Intelligent analysis and prediction method and system for text travel service

Abstract

The invention provides a text travel service intelligent analysis and prediction method and system, and the method comprises the following steps: building a space-time diagram model based on text travel multi-dimensional data; and utilizing the space-time diagram model to predict the travel traffic, evaluate the service quality and provide optimization suggestions. According to the technical scheme, multi-source and multi-dimensional dynamic data can be processed, and more accurate travel traffic prediction is realized; based on a graph structure retrieval and generation technology, context-related intelligent prediction reports and management suggestions can be generated; and the system has real-time feedback and dynamic adjustment capabilities, and can adapt to a rapidly changing text travel environment.

Description

Background Art

[0002] With the rapid development of the cultural tourism industry, scenic spots and cultural tourism management are facing increasingly complex challenges, especially in dealing with tourist flow, resource allocation, service quality evaluation, etc. Data-driven decision-making has become increasingly important. However, existing cultural tourism data analysis methods are mostly limited to static analysis and the use of a single data source, lacking comprehensive integration and analysis of spatiotemporal dynamic changes and multidimensional data.

[0003] Traditional cultural tourism forecasting models, such as time series-based forecasting methods or machine learning models, usually cannot handle complex data across domains, time and space well, and cannot provide managers with real-time, accurate forecasts and suggestions. In addition, most current systems lack comprehensive analysis of multi-factor data such as user behavior feedback, weather, and traffic, making it difficult to dynamically and effectively forecast cultural tourism traffic or evaluate service quality in real time. Summary of the invention

[0004] The present application provides a method and system for intelligent analysis and prediction of cultural and tourism services, which is used to predict cultural and tourism traffic and evaluate service quality through spatiotemporal graph data, and provide intelligent operation suggestions for scenic area managers.

[0005] In a first aspect, a method for intelligent analysis and prediction of cultural tourism services is provided, comprising the following steps:

[0006] Construct a spatiotemporal graph model based on multi-dimensional cultural and tourism data;

[0007] The spatiotemporal graph model is used to predict cultural and tourism traffic, evaluate service quality and provide optimization suggestions.

[0008] In the above technical solution, a spatiotemporal graph model is constructed based on multi-dimensional data of culture and tourism; the spatiotemporal graph model is used to predict cultural and tourism traffic, evaluate service quality and provide optimization suggestions; it can process dynamic data from multiple sources and dimensions to achieve more accurate cultural and tourism traffic prediction; the retrieval and generation technology based on the graph structure can generate context-related intelligent prediction reports and management suggestions; it has real-time feedback and dynamic adjustment capabilities, and can adapt to the rapidly changing cultural and tourism environment.

[0009] In a specific implementation plan, the steps of constructing a spatiotemporal graph model based on cultural and tourism multi-dimensional data specifically include:

[0010] Acquire the cultural and tourism multi-dimensional data;

[0011] Preprocessing the cultural and tourism multi-dimensional data;

[0012] Based on the preprocessed cultural and tourism multi-dimensional data, the spatiotemporal graph model is constructed.

[0013] In a specific implementation scheme, the spatiotemporal graph model includes nodes and edges, wherein:

[0014] The nodes represent different entities in the cultural tourism system;

[0015] The edges represent the relationships between the entities.

[0016] In a specific implementation scheme, the steps of using the spatiotemporal graph model to predict cultural and tourism traffic, evaluate service quality and provide optimization suggestions specifically include:

[0017] Using the spatiotemporal graph model, predicting cultural and tourism traffic;

[0018] Using the spatiotemporal graph model, evaluating service quality and providing optimization suggestions;

[0019] Output the cultural and tourism traffic forecast results and service quality evaluation results.

[0020] In a specific implementation scheme, the step of predicting cultural and tourism traffic using the spatiotemporal graph model specifically includes:

[0021] Extracting features of the nodes and the edges;

[0022] Retrieve relevant subgraphs for requirements;

[0023] By analyzing the graph structure, the calculation path is optimized;

[0024] Generate reports on cultural and tourism traffic forecasts or optimization management strategies based on search results;

[0025] Dynamically adjust reports with new forecasts and management recommendations.

[0026] In a specific implementation scheme, the steps of using the spatiotemporal graph model to evaluate the service quality and provide optimization suggestions are as follows:

[0027] Utilizing the spatiotemporal graph model, an evaluation report of the current service quality is generated in real time by combining visitor feedback, facility usage and behavioral data.

[0028] In a specific implementation scheme, the report includes: future traffic trend chart, tourist density forecast for each time period, resource allocation optimization suggestions, and the impact of weather changes.

[0029] In a specific implementation scheme, the GraphRAG technology is used to utilize the spatiotemporal graph model to predict cultural and tourism traffic, evaluate service quality, and provide optimization suggestions.

[0030] In a specific implementation scheme, the nodes include scenic spots and parking lots; the edges include tourist flow paths; and the features include historical flow, facility conditions, and geographical locations.

[0031] In the second aspect, a cultural tourism service intelligent analysis and prediction system is provided, including:

[0032] Model module, used to build a spatiotemporal graph model based on multi-dimensional cultural and tourism data;

[0033] The prediction and evaluation module is used to use the spatiotemporal graph model to predict cultural and tourism traffic, evaluate service quality and provide optimization suggestions.

[0034] In the above technical solution, a spatiotemporal graph model is constructed based on multi-dimensional data of culture and tourism; the spatiotemporal graph model is used to predict cultural and tourism traffic, evaluate service quality and provide optimization suggestions; it can process dynamic data from multiple sources and dimensions to achieve more accurate cultural and tourism traffic prediction; the retrieval and generation technology based on the graph structure can generate context-related intelligent prediction reports and management suggestions; it has real-time feedback and dynamic adjustment capabilities, and can adapt to the rapidly changing cultural and tourism environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flowchart of the intelligent analysis and prediction method for cultural and tourism services provided in the embodiment of the present application;

[0036] Figure 2 This is a structural block diagram of the cultural and tourism service intelligent analysis and prediction system provided in the embodiment of the present application. DETAILED DESCRIPTION

[0037] The present application is further described in detail below through the accompanying drawings and embodiments. Through these descriptions, the characteristics and advantages of the present application will become clearer and more specific.

[0038] The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise noted.

[0039] In addition, the technical features involved in different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0040] To facilitate understanding of the intelligent analysis and prediction method and system for cultural and tourism services provided in the embodiments of the present application, its application scenario is first explained. The intelligent analysis and prediction method and system for cultural and tourism services provided in the embodiments of the present application are used to predict cultural and tourism traffic and evaluate service quality through spatiotemporal graph data, and provide intelligent operation suggestions for scenic area managers. With the rapid development of the cultural and tourism industry, scenic spots and cultural and tourism management are facing increasingly complex challenges, especially in dealing with tourist traffic, resource allocation, service quality evaluation, etc., data-driven decision-making has become increasingly important. However, the existing cultural and tourism data analysis methods are mostly limited to static analysis and the use of a single data source, lacking comprehensive integration and analysis of spatiotemporal dynamic changes and multidimensional data. Traditional cultural and tourism prediction models, such as time series-based prediction methods or machine learning models, are usually not able to handle complex data across domains, time and space, and cannot provide managers with real-time and accurate predictions and suggestions. In addition, most current systems lack comprehensive analysis of multi-factor data such as user behavior feedback, weather, and traffic, making it difficult to dynamically and effectively predict cultural and tourism traffic or evaluate service quality in real time. To this end, the present application embodiment provides a method and system for intelligent analysis and prediction of cultural tourism services, so as to predict cultural tourism traffic and evaluate service quality through spatiotemporal graph data, and provide intelligent operation suggestions for scenic area managers. The following is a detailed description of the embodiments in conjunction with specific drawings.

[0041] Explanation of terms

[0042] GraphRAG (Graph Retrieval-Augmented Generation): GraphRAG is a technology that combines knowledge graphs and graph machine learning. It can improve the retrieval, generation, and reasoning capabilities based on spatiotemporal graph data, and is particularly suitable for prediction tasks in complex, multi-source data environments.

[0043] Advantages of GraphRAG over existing technologies

[0044] GraphRAG technology has unique advantages in processing spatiotemporal data, multi-dimensional data fusion and generating natural language forecast reports. Compared with traditional time series analysis and machine learning models, it demonstrates higher flexibility and intelligence.

[0045] 1. Differences in spatiotemporal data processing

[0046] 1.1. Limitations of Time Series Analysis

[0047] Traditional time series analysis methods, such as ARIMA, SARIMA, LSTM, etc., mainly build models based on single-dimensional time series data and focus on the temporal patterns of historical data. These models usually rely on the following assumptions:

[0048] Fixed time dependency: Assume that future data is a function of past data and that this dependency is linear or has a modelable periodicity. Traditional models are difficult to accurately capture abnormal events or complex spatial factors.

[0049] Single-dimensional analysis: Time series models can often only process data in one dimension (time), and their integration capabilities are limited when faced with multi-dimensional, cross-domain data (such as tourist flow, weather, traffic, etc.).

[0050] 1.2. Advantages of GraphRAG in spatiotemporal data

[0051] GraphRAG processes spatiotemporal data through graph structures, surpassing the limitations of traditional time series. It is particularly unique in integrating complex spatiotemporal data and multidimensional data:

[0052] Spatial dependency: GraphRAG not only considers the time dimension, but also captures the spatial dependencies between cultural and tourism entities. For example, the physical locations between scenic spots and the connection paths between transportation networks can be described by the edges in the graph, so as to better handle dynamic changes in space.

[0053] Dynamic spatiotemporal features: GraphRAG can process features in both time and space dimensions. For example, the tourist flow in a certain period of time depends not only on historical flow data, but also on the real-time flow, weather conditions, and traffic conditions of other attractions in the area.

[0054] Capturing nonlinear patterns: Traditional time series methods have strong linear assumptions about data, while GraphRAG can better capture nonlinear dependencies between data through graph neural networks (GNNs) and adapt to changes in complex cultural and tourism environments.

[0055] 2. Differences in multidimensional data fusion

[0056] 2.1. Limitations of Traditional Machine Learning

[0057] When processing multi-dimensional data, traditional machine learning models (such as decision trees, random forests, etc.) usually rely on feature engineering to manually extract features to fuse similar or related data. For example:

[0058] Independent features: Traditional models usually treat different features (such as weather, traffic, user ratings, etc.) as independent variables, and the performance of the model depends largely on the quality of feature extraction.

[0059] Lack of complex relationship modeling: These models cannot directly handle complex relationship networks and multi-level dependencies, such as traffic interactions between attractions and complex connectivity of transportation networks.

[0060] 2.2. GraphRAG’s multi-source data fusion capability

[0061] GraphRAG integrates multi-source data under the same framework through graph structure modeling, so that it can adaptively capture the complex interactive relationships between multi-dimensional data.

[0062] Multi-source data integration in graph structure: GraphRAG can integrate different types of nodes (such as attractions, transportation hubs, weather events) into the same graph, and each node and edge can carry information of different dimensions (such as geographic location, weather conditions, tourist flow, user ratings, etc.). This method greatly improves the efficiency of the model for multi-dimensional data fusion.

[0063] Cross-domain data modeling: GraphRAG allows data from different fields (such as traffic, weather, and tourist behavior) to interact naturally through edges and nodes in the graph, thereby better reflecting the actual situation in the prediction. For example, a traffic congestion node may directly affect the flow of tourists between attractions A and B. This relationship can be directly modeled through the graph structure, while traditional machine learning models may find it difficult to automatically discover such complex relationships.

[0064] 3. Differences in generating natural language prediction reports

[0065] 3.1. Limitations of Traditional Models

[0066] Traditional models (such as time series analysis or machine learning models) can usually only generate natural language reports through preset templated languages, which are difficult to dynamically adapt to different situations. The generated reports often lack flexibility, explainability, and detailed descriptions of complex situations.

[0067] Templated reports: Most existing systems generate reports using fixed templates and cannot dynamically generate flexible report content based on forecast results. This approach lacks context awareness and cannot adjust content based on real-time feedback.

[0068] Poor interpretability: The prediction results of traditional models (such as tourist flow figures) are usually difficult to explain. There is a lack of tracing of key influencing factors, making it difficult for managers to understand the reasons behind the predictions.

[0069] 3.2. GraphRAG’s Natural Language Generation Advantages

[0070] GraphRAG combines Retrieval-Augmented Generation (RAG) technology, which can not only extract relevant information from the spatiotemporal graph, but also generate interpretable natural language prediction reports.

[0071] Dynamic natural language generation: GraphRAG generates natural language reports based on the search results in the graph, and can dynamically generate reports for different scenarios and different needs. For example, the system can not only predict the tourist flow of a certain attraction, but also generate detailed explanations including factors such as traffic and weather, so that managers can have a more comprehensive understanding of future trends.

[0072] Explainability of generated results: Each generated forecast report can be traced back to specific nodes and edges in the graph, and the report will explain which factors (such as weather and traffic) led to a specific forecast result, thereby enhancing the credibility and actionability of the report.

[0073] 4. Security of access

[0074] 4.1. Security issues of traditional models

[0075] In the cultural and tourism management system, data security is of vital importance, especially when it comes to sensitive visitor data, operational data, etc. However, traditional prediction models usually have the following problems in terms of access and data security:

[0076] Data exposure risk: Traditional models usually rely on centralized data storage and processing systems, which are vulnerable to cyber attacks and data leakage risks.

[0077] Lack of fine-grained permission management: Traditional systems lack fine-grained permission control for different user roles, resulting in imprecise access control and increasing the risk of data leakage.

[0078] 4.2. GraphRAG’s security design

[0079] GraphRAG has adopted a multi-layer protection mechanism in terms of access and security to ensure the security of data throughout the entire processing process.

[0080] Graph-based permission control: GraphRAG can set different access permissions for different graph nodes and edges. For example, certain sensitive data (such as visitor privacy and key operational data) can be authorized only to specific user groups to prevent unauthorized users from accessing it.

[0081] Distributed graph storage: Different from traditional centralized storage, GraphRAG can use distributed graph storage to store sensitive data on different nodes, reducing the risk of single point failure and centralized attacks.

[0082] Data encryption and access auditing: The system uses encryption algorithms to protect data transmission and storage, and all access operations are subject to detailed audit records to ensure that every data access can be traced for anomaly detection and auditing.

[0083] refer to Figure 1 and Figure 2 , Figure 1 A flowchart of the intelligent analysis and prediction method for cultural and tourism services provided in the embodiment of the present application; Figure 2 This is a structural block diagram of the cultural and tourism service intelligent analysis and prediction system provided in the embodiment of the present application.

[0084] exist Figure 1 In the embodiment of the present application, a method for intelligent analysis and prediction of cultural and tourism services is provided, comprising the following steps:

[0085] Construct a spatiotemporal graph model based on multi-dimensional cultural and tourism data;

[0086] The spatiotemporal graph model is used to predict cultural and tourism traffic, evaluate service quality and provide optimization suggestions.

[0087] In the above technical solution, a spatiotemporal graph model is constructed based on multi-dimensional data of culture and tourism; the spatiotemporal graph model is used to predict cultural and tourism traffic, evaluate service quality and provide optimization suggestions; it can process dynamic data from multiple sources and dimensions to achieve more accurate cultural and tourism traffic prediction; the retrieval and generation technology based on the graph structure can generate context-related intelligent prediction reports and management suggestions; it has real-time feedback and dynamic adjustment capabilities, and can adapt to the rapidly changing cultural and tourism environment.

[0088] In a specific implementation plan, the steps of constructing a spatiotemporal graph model based on cultural and tourism multi-dimensional data specifically include:

[0089] Acquire the cultural and tourism multi-dimensional data;

[0090] Preprocessing the cultural and tourism multi-dimensional data;

[0091] Based on the preprocessed cultural and tourism multi-dimensional data, the spatiotemporal graph model is constructed.

[0092] In a specific implementation scheme, the spatiotemporal graph model includes nodes and edges, wherein:

[0093] The nodes represent different entities in the cultural tourism system;

[0094] The edges represent the relationships between the entities.

[0095] In a specific implementation scheme, the steps of using the spatiotemporal graph model to predict cultural and tourism traffic, evaluate service quality and provide optimization suggestions specifically include:

[0096] Using the spatiotemporal graph model, predicting cultural and tourism traffic;

[0097] Using the spatiotemporal graph model, evaluating service quality and providing optimization suggestions;

[0098] Output the cultural and tourism traffic forecast results and service quality evaluation results.

[0099] In a specific implementation scheme, the step of predicting cultural and tourism traffic using the spatiotemporal graph model specifically includes:

[0100] Extracting features of the nodes and the edges;

[0101] Retrieve relevant subgraphs for requirements;

[0102] By analyzing the graph structure, the calculation path is optimized;

[0103] Generate reports on cultural and tourism traffic forecasts or optimization management strategies based on search results;

[0104] Dynamically adjust reports with new forecasts and management recommendations.

[0105] Specifically, the intelligent analysis and prediction method for cultural tourism services includes the following steps:

[0106] 1. Data collection and spatiotemporal graph construction

[0107] First, information related to culture and tourism is collected from multiple data sources, including historical tourist flow data, user feedback (such as comments, ratings, etc.), weather forecast data, traffic flow, etc.

[0108] These data are processed through time and space to construct a multi-dimensional time and space graph data model. Specifically, nodes represent cultural and tourism related entities (such as scenic spots, attractions, transportation stations, etc.), edges represent the relationship between entities (such as tourist flows, transportation routes, etc.), and the graph also contains information on the time dimension.

[0109] 2. GraphRAG model training

[0110] The system preprocesses the spatiotemporal graph data and trains it through the GraphRAG model. The training goal of the model is to extract relevant historical information from the graph structure and generate future traffic forecasts and service quality assessments. During the training process, the model will combine multiple factors (such as weather, traffic, etc.) for feature extraction and learning.

[0111] 2.1. Cultural and tourism traffic prediction and generation

[0112] The system uses the GraphRAG model to generate forecast results for cultural and tourism traffic based on the input future time period, weather information and other dynamic factors. The forecast output includes both specific traffic data and management suggestions in natural language.

[0113] 2.2. Service quality evaluation and optimization suggestion generation

[0114] In addition to traffic forecasts, the system can also generate service quality evaluation reports to help managers adjust resource allocation strategies based on current cultural and tourism conditions and optimize the visitor experience. For example, when a peak period is predicted, the system will recommend adding maintenance personnel and increasing the frequency of use of public facilities.

[0115] 2.3. Real-time feedback and dynamic adjustment

[0116] The system continuously monitors dynamic factors such as actual tourist flow and weather, and adjusts the forecast results and generated management recommendations based on actual conditions to ensure the accuracy of the forecasts and recommendations.

[0117] In the above technical solution, GraphRAG technology is introduced into the field of culture and tourism. By combining spatiotemporal graph data, the system realizes intelligent analysis and prediction of complex and multidimensional data. Compared with the traditional culture and tourism prediction model, this application has the following advantages:

[0118] ① It can process dynamic data from multiple sources and dimensions to achieve more accurate cultural and tourism traffic forecasts;

[0119] ② Graph-based retrieval and generation technology can generate context-related intelligent prediction reports and management suggestions;

[0120] ③The system has real-time feedback and dynamic adjustment capabilities, and can adapt to the rapidly changing cultural and tourism environment;

[0121] ④ The adopted GraphRAG technology makes the content generated by the system explainable and traceable, and the data source can be traced;

[0122] ⑤ The adopted GraphRAG technology enables the system to have the permission management characteristics of graph database, which can divide the permissions for accessing data and has higher data access scope control and security.

[0123] In a specific implementation scheme, the steps of using the spatiotemporal graph model to evaluate the service quality and provide optimization suggestions are as follows:

[0124] Utilizing the spatiotemporal graph model, an evaluation report of the current service quality is generated in real time by combining visitor feedback, facility usage and behavioral data.

[0125] In a specific implementation scheme, the report includes: future traffic trend chart, tourist density forecast for each time period, resource allocation optimization suggestions, and the impact of weather changes.

[0126] In a specific implementation scheme, the GraphRAG technology is used to utilize the spatiotemporal graph model to predict cultural and tourism traffic, evaluate service quality, and provide optimization suggestions.

[0127] In a specific implementation scheme, the nodes include scenic spots and parking lots; the edges include tourist flow paths; and the features include historical flow, facility conditions, and geographical locations.

[0128] In a specific implementation scheme, the cultural tourism service intelligent analysis and prediction method includes:

[0129] 1. Data collection and preprocessing

[0130] Collect multi-dimensional data related to culture and tourism from multiple external data sources through API or data interface, including historical tourist flow records, user behavior feedback (such as tourist comments, ratings, etc.), weather forecast information, traffic data (such as real-time traffic flow, road conditions, etc.) and social event data.

[0131] The collected data is cleaned, organized and processed in time and space by the preprocessing module to ensure that the data structure is unified and time and space information can be effectively associated. Each data point (such as scenic spots, transportation nodes, weather change points, etc.) has a clear timestamp and geographic location information as the basis of the spatiotemporal graph model.

[0132] 2. Spatiotemporal graph data modeling

[0133] Based on the above preprocessed multidimensional data, the system constructs a spatiotemporal graph model. The model consists of nodes and edges, where nodes represent different entities in the cultural tourism system, including scenic spots, transportation hubs, accommodation facilities, etc.; edges represent the relationships between these entities, such as tourist flow paths, transportation connections, etc. In addition, information in the time dimension is also recorded as a type of edge, indicating the behavior pattern of entities changing over time.

[0134] Through this spatiotemporal graph model, the system can capture the dynamic changes and spatial correlations of cultural and tourism traffic, laying the foundation for subsequent GraphRAG analysis.

[0135] 3. Cultural and tourism traffic prediction and suggestion generation based on GraphRAG

[0136] The core function of the GraphRAG model is to combine graph structure data with the generative model to efficiently retrieve information related to user needs or future predictions from the spatiotemporal graph. Specifically, GraphRAG uses its graph retrieval capabilities to automatically find the nodes and edges most relevant to the prediction target (such as cultural and tourism traffic in a certain period of time in the future) from the graph. At the same time, the generative model is responsible for integrating this information and generating a prediction report described in natural language.

[0137] For example, when the system is asked to predict cultural and tourism traffic during a future holiday, the GraphRAG model will search for matching patterns in the space-time graph based on historical holiday data, weather conditions, user behavior patterns and other information, generate prediction results, and provide suggestions for dealing with potential peaks or troughs.

[0138] The GraphRAG model is used to generate cultural and tourism traffic forecast results based on the input future time period and various dynamic factors. The forecast results include: the expected total number of tourists in a certain time period, the traffic distribution of each scenic spot, etc. The system will also generate optimization suggestions for responding to the forecast results, such as adding service personnel, adjusting traffic routes, and optimizing security measures during periods of high traffic. For example, if the weather forecast shows heavy rain in the next two days, the system may recommend increasing the supply of rain gear or adjusting the open area, and notify scenic area managers to reduce the use of certain outdoor facilities.

[0139] The prediction process includes:

[0140] Feature extraction

[0141] In the system, each node (such as scenic spots, parking lots) and edge (such as tourist flow paths) contains some basic features, such as historical traffic, facility conditions, geographical location, etc. We extract the features of these nodes and edges from the graph data for subsequent analysis.

[0142] 3.2. Related subgraph retrieval

[0143] Use an attention-based strategy to preliminarily screen all nodes and edges. Determine which nodes and edges are most relevant to the input requirements (such as "predicting tourist traffic during the National Day holiday"). The screening process calculates the correlation between each node and edge and the query requirement, and finally selects the part that best matches the requirement.

[0144] Path Optimization

[0145] After completing the selection of nodes and edges, the system determines which paths (edges connecting nodes) are the most valuable. By analyzing the graph structure, the system automatically selects the shortest and most relevant paths to reduce the amount of calculation and improve efficiency. For example, when predicting the flow of tourists during the National Day holiday, the system may focus on scenic spots and paths with large tourist flows and ignore irrelevant parts.

[0146] 3.4. Generate Report

[0147] Once the relevant sub-graphs are retrieved, the system will generate reports on cultural and tourism traffic forecasts or optimized management strategies based on these data. These reports can include specific traffic forecasts, time periods of peak congestion, and management suggestions for dealing with these situations, such as adding staff and adjusting resources.

[0148] 3.5. Dynamic Adjustment

[0149] Since the data in the spatiotemporal graph changes dynamically over time (such as weather changes or updates to real-time traffic data), the system automatically adjusts the content retrieved and generated. When new data appears, the system recalculates the correlation and updates the forecast results and management recommendations to ensure that the system can respond to changes in actual conditions.

[0150] 4.Service quality evaluation and feedback

[0151] In addition to cultural and tourism traffic forecasts, the system is also able to evaluate service quality and provide optimization suggestions. By combining tourist feedback, facility usage, and other behavioral data, the system can generate an evaluation report on the current service quality in real time. For example, based on user comments on social media or feedback in the evaluation system, the system can generate improvement suggestions for certain attractions.

[0152] If user feedback from a scenic spot shows congestion or long queues, the system will predict whether similar situations will continue to occur in the next few days based on historical traffic data and generate mitigation suggestions, such as guiding tourists to divert or providing additional service facilities.

[0153] 5. Report generation and real-time feedback

[0154] The results of cultural and tourism traffic forecast and service quality assessment are output through the report generation module, which includes: future traffic trend chart, tourist density forecast for each period, resource allocation optimization suggestions, possible impact of weather changes, etc. These reports can help scenic area managers make more scientific and data-driven decisions.

[0155] The system has a real-time feedback function and can dynamically adjust the forecast results and optimization suggestions based on the latest tourist behavior data and weather changes. For example, when the actual tourist flow is much higher than the predicted value, the system can automatically generate an early warning and suggest that the scenic spot increase staff or open a backup channel.

[0156] Through this application, scenic area managers can obtain more accurate cultural and tourism traffic forecasts and management suggestions, which improves the scenic area's operational efficiency and tourists' satisfaction. The system uses GraphRAG technology to intelligently analyze and predict spatiotemporal graph data, and can achieve dynamic adjustment and efficient management in a complex and changing cultural and tourism environment. Its innovation lies in combining the powerful retrieval capabilities of graph structured data with the flexible response capabilities of the generation model, bringing advanced intelligent solutions to the cultural and tourism industry.

[0157] Specifically, in the process of data fusion from multiple sources, the following algorithm is used for adaptive data fusion. The algorithm uses an adaptive weight allocation mechanism and combines dynamic graph features to weightedly fuse the data features of each factor. By dynamically adjusting the weights at different times and locations, the retrieval module of GraphRAG can adjust the weights of different external information through this multi-factor fusion algorithm, thereby enhancing the reliability and context matching of the generated content. The specific steps include:

[0158] 1. Algorithm input:

[0159] The node feature matrix X of the spatiotemporal graph t ,in represents the feature vector of node i at time t;

[0160] The feature input set of different factors {F traffic , F weather , F userfeedback , ...};

[0161] Historical traffic data {y t-1 ,y t-2 , ..., y t-n}.

[0162] 2. Algorithm output:

[0163] Traffic forecast value at the next moment

[0164] 3. Algorithm flow:

[0165] (1) Spatiotemporal graph feature extraction: Use graph neural network (GNN) to extract spatiotemporal features and perform spatiotemporal feature extraction on the input spatiotemporal graph data X t The high-level representation of the nodes is generated through graph convolution operations, defined as H t =GNN(X t ), where H t is the hidden state matrix of the graph.

[0166] (2) Factor feature extraction: For each external factor F∈{F traffic , F weather , F user feedback , ...} Use a separate neural network extractor (such as a multi-layer perceptron, MLP) to obtain factor feature representations:

[0167]

[0168] (3) Historical traffic embedding: Encode historical traffic data to generate historical traffic feature vectors Use the Long Short-Term Memory (LSTM) network to generate the feature vector:

[0169]

[0170] (4) Adaptive weight allocation: An adaptive weight allocation module is used to automatically learn the weights of different factors under different time and space conditions. A multi-layer perceptron is used to generate the weight allocation coefficients w for each factor. traffic 、w weather wait:

[0171]

[0172] …

[0173] Among them, σ represents the Sigmoid activation function to ensure that the weight is between [0,1].

[0174] (5) The feature vectors of each factor are weighted and summed up to obtain the final comprehensive feature representation

[0175]

[0176] (6) Traffic prediction: Use the fully connected layer to predict the fused comprehensive features:

[0177]

[0178] (7) In order to train the model, the mean square error (MSE) is used as the loss function. The model parameters are optimized by minimizing the loss function so that the model can accurately predict the traffic at the next moment:

[0179]

[0180] This application uses GraphRAG technology to intelligently analyze and predict cultural and tourism data, builds a spatiotemporal graph model, and realizes efficient management and prediction of complex cultural and tourism environments. The system has dynamic adjustment capabilities, can respond to environmental changes in real time, and generate targeted optimization suggestions. The system has broad application prospects in the cultural and tourism industry, and can effectively improve the management efficiency of scenic spots and provide tourists with a better service experience.

[0181] exist Figure 2 In the embodiment of the present application, a cultural tourism service intelligent analysis and prediction system is provided, including:

[0182] Model module, used to build a spatiotemporal graph model based on multi-dimensional cultural and tourism data;

[0183] The prediction and evaluation module is used to use the spatiotemporal graph model to predict cultural and tourism traffic, evaluate service quality and provide optimization suggestions.

[0184] In the above technical solution, a spatiotemporal graph model is constructed based on multi-dimensional data of culture and tourism; the spatiotemporal graph model is used to predict cultural and tourism traffic, evaluate service quality and provide optimization suggestions; it can process dynamic data from multiple sources and dimensions to achieve more accurate cultural and tourism traffic prediction; the retrieval and generation technology based on the graph structure can generate context-related intelligent prediction reports and management suggestions; it has real-time feedback and dynamic adjustment capabilities, and can adapt to the rapidly changing cultural and tourism environment.

[0185] Those skilled in the art will appreciate that the present application may be implemented as a system, method or computer program product.

[0186] Therefore, the present disclosure may be specifically implemented in the following forms, namely: it may be completely hardware, it may be completely software (including firmware, resident software, microcode, etc.), or it may be a combination of hardware and software, generally referred to herein as a "circuit", "module" or "system". In addition, in some embodiments, the present application may also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium may contain computer-readable program code.

[0187] Any combination of one or more computer-readable media can be used. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, computer-readable storage media can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0188] Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art can change, modify, replace and modify the above embodiments within the scope of the present application. On this basis, a variety of replacements and improvements can be made to the present application, all of which fall within the scope of protection of the present application.

Claims

1. A method for intelligent analysis and prediction of cultural and tourism services, characterized in that: The following steps are involved: Construct a spatiotemporal graph model based on multi-dimensional cultural and tourism data; The spatiotemporal graph model is used to predict cultural and tourism traffic, evaluate service quality and provide optimization suggestions.

2. The intelligent analysis and prediction method for cultural and tourism services according to claim 1 is characterized in that: The steps to construct a spatiotemporal graph model based on multi-dimensional cultural and tourism data include: Acquire the cultural and tourism multi-dimensional data; Preprocessing the cultural and tourism multi-dimensional data; Based on the preprocessed cultural and tourism multi-dimensional data, the spatiotemporal graph model is constructed.

3. The intelligent analysis and prediction method for cultural and tourism services according to claim 2 is characterized in that: The spatiotemporal graph model includes nodes and edges, wherein: The nodes represent different entities in the cultural tourism system; The edges represent the relationships between the entities.

4. The intelligent analysis and prediction method for cultural and tourism services according to claim 3 is characterized in that: The steps of using the spatiotemporal graph model to predict cultural tourism traffic, evaluate service quality and provide optimization suggestions specifically include: Using the spatiotemporal graph model, predicting cultural and tourism traffic; Using the spatiotemporal graph model, evaluating service quality and providing optimization suggestions; Output the cultural and tourism traffic forecast results and service quality evaluation results.

5. The intelligent analysis and prediction method for cultural and tourism services according to claim 4 is characterized in that: The steps of predicting cultural tourism traffic using the spatiotemporal graph model specifically include: Extracting features of the nodes and the edges; Retrieve relevant subgraphs for requirements; By analyzing the graph structure, the calculation path is optimized; Generate reports on cultural and tourism traffic forecasts or optimization management strategies based on search results; Dynamically adjust reports with new forecasts and management recommendations.

6. The intelligent analysis and prediction method for cultural and tourism services according to claim 5 is characterized in that: The steps of using the spatiotemporal graph model to evaluate the service quality and provide optimization suggestions are as follows: By utilizing the spatiotemporal graph model, an evaluation report of the current service quality is generated in real time by combining visitor feedback, facility usage and behavioral data.

7. The intelligent analysis and prediction method for cultural and tourism services according to claim 6 is characterized in that: The report includes: future traffic trend chart, tourist density forecast for each time period, resource allocation optimization suggestions and the impact of weather changes.

8. The intelligent analysis and prediction method for cultural and tourism services according to claim 7, characterized in that: Through GraphRAG technology, the spatiotemporal graph model is used to predict cultural and tourism traffic, evaluate service quality and provide optimization suggestions.

9. The intelligent analysis and prediction method for cultural and tourism services according to claim 8, characterized in that: The nodes include scenic spots and parking lots; the edges include tourist flow paths; and the features include historical flow, facility conditions, and geographic locations.

10. A cultural tourism service intelligent analysis and prediction system, characterized in that: include: Model module, used to build a spatiotemporal graph model based on multi-dimensional cultural and tourism data; The prediction and evaluation module is used to use the spatiotemporal graph model to predict cultural and tourism traffic, evaluate service quality and provide optimization suggestions.

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