Scenic area management interactive method and system based on big data

Abstract

The invention discloses a scenic spot management interactive method and system based on big data, and particularly relates to the technical field of tourism management and intelligent scenic spots, an access control gate machine, an electronic ticket card reader and a tourist information management terminal are arranged to record incoming and outgoing data of tourists, and a ticket system is connected with a database to realize real-time transmission and storage of the data; high-definition monitoring cameras and visitor flow counting equipment are installed at main entrances and exits of scenic spots and important scenic spots, the number of tourists is monitored in real time and accurately counted, and RFID equipment is configured for recognizing and tracking the identities and positions of the tourists; the system is provided with an access control gate machine, a monitoring camera, RFID equipment and the like, acquires tourist data in real time, models by using multiple algorithms such as linear regression, random forest and LSTM, processes in real time and combines multiple algorithms to inform management personnel to take measures in time, so that excessive concentration of tourists is avoided, and the operation efficiency and safety of scenic spots are improved; and meanwhile, data support is provided for management decisions.

Description

Technical Field

[0001] The present invention relates to the field of tourism management and intelligent scenic spot technology, and more specifically, to an interactive method and system for scenic spot management based on big data. Background Art

[0002] At present, scenic spot management faces problems such as inaccurate prediction of tourist flow, low management efficiency, and difficulty in ensuring service quality. Existing systems mostly rely on manual operations and basic electronic guides, lacking intelligence and real-time performance, resulting in data silos and management lags. Traditional centralized storage is difficult to process massive data, and performance bottlenecks are prone to occur during high concurrent access. To solve these problems, a scenic spot tourist flow prediction system based on big data is proposed to solve the above problems. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide an interactive method and system for scenic area management based on big data. The scenic area tourist flow prediction system based on big data comprehensively improves the scenic area management efficiency and tourist experience through five steps: data collection, meteorological and activity data acquisition, data storage and management, data analysis and modeling, prediction and early warning. The system collects tourist data in real time by installing access control gates, surveillance cameras, RFID equipment, etc., and builds a distributed storage and database management system in combination with meteorological and activity data to ensure safe and efficient data storage. Using multiple algorithms such as linear regression, random forest and LSTM to model, the model is optimized through cross-validation and hyperparameter tuning to provide accurate tourist flow prediction. The prediction and early warning system combining real-time processing and multiple algorithms promptly notifies management personnel to take measures to avoid excessive concentration of tourists, improve the operational efficiency and safety of scenic spots, and provide data support for management decisions.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: S1: setting up access control gates, electronic ticket readers and visitor information management terminals to record the entry and exit data of visitors, and connecting the ticket system to the database to realize real-time transmission and storage of data; installing high-definition surveillance cameras and crowd counting equipment at the main entrances and exits of the scenic area and important attractions to monitor and accurately count the number of tourists in real time, and configuring RFID equipment to identify and track the identity and location of tourists.

[0005] S2: Install weather stations and weather sensors in the scenic area to monitor weather conditions in real time, and access third-party weather data interfaces to obtain comprehensive weather data; use the calendar system to record statutory holidays, scenic area activities and special events, and connect with the scenic area activity management system to obtain detailed activity arrangements and timetables;

[0006] S3: Deploy distributed storage servers and database management systems for large-scale data storage and structured query; use data cleaning software and data preprocessing tools to clean, filter and format the collected raw data;

[0007] S4: Use data analysis software to analyze the cleaned data, extract key features, and use the machine learning platform to establish a tourist flow prediction model; train and optimize the model through feature engineering tools, use cross-validation tools and error analysis tools to evaluate model performance and improve prediction accuracy;

[0008] S5: Use a real-time data processing platform to perform real-time data processing and streaming computing, integrate the prediction algorithm into the system, and realize real-time tourist flow prediction; set up an early warning system and set the flow threshold. Once the predicted flow exceeds the threshold, the system automatically issues a warning notification and configures an alarm to prompt the management personnel.

[0009] In a preferred embodiment: in step S1, access control gates, electronic ticket readers and tourist information management terminals are set up to record the entry and exit data of tourists, and the ticket system is connected to the database to achieve real-time transmission and storage of data; high-definition surveillance cameras and people flow counting equipment are installed at the entrances and exits of scenic spots and scenic spots to monitor and accurately count the number of tourists in real time, and RFID equipment is configured to identify and track the identity and location of tourists; the total number of tourists can be obtained by accumulating the records of all card readers: Among them, E i is the number of tourists recorded by the i-th card reader, and n is the total number of card readers; through the management terminal, the system records the name, ID number, ticket type, entry time and exit time of each tourist to provide data support; the camera and sensor equipment capture real-time tourist flow information, and the data is transmitted to the system to analyze the distribution and flow of tourists in the current scenic area; the total number of real-time tourists is obtained by accumulating the records of all cameras: Among them, C i is the number of tourists recorded by the i-th camera, and m is the total number of cameras; the RFID tag will emit a unique ID, the reader will capture this ID, and record the location information and timestamp to track the movements of each tourist; the RFID data set is represented as: RFID Data = {Tag ID, Location.Time}.

[0010] In a preferred embodiment: in step S2, a weather station and a weather sensor are installed in the scenic area to monitor the weather conditions in real time, and a third-party weather data interface is connected to obtain comprehensive weather data; a calendar system is used to record statutory holidays, scenic area activities and special events, and the system is connected to the scenic area activity management system to obtain detailed activity arrangements and timetables; the weather station and the sensor continuously collect data such as temperature, humidity and wind speed, and the data will be transmitted to the system to help analyze the impact of weather on tourist flow; the weather data set is represented by: Weather_Data = {T, H, W}, where T is temperature, H is humidity, and W is wind speed; through the third-party weather API interface, the system obtains more comprehensive weather information; the external weather data set is represented by: External_Weather_Data = {T ext ,H ext ,W ext}, where T ext is the external temperature, H ext is the external humidity, W ext is the external wind speed; the calendar system and activity management system record the arrangements of holidays and special events. The activity data set is expressed as: Event_Data_{Event_Name,Date,Time,Location}, where Event_Name is the event name, Date is the event date, Time is the event time, and Location is the event location.

[0011] In a preferred embodiment: in step S3, a distributed storage server and a database management system are deployed for storage and structured query of large-scale data; data cleaning software and data preprocessing tools are used to clean, filter and format the collected raw data to ensure data consistency and quality; the distributed storage server can process a large amount of visitor data, including real-time and historical data, to ensure secure storage and fast access to data, and the total storage capacity is obtained by summing up the storage capacity of all servers: Among them, S iis the storage capacity of the ith server, and p is the total number of servers; the database management system organizes data into tabular form to support complex query operations and fast retrieval and processing of required information. An example of a database query statement is as follows: DB_Query = SELECT {fields} FROM table WHERE condition, where fields is the query field, table is the data table, and condition is the query condition; data cleaning software and preprocessing tools are used to remove noise and error information in the data and format the data. The cleaned data is represented by: Cleaned_Data = Preprocess, where Cleaned_Data is the cleaned data, Raw_Data is the original data, and Preprocess is the data preprocessing function.

[0012] In a preferred embodiment: in step S4, the cleaned data is analyzed using data analysis software to extract key features, and a tourist flow prediction model is established using a machine learning platform; the model is trained and optimized using feature engineering tools, and the model performance is evaluated using cross-validation tools and error analysis tools to improve prediction accuracy. The data analysis software performs statistical analysis on the processed data to find out the key factors affecting tourist flow. The feature set is represented as: Features = {f1, f2, ..., f n}, where f i is the i-th feature; the machine learning platform uses historical data for training and builds a model to predict future tourist traffic. The algorithms include linear regression, random forest, and long short-term memory network; the linear regression model is expressed as: y = β0 + β1x1 + β2x2 + … + β n x n , where y is the predicted tourist flow, x i For each feature, β i is the corresponding regression coefficient. The random forest model makes predictions by constructing multiple decision trees and taking the average value: in, is the predicted value, T is the number of decision trees, f t (x) is the prediction result of the t-th tree; the long short-term memory network is a recurrent neural network used to process and predict time series data. The input of the LSTM model is a time series {x t}, the output is the predicted value y t : Among them, i t 、f t , o t are the activation values ​​of the input gate, forget gate, and output gate, respectively. is the candidate memory cell state, C t is the memory cell state, h t It is a hidden state; feature engineering tools are used to extract and optimize features to improve the accuracy and robustness of the model; model evaluation is carried out through cross-validation and error analysis, and commonly used evaluation indicators include mean square error and mean absolute error: and Among them, y i is the true value, is the predicted value, and n is the number of samples.

[0014] In a preferred embodiment: in step S5, a real-time data processing platform is used to perform real-time data processing and streaming computing, and a prediction algorithm is integrated into the system to achieve real-time tourist flow prediction; an early warning system is set, and a flow threshold is set. Once the predicted flow exceeds the threshold, the system automatically issues an early warning notification and configures an alarm to prompt management personnel. The real-time data processing platform can process real-time data from various sensors and systems, and immediately perform calculations and analyses to provide real-time tourist flow prediction results: Real_Time_Prediction = Predict(Real_Time_Data), where Real_Time_Prediction is the real-time prediction result, Real_Time_Data is the real-time data, and Predict is the prediction function; first, the ARIMA model is used to perform short-term prediction on time series data: y t =c+v1y t-1 +φ2y t-2 +…+φ p y t-p +θ1∈ t-1 +θ2∈ t-2 +…+θ q ∈ t-q +∈ t , where y t is the predicted value at time t, φ i is the autoregressive coefficient, θ j is the moving average coefficient, ∈ t is the error term; then the LSTM model is combined for time prediction, and finally the results of the two are weighted averaged to obtain the final prediction value: Final_Prediction=α×ARIMA_Prediction+(1-α)×LSTM_Prediction, where α is the weight parameter with a value range of [0,1]; the early warning system sets the flow threshold according to the output of the prediction model. When the predicted flow exceeds the threshold, the system will automatically issue an early warning notification: Among them, Alert is the warning signal, and Threshold is the flow threshold. Once the warning is triggered, the electronic display screen and voice alarm will notify the scenic area management personnel to take action to deal with the congestion.

[0015] Technical effects and advantages of the present invention: The scenic spot tourist flow prediction system based on big data comprehensively improves the scenic spot management efficiency and tourist experience through five steps: data collection, meteorological and activity data acquisition, data storage and management, data analysis and modeling, prediction and early warning. The system collects tourist data in real time by installing access control gates, surveillance cameras, RFID equipment, etc., and builds a distributed storage and database management system in combination with meteorological and activity data to ensure safe and efficient data storage. Using multiple algorithms such as linear regression, random forest and LSTM to model, the model is optimized through cross-validation and hyperparameter tuning to provide accurate tourist flow prediction. The prediction and early warning system that combines real-time processing and multiple algorithms promptly notifies management personnel to take measures to avoid excessive concentration of tourists, improve the operational efficiency and safety of the scenic spot, and provide data support for management decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the working steps of the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] An example implementation will now be described more fully with reference to the accompanying drawings. S1: Access control gates, electronic ticket readers and visitor information management terminals are set up to record visitor entry and exit data, and the ticket system is connected to a database to achieve real-time transmission and storage of data; high-definition surveillance cameras and crowd counting equipment are installed at the main entrances and exits of the scenic area and important attractions to monitor and accurately count the number of tourists in real time, and RFID equipment is configured to identify and track the identity and location of tourists.

[0019] S2: Install weather stations and weather sensors in the scenic area to monitor weather conditions in real time, and access third-party weather data interfaces to obtain comprehensive weather data; use the calendar system to record statutory holidays, scenic area activities and special events, and connect with the scenic area activity management system to obtain detailed activity arrangements and timetables;

[0020] S3: Deploy distributed storage servers and database management systems for large-scale data storage and structured query; use data cleaning software and data preprocessing tools to clean, filter and format the collected raw data;

[0021] S4: Use data analysis software to analyze the cleaned data, extract key features, and use the machine learning platform to establish a tourist flow prediction model; train and optimize the model through feature engineering tools, use cross-validation tools and error analysis tools to evaluate model performance and improve prediction accuracy;

[0022] S5: Use a real-time data processing platform to process data in real time and perform streaming calculations, integrate the prediction algorithm into the system, and realize real-time tourist flow prediction; set up an early warning system and set a flow threshold. Once the predicted flow exceeds the threshold, the system automatically issues an early warning notification and configures an alarm to alert management personnel; in step S1, set up access control gates, electronic ticket readers, and tourist information management terminals to record the entry and exit data of tourists, and connect the ticket system to the database to realize real-time transmission and storage of data; install high-definition surveillance cameras and traffic counting equipment at the entrances and exits of scenic spots and scenic spots to monitor and accurately count the number of tourists in real time, and configure RFID equipment to identify and track the identity and location of tourists; the total number of tourists can be obtained by accumulating the records of all card readers: Among them, E i is the number of tourists recorded by the i-th card reader, and n is the total number of card readers; through the management terminal, the system records the name, ID number, ticket type, entry time and exit time of each tourist to provide data support; the camera and sensor equipment capture real-time tourist flow information, and the data is transmitted to the system to analyze the distribution and flow of tourists in the current scenic area; the total number of real-time tourists is obtained by accumulating the records of all cameras: Among them, C iis the number of tourists recorded by the i-th camera, and m is the total number of cameras; the RFID tag will emit a unique ID, the reader will capture this ID, and record the location information and timestamp to track the movement of each tourist; the RFID data set is represented by: RFID Data = {Tag ID, Location.Time}; The first step of the scenic spot tourist flow prediction system is data collection and equipment deployment. The main task of this step is to install and set up various hardware devices to collect tourists' entry and exit data and monitor tourist flow in real time. These data are the basis for subsequent analysis and prediction, so the accuracy and real-time nature of the collection process are crucial. First of all, the access control gate and electronic ticket reader are the core equipment for data collection. The access control gate ensures that every tourist entering the scenic spot must swipe the card or scan the ticket through physical barriers and automatic control mechanisms. These devices record the entry and exit time of each tourist and their identity information, and transmit this data to the central database in real time. This method not only improves the accuracy of the data, but also reduces human errors and data delays. The tourist information management terminal further supplements this process to collect more detailed tourist information, such as name, ID number, and ticket purchase information. These data are helpful for more detailed analysis of tourist behavior and personalized services. It is necessary to install high-definition surveillance cameras and people flow counting equipment at the main entrances and exits and important scenic spots of the scenic area. These devices can monitor and count the flow of tourists in real time and provide accurate real-time data. High-definition surveillance cameras can capture high-resolution video images to help analysts observe the behavior patterns and flow trends of tourists. Infrared sensors and thermal imaging cameras can provide reliable data in low light or at night. These data are transmitted to the central system through the network for real-time processing and storage. In addition, RFID (radio frequency identification) equipment plays an important role in identifying and tracking the identity and location of tourists. Each tourist is equipped with an RFID tag when entering the scenic area, which has a unique ID number built in. RFID readers are distributed in various key locations in the scenic area, which can read the information of these tags and record the current location and timestamp of tourists in real time. This method can not only accurately track the movements of tourists, but also quickly locate the location of tourists in emergency situations, thereby improving the efficiency of safety management. In short, the purpose of data collection and equipment deployment is to ensure that relevant data of tourists can be collected comprehensively and accurately through the collaborative work of multiple advanced equipment. These data will provide a solid foundation for subsequent analysis and prediction.

[0023] In step S2, a weather station and weather sensors are installed in the scenic area to monitor weather conditions in real time, and a third-party weather data interface is connected to obtain comprehensive weather data; a calendar system is used to record statutory holidays, scenic area activities and special events, and the system is connected to the scenic area activity management system to obtain detailed activity arrangements and timetables; the weather station and sensors continuously collect data such as temperature, humidity and wind speed, and the data will be transmitted to the system to help analyze the impact of weather on tourist flow; the weather data set is represented by: Weather_Data = {T, H, W}, where T is temperature, H is humidity, and W is wind speed; through the third-party weather API interface, the system obtains more comprehensive weather information; the external weather data set is represented by: External_Weather_Data = {T ext ,H ext ,W ext}, where T ext is the external temperature, H ext is the external humidity, W extis the external wind speed; the calendar system and activity management system record the arrangement of holidays and special events, and the activity data set is expressed as: Event_Data_{Event_Name,Date,Time,Location}, where Event_Name is the name of the event, Date is the date of the event, Time is the time of the event, and Location is the location of the event; after completing the collection of basic data, the second step is to obtain meteorological data and activity data. These data have a great impact on the flow of tourists in the scenic area, so they need to be accurately and comprehensively collected and integrated. First of all, it is essential to install meteorological stations and meteorological sensors inside the scenic area. Meteorological stations are usually equipped with equipment such as temperature sensors, humidity sensors, and wind speed sensors, which can monitor the weather conditions in the scenic area in real time. These sensors continuously collect current temperature, humidity, and wind speed data and transmit them to the central database. Real-time meteorological data helps to analyze the impact of weather changes on tourist flow. For example, sunny weather usually attracts more tourists, while bad weather may lead to a significant reduction in the number of tourists. In addition to local meteorological data, it is also very important to access third-party meteorological data interfaces. These interfaces provide a wider range of meteorological data, including weather forecasts, climate change trends and other information. By integrating local meteorological data and third-party data, more comprehensive and accurate weather information can be obtained. Third-party meteorological data interfaces usually provide APIs, and the system can regularly obtain the latest weather data from these interfaces and combine them with local data for analysis. In this way, the system can not only monitor the current weather conditions, but also predict weather changes in the next few days, thereby more accurately predicting tourist flow. In addition, using a calendar system to record statutory holidays, scenic area activities, and special events is also a key step. Statutory holidays and special events usually have a significant impact on the number of tourists, so this information needs to be recorded in detail. The calendar system can pre-enter national statutory holidays and important festivals, which usually have a large number of tourists flocking to the scenic area. In addition, the scenic area’s own activity management system records various event arrangements, including cultural performances, exhibitions, and special events. Information about these activities, such as event name, date, time, and location, will be integrated into the system to predict tourist flow in a specific time period. By combining local meteorological data, third-party meteorological data, and activity data, a comprehensive data environment can be built. These data can not only be used to analyze and predict tourist flow, but also help scenic area managers make reasonable operational and management decisions. For example, during holidays or events when high traffic is expected, the number of staff and facility openings can be increased to cope with peak demand.

[0024] In step S3, distributed storage servers and database management systems are deployed for large-scale data storage and structured query. Data cleaning software and data preprocessing tools are used to clean, filter and format the collected raw data to ensure data consistency and quality. Distributed storage servers can process a large amount of visitor data, including real-time and historical data, to ensure secure storage and fast access to data. The total storage capacity is obtained by adding up the storage capacity of all servers: Among them, S iis the storage capacity of the i-th server, and p is the total number of servers; the database management system organizes data into a table form to support complex query operations and fast retrieval and processing of the required information. The following is an example of a database query statement: DB_Query = SELECT {fields} FROM table WHERE condition, where fields is the query field, table is the data table, and condition is the query condition; data cleaning software and preprocessing tools are used to remove noise and error information from the data and format the data. The cleaned data is represented by: Cleaned_Data = Preprocess, where Cleaned_Data is the cleaned data, Raw_Data is the original data, and Preprocess is the data preprocessing function; after collecting rich basic data and auxiliary data, the third step is data storage and management. The goal of this step is to ensure that all data can be stored safely and stably, and can be quickly accessed and processed. In order to achieve this goal, it is necessary to deploy advanced data storage devices and database management systems, and to clean and preprocess data at the same time. First, deploying distributed storage servers is the key to data storage and management. Distributed storage systems can handle large-scale data and support high concurrent access. Traditional centralized storage systems often encounter performance bottlenecks and single point failure problems when facing large-scale data and high concurrent access. Distributed storage improves the reliability of data storage and enhances the scalability of the system by distributing and storing data on multiple servers. In this system, each server only stores part of the data, but all data can be accessed through the network. This design not only improves data security, but also continues to provide data services when some servers fail. Secondly, the database management system (DBMS) plays a vital role in data storage and management. DBMS can organize data into structured tables, support complex query operations, and provide efficient data retrieval functions. DBMS also has data backup and recovery functions, which can quickly restore data when data is lost or damaged. In addition, DBMS also supports transaction management of data to ensure data consistency and integrity. In the database, all data is stored according to a predefined pattern, including tourist information, real-time monitoring data, meteorological data, and activity data. Before data storage, data cleaning and preprocessing are essential steps. The purpose of data cleaning is to remove noise and erroneous information in the data and ensure the quality of the data. For example, in tourist data, there may be duplicate records or missing values, which need to be solved during the data cleaning process. Data preprocessing is to format the data so that it meets the requirements of analysis and processing. For example, converting timestamps to standard date and time formats, encoding text data into digital formats, etc. Although these steps seem simple, they are crucial for subsequent data analysis and processing.In addition, data storage and management also include data security management. In order to protect data from unauthorized access and tampering, strict access control policies need to be implemented. Only authorized personnel can access and operate data, and the system also needs to conduct regular security audits to detect and prevent potential security threats. At the same time, data encryption is also an important measure to protect data security. By encrypting and storing sensitive data, data leakage can be effectively prevented. In short, the goal of data storage and management is to establish an efficient, secure, and reliable data storage environment to ensure that all data can be stably stored and quickly accessed and processed. Through advanced storage technology and management systems, a solid foundation can be provided for subsequent data analysis and prediction.

[0025] In step S4, the cleaned data is analyzed using data analysis software to extract key features, and a tourist flow prediction model is established using a machine learning platform. The model is trained and optimized using feature engineering tools, and cross-validation tools and error analysis tools are used to evaluate model performance and improve prediction accuracy. The data analysis software performs statistical analysis on the processed data to find out the key factors affecting tourist flow. The feature set is represented as: Features = {f1, f2, ..., f n}, where f i is the i-th feature; the machine learning platform uses historical data for training and builds a model to predict future tourist traffic. The algorithms include linear regression, random forest, and long short-term memory network; the linear regression model is expressed as: y = β0 + β1x1 + β2x2 + … + β n x n , where y is the predicted tourist flow, x i For each feature, β i is the corresponding regression coefficient. The random forest model makes predictions by constructing multiple decision trees and taking the average value: in, is the predicted value, T is the number of decision trees, f t (x) is the prediction result of the t-th tree; the long short-term memory network is a recurrent neural network used to process and predict time series data. The input of the LSTM model is a time series {x t}, the output is the predicted value y t : Among them, i t 、f t , o t are the activation values ​​of the input gate, forget gate, and output gate, respectively. is the candidate memory cell state, C t is the memory cell state, h tIt is a hidden state; feature engineering tools are used to extract and optimize features to improve the accuracy and robustness of the model; model evaluation is carried out through cross-validation and error analysis, and commonly used evaluation indicators include mean square error and mean absolute error: and Among them, y i is the true value, is the predicted value, and n is the number of samples; after completing data collection and storage, the fourth step is to perform data analysis and modeling. The purpose of this step is to use the collected data to establish an effective prediction model to predict future tourist flow. Data analysis and modeling are the core of the entire system because they directly affect the accuracy and reliability of the prediction results. First of all, data analysis is the basis of data modeling. The main task of data analysis is to conduct exploratory analysis on the cleaned data to find out the key factors affecting tourist flow. These factors may include weather conditions, holidays, scenic spot activities, etc. In the process of data analysis, data analysts will use various statistical methods and data visualization tools to gain an in-depth understanding of the distribution and characteristics of the data. For example, by drawing distribution graphs and time series graphs of data, you can intuitively see the trends and patterns of tourist flow over time. Through correlation analysis, you can identify the relationship between various factors and determine which factors have a significant impact on tourist flow. Based on data analysis, feature extraction and feature engineering are important steps in data modeling. The purpose of feature extraction is to extract useful features from the raw data, which will be used as input to the model. For example, features such as temperature, humidity, and wind speed are extracted from weather data, and features such as historical tourist flow and tourist age distribution are extracted from tourist data. Feature engineering is to optimize and transform features to make them more suitable for model training. For example, standardize numerical features and encode categorical features. These processes can improve the training effect and prediction performance of the model. Next, using the machine learning platform to establish a prediction model is the core task of data analysis and modeling. Common machine learning algorithms include linear regression, random forest, and long short-term memory network (LSTM). Linear regression is a simple and effective algorithm suitable for modeling linear relationships. Random forest can effectively handle nonlinear relationships and high-dimensional data by constructing multiple decision trees and averaging the results. LSTM is a special recurrent neural network that is suitable for processing time series data and can capture long-term dependencies. In practical applications, multiple algorithms are often combined to improve the accuracy and robustness of predictions. For example, the ARIMA model can be used to make short-term predictions for time series data, and then combined with the LSTM model for longer-term predictions, and finally the final prediction results are obtained by weighted averaging. After building the model, model training and optimization are important steps to improve model performance. Through cross-validation and hyperparameter tuning, the optimal model parameters can be found and the generalization ability of the model can be improved. Cross-validation can effectively evaluate the stability and predictive ability of the model by dividing the data into multiple subsets and training and validating the model multiple times. Hyperparameter tuning searches for different parameter combinations to find the optimal parameter configuration and improve the predictive performance of the model. Finally, model evaluation is a key step to verify the model effect. Common evaluation indicators include mean square error (MSE) and mean absolute error (MAE).These indicators can quantify the prediction error of the model and help analysts evaluate the accuracy and reliability of the model. By comparing the evaluation results of different models, the best model can be selected for actual prediction.

[0027] In step S5, a real-time data processing platform is used to perform real-time data processing and streaming computing, and the prediction algorithm is integrated into the system to realize real-time tourist flow prediction; an early warning system is set, and a flow threshold is set. Once the predicted flow exceeds the threshold, the system automatically issues an early warning notification and configures an alarm to remind the management personnel. The real-time data processing platform can process the real-time data transmitted from various sensors and systems, and immediately perform calculations and analysis to provide real-time tourist flow prediction results: Real_Time_Prediction = Predict(Real_Time_Data), where Real_Time_Prediction is the real-time prediction result, Real_Time_Data is the real-time data, and Predict is the prediction function; first, the ARIMA model is used to perform short-term prediction on the time series data: y t =c+φ1y t-1 +φ2y t-2 +…+φ p y t-p +θ1∈ t-1 +θ2∈ t-2 +…+θ q ∈ t-q +∈ t , where y t is the predicted value at time t, φ i is the autoregressive coefficient, θ j is the moving average coefficient, ∈ t is the error term; then the LSTM model is combined for time prediction, and finally the results of the two are weighted averaged to obtain the final prediction value: Final_Prediction=α×ARIMA_Prediction+(1-α)×LSTM_Prediction, where α is the weight parameter with a value range of [0,1]; the early warning system sets the flow threshold according to the output of the prediction model. When the predicted flow exceeds the threshold, the system will automatically issue an early warning notification: Among them, Alert is the warning signal, and Threshold is the flow threshold. Once the warning is triggered, the electronic display screen and voice alarm notify the scenic area management personnel to take action to deal with the congestion; after establishing an effective prediction model, the last step is to make predictions and warnings. The purpose of this step is to use the prediction model to predict the future tourist flow and issue a warning notification when the tourist flow exceeds the preset threshold. First, the real-time data processing platform is used to perform real-time data processing and streaming computing. The real-time data processing platform can process real-time data from various sensors and systems, perform calculations and analysis immediately, and provide real-time tourist flow prediction results. By integrating the prediction algorithm into the system, a rapid response to real-time data can be achieved and the timeliness of the prediction can be improved. In order to improve the accuracy of real-time predictions, multiple algorithms can be used in combination. For example, the ARIMA model is first used to make short-term predictions on time series data, and then the LSTM model is combined for longer-term predictions, and finally the final prediction results are obtained by weighted average. This multi-algorithm combination method can make full use of the advantages of each algorithm and improve the accuracy and robustness of the prediction. In practical applications, the system will continuously receive real-time data from devices such as access control gates, surveillance cameras, and meteorological sensors. After cleaning and preprocessing, these data are input into the prediction model to obtain real-time tourist flow prediction results. Based on the prediction results, the system will automatically determine whether the tourist flow exceeds the preset threshold. If the predicted flow exceeds the threshold, the system will automatically issue an early warning notification. The early warning system sets the flow threshold according to the output of the prediction model. When the predicted flow exceeds the threshold, the system will automatically issue an early warning notification. The early warning system can notify scenic area managers in a variety of ways, such as electronic display screens, voice alarms, and SMS notifications. After receiving the early warning, managers can take timely measures to adjust the management and services of the scenic area, such as adding staff, adjusting open areas, and guiding the flow of tourists to cope with possible congestion. In this way, it is possible to effectively avoid excessive concentration of tourists and improve the management efficiency and service quality of the scenic area. In addition, the system can also generate detailed forecast reports to help managers make long-term plans and decisions. For example, based on historical data and forecast results, analyze the seasonal changes and long-term trends of tourist flow, and formulate reasonable marketing strategies and service plans. In short, the prediction and early warning system improves the accuracy and timeliness of tourist flow prediction through real-time data processing and multi-algorithm combination. Through timely early warning notifications and effective management measures, tourists' travel experience can be improved and the operational efficiency and safety of scenic spots can be improved.

[0028] The above embodiments are implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments are implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer is a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions are stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions are transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium is any available medium that a computer can access or a data storage device such as a server or data center containing one or more available media sets. The available medium is a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium is a solid-state hard disk.

[0029] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0030] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0031] The units described as separate components are or are not physically separated, and the components shown as units are or are not physical units, that is, located in one place or distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0032] In addition, each functional unit in each embodiment of the present application is integrated into one processing unit, each unit exists physically separately, or two or more units are integrated into one unit.

[0033] If the functions are implemented in the form of software functional units and sold or used as independent products, they are stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or partly in other words, the part that contributes to the prior art or part of the technical solution is embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for enabling a computer device (such as a personal computer, a server, or a network device) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media for storing program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0034] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. An interactive method and system for scenic spot management based on big data, characterized by: The method for measuring the depth of concrete cracks comprises the following steps: S1: Set up access control gates, electronic ticket readers and visitor information management terminals to record the entry and exit data of tourists, and connect the ticket system to the database to achieve real-time transmission and storage of data; install high-definition surveillance cameras and crowd counting equipment at the main entrances and exits of the scenic area and important attractions to monitor and accurately count the number of tourists in real time, and configure RFID equipment to identify and track the identity and location of tourists. S2: Install weather stations and weather sensors in the scenic area to monitor weather conditions in real time, and access third-party weather data interfaces to obtain comprehensive weather data; use the calendar system to record statutory holidays, scenic area activities and special events, and connect with the scenic area activity management system to obtain detailed activity arrangements and timetables; S3: Deploy distributed storage servers and database management systems for large-scale data storage and structured query; use data cleaning software and data preprocessing tools to clean, filter and format the collected raw data; S4: Use data analysis software to analyze the cleaned data, extract key features, and use the machine learning platform to establish a tourist flow prediction model; train and optimize the model through feature engineering tools, use cross-validation tools and error analysis tools to evaluate model performance and improve prediction accuracy; S5: Use a real-time data processing platform to perform real-time data processing and streaming computing, integrate the prediction algorithm into the system, and realize real-time tourist flow prediction; set up an early warning system and set the flow threshold. Once the predicted flow exceeds the threshold, the system automatically issues a warning notification and configures an alarm to prompt the management personnel.

2. The interactive method and system for scenic area management based on big data according to claim 1, characterized in that: In step S1, access control gates, electronic ticket readers and tourist information management terminals are set up to record the entry and exit data of tourists, and the ticket system is connected to the database to achieve real-time transmission and storage of data; high-definition surveillance cameras and crowd counting equipment are installed at the entrances and exits of the scenic area and scenic spots to monitor and accurately count the number of tourists in real time, and RFID equipment is configured to identify and track the identity and location of tourists; The total number of visitors can be obtained by adding up the records of all card readers: Among them, E i is the number of tourists recorded by the i-th card reader, and n is the total number of card readers; through the management terminal, the system records the name, ID number, ticket type, entry time and exit time of each tourist to provide data support; the camera and sensor equipment capture real-time tourist flow information, and the data is transmitted to the system to analyze the distribution and flow of tourists in the current scenic area; the total number of real-time tourists is obtained by accumulating the records of all cameras: Among them, C i is the number of tourists recorded by the i-th camera, and m is the total number of cameras; the RFID tag will emit a unique ID, the reader will capture this ID, and record the location information and timestamp to track the movements of each tourist; the RFID data set is represented as: RFID Data = {Tag ID, Location.Time}.

3. The interactive method and system for scenic spot management based on big data according to claim 1, characterized in that: In step S2, a weather station and weather sensors are installed in the scenic area to monitor weather conditions in real time, and a third-party weather data interface is connected to obtain comprehensive weather data; a calendar system is used to record statutory holidays, scenic area activities and special events, and the system is connected to the scenic area activity management system to obtain detailed activity arrangements and timetables; the weather station and sensors continuously collect data such as temperature, humidity and wind speed, and the data will be transmitted to the system to help analyze the impact of weather on tourist flow; the weather data set is represented by: Weather_Data = {T, H, W}, where T is temperature, H is humidity, and W is wind speed; through the third-party weather API interface, the system obtains more comprehensive weather information; the external weather data set is represented by: External_Weather_Data = {T ext ,H ext ,W ext }, where T ext is the external temperature, H ext is the external humidity, W ext is the external wind speed; the calendar system and activity management system record the arrangements of holidays and special events. The activity data set is expressed as: Event_Data_{Event_Name,Date,Time,Location}, where Event_Name is the event name, Date is the event date, Time is the event time, and Location is the event location.

4. The interactive method and system for scenic area management based on big data according to claim 1, characterized in that: In step S3, distributed storage servers and database management systems are deployed for large-scale data storage and structured query. Data cleaning software and data preprocessing tools are used to clean, filter and format the collected raw data to ensure data consistency and quality. Distributed storage servers can process a large amount of visitor data, including real-time and historical data, to ensure secure storage and fast access to data. The total storage capacity is obtained by adding up the storage capacity of all servers: Among them, S i is the storage capacity of the ith server, and p is the total number of servers; the database management system organizes data into tabular form to support complex query operations and fast retrieval and processing of required information. An example of a database query statement is as follows: DB_Query = SELECT {fields} FROM table WHERE condition, where fields is the query field, table is the data table, and condition is the query condition; data cleaning software and preprocessing tools are used to remove noise and error information in the data and format the data. The cleaned data is represented by: Cleaned_Data = Preprocess, where Cleaned_Data is the cleaned data, Raw_Data is the original data, and Preprocess is the data preprocessing function.

5. The interactive method and system for scenic area management based on big data according to claim 1, characterized in that: In step S4, the cleaned data is analyzed using data analysis software to extract key features, and a tourist flow prediction model is established using a machine learning platform. The model is trained and optimized using feature engineering tools, and cross-validation tools and error analysis tools are used to evaluate model performance and improve prediction accuracy. The data analysis software performs statistical analysis on the processed data to find out the key factors affecting tourist flow. The feature set is represented as: Features = {f1, f2, ..., f n }, where f i is the i-th feature; the machine learning platform uses historical data for training and builds a model to predict future tourist traffic. The algorithms include linear regression, random forest, and long short-term memory network; the linear regression model is expressed as: y = β0 + β1x1 + β2x2 + … + β n x n , where y is the predicted tourist flow, x i For each feature, β i is the corresponding regression coefficient. The random forest model makes predictions by constructing multiple decision trees and taking the average value: in, is the predicted value, T is the number of decision trees, f t (x) is the prediction result of the tth tree; the long short-term memory network is a recurrent neural network used to process and predict time series data. The input of the LSTM model is a time series {x t }, the output is the predicted value y t : Among them, i t 、f t , o t are the activation values ​​of the input gate, forget gate, and output gate, respectively. is the candidate memory cell state, C t is the memory cell state, h t It is a hidden state; feature engineering tools are used to extract and optimize features to improve the accuracy and robustness of the model; model evaluation is carried out through cross-validation and error analysis, and commonly used evaluation indicators include mean square error and mean absolute error: and Among them, y i is the true value, is the predicted value, and n is the number of samples.

6. The interactive method and system for scenic area management based on big data according to claim 1, characterized in that: In step S5, a real-time data processing platform is used to perform real-time data processing and streaming computing, and a prediction algorithm is integrated into the system to achieve real-time tourist flow prediction; Set up an early warning system and set a flow threshold. Once the predicted flow exceeds the threshold, the system automatically issues an early warning notification and configures an alarm to remind management personnel. The real-time data processing platform can process the real-time data from various sensors and systems, and immediately calculate and analyze it to provide real-time tourist flow prediction results: Real_Time_Prediction = Predict(Real_Time_Data), where Real_Time_Prediction is the real-time prediction result, Real_Time_Data is the real-time data, and Predict is the prediction function. First, use the ARIMA model to make short-term predictions on time series data: t =c+φ1y t-1 +φ2y t-2 +…+φ p y t-p +θ1∈ t-1 +θ2∈ t-2 +…+θ q ∈ t-q +∈ t , where y t is the predicted value at time t, φ i is the autoregressive coefficient, θ j is the moving average coefficient, ∈ t is the error term; then the LSTM model is combined for time prediction, and finally the results of the two are weighted averaged to obtain the final prediction value: Final_Prediction=α×ARIMA_Prediction+(1-α)×LSTM_Prediction, where α is the weight parameter with a value range of [0,1]; the early warning system sets the flow threshold according to the output of the prediction model. When the predicted flow exceeds the threshold, the system will automatically issue an early warning notification: Among them, Alert is the warning signal, and Threshold is the flow threshold. Once the warning is triggered, the electronic display screen and voice alarm will notify the scenic area management personnel to take action to deal with the congestion.

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