File travel multi-source heterogeneous data management platform

Abstract

The invention discloses a text travel multi-source heterogeneous data management platform, belongs to the field of data management, and aims to solve the problems that the utilization rate of existing text travel multi-source heterogeneous data is relatively low, and invalid data is difficult to screen out. According to the invention, through the text travel data acquisition module, the multi-source data fusion module, the big data calculation module and the intelligent application algorithm module, an external data source is connected through a multi-protocol interface, so that full-type coverage, high-concurrency acquisition and invalid data pre-screening of structured and unstructured data are realized; precise screening and value mining of text travel multi-source data are achieved, while data quality is guaranteed, potential effective data are reserved to the maximum extent, reliable input is provided for subsequent data fusion and intelligent application, after the data are screened, the structural difference of the multi-source data is eliminated through full-process operation of data extraction, conversion, cleaning and loading, and the real-time value mining of the text travel multi-source data is achieved. A unified data model in the text travel field is constructed, standardized input is provided for subsequent calculation and analysis, and sharing of data resources is better achieved.

Description

Technical Field

[0001] The present invention relates to the field of data management technology, and in particular to a cultural tourism multi-source heterogeneous data management platform. Background Art

[0002] With the advancement of the all-for-one tourism strategy and the deepening of smart cultural tourism development, the cultural tourism industry is transforming from a resource-driven to a data-driven one. As a core production factor, the management of cultural tourism data directly impacts scenic area operational efficiency, visitor service quality, and industry decision-making.

[0003] In the existing technology, the utilization rate of data in related fields that are prevalent in the cultural and tourism industry is not high, the degree of fusion of multi-source heterogeneous data is insufficient, the application capabilities of cultural and tourism big data are insufficient, and the intelligent application services are not strong. Among them, traditional data collection tools only support a single protocol and are difficult to adapt to multi-source interfaces such as SQL databases, IoTMQTT protocols, and social media APIs in cultural and tourism scenarios; and data loss is prone to occur in high-concurrency scenarios (such as peak tourist flow in scenic spots during holidays). At the same time, there is a lack of a pre-screening mechanism for invalid data (such as advertising comments and equipment false alarm data), which leads to a 30%-50% increase in subsequent processing load.

[0004] To this end, we propose a multi-source heterogeneous data management platform for cultural tourism. Summary of the Invention

[0005] The purpose of the present invention is to provide a cultural and tourism multi-source heterogeneous data management platform, which solves the problem in the background technology that the utilization rate of cultural and tourism multi-source heterogeneous data is low and it is difficult to screen out invalid data.

[0006] To achieve the above objectives, the present invention provides the following technical solutions: a cultural tourism multi-source heterogeneous data management platform, comprising:

[0007] Cultural and tourism data collection module, used for:

[0008] Connect to external data sources through multi-protocol interfaces to achieve full coverage of structured and unstructured data, high-concurrency collection, and pre-screening of invalid data;

[0009] Multi-source data fusion module, used for:

[0010] After the data is screened, the whole process of data extraction, conversion, cleaning, and loading is carried out to eliminate the structural differences of multi-source data, build a unified data model in the field of culture and tourism, and provide standardized input for subsequent calculations and analysis;

[0011] Big data computing module, used for:

[0012] Build a distributed data processing architecture to support the storage, buffering, stream batch processing, and performance optimization of massive data, providing stable data services for upper-level intelligent algorithms;

[0013] Intelligent application algorithm module for:

[0014] Through multimodal data feature analysis and intelligent algorithm modeling, data is converted into information that can guide business decisions and realize intelligent applications in cultural and tourism scenarios;

[0015] Visualization application modules for:

[0016] Convert complex data processing results into easy-to-understand, interactive, and multi-terminal-adaptable visual content to support users in quickly obtaining key information.

[0017] Furthermore, the cultural tourism data collection module includes:

[0018] Structured data acquisition unit for:

[0019] Through SQL adapters, API connectors, and IoT protocol parsers, it enables real-time streaming data collection from relational databases, government systems, and IoT devices. It uses a multi-threaded concurrent mechanism to improve data capture efficiency in high-throughput scenarios.

[0020] Unstructured data acquisition unit for:

[0021] Use a web crawler engine to capture social media text, a multimedia collector to acquire video data, and a document parser to process PDF reports. Support batch collection and configure content-aware filters to pre-screen invalid data during the collection phase, reducing subsequent processing pressure.

[0022] The data judgment unit is used to verify the basic attributes, format specifications and business value of the collected data.

[0023] Furthermore, the multi-source data fusion module includes:

[0024] The data extraction unit is used to: automatically identify the heterogeneous structures of different data sources using a dynamic pattern matching algorithm to resolve data format incompatibility issues;

[0025] The data conversion unit is used to: map heterogeneous data to a unified model based on ontology mapping technology, and standardize the coordinate system and time zone of scenic spot passenger flow data and GPS trajectory data;

[0026] The data loading unit is used to write the cleaned and standardized data into the database, completing the final conversion from raw data to usable data;

[0027] The data cleaning unit is used to detect data anomalies in real time through the rule engine and trigger repair or discard operations to ensure data quality.

[0028] Furthermore, the big data calculation module includes:

[0029] The Kafka data processing platform is used to: buffer and smooth out the instantaneous data accumulation during sudden passenger flow peaks, balance the processing rate of data sources and downstream computing tasks, and prevent downstream systems from crashing due to data surges;

[0030] HBase database is used to: use spatiotemporal joint indexes to store real-time monitoring data, support high-concurrency real-time queries, and use dynamic partitioning strategies to optimize data partitioning based on scenic area location codes and time period information to improve query efficiency;

[0031] The HDFS module is used to reduce storage costs through hot and cold tiered storage strategies while supporting distributed storage of large-scale data.

[0032] Furthermore, the intelligent application algorithm module includes:

[0033] The data parsing module is used to extract multimodal data features through a feature engineering pipeline. For unstructured data, it uses a neural network with an attention mechanism, BERT for sentiment analysis, and 3D-CNN for video anomaly detection to accurately capture implicit information in the data.

[0034] The intelligent method module is used to: integrate spatiotemporal sequence prediction and tourist behavior clustering, use spatiotemporal graph neural networks to model passenger flow migration between scenic spots, use multi-task learning to simultaneously predict scenic spot popularity and traffic congestion, use a reinforcement learning recommendation engine to generate personalized routes based on tourist preferences, and improve model generalization capabilities. Through the federated learning mechanism, the model is trained locally in each scenic spot, and only model parameters are exchanged, achieving cross-regional model optimization while protecting data privacy.

[0035] The intelligent application module is used to convert the algorithm output into a specific business model, including a real-time warning unit, a decision support unit, and an intelligent report generation unit. The real-time warning unit is specifically used for anomaly detection based on a sliding window and triggering a passenger flow overload alarm. The decision support unit is specifically used for multi-dimensional correlation analysis of the influence relationship of "passenger flow-weather-marketing activities". The intelligent report generation unit is specifically used to automatically integrate multi-source analysis results and output daily or weekly reports on scenic area operations.

[0036] Furthermore, the intelligent method module includes:

[0037] A spatiotemporal graph neural network model is used to process the relationship between tourist flow migration between scenic spots;

[0038] A multi-task learning framework for simultaneously predicting scenic spot popularity and traffic congestion index;

[0039] Reinforcement learning recommendation engine for generating personalized travel itineraries.

[0040] Furthermore, the visualization application module includes:

[0041] The large-screen display unit is used to overlay a display layer with geographic information, using GIS maps to overlay a scenic spot passenger flow heat map, and present an overall situation on the command center's large screen, specifically the real-time passenger flow distribution and congested areas of the city's scenic spots;

[0042] Web-side interactive unit: Provides multi-dimensional data drilling function, from "city-wide passenger flow" to "single scenic spot", and then to "specific time period", allowing users to independently explore data details and analyze the specific reasons for the decline in passenger flow in a certain scenic spot;

[0043] Mobile adaptation unit: This unit meets the instant information needs of mobile scenarios through real-time push of lightweight data. It uses dynamic data binding technology and a unified rendering engine to achieve multi-terminal visual style adaptation and supports automatic updating of visual elements. Specifically, the color of the heat map is refreshed in real time when the passenger flow changes.

[0044] Furthermore, the intelligent application module also includes: a passenger flow prediction algorithm based on the spatiotemporal attention mechanism and a prediction verification algorithm based on statistical distribution. The passenger flow prediction algorithm is used to predict the passenger flow of each scenic spot in the future period, capture the spatiotemporal correlation characteristics, and the prediction verification algorithm is used to verify whether the prediction results of the passenger flow prediction algorithm are consistent with the historical data distribution, so as to avoid misjudgment caused by model overfitting or data mutation.

[0045] Furthermore, in the passenger flow prediction algorithm, capturing spatiotemporal correlation features includes the following steps:

[0046] S1: Spatiotemporal feature extraction, calculating the dependency weight of the time dimension, capturing periodicity and trend characteristics: α t =softmax(W t ·concat(h t , h t-1 )+b t ), where h t is the hidden state at time step t, W t , b t is a learnable parameter, α t ∈[0, 1] represents the contribution of time step t to the prediction, and then calculates the association weight of the spatial dimension to capture the migration pattern of tourist flow between scenic spots: β d =softmax(W s ·cosine(v d ,v0)+b s ), where v d is the feature vector of the neighborhood scenic spot d, v0 is the feature vector of the current scenic spot, W s , b sis a learnable parameter, β d ∈[0,1] represents the influence weight of the neighboring scenic spot d on the current scenic spot;

[0047] S2: Fusion of spatiotemporal attention features, output of passenger flow in the future τ time steps through the fully connected layer

[0048] Where W O , b o is the output layer parameter;

[0049] The prediction verification algorithm specifically includes the following steps:

[0050] S1: Distribution difference calculation, using KS test to measure P hist With P pred The difference between the two, the test statistic D is defined as: D = max x |F hist (x)-F pred (x)|, where F hist (x), F pred (x) are the cumulative distribution functions of historical distribution and forecast distribution respectively;

[0051] S2: significance determination, set the significance level α = 0.05, if D ≤ D α , then the null hypothesis is accepted, which means that there is no significant difference between the predicted distribution and the historical distribution; otherwise, the null hypothesis is rejected, which means that the prediction is unreliable;

[0052] The passenger flow prediction algorithm and prediction verification algorithm implement collaborative verification logic as follows:

[0053] when Then a real-time warning is triggered, and θ is the warning threshold;

[0054] when And D>D α , the prediction is marked as doubtful, triggering the model retraining process.

[0055] Furthermore, in the data determination unit, the basic attributes, format specifications and business value are evaluated, specifically:

[0056] The collected data is evaluated in three dimensions and the quantitative scores are output as follows:

[0057] pass Calculate a data integrity score to evaluate the basic attributes of the data;

[0058] pass Calculate a compliance score to assess whether the data format is standardized;

[0059] pass Calculate the data business value score to assess the relevance of data content to cultural and tourism business scenarios;

[0060] Based on the three-dimensional evaluation scores, the weighted comprehensive score S is calculated and the data is graded. The calculation formula is as follows: S = 0.4I + 0.3C + 0.3R. Setting:

[0061] When S≥85, the collected data is high-confidence valid data and is directly output to the multi-source data fusion module without manual intervention;

[0062] When 60≤S<85, the collected data is considered medium confidence data awaiting verification, triggering the manual review process, and the business personnel will decide whether to correct the data;

[0063] When S<60, the collected data is low-confidence invalid data, marked as invalid data, and directly screened out;

[0064] For medium-confidence data awaiting verification, a data correction tool set is provided. The corrected data is automatically returned to the data judgment unit, where the score is recalculated and a second judgment is made. When S ≥ 85, it is judged as valid data.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] The present invention proposes a cultural and tourism multi-source heterogeneous data management platform. In the existing technology, the utilization rate of cultural and tourism multi-source heterogeneous data is currently low, and it is difficult to screen out invalid data. The present invention uses a cultural and tourism data acquisition module, a multi-source data fusion module, a big data calculation module, and an intelligent application algorithm module to connect to external data sources through a multi-protocol interface to achieve full type coverage of structured and unstructured data, high concurrency collection, and pre-screening of invalid data, thereby realizing accurate screening and value mining of cultural and tourism multi-source data. While ensuring data quality, it maximizes the retention of potential valid data and provides reliable input for subsequent data fusion and intelligent applications. After the data is screened, the structural differences of multi-source data are eliminated through the full process of data extraction, conversion, cleaning, and loading, and a unified data model for the cultural and tourism field is constructed to provide standardized input for subsequent calculations and analysis, thereby better realizing the sharing and utilization of data resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is the overall program flowchart of the cultural tourism multi-source heterogeneous data management platform of the present invention;

[0068] Figure 2 This is a logic determination flow chart of the cultural tourism multi-source heterogeneous data management platform of the present invention. DETAILED DESCRIPTION

[0069] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0070] In order to solve the technical problem of how to effectively process data, such as Figure 1-Figure 2 As shown, the following preferred technical solutions are provided:

[0071] A cultural tourism multi-source heterogeneous data management platform, including:

[0072] Cultural and tourism data collection module, used for:

[0073] Connect to external data sources through multi-protocol interfaces to achieve full coverage of structured and unstructured data, high-concurrency collection, and pre-screening of invalid data;

[0074] Multi-source data fusion module, used for:

[0075] After the data is screened, the whole process of data extraction, conversion, cleaning, and loading is carried out to eliminate the structural differences of multi-source data, build a unified data model in the field of culture and tourism, and provide standardized input for subsequent calculations and analysis;

[0076] Big data computing module, used for:

[0077] Build a distributed data processing architecture to support the storage, buffering, stream batch processing, and performance optimization of massive data, providing stable data services for upper-level intelligent algorithms;

[0078] Intelligent application algorithm module for:

[0079] Through multimodal data feature analysis and intelligent algorithm modeling, data is converted into information that can guide business decisions and realize intelligent applications in cultural and tourism scenarios;

[0080] Visualization application modules for:

[0081] Convert complex data processing results into easy-to-understand, interactive, and multi-terminal-adaptable visual content to support users in quickly obtaining key information.

[0082] The cultural and tourism data collection module includes:

[0083] Structured data acquisition unit for:

[0084] Through SQL adapters, API connectors, and IoT protocol parsers, it enables real-time streaming data collection from relational databases, government systems, and IoT devices. It uses a multi-threaded concurrent mechanism to improve data capture efficiency in high-throughput scenarios.

[0085] Unstructured data acquisition unit for:

[0086] Use a web crawler engine to capture social media text, a multimedia collector to acquire video data, and a document parser to process PDF reports. Support batch collection and configure content-aware filters to pre-screen invalid data during the collection phase, reducing subsequent processing pressure.

[0087] The data judgment unit is used to verify the basic attributes, format specifications and business value of the collected data.

[0088] The multi-source data fusion module includes:

[0089] The data extraction unit is used to: automatically identify the heterogeneous structures of different data sources using a dynamic pattern matching algorithm to resolve data format incompatibility issues;

[0090] The data conversion unit is used to: map heterogeneous data to a unified model based on ontology mapping technology, and standardize the coordinate system and time zone of scenic spot passenger flow data and GPS trajectory data;

[0091] The data loading unit is used to write the cleaned and standardized data into the database, completing the final conversion from raw data to usable data;

[0092] The data cleaning unit is used to detect data anomalies in real time through the rule engine and trigger repair or discard operations to ensure data quality.

[0093] The big data computing module includes:

[0094] The Kafka data processing platform is used to: buffer and smooth out the instantaneous data accumulation during sudden passenger flow peaks, balance the processing rate of data sources and downstream computing tasks, and prevent downstream systems from crashing due to data surges;

[0095] HBase database is used to: use spatiotemporal joint indexes to store real-time monitoring data, support high-concurrency real-time queries, and use dynamic partitioning strategies to optimize data partitioning based on scenic area location codes and time period information to improve query efficiency;

[0096] The HDFS module is used to reduce storage costs through hot and cold tiered storage strategies while supporting distributed storage of large-scale data.

[0097] Intelligent application algorithm modules include:

[0098] The data parsing module is used to extract multimodal data features through a feature engineering pipeline. For unstructured data, it uses a neural network with an attention mechanism, BERT for sentiment analysis, and 3D-CNN for video anomaly detection to accurately capture implicit information in the data.

[0099] The intelligent method module is used to: integrate spatiotemporal sequence prediction and tourist behavior clustering, use spatiotemporal graph neural networks to model passenger flow migration between scenic spots, use multi-task learning to simultaneously predict scenic spot popularity and traffic congestion, use a reinforcement learning recommendation engine to generate personalized routes based on tourist preferences, and improve model generalization capabilities. Through the federated learning mechanism, the model is trained locally in each scenic spot, and only model parameters are exchanged, achieving cross-regional model optimization while protecting data privacy.

[0100] The intelligent application module is used to convert the algorithm output into a specific business model, including a real-time warning unit, a decision support unit, and an intelligent report generation unit. The real-time warning unit is specifically used for anomaly detection based on a sliding window and triggering a passenger flow overload alarm. The decision support unit is specifically used for multi-dimensional correlation analysis of the influence relationship of "passenger flow-weather-marketing activities". The intelligent report generation unit is specifically used to automatically integrate multi-source analysis results and output daily or weekly reports on scenic area operations.

[0101] The smart method module includes:

[0102] A spatiotemporal graph neural network model is used to process the relationship between tourist flow migration between scenic spots;

[0103] A multi-task learning framework for simultaneously predicting scenic spot popularity and traffic congestion index;

[0104] Reinforcement learning recommendation engine for generating personalized travel itineraries.

[0105] Visualization application modules include:

[0106] The large-screen display unit is used to overlay a display layer with geographic information, using GIS maps to overlay a scenic spot passenger flow heat map, and present an overall situation on the command center's large screen, specifically the real-time passenger flow distribution and congested areas of the city's scenic spots;

[0107] Web-side interactive unit: Provides multi-dimensional data drilling function, from "city-wide passenger flow" to "single scenic spot", and then to "specific time period", allowing users to independently explore data details and analyze the specific reasons for the decline in passenger flow in a certain scenic spot;

[0108] Mobile adaptation unit: This unit meets the instant information needs of mobile scenarios through real-time push of lightweight data. It uses dynamic data binding technology and a unified rendering engine to achieve multi-terminal visual style adaptation and supports automatic updating of visual elements. Specifically, the color of the heat map is refreshed in real time when the passenger flow changes.

[0109] The intelligent application module also includes: a passenger flow prediction algorithm based on the spatiotemporal attention mechanism and a prediction verification algorithm based on statistical distribution. The passenger flow prediction algorithm is used to predict the passenger flow of each scenic spot in the future period, capture the spatiotemporal correlation characteristics, and verify whether the prediction results of the passenger flow prediction algorithm are consistent with the historical data distribution through the prediction verification algorithm, so as to avoid misjudgment caused by model overfitting or data mutation.

[0110] In the passenger flow prediction algorithm, capturing spatiotemporal correlation features includes the following steps:

[0111] S1: Spatiotemporal feature extraction, calculating the dependency weight of the time dimension, capturing periodicity and trend characteristics: α t =softmax(W t ·concat(h t , h t-1 )+b t ), where h t is the hidden state at time step t, W t , b t is a learnable parameter, α t ∈[0, 1] represents the contribution of time step t to the prediction, and then calculates the association weight of the spatial dimension to capture the migration pattern of tourist flow between scenic spots: β d =softmax(W s ·cosine(v d ,v0)+b s ), where v d is the feature vector of the neighborhood scenic spot d, v0 is the feature vector of the current scenic spot, W s , b s is a learnable parameter, β d ∈[0,1] represents the influence weight of the neighboring scenic spot d on the current scenic spot;

[0112] S2: Fusion of spatiotemporal attention features, output of passenger flow in the future τ time steps through the fully connected layer

[0113] Where W O , b o is the output layer parameter;

[0114] The prediction verification algorithm specifically includes the following steps:

[0115] S1: Distribution difference calculation, using KS test to measure P hist With P pred The difference between the two, the test statistic D is defined as: D = max x |F hist (x)-F pred (x)|, where Fhist (x), F pred (x) are the cumulative distribution functions of historical distribution and forecast distribution respectively;

[0116] S2: significance determination, set the significance level α = 0.05, if D ≤ D α , then the null hypothesis is accepted, which means that there is no significant difference between the predicted distribution and the historical distribution; otherwise, the null hypothesis is rejected, which means that the prediction is unreliable;

[0117] The passenger flow prediction algorithm and prediction verification algorithm implement collaborative verification logic as follows:

[0118] when Then a real-time warning is triggered, and θ is the warning threshold;

[0119] when And D>D α , the prediction is marked as doubtful, triggering the model retraining process.

[0120] For example, a popular scenic spot plans to hold a concert on the weekend. The passenger flow prediction algorithm predicts that the passenger flow from 14:00 to 16:00 will be 12,000 people. The maximum capacity of the scenic spot is 15,000 people. θ = 15,000. The passenger flow prediction algorithm outputs Trigger the warning condition, and the prediction verification algorithm calculates the passenger flow distribution P in the same period of history. hist (mean 9500 people, standard deviation 1200 people), and generate the predicted distribution P through Monte Carlo sampling pred (mean 12000, standard deviation 800), KS test calculation D = 0.25, critical value (n = 30 days), D ≤ D α , verifying the credibility of the prediction, triggering real-time warnings, and the command center dispatching security personnel through the visualization application module to avoid passenger flow congestion.

[0121] In the data judgment unit, basic attributes, format specifications, and business value are evaluated, specifically:

[0122] The collected data is evaluated in three dimensions and the quantitative scores are output as follows:

[0123] pass Calculate a data integrity score to evaluate the basic attributes of the data;

[0124] pass Calculate a compliance score to assess whether the data format is standardized;

[0125] pass Calculate the data business value score to assess the relevance of data content to cultural and tourism business scenarios;

[0126] Based on the three-dimensional evaluation scores, the weighted comprehensive score S is calculated and the data is graded. The calculation formula is as follows: S = 0.4I + 0.3C + 0.3R. Setting:

[0127] When S≥85, the collected data is high-confidence valid data and is directly output to the multi-source data fusion module without manual intervention;

[0128] When 60≤S<85, the collected data is considered medium confidence data awaiting verification, triggering the manual review process, and the business personnel will decide whether to correct the data;

[0129] When S<60, the collected data is low-confidence invalid data, marked as invalid data, and directly screened out;

[0130] For medium-confidence data awaiting verification, a data correction tool set is provided. The corrected data is automatically returned to the data judgment unit, where the score is recalculated and a second judgment is made. When S ≥ 85, it is judged as valid data.

[0131] In the data judgment unit, regular expressions are used to verify field integrity, such as ensuring that "visitor ID" is not empty. JSON / XML schemas are used to verify format compliance, such as attribute type matching. SPARQL queries are used to match business keywords, such as "scenic spot" and "passenger flow," using the ontology library. NLP word segmentation is used to extract text keywords, OCR is used to identify key information in documents, and video key frames are extracted to assess integrity. Semantic similarity with the cultural and tourism ontology library is calculated using a pre-trained model.

[0132] For example:

[0133] Case 1

[0134] The real-time number of people in Scenic Area A is recorded as 500, but the "tourist type" (individual tourist / group) information is missing. The time format is correct.

[0135] Now judge the data. First, calculate the data integrity score. There are 4 fields in total, and 1 is missing. Then I = (1-1 / 4) × 100 = 75. Then calculate the format compliance score. If the time format is correct, C = 100. Finally, calculate the data business value score. If it includes "Scenic Area ID" and "Real-time Number of People", R = 90, and the comprehensive score S = 0.4 × 75 + 0.3 × 100 + 0.3 × 90 = 87. The result is high confidence and passed directly. If the "Collection Time" format is incorrect, C = 70, the total score S = 0.4 × 75 + 0.3 × 70 + 0.3 × 90 = 78, the result is medium confidence, triggering manual review.

[0136] Case 2

[0137] The original text is "A certain scenic area is good! But the surrounding traffic is too congested. It is recommended that friends who drive here come early~ #Travel攻略#交通吐槽(附带无关广告链接:点击领取手机优惠券)";

[0138] Data integrity: If the key information of the text is complete, then I = 90;

[0139] Format compliance: There are no special format requirements, and by default, C = 100;

[0140] Data business value: It contains the keywords "scenic area" and "traffic", but also contains an advertising link. 3 out of 5 keywords are matched in the ontology library, so R = 60;

[0141] Comprehensive score: S = 0.4×90 + 0.3×100 + 0.3×60 = 84. The result is medium confidence, triggering manual review;

[0142] After manual intervention and rollback for rejudgment, the advertising link is deleted, and the business labels of "traffic advice" and "tourist experience" are marked. The data business value is improved, and 4 out of 5 keywords are matched, so R = 80. Recalculate S = 0.4×90 + 0.3×100 + 0.3×80 = 90. The result is high confidence, and it is retained as valid data. If not corrected, the advertising proportion exceeds 40%, and it is manually confirmed as invalid data and directly screened out;

[0143] Case Three

[0144] Original data (false alarm of IoT device): "Sensor ID: S001, Temperature: -200°C, Humidity: 150%" (significantly exceeding the physical range)";

[0145] Data integrity: The fields are complete, so I = 100;

[0146] Format compliance: The numerical format is correct, so C = 100;

[0147] Data business value: The temperature or humidity value is unreasonable and has nothing to do with the cultural and tourism business, so R = 20;

[0148] Comprehensive score: S = 0.4×100 + 0.3×100 + 0.3×20 = 76. The result is medium confidence, but the value exceeds the reasonable range (for example, the temperature cannot be lower than -273°C), and it is directly marked as invalid.

[0149] From the processing process of different types of data, it can be seen that this solution realizes precise filtering and value maximization of cultural and tourism data at the collection source through quantitative evaluation, hierarchical processing, and correction of the closed loop, avoiding both the problem of "loss of valid data caused by strict format verification" in the traditional solution and the defect of residual invalid data caused by extensive screening.

[0150] Specifically, in the data collection phase, a multi-protocol interface is used to achieve high-concurrency collection of all types of data. A data judgment unit is used to filter and pre-screen invalid data and process it in a graded manner, laying the foundation for high-quality data. The Kafka data processing platform, HBase database, and HDFS module are combined to achieve massive data storage and processing, balancing processing efficiency and cost.

[0151] The visualization application module supports cross-terminal interaction across large screens, web terminals, and mobile terminals, dynamically presenting data details and overall trends;

[0152] It achieves accurate screening and value mining of multi-source cultural and tourism data, maximizes the retention of potential effective data while ensuring data quality, and provides reliable input for subsequent data fusion and intelligent applications.

[0153] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A cultural tourism multi-source heterogeneous data management platform, characterized by: include: Cultural and tourism data collection module, used for: Connect to external data sources through multi-protocol interfaces to achieve full coverage of structured and unstructured data, high-concurrency collection, and pre-screening of invalid data; Multi-source data fusion module, used for: After the data is screened, the whole process of data extraction, conversion, cleaning, and loading is carried out to eliminate the structural differences of multi-source data, build a unified data model in the field of culture and tourism, and provide standardized input for subsequent calculations and analysis; Big data computing module, used for: Build a distributed data processing architecture to support the storage, buffering, stream batch processing, and performance optimization of massive data, providing stable data services for upper-level intelligent algorithms; Intelligent application algorithm module for: Through multimodal data feature analysis and intelligent algorithm modeling, data is converted into information that can guide business decisions and realize intelligent applications in cultural and tourism scenarios; Visualization application modules for: Convert complex data processing results into easy-to-understand, interactive, and multi-terminal-adaptable visual content to support users in quickly obtaining key information.

2. A cultural tourism multi-source heterogeneous data management platform according to claim 1, characterized in that: The cultural tourism data collection module includes: Structured data acquisition unit for: Through SQL adapters, API connectors, and IoT protocol parsers, it enables real-time streaming data collection from relational databases, government systems, and IoT devices. It uses a multi-threaded concurrent mechanism to improve data capture efficiency in high-throughput scenarios. Unstructured data acquisition unit for: Use a web crawler engine to capture social media text, a multimedia collector to acquire video data, and a document parser to process PDF reports. Support batch collection and configure content-aware filters to pre-screen invalid data during the collection phase, reducing subsequent processing pressure. The data judgment unit is used to verify the basic attributes, format specifications and business value of the collected data.

3. A cultural tourism multi-source heterogeneous data management platform according to claim 2, characterized in that: The multi-source data fusion module includes: The data extraction unit is used to: automatically identify the heterogeneous structures of different data sources using a dynamic pattern matching algorithm to resolve data format incompatibility issues; The data conversion unit is used to: map heterogeneous data to a unified model based on ontology mapping technology, and standardize the coordinate system and time zone of scenic spot passenger flow data and GPS trajectory data; The data loading unit is used to write the cleaned and standardized data into the database, completing the final conversion from raw data to usable data; The data cleaning unit is used to detect data anomalies in real time through the rule engine and trigger repair or discard operations to ensure data quality.

4. A cultural tourism multi-source heterogeneous data management platform according to claim 3, characterized in that: The big data computing module includes: The Kafka data processing platform is used to: buffer and smooth out the instantaneous data accumulation during sudden passenger flow peaks, balance the processing rate of data sources and downstream computing tasks, and prevent downstream systems from crashing due to data surges; HBase database is used to: use spatiotemporal joint indexes to store real-time monitoring data, support high-concurrency real-time queries, and use dynamic partitioning strategies to optimize data partitioning based on scenic area location codes and time period information to improve query efficiency; The HDFS module is used to reduce storage costs through hot and cold tiered storage strategies while supporting distributed storage of large-scale data.

5. A cultural tourism multi-source heterogeneous data management platform according to claim 4, characterized in that: The intelligent application algorithm module includes: The data parsing module is used to extract multimodal data features through a feature engineering pipeline. For unstructured data, it uses a neural network with an attention mechanism, BERT for sentiment analysis, and 3D-CNN for video anomaly detection to accurately capture implicit information in the data. The intelligent method module is used to: integrate spatiotemporal sequence prediction and tourist behavior clustering, use spatiotemporal graph neural networks to model passenger flow migration between scenic spots, use multi-task learning to simultaneously predict scenic spot popularity and traffic congestion, use a reinforcement learning recommendation engine to generate personalized routes based on tourist preferences, and improve model generalization capabilities. Through the federated learning mechanism, the model is trained locally in each scenic spot, and only model parameters are exchanged, achieving cross-regional model optimization while protecting data privacy. The intelligent application module is used to convert the algorithm output into a specific business model, including a real-time warning unit, a decision support unit, and an intelligent report generation unit. The real-time warning unit is specifically used for anomaly detection based on a sliding window and triggering a passenger flow overload alarm. The decision support unit is specifically used for multi-dimensional correlation analysis of the influence relationship between "passenger flow-weather-marketing activities". The intelligent report generation unit is specifically used to automatically integrate multi-source analysis results and output daily or weekly reports on scenic area operations.

6. A cultural tourism multi-source heterogeneous data management platform according to claim 5, characterized in that: The intelligent method module includes: A spatiotemporal graph neural network model is used to process the relationship between tourist flow migration between scenic spots; A multi-task learning framework for simultaneously predicting scenic spot popularity and traffic congestion index; Reinforcement learning recommendation engine for generating personalized travel itineraries.

7. A cultural tourism multi-source heterogeneous data management platform according to claim 6, characterized in that: The visualization application module includes: The large-screen display unit is used to overlay a display layer with geographic information, using GIS maps to overlay a scenic spot passenger flow heat map, and present an overall situation on the command center's large screen, specifically the real-time passenger flow distribution and congested areas of the city's scenic spots; Web-based interactive unit: Provides multi-dimensional data drilling capabilities, from "city-wide passenger flow" to "individual scenic spot", and then to "specific time period", allowing users to independently explore data details and analyze the specific reasons for the decline in passenger flow at a certain scenic spot; Mobile adaptation unit: This unit meets the instant information needs of mobile scenarios through real-time push of lightweight data. It uses dynamic data binding technology and a unified rendering engine to achieve multi-terminal visual style adaptation and supports automatic updating of visual elements. Specifically, the color of the heat map is refreshed in real time when the passenger flow changes.

8. A cultural tourism multi-source heterogeneous data management platform according to claim 7, characterized in that: The intelligent application module also includes: a passenger flow prediction algorithm based on the spatiotemporal attention mechanism and a prediction verification algorithm based on statistical distribution. The passenger flow prediction algorithm is used to predict the passenger flow of each scenic spot in the future period, capture the spatiotemporal correlation characteristics, and the prediction verification algorithm is used to verify whether the prediction results of the passenger flow prediction algorithm are consistent with the historical data distribution, thereby avoiding misjudgment caused by model overfitting or data mutation.

9. A cultural tourism multi-source heterogeneous data management platform according to claim 8, characterized in that: In the passenger flow prediction algorithm, capturing spatiotemporal correlation features includes the following steps: S1: Spatiotemporal feature extraction, calculating the dependency weight of the time dimension, capturing periodicity and trend characteristics: α t =softmax(W t ·concat(h t , h t-1 )+b t ), where h t is the hidden state at time step t, W t , b t is a learnable parameter, α t ∈[0, 1] represents the contribution of time step t to the prediction, and then calculates the association weight of the spatial dimension to capture the migration pattern of tourist flow between scenic spots: β d =softmax(W s ·cosine(v d ,v0)+b s ), where v d is the feature vector of the neighborhood scenic spot d, v0 is the feature vector of the current scenic spot, W s , b s is a learnable parameter, β d ∈[0,1] represents the influence weight of the neighboring scenic spot d on the current scenic spot; S2: Fusion of spatiotemporal attention features, output of passenger flow in the future τ time steps through the fully connected layer Where W O , b o is the output layer parameter; The prediction verification algorithm specifically includes the following steps: S1: Distribution difference calculation, using KS test to measure P hist With P pred The difference between the two, the test statistic D is defined as: D = max x |F hist (x)-F pred (x)|, where F hist (x), F pred (x) are the cumulative distribution functions of historical distribution and forecast distribution respectively; S2: significance determination, set the significance level α = 0.05, if D ≤ D α , then the null hypothesis is accepted, which means that there is no significant difference between the predicted distribution and the historical distribution; otherwise, the null hypothesis is rejected, which means that the prediction is unreliable; The passenger flow prediction algorithm and prediction verification algorithm implement collaborative verification logic as follows: when Then a real-time warning is triggered, and θ is the warning threshold; when And D>D α , the prediction is marked as doubtful, triggering the model retraining process.

10. A cultural tourism multi-source heterogeneous data management platform according to claim 9, characterized in that: In the data determination unit, the basic attributes, format specifications and business value are evaluated, specifically: The collected data is evaluated in three dimensions and the quantitative scores are output as follows: pass Calculate a data integrity score to evaluate the basic attributes of the data; pass Calculate a compliance score to assess whether the data format is standardized; pass Calculate the data business value score to assess the relevance of data content to cultural and tourism business scenarios; Based on the three-dimensional evaluation scores, the weighted comprehensive score S is calculated and the data is graded. The calculation formula is as follows: S = 0.4I + 0.3C + 0.3R. Setting: When S≥85, the collected data is high-confidence valid data and is directly output to the multi-source data fusion module without manual intervention; When 60≤S<85, the collected data is considered medium confidence data awaiting verification, triggering the manual review process, and the business personnel will decide whether to correct the data; When S<60, the collected data is low-confidence invalid data, marked as invalid data, and directly screened out; For medium-confidence data awaiting verification, a data correction tool set is provided. The corrected data is automatically returned to the data judgment unit, where the score is recalculated and a second judgment is made. When S ≥ 85, it is judged as valid data.

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