and forth is more and more, this path The pheromone concentration is significantly higher than the longer round trip time, and the path with the highest pheromone concentration is the optimal path from the nest to the food. In addition to the shortest path, this process provides several suboptimal paths. Therefore, each time the ant colony algorithm completes a related search, in addition to the optimal solution, it can provide several suboptimal solutions, which will The defect that causes the ant colony algorithm to fall into the local optimal solution will affect the choice of the shortest path to some extent.

A* algorithm
The now popular heuristic search algorithm should belong to the A* algorithm, and the A* algorithm is a direct search method. The so-called direct search method refers to the method of directly searching without any preprocessing. The difference between the algorithm is that the global information is introduced when searching for each possible node in the shortest path, and the distance between the current node and the end point is estimated, and used as a criterion to judge whether the node is in the On the shortest path. In addition, the A* algorithm also introduces the evaluation function f(n) = g(n) + The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W10, 2020 International Conference on Geomatics in the Big Data Era (ICGBD), 15-17 November 2019, Guilin, Guangxi, China h(n) , where g( n) is the shortest path value known from the starting raster to the grid n, h ( n) is the valuation heuristic value of the grid n to the target. If the actual cost from n to the target is always greater than or equal to h(n), then the shortest path will definitely be found. For the three-dimensional A* algorithm h(n), the Euclidean distance is usually taken as the heuristic value because it is allowed to move in any direction in three-dimensional space. Since the actual distance from the grid n to the destination grid is generally larger than this heuristic value, it is guaranteed that the search path distance is the shortest.The A* shortest path search algorithm can be implemented by C language using VC++6.0 software. The result of running in VC++ is shown in the following figure.
This is a simulated flat map. The blank squares represent the roads that can pass, and the white squares are obstacles that are impassable (each grid in the program represents one step, Understand that the map is a 20*20 matrix, and solve the path by inputting the coordinates of the starting point and the coordinates of the end point).
In Figure 1, "□" refers to a road that can pass, and "•" represents an obstacle, meaning that it is impassable. Figure 1 In Fig. 2, when the starting point coordinates (1, 1) and the ending point coordinates (9, 10) are input, the shortest path can be obtained, and the minimum number of steps can be obtained.
"※" stands for the starting point, "◎" stands for the end point, and "☆" stands for the shortest path.

Figure 2
When the 3D path planning algorithm is applied to the path solving of the 3D scenic area model, many algorithms lose their advantages and even complete the solving task. The real scenic terrain is more complex, the road is rugged, and there are many hidden points and obstacles. When these inaccessible factors exist in the path, Then this path will not be feasible. Among the above several suitable algorithms, the A* algorithm is more suitable for the path solving of the real scene, because the A* algorithm is more suitable for multiple obstacles, and the algorithm is fast and efficient, and the terrain is fluctuating.
There are advantages in maps with many obstacles. Like the literature, the slope distance is equivalent to a horizontal distance, and a new g(n) representation method is introduced to improve the A* algorithm, and the final practical application effect is ideal, which is very good for the three-dimensional scenic path planning. Great practicality.   Improve hardware performance to optimize server information acceptance and processing.

Network environment
ArcGIS Server built by ArcObjects is a development tool B\S tool (browser/server). When the user uses the software, the browser sends an access request to the server on the other end of the Internet, and then the server receives the application signal and analyzes, calculates, and processes it. The information obtained after processing is fed back to the user. In this series of processes, a large amount of data is constantly being uploaded and downloaded, and the high speed and smoothness of the network environment is particularly important. Improve network speed, good network environment can improve server information reception and processing

system layout
The architecture of the ArcGIS Server system is a gis server, a web server, and a client. They can be on the same machine or on different machines. When the server receives less processing information, we can choose to have the gis server, web server and client deployed on the same machine. When the server is frequently accessed and the received data exceeds the load, the ArcGIS Server system components are laid out. On different machines, the purpose of optimizing the server information acceptance and processing process is achieved.

CONCLUSION
With the rapid development of the tourism industry, the number of tourists continues to rise, tourists are increasingly demanding the intelligent service quality and navigation system The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W10, 2020 International Conference on Geomatics in the Big Data Era (ICGBD), 15-17 November 2019, Guilin, Guangxi, China This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-3-W10-1139-2020 | © Authors 2020. CC BY 4.0 License. of the scenic spot, and the two-dimensional map can no longer meet the individual needs of tourists. This paper integrates the actual needs of passengers and the current status of the construction of the navigation service system, and turns the research direction to the map guide design of the three-dimensional model.
The study takes a certain scenic spot as the specific research object. In the aspect of path optimization, based on the A* algorithm, combined with the straight line intersection algorithm in computer graphics, it is applied to the path search to improve the accuracy of the raster data path solution. In terms of service port, the online map service based on ArcGIS Server is adopted, and the information receiving and information processing process of the server is optimized. In the design of the guide system map model, the terrain data interpolation method is used to improve the data precision and establish a high-precision three-dimensional navigation map.
Model; in the aspect of symbol system design, the three-dimensional symbol database is combined, and the basic characteristics of the three-dimensional symbol are summarized, and a concrete construction scheme is proposed for the three-dimensional guide system of the scenic spot.
The research and design of the guide system and the principle and application of intelligent service have reduced the risk of tourists during the tour. To a certain extent, it also improved the quality and efficiency of the tour, providing a high-precision map model design for the three-dimensional navigation system.
The smart scenic three-dimensional tour guide system and its intelligent service provide a practical service optimization solution, which reduces the construction and use cost of the high-precision three-dimensional navigation system in the scenic spot to a certain extent.