Scenic area service optimization method and system based on big data

Abstract

The invention relates to the technical field of data processing, and discloses a scenic spot service optimization method and system based on big data. The method comprises the following steps: acquiring scenic spot operation data through data acquisition equipment, and performing classification arrangement and standardization processing to obtain a basic data set; performing feature decomposition and clustering analysis on the basic data set to obtain behavior feature data; performing space-time correlation analysis on the basic data set and the behavior characteristic data to obtain dynamic prediction data; performing multi-dimensional fusion according to the feature data and the prediction data to obtain recommendation data; performing association analysis on various data to obtain evaluation data; and performing semantic analysis based on the evaluation data to obtain an optimization scheme. According to the application, comprehensive acquisition, deep analysis and dynamic optimization of scenic spot service data are realized through a big data technology, and the accuracy and efficiency of scenic spot service management are improved.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a method and system for optimizing scenic area services based on big data. Background Technology

[0002] With the rapid development of the tourism industry, improving the service quality of scenic spots has become a key focus for the sector. Current scenic spot service management methods primarily rely on traditional manual statistics and experience-based decision-making, obtaining service quality information through regular collection of visitor feedback, manual inspections, and questionnaires. Some scenic spots have begun to introduce basic information management systems, enabling functions such as visitor flow statistics, electronic ticket management, and simple service reservations. Simultaneously, some scenic spots are experimenting with using technologies such as video surveillance and electronic maps to assist service management, providing visitors with basic information inquiry and navigation services.

[0003] However, existing scenic area service management methods have significant shortcomings. First, data collection methods are limited, making it difficult to comprehensively capture the scenic area's operational status and visitor behavior characteristics. Second, data analysis methods are simplistic, lacking the ability to deeply mine and correlate multi-source data. Third, service optimization processes lack scientific basis, often relying on subjective experience for decision-making, making it difficult to adapt to rapidly changing service demands. Finally, the service evaluation system is incomplete, making it difficult to accurately quantify and continuously improve service quality. These problems hinder the effective improvement of scenic area service quality, impacting visitor experience and scenic area operational efficiency. Summary of the Invention

[0004] This application provides a method and system for optimizing scenic area services based on big data, which uses big data technology to achieve comprehensive collection, in-depth analysis and dynamic optimization of scenic area service data, thereby improving the accuracy and efficiency of scenic area service management.

[0005] Firstly, this application provides a big data-based method for optimizing scenic area services. This method includes: collecting scenic area operational data using data acquisition equipment; classifying and organizing the operational data; cleaning and standardizing the classification results according to data evaluation standards to obtain a basic scenic area dataset; performing feature decomposition and dimensionality reduction on tourist behavior data based on the basic dataset; performing cluster analysis and numerical calculations on the dimensionality-reduced data to obtain tourist behavior feature data; and conducting spatiotemporal correlation analysis between the basic dataset and the tourist behavior feature data, and performing data interpolation and trend fitting to obtain numerical data. According to calculations, data is filtered based on threshold conditions to obtain dynamic prediction data for scenic areas. Correlation analysis and weight calculation are performed on the data based on the tourist behavior characteristic data and the dynamic prediction data for scenic areas. The calculation results are then subjected to multi-dimensional data fusion and sorting to obtain service recommendation data. Data association and cross-analysis are performed on the basic dataset of scenic areas, the dynamic prediction data for scenic areas, and the service recommendation data. Difference calculation and normalization are performed through data comparison to obtain service evaluation data. Based on the service evaluation data, semantic extraction and feature mapping are performed on the feedback data. The mapping results are then aggregated and similarity calculated to obtain a service optimization scheme.

[0006] Secondly, this application provides a big data-based scenic area service optimization system, which includes:

[0007] The data acquisition module is used to collect scenic area operation data through data acquisition equipment, classify and organize the scenic area operation data, clean and standardize the classification results according to data evaluation standards, and obtain the basic dataset of the scenic area.

[0008] The dimensionality reduction module is used to perform feature decomposition and dimensionality reduction on tourist behavior data based on the scenic area's basic dataset, and then perform cluster analysis and numerical calculation on the dimensionality-reduced data to obtain tourist behavior feature data.

[0009] The association module is used to perform spatiotemporal association analysis between the basic dataset of the scenic area and the tourist behavior feature data, perform data calculation through data interpolation and trend fitting, and filter data according to threshold conditions to obtain dynamic prediction data of the scenic area.

[0010] The calculation module is used to perform correlation analysis and weight calculation on the tourist behavior characteristic data and the scenic area dynamic prediction data, and to perform multi-dimensional data fusion and sorting processing on the calculation results to obtain service recommendation data.

[0011] The cross-analysis module is used to perform data association and cross-analysis on the basic dataset of the scenic area, the dynamic prediction data of the scenic area and the service recommendation data. Through data comparison, difference calculation and normalization are performed to obtain service evaluation data.

[0012] The mapping module is used to perform semantic extraction and feature mapping on the feedback data based on the service evaluation data, and to perform data aggregation and similarity calculation on the mapping results to obtain a service optimization scheme.

[0013] The technical solution provided in this application achieves comprehensive acquisition and effective integration of scenic area operation data through multi-source data collection and processing. During data processing, classification, organization, and standardization ensure the reliability and consistency of data quality. Feature decomposition and dimensionality reduction analysis of tourist behavior data effectively extract key features of tourist behavior patterns, reducing the complexity of data processing. Spatiotemporal correlation analysis and data interpolation accurately capture the dynamic changes in scenic area service demand, improving prediction accuracy. In the service recommendation stage, correlation analysis and weight calculation methods achieve precise matching of personalized service recommendations. The application of data association and cross-analysis makes service quality assessment more comprehensive and objective. Semantic extraction and feature mapping effectively mine valuable information from feedback data, providing a reliable decision-making basis for service optimization. The overall solution, through a data-driven approach, achieves refined management and dynamic optimization of scenic area services, improves the tourist service experience, and enhances the operational efficiency of the scenic area. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of one embodiment of the scenic area service optimization method based on big data in this application.

[0016] Figure 2 This is a schematic diagram of one embodiment of the scenic area service optimization system based on big data in this application. Detailed Implementation

[0017] This application provides a method and system for optimizing scenic area services based on big data. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0018] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the scenic area service optimization method based on big data in this application includes:

[0019] Step S101: Collect scenic area operation data through data acquisition equipment, classify and organize the scenic area operation data, clean and standardize the classification results according to data evaluation standards, and obtain the basic dataset of the scenic area.

[0020] Step S102: Based on the basic dataset of the scenic area, perform feature decomposition and dimensionality reduction on the tourist behavior data, and perform cluster analysis and numerical calculation on the dimensionality-reduced data to obtain tourist behavior feature data.

[0021] Step S103: Perform spatiotemporal correlation analysis on the basic dataset of the scenic area and the tourist behavior characteristic data, perform data calculation through data interpolation and trend fitting, and filter the data according to the threshold conditions to obtain the dynamic prediction data of the scenic area.

[0022] Step S104: Based on tourist behavior characteristic data and scenic area dynamic prediction data, perform correlation analysis and weight calculation on the data, and perform multi-dimensional data fusion and sorting processing on the calculation results to obtain service recommendation data.

[0023] Step S105: Perform data association and cross-analysis on the basic dataset of the scenic area, the dynamic prediction data of the scenic area, and the service recommendation data. Through data comparison, perform difference calculation and normalization processing to obtain service evaluation data.

[0024] Step S106: Based on the service evaluation data, perform semantic extraction and feature mapping on the feedback data, aggregate the mapping results and calculate similarity to obtain a service optimization plan.

[0025] Specifically, comprehensive data collection of the scenic area's operations is achieved through various data acquisition devices deployed within the area, including mobile terminal signal collectors, electronic payment terminals, and environmental sensors. Mobile terminal signal collectors primarily acquire tourists' location trajectory information within the scenic area, recording key information such as timestamps, location coordinates, and tourist identifiers. Electronic payment terminals record tourists' consumption data, covering consumption time, amount, items consumed, and payment methods. Environmental sensors collect environmental parameter data such as temperature, humidity, and air quality. This raw data needs to be categorized and organized, initially classified according to data type, collection time, and data source, forming structured data tables. Then, based on data evaluation standards, the categorized data undergoes cleaning and standardization, including removing duplicate data, adding missing values, correcting outliers, and unifying data formats, resulting in an accurate, complete, and consistent basic dataset for the scenic area.

[0026] Based on the existing basic dataset of the scenic area, the next step is to conduct in-depth analysis of tourist behavior characteristics. Feature decomposition of the tourist behavior data will be performed, breaking down complex behavioral data into multiple basic feature dimensions, such as tour routes, dwell time, consumption habits, and activity preferences. Then, dimensionality reduction techniques will be used to map the high-dimensional feature space to a low-dimensional space, preserving key feature information while reducing data complexity. In this process, principal component analysis (PCA) will be employed, calculating eigenvalues ​​and eigenvectors, and selecting principal components with higher contribution rates as the feature representations after dimensionality reduction. Next, cluster analysis will be performed on the dimensionality-reduced data, using density clustering algorithms to group similar behavioral features. The centroid and distribution characteristics of each cluster will be obtained through numerical calculations, ultimately forming tourist behavior characteristic data that accurately describes the behavioral patterns of different types of tourists. When performing correlation analysis between the basic scenic area dataset and the tourist behavior characteristic data, the correlation between the temporal and spatial dimensions will be emphasized. In the temporal dimension, the two types of data will be aligned according to time series, and time windows will be established for data pairing. For missing data points in the time series, interpolation techniques will be used to supplement them, estimating missing values ​​through methods such as linear interpolation or spline interpolation. Then, trend fitting is performed on the data, using methods such as multinomial fitting or exponential smoothing to capture the changing trends and periodic characteristics of the data. Based on the fitting results, threshold conditions are set to select data samples with significant correlation, forming dynamic prediction data for the scenic area. This data can reflect the dynamic relationship between tourist behavior and the operational status of the scenic area.

[0027] In the service recommendation stage, correlation analysis is performed on tourist behavior characteristic data and scenic area dynamic prediction data to calculate correlation coefficients between different characteristics and identify feature combinations with strong correlations. Simultaneously, weight calculations are performed, assigning weight values ​​to different features based on their importance and prediction accuracy. The results of correlation analysis and weight calculations are then multidimensionally fused, and ranking processing is performed to obtain personalized service recommendation data for different types of tourists. This recommendation data includes multiple aspects such as suggested tour routes, recommended attractions, and suggestions for using service facilities.

[0028] To assess service quality, in-depth correlation analysis was conducted on the scenic area's basic dataset, dynamic prediction data, and service recommendation data. Cross-analysis was used to study the inherent relationships between different data sets and identify factors influencing service quality. During data comparison, the difference between actual service effects and expected goals was calculated, and normalization was applied to ensure comparability of evaluation indicators across different dimensions. This resulted in standardized service evaluation data that intuitively reflects various aspects of service quality. Finally, service plans were optimized based on the service evaluation data. Semantic analysis was performed on the feedback data to extract keywords and sentiment features, transforming the text data into structured feature vectors. Feature mapping techniques were used to establish a correspondence between service evaluations and improvement measures. The mapping results were aggregated, similar improvement suggestions were merged, and similarity calculations ensured the consistency and feasibility of the optimization plan. The final service optimization plan included specific improvement measures and implementation suggestions.

[0029] For example, in the process of optimizing scenic area services, signal collectors acquire tourist location data, recording trajectory information including timestamps and location coordinates. Simultaneously, electronic payment records are collected to establish a correlation between tourist location and consumption behavior. Outliers, such as location data significantly deviating from normal activity ranges and unreasonable consumption records, are removed through data cleaning. Feature extraction is performed on the cleaned data to identify tourist activity patterns, such as frequently used routes, length of stay, and consumption preferences. Based on these features, cluster analysis is conducted to identify different types of tourist groups, such as fast-paced tourists, in-depth experience tourists, and family-oriented tourists. Then, combined with real-time monitoring data, the system predicts the changing trends of visitor flow at each attraction and formulates differentiated service strategies. For example, for tourists seeking in-depth experiences, the system recommends less crowded, less popular attractions and unique experiences; for family-oriented tourists, it prioritizes activities suitable for children and related facilities. By continuously collecting service feedback, the recommendation strategy is continuously optimized to improve service quality.

[0030] In this embodiment, comprehensive acquisition and effective integration of scenic area operation data are achieved through multi-source data collection and processing. During data processing, classification, organization, and standardization ensure the reliability and consistency of data quality. Feature decomposition and dimensionality reduction analysis of tourist behavior data effectively extract key features of tourist behavior patterns, reducing the complexity of data processing. Spatiotemporal correlation analysis and data interpolation accurately capture the dynamic changes in scenic area service demand, improving prediction accuracy. In the service recommendation stage, correlation analysis and weight calculation methods achieve precise matching of personalized service recommendations. The application of data association and cross-analysis makes service quality assessment more comprehensive and objective. Semantic extraction and feature mapping effectively mine valuable information from feedback data, providing a reliable decision-making basis for service optimization. The overall solution, through a data-driven approach, achieves refined management and dynamic optimization of scenic area services, improves the tourist service experience, and enhances the operational efficiency of the scenic area.

[0031] In one specific embodiment, the process of performing step S101 may specifically include the following steps:

[0032] (1) Obtain tourist location information data through signal acquisition device, tourist consumption transaction data through electronic payment terminal, and scenic area environmental parameter data through environmental monitoring equipment. Combine tourist location information data, tourist consumption transaction data, and scenic area environmental parameter data to obtain scenic area operation data.

[0033] (2) Arrange the scenic area operation data in chronological order according to the timestamp, label the scenic area operation data with categories through the data classification algorithm, remove duplicate values ​​from the labeled scenic area operation data, and obtain preliminary classification data.

[0034] (3) Perform data integrity checks based on the preliminary classification data, fill in the missing data in the preliminary classification data using linear interpolation, and perform interval limitation processing on the outliers in the preliminary classification data to obtain the preliminary cleaned data;

[0035] (4) The preliminary cleaned data is processed through data normalization. The numerical data in the preliminary cleaned data is normalized and the categorical data in the preliminary cleaned data is encoded and converted to obtain standardized processed data.

[0036] (5) Calculate the quality score of the standardized data, set evaluation weights based on the three dimensions of timeliness, accuracy and completeness of the standardized data, and calculate the quality score of the standardized data by weighted average method.

[0037] (6) Standardized data with quality scores higher than the preset threshold are divided into time windows and merged. Through data structure transformation, the basic dataset of the scenic area is obtained.

[0038] Specifically, raw data is acquired through various data collection devices: signal acquisition devices capture tourists' mobile phone signals, electronic tickets, and other information to record tourists' real-time location and movement trajectory within the scenic area, including timestamps, location coordinates, and device identification; electronic payment terminals record all tourist consumption behavior within the scenic area, including transaction time, transaction amount, product category, and payment method; environmental monitoring equipment collects environmental parameters such as temperature, humidity, air quality, and noise within the scenic area. This data from different sources is combined according to a unified data structure standard to constitute the scenic area's operational data.

[0039] The acquired scenic area operation data is first sorted by timestamp to ensure its temporal sequence. Next, data classification algorithms are used to categorize the data, such as labeling location data as "movement trajectory" or "stop point," and consumption data as "food consumption," "shopping consumption," or "ticket consumption." During the labeling process, duplicate data from the same time, device, or type are removed, retaining only the most complete or reliable record. Data integrity checks focus on the continuity and completeness of the data. Missing values ​​in the time series are supplemented using linear interpolation. For example, if an environmental monitoring point has data at 12:00 and 12:10, but data is missing at 12:05, an estimated value for 12:05 can be calculated using linear interpolation of the two time points. For outliers, upper and lower thresholds are set based on reasonable ranges for each data type, and data exceeding these ranges is corrected or marked. Data normalization involves two main aspects: normalizing numerical data (such as consumption amounts and environmental parameters) and mapping them to the [0,1] interval; and encoding categorical data (such as consumption type and facility type) by converting text labels into numerical codes. This ensures that data of different types and magnitudes are comparable and computable.

[0040] The quality score calculation considers three key dimensions: timeliness (assessing the timeliness and validity of the data); accuracy (assessing the reliability and precision of the data); and completeness (assessing the missing data and coverage). Weighting coefficients are assigned to each of these three dimensions, and a weighted average method is used to calculate the overall quality score. The specific calculation formula is as follows:

[0041] Score = w1*T + w2*A + w3*C

[0042] Where w1, w2, and w3 are the weighting coefficients for timeliness, accuracy, and completeness, respectively, and T, A, and C are the scores for the corresponding dimensions.

[0043] Finally, data with quality scores exceeding a preset threshold were divided into time windows (e.g., hourly, daily), and data within the same window were merged and organized. By adjusting the data structure and standardizing the format, a standardized basic dataset for the scenic area was ultimately formed, providing a foundation for subsequent data analysis and mining. This dataset contains tourist behavior trajectories, consumption records, and environmental parameter information, and is characterized by high quality, standardization, and computability.

[0044] In one specific embodiment, the process of performing step S102 may specifically include the following steps:

[0045] (1) Separate the time dimension, location dimension and consumption dimension data in the basic dataset of the scenic area, perform numerical statistics on the separated data of each dimension, and obtain the feature significance data by calculating the standard deviation of the data;

[0046] (2) Perform dimensionality reduction spatial mapping on the feature saliency data, reduce the dimension through principal component analysis, calculate the variance contribution of the reduced data, and obtain the dimensionality reduction feature data;

[0047] (3) Reconstruct the dimensionality-reduced feature data according to the time series, divide the reconstructed data into time windows, and obtain the feature association data through data correlation calculation;

[0048] (4) Perform hierarchical division of the feature-related data, group the data points by data distance calculation, extract the center value of the grouping results, and obtain the cluster center data;

[0049] (5) Calculate the distance between the cluster center data and the feature association data, classify the data according to the minimum distance principle, perform numerical statistics on the classification results, and obtain cluster statistics data.

[0050] (6) Reconstruct the clustered statistical data by combining features, perform numerical transformation through data normalization, and merge the transformation results to obtain tourist behavior characteristic data.

[0051] Specifically, the basic dataset of the scenic area is separated into three dimensions. The time dimension includes temporal features such as visit time, duration of stay, and frequency of repeat visits; the location dimension includes spatial features such as visitor movement trajectories, stop locations, and order of attraction visits within the scenic area; and the consumption dimension includes the amount, frequency, and type of spending on dining, shopping, and entertainment. Numerical statistics are performed on the data from each of these three dimensions, calculating the mean, standard deviation, and other statistical measures for each feature. The standard deviation reflects the dispersion of the data; a larger value indicates higher discriminative power and is more suitable as a feature identifier for visitor behavior. By comparing the standard deviations of different features, significant feature indicators are determined, forming significant feature data.

[0052] Then, dimensionality reduction is performed on the saliency data to reduce data complexity. Principal component analysis (PCA) is a commonly used dimensionality reduction method. Its core idea is to transform the original high-dimensional feature space into a new low-dimensional feature space while preserving the main information of the data. Specific steps include: calculating the covariance matrix of the features, solving for eigenvalues ​​and eigenvectors, and selecting eigenvectors with larger contribution rates as principal components. Variance contribution rate represents the degree to which each principal component explains the variability of the original data. By calculating the cumulative variance contribution rate, an appropriate number of principal components are selected to ensure that the dimensionality-reduced data retains the main information of the original data. The dimensionality-reduced feature data is then reorganized chronologically to form time series data. By setting time windows (such as hours, days, weeks, etc.), the continuous time series is divided into multiple time segments. Within each time window, the correlation between features is analyzed, the correlation coefficient between feature pairs is calculated, and feature combinations with strong correlations are identified.

[0053] When hierarchically partitioning feature-related data, distance calculation methods are used to measure the similarity between data points. Commonly used distance calculation methods include Euclidean distance and Manhattan distance. Based on the distance matrix, similar data points are divided into different groups, each group representing a class of similar behavioral patterns. A centroid value is calculated for each group to obtain cluster centers that represent the characteristics of that class of behavior. Then, the distance between the cluster centers and the original feature-related data is calculated, and each data point is assigned to the category of the nearest cluster center according to the principle of minimum distance. Statistical analysis is performed on the data within each category, calculating statistical indicators such as the amount of data and feature distribution for each category, forming cluster statistics. These statistics reflect the distribution characteristics and main features of different behavioral patterns.

[0054] The clustering statistics are reorganized by recombining features from different dimensions according to predetermined rules. Data normalization ensures the comparability of numerical ranges for different features. The reorganized features are then merged and organized to obtain a dataset that comprehensively describes tourist behavior characteristics.

[0055] For example, when tourists enter a scenic area, their location changes are recorded using data collection devices. Continuous location data is segmented by time to obtain the activity areas of tourists in different time periods. Simultaneously, the dwell time and spending behavior of tourists in each area are recorded. Standard deviation calculations reveal that the distribution of tourists' dwell time within the scenic area varies significantly, exhibiting high discriminative power and suitable as a behavioral feature; while the differences in movement speed are relatively small, resulting in lower significance as a feature. Principal component analysis reduces the original multidimensional features (including dwell time, spending amount, activity range, etc.) to a smaller set of principal feature combinations. In the time dimension, tourist activity data is segmented by hour to analyze the correlation between behavioral features in different time periods. Based on this hierarchical segmentation and cluster analysis, several typical tourist behavior patterns are identified, such as in-depth experience-oriented (characterized by long dwell time at a single attraction and high spending), fast-paced tour-oriented (characterized by rapid transitions between attractions and fewer stops), and family-oriented tour-oriented (characterized by concentration in areas suitable for children and spending primarily on food and beverages).

[0056] In one specific embodiment, the process of executing step S103 may specifically include the following steps:

[0057] (1) Based on the time labels of the scenic area basic dataset and tourist behavior characteristic data, the data is aligned according to the time dimension, and time-series aligned data is obtained through data matching calculation;

[0058] (2) The missing time point information in the time-series aligned data is supplemented by linear interpolation, and the supplemented data is smoothed to obtain the interpolated and completed data.

[0059] (3) Based on the historical distribution pattern of the interpolated data, the change function is obtained by calculating the data trend, and the change function is fitted with parameters to obtain the trend function data;

[0060] (4) Cross-validate the trend function data with the interpolated data, calculate the prediction error value through residual calculation, and select and retain the data segment with high prediction accuracy according to the error threshold to obtain the prediction verification data.

[0061] (5) Calculate the correlation degree of the prediction verification data according to the spatial distance, aggregate the data points with high correlation coefficients into regions, and obtain spatiotemporal aggregated data through data fusion processing;

[0062] (6) Periodically analyze the spatiotemporal aggregated data, extract the change characteristics according to the data fluctuation pattern, and obtain the dynamic prediction data of the scenic area through data reconstruction processing.

[0063] Specifically, in the process of integrating the scenic area's basic dataset and tourist behavior characteristic data, the first step is to align the two types of data according to the time dimension. A time tag refers to the timestamp of a data record, containing complete time information including year, month, day, hour, minute, and second. The core of data alignment is to establish a unified time reference system, mapping data from different sources to the same point in time. Data matching calculations are performed using the following formula:

[0064]

[0065] Where M(x,y) represents the data matching metric, x i and γ i Let α represent the data values ​​of the two data sources at time point i. i γ is the time weighting coefficient, δ is the data similarity function, and γ is the data similarity coefficient. i λ(t) is the data reliability coefficient. i ) is the time decay function, and n is the total number of time points.

[0066] For missing time points in time-series aligned data, linear interpolation is used to fill in the gaps. Linear interpolation estimates data based on adjacent time points, assuming the data changes linearly over a short period. To eliminate random fluctuations, the interpolated data is smoothed using methods such as moving averages to reduce noise. The interpolated data maintains the continuity of the time series. After obtaining the time-series data, it is necessary to analyze the historical distribution patterns and establish a trend model. Through statistical analysis of historical data, the periodicity, seasonality, and long-term trends of the data are identified. The trend function is obtained by fitting historical data; common fitting methods include polynomial fitting and exponential smoothing. The fitted trend function can describe the general pattern of data change over time. To verify the accuracy of the trend function, the predicted results are compared with the actual observed data. The accuracy of the prediction is evaluated by calculating the residual between the predicted and actual values. A reasonable error threshold is set to select data segments with high prediction accuracy, which can better reflect the actual change patterns.

[0067] Spatial analysis is performed on the prediction and verification data to calculate the correlation between data from different locations. The correlation calculation considers both spatial distance and data similarity, grouping points that are spatially close and have similar data characteristics into a single region. Through data fusion processing, multiple data points within the same region are integrated to obtain spatiotemporal aggregated data that represents the overall characteristics of that region. Finally, periodic analysis is performed on the spatiotemporal aggregated data to study its changing characteristics at different time scales. The periodic components of the data are decomposed using methods such as Fourier transform to extract the main periodic features. Based on these features, the data is reconstructed to form dynamic prediction data for the scenic area capable of predicting future trends.

[0068] For example, at a major scenic spot, visitor flow data and behavioral characteristic data were recorded using data collection devices. First, the visitor flow data (recorded every minute) and visitor behavior data (which may be irregularly recorded) were aligned according to a unified time standard. For instance, if visitor flow data was available at a certain point in time but behavioral characteristic data was missing, linear interpolation was used to supplement it. Interpolation referenced data from preceding and following time points, and a moving average method was used to eliminate random fluctuations. Then, the patterns of historical data were analyzed, revealing significant differences in visitor flow distribution between weekdays and weekends, and unique distribution characteristics during holidays. Based on these patterns, a trend prediction model was established and validated using actual data. Spatially, visitor flow between adjacent attractions often exhibits correlation. By calculating spatial correlation, the data from related attractions were integrated. The resulting dynamic prediction data not only reflects patterns of change in the temporal dimension but also includes spatial correlation characteristics.

[0069] In one specific embodiment, the process of executing step S104 may specifically include the following steps:

[0070] (1) Based on spatial attributes, tourist behavior characteristic data and scenic area dynamic prediction data are associated and paired, and a data association set is formed by thematic attribute judgment and processing.

[0071] (2) Perform correlation coefficient calculation on the data association set, extract strongly correlated data based on significance determination, and obtain correlation result data;

[0072] (3) Weight coefficients are calibrated for the correlation results data through data feature distribution, and weight allocation is completed through numerical balance calculation to form weight allocation data;

[0073] (4) Under the multi-dimensional indicators, the weighted data is verified and grouped, and the grouped information is cross-merged to generate multi-dimensional fused data;

[0074] (5) Divide the multidimensional fused data into hierarchical levels according to the priority principle, and perform data noise reduction and filtering in combination with hierarchical thresholds to output sorted and filtered data.

[0075] (6) Based on the inter-block association, the sorted and filtered data is reconstructed and integrated, and the data is organized according to the service recommendation rules to obtain the service recommendation data.

[0076] Specifically, tourist behavior data is linked with scenic area dynamic prediction data. Tourist behavior data includes indicators such as tourist activity trajectories, dwell time, and consumption habits, while scenic area dynamic prediction data includes information such as visitor flow predictions and service load predictions for different areas. Spatial attribute association refers to matching these two types of data along a geographic spatial dimension, establishing a correspondence between behavioral characteristics and prediction data within the same spatial area. Thematic attribute determination involves matching data based on its business attributes, such as linking tourist dining consumption behavior with visitor flow predictions for dining areas, and shopping behavior with prediction data for shopping areas, thereby forming a data association set with clear correspondences.

[0077] Correlation analysis of a dataset aims to quantify the strength of relationships between different features. Correlation coefficient calculations employ statistical methods such as Pearson correlation coefficient and Spearman rank correlation coefficient to calculate the correlation between feature pairs. Significance determination involves setting confidence levels and conducting statistical tests on the correlation coefficients to screen for statistically significant strongly correlated feature pairs. The resulting correlation data reflects the actual degree of association between each feature, providing a basis for subsequent weight allocation. After determining the correlation between features, weight coefficients need to be calibrated. The distribution of data features reflects the importance of each feature within the overall dataset; by analyzing the distribution range, central tendency, and dispersion of feature values, initial weights are determined for each feature. Numerical balance calculations consider the mutual influence between features and adjust the weight coefficients to achieve overall balance. For example, for highly correlated features, the weight of one feature is appropriately reduced to avoid information duplication; for relatively independent but important features, a higher weight coefficient is maintained.

[0078] The weighted data needs to be validated under multi-dimensional indicators. These indicators include time dimension (data performance over different time periods), spatial dimension (data characteristics of different regions), and user dimension (behavioral characteristics of different types of tourists). Cross-validation is used to group the data according to these dimensions to verify the rationality of the weighted allocation. Data cross-merging then integrates the validated data from each dimension to form unified multi-dimensional fused data. The hierarchical division of the multi-dimensional fused data is based on priority principles. Priority settings consider factors such as service importance, timeliness, and personalization, dividing the data into different levels. The grading thresholds are set based on data quality and reliability indicators; data that does not meet quality requirements is removed through noise reduction and filtering, retaining high-quality sorted and filtered data.

[0079] Based on the actual needs of the service scenarios, the sorted and filtered data is reconstructed and integrated. Inter-block correlation analysis considers the logical relationships between different data blocks, ensuring the coherence and usability of the reconstructed data. Service recommendation rules include temporal rules (considering the rationality of the recommendation order), correlation rules (considering the complementary relationships between service items), and personalized rules (considering user preferences and demand characteristics). These rules are used to organize the data, ultimately forming the service recommendation data.

[0080] For example, in scenic area service recommendations, behavioral data of tourist A was first collected, including their movement trajectory within the scenic area, dwell time at various attractions, and consumption records. Simultaneously, dynamic prediction data for the scenic area showed the expected visitor flow for each area at different times. Through spatial attribute association, tourist A's dwell time at attraction 1 was matched with the predicted visitor flow data for that attraction; through thematic attribute determination, tourist A's dining consumption behavior was correlated with the service prediction data for the dining area. Correlation analysis revealed significant correlations between tourist dwell time and attraction visitor flow, and between consumption behavior and service area capacity. Based on these correlations, weights were assigned to different features, such as higher weights for historical preferences, medium weights for real-time visitor flow prediction, and lower weights for weather factors. Multi-dimensional validation verified the accuracy of recommendations at different times from a time perspective, the rationality of recommendations for different areas from a spatial perspective, and the applicability to different types of tourists from a user perspective. After hierarchical segmentation and noise reduction filtering, the most valuable recommendation data was retained. Ultimately, based on the service recommendation rules, a personalized tour suggestion was generated, including recommended attractions, best times to visit, and dining recommendations. These suggestions took into account both tourists' personal preferences and the real-time conditions of the scenic area, ensuring that the recommended services met tourists' needs while avoiding localized congestion.

[0081] In one specific embodiment, the process of executing step S105 may specifically include the following steps:

[0082] (1) Group the basic dataset of scenic spots, dynamic prediction data of scenic spots and service recommendation data under the associated theme attribute, and perform field mapping on the grouping results to form multi-source associated data;

[0083] (2) Based on the data structure, type identification is performed on multi-source associated data, field splitting is completed according to the identification criteria, and the split fields are organized by dimension to obtain field representation data;

[0084] (3) Divide the field representation data into periods along the time axis, calculate the numerical differences based on the periodic characteristics, determine the trend of change through difference mapping, and generate difference comparison data;

[0085] (4) Divide the difference comparison data into intervals according to the numerical distribution, normalize the segmentation results, standardize the data according to the statistical rules, and output the standardized data.

[0086] (5) The standardized data are validated by cross-validation, and confidence data are selected by combining the validation rules. Validation evaluation data is generated by data correction.

[0087] (6) The verification and evaluation data are scored and the indicators are synthesized. The data is then quantified according to the scoring rules, and the service evaluation data is obtained through data reconstruction.

[0088] Specifically, the associated thematic attributes refer to the business relationships between data, including service type relationships (such as catering services, tourism services, shopping services, etc.), spatial location relationships (such as adjacent areas, functional areas, etc.), and time series relationships (such as service time period correspondence). Based on these associated attributes, the scenic area basic dataset (including infrastructure information, service resource allocation, etc.), scenic area dynamic prediction data (including visitor flow prediction, service demand prediction, etc.), and service recommendation data (including recommendation schemes, service strategies, etc.) are grouped by theme. Field mapping is performed on the grouped data to establish the correspondence between fields in different datasets. For example, the attraction numbers in the basic data are mapped to the area identifiers in the prediction data, and the service items in the service recommendation data are mapped to the resource allocation in the basic data, forming a multi-source associated data with a unified structure.

[0089] Type identification is performed on multi-source correlated data, focusing on the structural characteristics and business implications of the data. Identification criteria are established based on data type (e.g., numerical, categorical, time-series) and business attributes (e.g., resource, service, evaluation attributes) to split data fields. The splitting process considers the dependencies between fields, avoiding the fragmentation of related data. The split fields are then reorganized according to business dimensions, combining related fields to form field representation data with clear business meaning. Periodic analysis is performed on the field representation data over time. Period division is based on business characteristics, such as daily cycles (peak hours), weekly cycles (weekdays and weekends), and monthly cycles (peak and off-peak seasons). Within each period, the numerical differences of key indicators, such as service quality indicators, resource utilization, and customer satisfaction, are calculated. By mapping these differences, regular trends are identified, generating comparative data reflecting changes in service quality.

[0090] Analyze the distribution characteristics of the comparative data to determine a reasonable interval division scheme based on the data's distribution patterns. Interval division should consider the central tendency and dispersion of the data to ensure the intervals are meaningful for business operations. Normalize the data within the divided intervals, transforming data of different dimensions to a unified scale. Standardize the data using statistical rules (such as mean standardization, maximum / minimum standardization, etc.) to make different types of evaluation indicators comparable. The standardized data needs to be validated using cross-validation to verify its reliability from multiple perspectives. The validation process includes data consistency checks (checking for contradictions between data from different sources), temporal continuity checks (verifying the temporal consistency of the data), and spatial correlation checks (verifying the rationality of the spatial distribution). Based on the validation results, select a subset of data with high confidence levels and correct any biased data using data correction methods to form the validation and evaluation data.

[0091] Finally, a comprehensive score is calculated based on the verification and evaluation data. The scoring rules include indicators across multiple dimensions, such as service quality indicators (service timeliness, accuracy, etc.), resource utilization indicators (facility utilization rate, manpower allocation efficiency, etc.), and customer experience indicators (satisfaction, complaint rate, etc.). The scores from each dimension are integrated using an indicator synthesis method to obtain a comprehensive evaluation result reflecting service quality.

[0092] For example, in the service evaluation of a scenic area, basic data (including the carrying capacity of 10 major attractions and the distribution of 95 service facilities), dynamic forecast data (including visitor flow forecasts and service demand forecasts for different time periods during holidays), and service recommendation data (including personalized route recommendations and peak-hour diversion plans) were collected. The data was categorized into thematic groups such as sightseeing services, catering services, and rest services according to service type, and a data field mapping relationship was established. In the type identification stage, service facility data was broken down into location attributes, capacity attributes, and service item attributes, and then reorganized according to service function. Through time-period analysis, significant differences were found between different types of services on weekdays and weekends, and the service quality of catering services fluctuated between peak and off-peak periods. These differential data were divided into intervals and standardized, and cross-validation was used to ensure data reliability. The generated service evaluation data intuitively reflects the quality level of various services in different time periods and regions.

[0093] In one specific embodiment, the process of executing step S106 may specifically include the following steps:

[0094] (1) Separate textual and numerical information from service evaluation data, perform syntactic segmentation on text paragraphs, and mark and divide them according to semantic rules to obtain semantically labeled data;

[0095] (2) Focus the semantic annotation data on the theme under the data structure dimension, calculate the keyword frequency of semantic segments, extract semantic features based on word frequency relationship, and form feature sequence data;

[0096] (3) Perform vector space transformation on the feature sequence data, calculate the feature distance in the vector space, select data through the distance threshold, and obtain feature mapping data;

[0097] (4) Group and aggregate the feature mapping data based on time-series features, accumulate the aggregation results, determine the correlation strength according to the numerical distribution, and output the data aggregation results.

[0098] (5) Calculate the similarity measure of the data aggregation results, classify the data based on the similarity threshold, classify the data according to the classification criteria, and generate similarity analysis data;

[0099] (6) Based on the optimization criteria, the similarity analysis data is quantified, and the solutions are organized in combination with the quantitative indicators. The service optimization solution is obtained through solution reconstruction.

[0100] Specifically, service evaluation data undergoes text and numerical separation. Textual information includes unstructured data such as visitor reviews, service records, and operational logs, while numerical information includes quantitative indicators such as ratings, duration, and frequency. Syntactic segmentation of text paragraphs involves dividing long texts into basic sentence units based on linguistic features such as punctuation and keywords. Semantic rule tagging adds semantic labels to each sentence unit according to predefined semantic rules, such as sentiment (positive, negative, neutral), topic type (service quality, facility status, staff attitude, etc.), and time sequence markers, resulting in structured semantically labeled data. In terms of data structure, the semantically labeled data undergoes topic-focused processing. Topic focus refers to identifying core topics of concern based on business needs, such as service improvement, resource allocation, and process optimization. For each topic, the frequency of keywords in the semantic fragments is calculated; keywords include service item names, problem descriptions, and improvement suggestion terms. Through word frequency statistics and association analysis, key semantic elements representing topic characteristics are extracted, constructing a feature sequence reflecting the topic content.

[0101] Feature sequence data needs to be transformed into a vector space for processing. Vector space transformation is the process of converting text features into numerical vectors, with each feature corresponding to one dimension in the vector space. In the vector space, the relationships between different features are measured by calculating metrics such as Euclidean distance and cosine similarity between feature vectors. By setting appropriate distance thresholds, significantly correlated feature combinations are selected to form feature mapping data. Temporal analysis is then performed on the feature mapping data, grouping the data according to time attributes. During grouping and aggregation, feature data within the same time window are merged, and the frequency and intensity of each type of feature are statistically analyzed. The distribution of features across different time periods is obtained through cumulative calculations, and the correlation strength between features is determined based on the distribution characteristics, generating data aggregation results that reflect the temporal feature associations.

[0102] Similarity analysis is performed on the aggregated data to calculate the degree of similarity between different data groups. Similarity measures employ multiple dimensions, such as feature overlap, distribution similarity, and temporal correlation. Data is categorized based on a set similarity threshold, with highly similar data grouped into the same category to form similarity analysis data with clear classification characteristics. Based on the goals of scenic area service optimization, optimization criteria are formulated, including quantitative indicators for improving service efficiency, optimizing resource utilization, and enhancing tourist satisfaction. The similarity analysis data is quantified according to these indicators to evaluate the feasibility and expected effects of various optimization measures. Through scheme reconstruction, different optimization measures are combined and adjusted to form a service optimization plan.

[0103] For example, during the optimization of scenic area services, visitor feedback data was collected. Syntactic analysis was used to segment comments such as "insufficient number of sanitary facilities in the scenic area, requiring queuing during peak hours" and label them as "facilities configuration - negative feedback" semantically. Keyword analysis of all comments revealed high frequencies of words like "queue," "waiting," and "crowded," indicating that facilities configuration is a key issue requiring attention. Converting these features into vector representations revealed a close proximity between the features "sanitary facilities" and "peak hours," indicating that the problem mainly occurs during peak visitor times. Time-series analysis showed that these problems are concentrated in the mornings of weekends and holidays, showing a positive correlation with visitor flow data. Categorizing these problems revealed similar issues in other high-traffic areas such as dining areas and parking lots. The final optimization plan included specific measures such as increasing the deployment of mobile facilities, optimizing staffing during peak hours, and implementing time-slot reservation management.

[0104] In one specific embodiment, the process of grouping and aggregating feature mapping data based on time-series features may specifically include the following steps:

[0105] (1) The feature mapping data is segmented according to the time span, and the data window is divided according to the time unit. The time series change values ​​are extracted from the data window to obtain the time series grouped data.

[0106] (2) Perform time-period overlap processing on the time-series grouped data, calculate the data intersection based on the overlapping part, select data according to the size of the intersection, and form overlapping associated data;

[0107] (3) Accumulate the values ​​of overlapping and related data in the time period dimension, calculate the frequency distribution according to the accumulation rules, determine the numerical weights based on the frequency relationship, and generate time-series accumulated data;

[0108] (4) Calculate the distribution density of the time series cumulative data, extract the data according to the density threshold, perform intra-group aggregation based on the extraction results, and output the distributed aggregated data.

[0109] (5) Calculate the intensity of the distributed clustered data based on the changing trend, determine the numerical range based on the calculation results, classify the data according to the range, and obtain the intensity classification data.

[0110] (6) The intensity grading data is correlated and quantified, the quantification results are integrated, and the data organization and processing are completed through integration rules to obtain the data aggregation results.

[0111] Specifically, the data is segmented based on its temporal attributes. Time spans are divided based on business needs, including time units of different granularities such as hourly, daily, and weekly. Each time unit constitutes a data window, from which key indicators reflecting data changes are extracted, such as passenger flow change rate, service request frequency, and resource utilization rate, forming time-series grouped data organized chronologically. Time-series grouped data undergoes period overlap analysis, with overlapping portions between adjacent time windows. This overlap design helps capture the continuous change characteristics of the data. The intersection of data in the overlapping areas is calculated, reflecting the data continuity between adjacent time periods. The strength of the data association is determined based on the size of the intersection, and data with high association are selected to form overlapping associated data, reflecting the continuous change patterns of features over time. After determining the time-series associations, the overlapping associated data is cumulatively statistically analyzed. Within each time period, the frequency and intensity of feature occurrences are statistically analyzed according to preset cumulative rules. The frequency distribution reflects the activity level of features in different time periods, and weight coefficients are assigned to different features by analyzing frequency relationships. The weight setting considers the importance and timeliness of features, generating time-series cumulative data containing weight information.

[0112] Density analysis is performed on time-series cumulative data to calculate the distribution density of the data along the time axis. The density calculation considers the concentration of data points and the time interval, and data segments with significant clustering characteristics are selected by setting a density threshold. Data points within these segments are then aggregated within groups to form distributed clustered data reflecting the central trend of the data. Based on the distributed clustered data, the trend of change is analyzed, and the intensity of data change is calculated. Intensity calculation includes multiple dimensions such as rate of change, fluctuation amplitude, and duration. Based on the calculation results, the range of numerical changes is determined, and the data is classified according to intensity level, generating hierarchical intensity classification data.

[0113] The intensity grading data undergoes correlation metric processing. Correlation metric processing refers to transforming the relationships between data at different levels into calculable numerical indicators. The correlation metric results are organized using integration rules to ensure that the logical relationships between the data are preserved, ultimately forming a structured data aggregation result.

[0114] For example, in the analysis of scenic area service data, the daily data is first segmented by hour. Each hourly window records indicators such as the number of tourists, the number of service requests, and facility utilization. A 15-minute overlap is set between adjacent hours to analyze data continuity. For instance, in the analysis of catering services, the data from the 11:45-12:15 overlap period reflects the initial characteristics of the lunch peak. Through cumulative statistics, it was found that catering service requests peak between 12:00 and 13:00, with significantly higher data density during this period than other times. Based on the growth rate and duration of service requests, service pressure is divided into three levels: low, medium, and high. The data aggregation results show that the weekday lunch peak is mainly concentrated between 12:00 and 13:00, lasting approximately 45 minutes, indicating a high level of service pressure, requiring increased service resource allocation starting at 11:30.

[0115] In one specific embodiment, the process of quantifying similarity analysis data according to optimization criteria may specifically include the following steps:

[0116] (1) Separate and extract the feature attributes of the similarity analysis data, establish numerical mapping based on the attribute relationships, and perform dimensional transformation according to the mapping standard to obtain feature quantification data;

[0117] (2) Classify the feature quantification data into indicators, calculate the threshold range based on the classification rules, and classify the data according to the range limits to form indicator classification data.

[0118] (3) Sort the indicator classification data according to priority, establish a hierarchical structure based on the sorting results, combine the data through the hierarchical relationship, and generate hierarchical combination data;

[0119] (4) Perform adaptation analysis on the hierarchical combination data based on the scenario conditions, calculate the scheme parameters according to the degree of adaptation, adjust the data according to the parameter relationship, and output the scheme parameter data.

[0120] (5) Verify the feasibility of the scheme parameter data, combine the verified data into scheme data, and complete the data sorting according to the splicing rules to obtain the scheme integrated data.

[0121] (6) The solution integrates data for optimization and evaluation, and the data is reorganized according to the evaluation criteria. The solution is constructed through the reorganization rules to obtain the service optimization solution.

[0122] Specifically, the characteristic attributes include service type characteristics (such as catering services, tour guide services, facility services, etc.), time characteristics (such as service duration, response time, waiting time, etc.), and quality characteristics (such as satisfaction rate, complaint rate, repurchase rate, etc.). A numerical mapping relationship is established for these characteristics, converting qualitative descriptions into quantitative indicators, such as converting service satisfaction into a score of 1-5, and response time into a value in minutes. The conversion is performed according to a unified dimensional standard to ensure comparability between different characteristics, forming quantitative characteristic data. The quantitative characteristic data is then graded, and evaluation levels are set for the indicators. The grading rules are based on business needs and historical data distribution, calculating the threshold range for each indicator. For example, service response time is divided into fast response, normal response, and delayed response; service quality is divided into excellent, good, average, and need improvement levels. The data is then categorized according to these threshold ranges, forming structured indicator classification data.

[0123] The categorized indicator data needs to be sorted according to priority. Determining the priority order considers multiple factors, including service importance (core services first), urgency of improvement (severity of the problem), and implementation difficulty (resource investment requirements). Based on the sorting results, a hierarchical structure is established, combining related indicators to form data combinations with clear hierarchical relationships. Adaptability analysis is performed on the hierarchical combination data for different service scenarios. Scenario conditions include environmental factors such as passenger flow levels, resource allocation, and seasonal characteristics. Specific solution parameters are calculated based on the scenario adaptability, such as the number of service personnel, facility opening hours, and reservation limits. These parameters need to be adjusted and optimized according to actual conditions to ultimately form feasible solution parameter data.

[0124] The feasibility of the proposed solutions needs to be verified. This verification includes assessing resource availability (sufficient human and material resources), cost-effectiveness (reasonable return on investment), and implementation difficulty (successful implementation). Verified solution parameters are then integrated and combined, organically combining various solution modules based on business processes and resource constraints to form integrated solution data. Finally, the integrated solution data undergoes optimization and evaluation. Evaluation criteria include quantitative indicators such as service efficiency improvement, resource utilization improvement, and tourist satisfaction improvement. Based on the evaluation results, the solutions are adjusted and restructured to ensure that the service optimization solutions not only address existing problems but also possess strong executability.

[0125] For example, in optimizing catering services at a scenic spot, multiple characteristic attributes of service data were analyzed. Waiting times exceeding 30 minutes were marked as service delays, and customer satisfaction scores below 3 points were marked as requiring improvement. Data grading showed that service delays mainly occurred during peak dining hours and were highly correlated with staff shortages. In the priority ranking, service improvement during peak dining hours was listed as the highest priority. Scenario analysis indicated that service pressure during peak hours mainly stemmed from the concentrated dining needs of group tourists. Based on these analyses, specific measures were developed, including reservation and flow control, flexible staff allocation, and the establishment of fast-track lanes. Feasibility verification confirmed the required human resources and venue conditions. The resulting optimization plan included specific measures such as increasing service staff allocation 30 minutes before peak hours, setting up dedicated dining areas for group tourists, and implementing a time-slot reservation system.

[0126] The above describes the scenic area service optimization method based on big data in the embodiments of this application. The following describes the scenic area service optimization system based on big data in the embodiments of this application. Please refer to [link / reference]. Figure 2 One embodiment of the big data-based scenic area service optimization system in this application includes:

[0127] The data acquisition module 201 is used to collect scenic area operation data through data acquisition equipment, classify and organize the scenic area operation data, clean and standardize the classification results according to data evaluation standards, and obtain the basic dataset of the scenic area.

[0128] The dimensionality reduction module 202 is used to perform feature decomposition and dimensionality reduction on tourist behavior data based on the scenic area basic dataset, and to perform cluster analysis and numerical calculation on the dimensionality-reduced data to obtain tourist behavior feature data.

[0129] The association module 203 is used to perform spatiotemporal correlation analysis between the basic dataset of the scenic area and the tourist behavior feature data, perform data calculation through data interpolation and trend fitting, and filter data according to threshold conditions to obtain dynamic prediction data of the scenic area.

[0130] The calculation module 204 is used to perform correlation analysis and weight calculation on the tourist behavior characteristic data and the scenic area dynamic prediction data, and to perform multi-dimensional data fusion and sorting processing on the calculation results to obtain service recommendation data.

[0131] The cross module 205 is used to perform data association and cross analysis on the basic dataset of the scenic area, the dynamic prediction data of the scenic area and the service recommendation data, and to obtain service evaluation data by performing difference calculation and normalization processing through data comparison.

[0132] The mapping module 206 is used to perform semantic extraction and feature mapping on the feedback data based on the service evaluation data, and to perform data aggregation and similarity calculation on the mapping results to obtain a service optimization scheme.

[0133] Through the collaborative efforts of the aforementioned components and the collection and processing of multi-source data, comprehensive acquisition and effective integration of scenic area operational data were achieved. During data processing, classification, organization, and standardization ensured the reliability and consistency of data quality. Feature decomposition and dimensionality reduction analysis of tourist behavior data effectively extracted key features of tourist behavior patterns, reducing the complexity of data processing. Spatiotemporal correlation analysis and data interpolation accurately captured the dynamic changes in scenic area service demand, improving prediction accuracy. In the service recommendation stage, correlation analysis and weight calculation methods enabled precise matching of personalized service recommendations. The application of data correlation and cross-analysis made service quality assessment more comprehensive and objective. Semantic extraction and feature mapping effectively mined valuable information from feedback data, providing a reliable basis for service optimization decisions. The overall solution, through a data-driven approach, achieved refined management and dynamic optimization of scenic area services, improved the tourist service experience, and enhanced the operational efficiency of the scenic area.

[0134] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing scenic area services based on big data, characterized in that, The big data-based scenic area service optimization method includes: The scenic area operation data is collected by data acquisition equipment, classified and organized, and the classification results are cleaned and standardized according to data evaluation standards to obtain the basic dataset of the scenic area. Based on the aforementioned basic dataset of the scenic area, feature decomposition and dimensionality reduction are performed on the tourist behavior data. The dimensionality-reduced data is then subjected to cluster analysis and numerical calculation to obtain tourist behavior feature data. The basic dataset of the scenic area and the tourist behavior characteristic data are subjected to spatiotemporal correlation analysis. Data calculation is performed through data interpolation and trend fitting. Data is filtered according to threshold conditions to obtain dynamic prediction data of the scenic area. Based on the tourist behavior characteristic data and the scenic area dynamic prediction data, correlation analysis and weight calculation are performed on the data, and the calculation results are subjected to multi-dimensional data fusion and sorting processing to obtain service recommendation data. The basic dataset of the scenic area, the dynamic prediction data of the scenic area, and the service recommendation data are correlated and cross-analyzed. The difference is calculated and normalized by data comparison to obtain service evaluation data. Based on the service evaluation data, semantic extraction and feature mapping are performed on the feedback data. The mapping results are then aggregated and similarity calculated to obtain a service optimization scheme.

2. The method for optimizing scenic area services based on big data according to claim 1, characterized in that, The process involves collecting scenic area operation data through data acquisition equipment, classifying and organizing the data, and cleaning and standardizing the classification results according to data evaluation standards to obtain a basic dataset for the scenic area, including: Tourist location information data is obtained through signal acquisition devices, tourist consumption transaction data is obtained through electronic payment terminals, and scenic area environmental parameter data is obtained through environmental monitoring equipment. The tourist location information data, tourist consumption transaction data, and scenic area environmental parameter data are combined to obtain the scenic area operation data. The scenic area operation data is arranged chronologically according to timestamps. The data is then categorized using a data classification algorithm. Duplicate values ​​are removed from the categorized data to obtain preliminary classification data. Based on the preliminary classification data, data integrity is checked, missing data in the preliminary classification data is filled in using linear interpolation, and outliers in the preliminary classification data are range-limited to obtain preliminary cleaned data. The preliminary cleaned data is processed through data normalization, which involves normalizing the numerical data and encoding the categorical data to obtain standardized data. A quality score is calculated for the standardized data. Evaluation weights are set according to three dimensions: timeliness, accuracy, and completeness of the standardized data. The quality score of the standardized data is calculated by a weighted average method. The standardized data with quality scores higher than a preset threshold are divided into time windows and merged. Through data structure transformation, the basic dataset of the scenic area is obtained.

3. The method for optimizing scenic area services based on big data according to claim 1, characterized in that, The process involves performing feature decomposition and dimensionality reduction on tourist behavior data based on the scenic area's basic dataset, followed by cluster analysis and numerical computation on the dimensionality-reduced data to obtain tourist behavior feature data, including: The time dimension, location dimension, and consumption dimension data in the basic dataset of the scenic area are separated, and numerical statistics are performed on the separated data of each dimension. The feature significance data is obtained by calculating the standard deviation of the data. The saliency data of the features are subjected to dimensionality reduction spatial mapping, and dimensionality reduction is performed by principal component analysis. The variance contribution of the reduced data is calculated to obtain the dimensionality-reduced feature data. The dimensionality-reduced feature data is reconstructed according to a time series, the reconstructed data is segmented into time windows, and feature-related data is obtained through data correlation calculation. The feature-related data is hierarchically divided, data points are grouped by data distance calculation, and the center value is extracted from the grouping results to obtain cluster center data. The distance between the cluster center data and the feature association data is calculated, and the data is classified according to the minimum distance principle. The classification results are statistically analyzed to obtain cluster statistics. The clustering statistics are reconstructed by combining features, and numerical transformation is performed through data normalization. The transformation results are then merged to obtain the tourist behavior feature data.

4. The method for optimizing scenic area services based on big data according to claim 1, characterized in that, The step of performing spatiotemporal correlation analysis between the basic dataset of the scenic area and the tourist behavior characteristic data, calculating data through data interpolation and trend fitting, and filtering data according to threshold conditions to obtain dynamic prediction data for the scenic area includes: Based on the time tags of the scenic area's basic dataset and the tourist behavior feature data, the data is aligned according to the time dimension, and time-series aligned data is obtained through data matching calculation. The missing time point information in the time-series aligned data is supplemented by linear interpolation calculation, and the supplemented data is smoothed to obtain interpolated and completed data. Based on the historical distribution pattern of the interpolated and completed data, a change function is obtained by calculating the data trend, and the change function is fitted with parameters to obtain trend function data; The trend function data and the interpolated data are cross-validated, and the prediction error value is obtained by calculating the residual. The data segments with high prediction accuracy are selected and retained according to the error threshold to obtain the prediction verification data. The correlation degree of the predicted verification data is calculated according to spatial distance. Data points with high correlation coefficients are aggregated into regions. Through data fusion processing, spatiotemporal aggregated data is obtained. The spatiotemporal aggregated data is periodically analyzed, and the change characteristics are extracted based on the data fluctuation patterns. Through data reconstruction processing, the dynamic prediction data of the scenic area is obtained.

5. The method for optimizing scenic area services based on big data according to claim 1, characterized in that, The process involves performing correlation analysis and weight calculation on the tourist behavior characteristic data and the scenic area dynamic prediction data, and then performing multi-dimensional data fusion and sorting processing on the calculation results to obtain service recommendation data, including: The tourist behavior feature data is associated and paired with the scenic area dynamic prediction data according to spatial attributes, and a data association set is formed through theme attribute determination processing; The correlation coefficient is calculated on the data association set, and the strongly correlated data is extracted based on the significance determination to obtain the correlation result data; The correlation result data is weighted by the data feature distribution, and the weight allocation is completed by numerical balance calculation to form weight allocation data. The weighted data is validated and grouped under multi-dimensional indicators, and the grouped information is cross-merged to generate multi-dimensional fused data. The multidimensional fused data is hierarchically divided according to the priority principle, and data noise reduction and filtering are performed in combination with hierarchical thresholds to output sorted and filtered data. The sorted and filtered data is reconstructed and integrated based on inter-block associations, and the data is organized according to service recommendation rules to obtain the service recommendation data.

6. The method for optimizing scenic area services based on big data according to claim 1, characterized in that, The process involves associating and cross-analyzing the basic dataset of the scenic area, the dynamic prediction data of the scenic area, and the service recommendation data. Through data comparison, difference calculations and normalization are performed to obtain service evaluation data, including: Under the associated topic attribute, the basic dataset of the scenic area, the dynamic prediction data of the scenic area and the service recommendation data are grouped, and the grouping results are processed by field mapping to form multi-source associated data; Based on the data structure, the multi-source associated data is type-identified, and the fields are split according to the identification criteria. The split fields are then organized by dimensions to obtain field representation data. The field characterization data is periodically divided along the time axis, the numerical differences are calculated based on the periodic characteristics, the trend of change is determined by the difference mapping, and the difference comparison data is generated. The difference comparison data is segmented into intervals according to the numerical distribution, the segmentation results are normalized, the data is standardized according to statistical rules, and the standardized data is output. The standardized data is validated by cross-validation, and confidence data is selected based on the validation rules. Validation and evaluation data are generated through data correction. The verification and evaluation data are scored and indexed, quantitatively processed according to the scoring rules, and the service evaluation data is obtained through data reconstruction.

7. The method for optimizing scenic area services based on big data according to claim 1, characterized in that, The step involves semantic extraction and feature mapping of the feedback data based on the service evaluation data, followed by data aggregation and similarity calculation of the mapping results to obtain a service optimization scheme, including: Textual and numerical information are separated from the service evaluation data, the text paragraphs are syntactically segmented, and the text is labeled and divided according to semantic rules to obtain semantically labeled data. In terms of data structure, the semantically labeled data is subject-focused, the keyword frequency of semantic segments is calculated, and semantic features are extracted based on word frequency relationships to form feature sequence data. The feature sequence data is transformed into a vector space, the feature distance is calculated in the vector space, and the data is selected by using a distance threshold to obtain feature mapping data; The feature mapping data is grouped and aggregated based on time-series characteristics. The aggregation results are numerically accumulated, the correlation strength is determined according to the numerical distribution, and the data aggregation results are output. The data aggregation results are subjected to similarity measurement calculation, data are classified based on similarity threshold, and data are categorized according to classification criteria to generate similarity analysis data; The similarity analysis data is quantified according to the optimization criteria, and the solution is organized in combination with the quantified indicators. The service optimization solution is obtained through solution reconstruction.

8. The method for optimizing scenic area services based on big data according to claim 7, characterized in that, The process of grouping and aggregating the feature mapping data based on time-series features, accumulating the aggregation results, determining the association strength according to the numerical distribution, and outputting the data aggregation results includes: The feature mapping data is segmented according to the time span, and data windows are divided according to the time unit. The time-series change values ​​are extracted from the data windows to obtain time-series grouped data. The time-series grouped data is subjected to time period overlap processing. The data intersection is calculated based on the overlapping part. Data is selected based on the size of the intersection to form overlapping and associated data. The overlapping and related data are accumulated in terms of time period, the frequency distribution is calculated according to the accumulation rules, the numerical weights are determined based on the frequency relationship, and time-series accumulated data is generated. The time-series accumulated data is subjected to distribution density calculation, data is extracted according to the density threshold, and intra-group aggregation is performed based on the extraction results to output the distributed aggregated data. The intensity of the distributed clustered data is calculated based on the changing trend. The numerical range is determined according to the calculation result. The data is then classified according to the range to obtain intensity classification data. The intensity grading data is correlated and quantified, the quantification results are integrated, and the data is organized and processed through integration rules to obtain the data aggregation result.

9. The method for optimizing scenic area services based on big data according to claim 7, characterized in that, The similarity analysis data is quantified according to optimization criteria, and a solution is organized based on the quantified indicators. The service optimization solution is obtained through solution reconstruction, including: The feature attributes of the similarity analysis data are separated and extracted, numerical mapping is established based on the attribute relationships, and dimensional transformation is performed according to the mapping standard to obtain feature quantification data. The quantified feature data is classified into indicators, and the threshold range is calculated based on the classification rules. The data is then categorized according to the range limits to form indicator classification data. The index classification data are sorted according to priority, a hierarchical structure is established based on the sorting results, and the data is combined through the hierarchical relationship to generate hierarchical combined data. Based on the scenario conditions, the hierarchical combination data is adapted and analyzed. The scheme parameters are calculated according to the degree of adaptation. The data is adjusted according to the parameter relationship, and the scheme parameter data is output. The feasibility of the proposed solution parameters is verified. The verified data is then combined into a solution, and the data is organized according to the combination rules to obtain integrated solution data. The proposed solution integrates data for optimization and evaluation, reorganizes the data according to the evaluation criteria, and completes the solution construction through reorganization rules to obtain the service optimization solution.

10. A big data-based scenic area service optimization system, used to implement the big data-based scenic area service optimization method as described in any one of claims 1-9, characterized in that, The big data-based scenic area service optimization system includes: The data acquisition module is used to collect scenic area operation data through data acquisition equipment, classify and organize the scenic area operation data, clean and standardize the classification results according to data evaluation standards, and obtain the basic dataset of the scenic area. The dimensionality reduction module is used to perform feature decomposition and dimensionality reduction on tourist behavior data based on the scenic area's basic dataset, and then perform cluster analysis and numerical calculation on the dimensionality-reduced data to obtain tourist behavior feature data. The association module is used to perform spatiotemporal association analysis between the basic dataset of the scenic area and the tourist behavior feature data, perform data calculation through data interpolation and trend fitting, and filter data according to threshold conditions to obtain dynamic prediction data of the scenic area. The calculation module is used to perform correlation analysis and weight calculation on the tourist behavior characteristic data and the scenic area dynamic prediction data, and to perform multi-dimensional data fusion and sorting processing on the calculation results to obtain service recommendation data. The cross-analysis module is used to perform data association and cross-analysis on the basic dataset of the scenic area, the dynamic prediction data of the scenic area and the service recommendation data. Through data comparison, difference calculation and normalization are performed to obtain service evaluation data. The mapping module is used to perform semantic extraction and feature mapping on the feedback data based on the service evaluation data, and to perform data aggregation and similarity calculation on the mapping results to obtain a service optimization scheme.

Images