Tourism behavior analysis and prediction system and method

Abstract

The invention relates to the technical field of user behavior analysis, and particularly discloses a tourism behavior analysis and prediction system and method, and the system comprises a data perception module, a data enhancement module, a behavior analysis module, a real-time prediction module, a decision recommendation module, and a system monitoring module. The data sensing module is used for performing interface integration with various data sources and collecting data related to tourism behaviors to obtain original data; the data processing module is used for cleaning, integrating and standardizing the original data, and adding enhanced data to supplement the blank of the original data to obtain preprocessed data; the behavior analysis module is used for analyzing the preprocessed data through data mining and deep learning, generating an analysis result and predicting behavior changes and demands of tourists; according to the invention, not only can the tourist attraction be helped to optimize the tourist experience, but also scientific decision support can be provided for tourist attraction managers by predicting the tourist flow, the consumption mode and the tourist attraction demand in real time.

Description

Technical Field

[0001] The present invention belongs to the technical field of user behavior analysis, and in particular relates to a tourism behavior analysis and prediction system and method. Background Art

[0002] The meaning of "tourism" is easy to understand. "Travel" means to travel, to go out, that is, to move from one place to another in order to achieve a certain purpose; "tour" means to go out for sightseeing, sightseeing, and entertainment, that is, to travel to achieve these purposes.

[0003] At present, the continuously growing tourism demand has kept the number of tourists in tourist destinations at a high level, and tourist destinations have made a lot of investment in the infrastructure of tourism public services, but in the face of the surge in passenger flow, it still cannot meet the demand. There are various software for recording travel itineraries on the market, such as Chanyouji, Baidu Maps and Gaode Maps, etc. These software mainly record the images of the attractions visited by tourists and the time of arrival at each attraction. Through the relevant information system, relevant data such as tourist flow can be monitored in time, but due to the lack of data accumulation and big data analysis capabilities, it is impossible to mine the travel patterns and behavioral habits of tourists. During the entire travel process, travel agencies, tourism regulatory agencies and scenic spots are unable to share tourist behavior information in a timely manner, resulting in the inability to solve the tourist interaction problems that occur during the travel process in a timely manner. How to conduct in-depth research and analysis on tourist behavior, realize standardized management, provide more professional and systematic tourism services, and provide data support for the standardization of tourism market management and the intelligent guidance of tourist travel.

[0004] Therefore, it is necessary to propose a tourism behavior analysis and prediction system and method to solve the problem of how to accurately analyze and mine tourists' behavior habits and then predict tourists' future needs in the existing technology.

[0005] The above information disclosed in this background technology is only used to increase the understanding of the background technology of the present invention and therefore, it may include information that does not constitute the prior art known to ordinary technicians in this field. Summary of the invention

[0006] The purpose of the present invention is to provide a tourism behavior analysis and prediction system and method to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A tourism behavior analysis and prediction system, comprising: a data perception module, a data enhancement module, a behavior analysis module, a real-time prediction module, a decision recommendation module, and a system monitoring module;

[0009] The data perception module is used to integrate interfaces with various data sources, collect data related to tourism behavior, and obtain raw data;

[0010] The data processing module is used to clean, integrate and standardize the original data, and add enhanced data to fill in the gaps in the original data to obtain preprocessed data;

[0011] The behavior analysis module is used to analyze the pre-processed data through data mining and deep learning, generate analysis results and predict the behavior changes and needs of tourists;

[0012] The real-time prediction module is used to analyze the pre-processed data and analysis results through a preset prediction model, predict the tourism demand, flow changes and preference trends of tourist attractions within a preset time period, and generate demand prediction results;

[0013] The decision-making recommendation module is used to adjust the recommended content and travel routes of tourist attractions in real time based on the analysis results and demand forecast results;

[0014] The system monitoring module is used to monitor the operating status of the entire system in real time, provide security protection, and record operation logs.

[0015] Preferably, the IoT devices are used to collect tourists' physical activities, health conditions, and location change data in real time, and obtain tourists' personal information, consumption records, and environmental information; the crawler technology is used to automatically capture tourists' interactive information on social media and analyze tourists' emotions and preferences; the data obtained through the IoT devices and crawler technology are integrated to obtain original data.

[0016] Preferably, the raw data is dynamically preprocessed using artificial intelligence and adaptive algorithms, including data cleaning, standardization and time series processing; combined with real-time data streams, the data processing flow and strategy are adjusted according to the characteristics of the raw data, and enhanced data is added to supplement the gaps in the raw data to obtain preprocessed data.

[0017] Preferably, the deep learning model is introduced to conduct a comprehensive analysis in combination with the preprocessed data, to construct a personalized behavior model, to understand and predict the behavior and preferences of tourists; natural language processing and sentiment analysis technology are used to conduct a deep analysis of the preprocessed data, to explore the potential needs and motivations of tourists, and to generate sentiment analysis results;

[0018] Based on the personalized behavior model and sentiment analysis results, the behavior changes and needs of tourists are predicted in real time to generate behavior prediction results; comprehensive analysis is performed on the behavior prediction results and sentiment analysis results to generate analysis results;

[0019] Define the dimensions of user portraits based on preprocessed data, and build user portraits based on analysis results; use Kafka to obtain tourists' latest data related to travel behavior in real time to achieve dynamic updates of user portraits.

[0020] Preferably, the tourist flow and consumption patterns of tourist attractions are generated based on the preprocessed data; a reinforcement learning algorithm is introduced to automatically adjust the recommendation strategy or operation decision of the tourist attractions according to the real-time tourist flow and consumption patterns;

[0021] According to the current tourist flow and demand forecast results, combined with external factors such as weather and holidays, the tourist flow of tourist attractions is dynamically optimized; based on the current travel behavior and sentiment analysis results of tourists, the recommended content and travel routes are adjusted in real time.

[0022] Preferably, the method adjusts the opening hours of tourist attractions, tour guide arrangements and service personnel configurations through a simulated annealing algorithm according to the real-time tourist flow, the capacity of tourist attractions and the preferences of tourists; and automatically recommends and generates customized tourist products and itineraries for tourist attractions based on the prediction results of tourist behavior;

[0023] During the travel process, natural language processing and sentiment analysis technology are used to conduct in-depth analysis of tourists’ real-time interactive information on social media, predict tourists’ satisfaction, and adjust the service content and operation strategies of tourist attractions.

[0024] Preferably, the system operating status and data flow are monitored in real time through artificial intelligence algorithms to automatically identify and respond to potential security threats; blockchain technology is used to protect tourists' sensitive data, and abnormal operations and behaviors in the system are automatically detected to report potential risks in a timely manner.

[0025] A tourist behavior analysis and prediction method, comprising:

[0026] Through the integration of interfaces with various data sources, IoT devices are used to monitor tourists' dynamic behaviors in real time. At the same time, interactive information on social media is automatically captured through crawler technology, and various data obtained are integrated to form raw data;

[0027] Apply artificial intelligence and adaptive algorithms to dynamically adjust the processing flow according to the characteristics of the original data, clean, format, and standardize the data, solve the problem of blank data, and perform time series processing;

[0028] Analyze tourists' travel behavior through deep learning models, build personalized behavior models and generate behavior prediction results. Combined with sentiment analysis technology, deeply explore tourists' emotional fluctuations and preference trends during the travel process and generate sentiment analysis results.

[0029] Based on the behavior prediction results, the preset prediction model is used to make real-time predictions of the tourist flow and consumption patterns of tourist attractions, evaluate the tourism trends within the preset time period, and dynamically adjust the demand prediction results in combination with external factors such as weather and holidays;

[0030] According to the behavior prediction results and demand prediction results, the recommended content and travel routes of tourist attractions are adjusted in real time. According to the real-time tourist flow, the simulated annealing algorithm is used to adjust the opening hours of tourist attractions, tour guide arrangements, and service personnel configuration;

[0031] The operation status and data flow of the entire system are monitored through artificial intelligence algorithms, potential security threats are automatically identified and responded to, and blockchain technology is used to protect tourists' sensitive data, and abnormal operations are detected to report potential security threats in a timely manner;

[0032] The dimensions of the user portrait are defined based on the preprocessed data, and the user portrait is constructed by combining the behavior prediction results and sentiment analysis results. With the help of Kafka, the latest data related to tourists' travel behavior is obtained in real time to achieve dynamic updating of the user portrait.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention acquires tourists' dynamic data and interactive information in real time through integration with IoT devices and social media, forms a comprehensive raw data foundation, applies artificial intelligence and adaptive algorithms to dynamically process and clean raw data, solves blank data and inconsistent format problems, and thus improves data quality and accuracy; introduces deep learning models and sentiment analysis technology to analyze tourists' behavior patterns and emotional fluctuations, and builds personalized behavior models to gain insight into tourists' preferences and demand trends during the travel process; combines external factors such as weather and holidays to dynamically adjust prediction results, adjust travel recommendation content and travel routes in real time, and optimize the opening hours of tourist attractions, tour guide arrangements, and service personnel configuration to maximize tourist experience and improve the operational efficiency of tourist attractions. This process can not only help tourist attractions optimize tourists' experience, but also provide scientific decision-making support for managers of tourist attractions by predicting tourist flow, consumption patterns, and demand for attractions in real time.

[0035] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a framework diagram of the tourist behavior analysis and prediction system of the present invention;

[0037] Figure 2This is a flow chart of the tourist behavior analysis and prediction method of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Embodiment 1:

[0040] See also Figure 1 As shown, a tourism behavior analysis and prediction system includes: a data perception module, a data enhancement module, a behavior analysis module, a real-time prediction module, a decision recommendation module, and a system monitoring module;

[0041] The data perception module is used to integrate interfaces with various data sources, collect data related to tourism behavior, and obtain raw data;

[0042] The data processing module is used to clean, integrate and standardize the original data, and add enhanced data to fill in the gaps in the original data to obtain preprocessed data;

[0043] The behavior analysis module is used to analyze the pre-processed data through data mining and deep learning, generate analysis results and predict the behavior changes and needs of tourists;

[0044] The real-time prediction module is used to analyze the pre-processed data and analysis results through a preset prediction model, predict the tourism demand, flow changes and preference trends of tourist attractions within a preset time period, and generate demand prediction results;

[0045] The decision-making recommendation module is used to adjust the recommended content and travel routes of tourist attractions in real time based on the analysis results and demand forecast results;

[0046] The system monitoring module is used to monitor the operating status of the entire system in real time, provide security protection, and record operation logs.

[0047] Through IoT devices (such as smart bracelets, environmental sensors, etc.), real-time data on tourists’ physical activities, health conditions, and location changes are collected, as well as tourists’ personal information, consumption records, and environmental information;

[0048] For example, sensors on smart bracelets and mobile devices can be used to track tourists' physical condition (such as pace, body temperature, etc.), travel habits (such as length of stay, travel style, etc.) and environmental perception (weather, air quality, etc.).

[0049] Automatically capture tourists’ interactive information (such as pictures, dynamics, comments, etc.) on social media through crawler technology to analyze tourists’ emotions and preferences;

[0050] Integrate the data obtained through IoT devices and crawler technology to obtain the original data.

[0051] Use artificial intelligence and adaptive algorithms to dynamically pre-process raw data, including data cleaning, standardization, and time series processing;

[0052] Combined with real-time data streams (using Kafka to manage real-time data streams), data processing processes and strategies are adjusted according to the characteristics of the original data;

[0053] Enhanced data (such as virtual reality attraction information, user travel logs, etc.) are added to fill in the gaps in the original data to obtain preprocessed data.

[0054] Introduce deep learning models combined with pre-processed data for comprehensive analysis, build personalized behavior models, and understand and predict tourists' behaviors and preferences;

[0055] Using natural language processing and sentiment analysis techniques, we conduct in-depth analysis of pre-processed data to explore tourists’ potential needs and motivations (such as adding facilities, improving services, etc.) and generate sentiment analysis results. In addition to basic sentiment classification (such as positive, negative, and neutral), we can expand to multi-level sentiment analysis to identify the deep meaning of emotions (such as anxiety, surprise, satisfaction, disappointment, etc.), thereby gaining a more detailed understanding of tourists’ motivations and behaviors.

[0056] Based on personalized behavior models and sentiment analysis results, predict tourists' behavior changes and needs in real time and generate behavior prediction results;

[0057] In order to better perform personalized recommendations, the collaborative filtering method is combined with the model. For example, based on the tourists' historical behavior, interest preferences, and the behavior of other similar tourists, the deep learning model is used to optimize the recommendation results, thereby improving the accuracy of personalized recommendations.

[0058] Comprehensively analyze the behavior prediction results and sentiment analysis results to generate analysis results;

[0059] Define the dimensions of user portraits based on preprocessed data, and build user portraits based on analysis results, including travel preferences, psychological state, health status, etc.; integrate data on tourists' behaviors on different platforms (such as hotel booking platforms, travel guide websites, etc.) to build a more comprehensive tourist portrait.

[0060] With the help of Kafka, tourists' latest data related to travel behavior can be obtained in real time, user portraits can be dynamically updated, and the best restaurants, shopping places and rest areas can be recommended to tourists, thereby providing tourists with a full range of personalized services.

[0061] Generate tourist flow and consumption patterns of tourist attractions based on preprocessed data;

[0062] Introducing reinforcement learning algorithms to automatically adjust the recommendation strategies or operational decisions of tourist attractions based on real-time tourist traffic and consumption patterns;

[0063] Based on the current tourist flow and demand forecast results, combined with external factors such as weather and holidays, the tourist flow of tourist attractions can be dynamically optimized; for example, tourist behavior will be different in different seasons, holidays or weather conditions. The system can combine time series data modeling to capture the laws of tourist behavior changes and further improve prediction accuracy.

[0064] Based on tourists' current travel behavior and sentiment analysis results, recommended content and travel routes are adjusted in real time.

[0065] According to the real-time tourist flow, the capacity of tourist attractions and the preferences of tourists, the opening hours of tourist attractions, tour guide arrangements and service personnel configuration are adjusted through the simulated annealing algorithm;

[0066] Automatically recommend and generate customized tourist products and itineraries for tourist attractions based on the predicted results of tourists’ behavior;

[0067] During the travel process, natural language processing and sentiment analysis technology are used to conduct in-depth analysis of tourists’ real-time interactive information on social media, predict tourists’ satisfaction, and adjust the service content and operation strategies of tourist attractions.

[0068] Use artificial intelligence algorithms to monitor system operating status and data flow in real time, automatically identify and respond to potential security threats;

[0069] Blockchain technology is used to protect tourists' sensitive data, automatically detect abnormal operations and behaviors in the system, and report potential risks in a timely manner.

[0070] Provide tourists with a virtual scenic spot tour experience through VR / AR technology to help tourists make better travel decisions;

[0071] Dynamically display tourist behavior data, attraction popularity, travel suggestions, etc. to enhance tourists' interactivity and participation in the system;

[0072] Provide personalized navigation and attraction introductions based on tourists' real-time location, and enhance the travel experience through voice assistants or AR glasses.

[0073] Embodiment 2:

[0074] See also Figure 2 As shown, a tourist behavior analysis and prediction method includes:

[0075] Through the integration of interfaces with various data sources, IoT devices are used to monitor tourists' dynamic behaviors in real time. At the same time, interactive information on social media is automatically captured through crawler technology, and various data obtained are integrated to form raw data;

[0076] Tourist attractions have set up multiple data collection points, including scenic spot entrances, major sightseeing areas, souvenir shops, etc. When tourists enter a tourist attraction, for example, they can use Wi-Fi positioning systems and smart guide devices. After entering the tourist attraction, tourists log in through mobile phone applications and allow the collection of their location, stay time, browsing habits, etc. At the same time, when tourists purchase goods in the scenic area, the system will automatically record consumption data, including the type, quantity, and payment amount of the purchased goods.

[0077] Apply artificial intelligence and adaptive algorithms to dynamically adjust the processing flow according to the characteristics of the original data, clean, format, and standardize the data, solve the problem of blank data, and perform time series processing;

[0078] Analyze tourists' travel behavior through deep learning models, build personalized behavior models and generate behavior prediction results. Combined with sentiment analysis technology, deeply explore tourists' emotional fluctuations and preference trends during the travel process and generate sentiment analysis results.

[0079] For example, some tourists habitually buy history books or handicrafts. Based on these behaviors, we can analyze and predict, recommend more similar products to tourists, and provide small coupons during their purchase process to stimulate tourists' desire to consume.

[0080] Based on the behavior prediction results, the preset prediction model is used to make real-time predictions of the tourist flow and consumption patterns of tourist attractions, evaluate the tourism trends within the preset time period, and dynamically adjust the demand prediction results in combination with external factors such as weather and holidays;

[0081] According to the behavior prediction results and demand prediction results, the recommended content and travel routes of tourist attractions are adjusted in real time. According to the real-time tourist flow, the simulated annealing algorithm is used to adjust the opening hours of tourist attractions, tour guide arrangements, and service personnel configuration;

[0082] During the tour, whenever tourists stay for a period of time or approach certain specific products, personalized recommendations will be pushed through the mobile terminal to remind tourists to buy related products. For example, if a tourist visits the ancient cultural relics exhibition area, relevant cultural relics replicas or history books will be recommended based on his browsing history; if a tourist stays in the main street, discount information of nearby restaurants or promotions of local specialty products will be pushed.

[0083] The operation status and data flow of the entire system are monitored through artificial intelligence algorithms, potential security threats are automatically identified and responded to, and blockchain technology is used to protect tourists' sensitive data, and abnormal operations are detected to report potential security threats in a timely manner;

[0084] Define the dimensions of the user portrait based on the preprocessed data, and build the user portrait by combining the behavior prediction results and sentiment analysis results;

[0085] With the help of Kafka, tourists’ latest data related to travel behavior can be obtained in real time to dynamically update user portraits.

[0086] As can be seen from the above, the present invention acquires tourists' dynamic data and interactive information in real time through integration with IoT devices and social media, forms a comprehensive raw data foundation, applies artificial intelligence and adaptive algorithms to dynamically process and clean raw data, solves blank data and inconsistent format problems, thereby improving data quality and accuracy; introduces deep learning models and sentiment analysis technology to analyze tourists' behavior patterns and emotional fluctuations, and builds personalized behavior models to gain insight into tourists' preferences and demand trends during the travel process; combines external factors such as weather and holidays to dynamically adjust prediction results, adjust travel recommendation content and travel routes in real time, and optimize the opening hours of tourist attractions, tour guide arrangements, and service personnel configuration to maximize tourist experience and improve the operational efficiency of tourist attractions. This process can not only help tourist attractions optimize tourists' experience, but also provide scientific decision-making support for managers of tourist attractions by predicting tourist flow, consumption patterns, and demand for attractions in real time.

[0087] Embodiment 3:

[0088] The embodiment of the present invention also provides a computer-readable storage medium, on which a program of a tourism behavior analysis and prediction system as described above is stored, and when the program is executed by a processor, each process of the above analysis and prediction system embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. Among them, the computer-readable storage medium, such as a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0089] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0090] In the drawings of the embodiments disclosed in the present invention, only the structures involved in the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0091] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0092] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A tourist behavior analysis and prediction system, characterized in that: include: Data perception module, data enhancement module, behavior analysis module, real-time prediction module, decision recommendation module, system monitoring module; The data perception module is used to integrate interfaces with various data sources, collect data related to tourism behavior, and obtain original data; The data processing module is used to clean, integrate and standardize the original data, and add enhanced data to fill in the blanks of the original data to obtain pre-processed data; The behavior analysis module is used to analyze the pre-processed data through data mining and deep learning, generate analysis results and predict the behavior changes and needs of tourists; The real-time prediction module is used to analyze the pre-processed data and the analysis results through a preset prediction model, predict the tourism demand, flow changes and preference trends of tourist attractions within a preset time period, and generate demand prediction results; The decision recommendation module is used to adjust the recommended content and travel routes of the tourist attractions in real time based on the analysis results and the demand forecast results; The system monitoring module is used to monitor the operating status of the entire system in real time, provide security protection, and record operating logs.

2. A tourist behavior analysis and prediction system according to claim 1, characterized in that: The data perception module is also used for: Collect the tourist's physical activity, health status, and location change data in real time through IoT devices, and obtain the tourist's personal information, consumption records, and environmental information; Automatically capture the tourists' interactive information on social media through crawler technology to analyze the tourists' emotions and preferences; The data obtained through the IoT device and the crawler technology are integrated to obtain the original data.

3. A tourist behavior analysis and prediction system according to claim 2, characterized in that: The data processing module is also used for: Dynamically preprocessing the raw data using artificial intelligence and adaptive algorithms, including data cleaning, standardization, and time series processing; In combination with real-time data streams, data processing procedures and strategies are adjusted according to the characteristics of the raw data; Enhanced data is added to supplement the blanks of the original data to obtain the preprocessed data.

4. A tourist behavior analysis and prediction system according to claim 3, characterized in that: The behavior analysis module is also used to: Introducing a deep learning model to conduct a comprehensive analysis in combination with the preprocessed data, building a personalized behavior model, and understanding and predicting the behavior and preferences of the tourists; Using natural language processing and sentiment analysis technology, the pre-processed data is deeply analyzed to explore the potential needs and motivations of the tourists and generate sentiment analysis results; Based on the personalized behavior model and the sentiment analysis result, predict the behavior changes and needs of the tourists in real time and generate behavior prediction results; Comprehensively analyzing the behavior prediction result and the sentiment analysis result to generate the analysis result; Defining dimensions of the user portrait according to the preprocessed data, and constructing the user portrait in combination with the analysis result; With the help of Kafka, the latest data related to the tourist's travel behavior is obtained in real time to achieve dynamic updating of the user portrait.

5. A tourist behavior analysis and prediction system according to claim 4, characterized in that: The real-time prediction module is also used for: generating the visitor flow and consumption pattern of the tourist attraction based on the preprocessed data; Introducing a reinforcement learning algorithm to automatically adjust the recommendation strategy or operation decision of the tourist attraction according to the real-time tourist flow and consumption pattern; According to the current tourist flow and the demand forecast results, combined with external factors such as weather and holidays, dynamically optimize the tourist flow of the tourist attractions; Based on the tourist's current travel behavior and the sentiment analysis result, the recommended content and travel route are adjusted in real time.

6. A tourist behavior analysis and prediction system according to claim 5, characterized in that: The decision recommendation module is also used for: According to the real-time tourist flow, the capacity of the tourist attractions and the preferences of the tourists, adjusting the opening hours, tour guide arrangements and service personnel configuration of the tourist attractions through a simulated annealing algorithm; Based on the predicted behavior results of the tourists, automatically recommend and generate customized tourism products and itineraries for the tourist attractions; During the tour, natural language processing and sentiment analysis technologies are used to deeply analyze the tourists' real-time interactive information on social media, predict the tourists' satisfaction and adjust the service content and operation strategy of the tourist attractions.

7. A tourist behavior analysis and prediction system according to claim 6, characterized in that: The system monitoring module is also used for: Monitor the system operation status and data flow in real time through artificial intelligence algorithms, and automatically identify and respond to potential security threats; Blockchain technology is used to protect the sensitive data of the tourists, and abnormal operations and behaviors in the system are automatically detected, and potential risks are reported in a timely manner.

8. A tourist behavior analysis and prediction method, characterized in that: include: Through the integration of interfaces with various data sources, IoT devices are used to monitor tourists' dynamic behaviors in real time. At the same time, interactive information on social media is automatically captured through crawler technology, and various data obtained are integrated to form raw data; Apply artificial intelligence and adaptive algorithms to dynamically adjust the processing flow according to the characteristics of the raw data, clean, format and standardize the data, solve the blank data problem, and perform time series processing; Analyze the tourist's travel behavior through a deep learning model, build a personalized behavior model and generate behavior prediction results, combine sentiment analysis technology to deeply explore the tourist's emotional fluctuations and preference trends during the travel process, and generate sentiment analysis results; Based on the behavior prediction results, the preset prediction model is used to make real-time predictions of the tourist flow and consumption patterns of tourist attractions, evaluate the tourism trends within a preset time period, and dynamically adjust the demand prediction results in combination with external factors such as weather and holidays; According to the behavior prediction results and the demand prediction results, the recommended content and tourist routes of tourist attractions are adjusted in real time, and the opening hours, tour guide arrangements, and service personnel configuration of the tourist attractions are adjusted using a simulated annealing algorithm according to the real-time tourist flow; The operating status and data flow of the entire system are monitored through artificial intelligence algorithms, potential security threats are automatically identified and responded to, and blockchain technology is combined to protect the sensitive data of the tourists, and abnormal operations are detected and potential security threats are reported in a timely manner.

9. A tourist behavior analysis and prediction method according to claim 8, characterized in that: The method further comprises: Defining the dimensions of the user portrait according to the preprocessed data, and constructing the user portrait in combination with the behavior prediction result and the sentiment analysis result; With the help of Kafka, the latest data related to the tourist's travel behavior is obtained in real time to achieve dynamic updating of the user portrait.

Images