An AI-driven hotel static information intelligent matching and aggregation system
By using a multi-level feature vectorization engine and a cascaded matching decision pipeline, combined with deep semantic understanding and business rules, the accuracy and efficiency issues in hotel static information matching and aggregation are solved, achieving high efficiency, automation and scalability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- MARCO POLO TRAVEL TECH CO LTD
- Filing Date
- 2026-02-28
- Publication Date
- 2026-06-19
Smart Images
Figure CN122241248A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of computer information processing, and particularly to an AI-driven intelligent matching and aggregation system for hotel static information. Background Art
[0002] In the field of hotel distribution and online travel, efficiently integrating hotel static information from multiple suppliers is the key to improving the platform service quality and operation efficiency. As the global hotel distribution network becomes increasingly complex, hotel distribution platforms, online travel agents, and global distribution systems need to aggregate hotel data from different sources to provide a unified and accurate hotel list for users to choose. Among them, the intelligent matching and aggregation technology of hotel static information aims to solve the problems of data duplication and inconsistency caused by multi-source data heterogeneity, and belongs to an important direction of the cross-application of artificial intelligence natural language processing and data engineering technology.
[0003] Among them, the intelligent matching and aggregation of multi-supplier hotel static information is the core link to ensure data quality. This technology aims to identify and merge duplicate entries describing the same hotel from different suppliers through automated means, so as to construct a unified and authoritative view of hotel master data. Its basic principle is to judge whether it points to the same entity by comparing key attributes such as the name, address, and geographical coordinates of the hotel through algorithms.
[0004] The existing technology mainly relies on rule-based matching methods, such as calculating the string similarity of hotel names or comparing address keywords. However, this method cannot effectively handle scenarios where semantic equivalence exists but the expression forms are very different. For example, it cannot accurately identify "Marriott Shanghai Pudong" and "Shanghai Pudong Marriott Hotel" as the same hotel. Although some solutions try to introduce artificial intelligence models for semantic understanding to improve the matching accuracy, they often lack in-depth integration with business rules and do not introduce an effective secondary verification mechanism. For example, the consideration of price differences for the same room type is ignored, resulting in a high false matching rate. In addition, the entire matching and aggregation process heavily relies on manual intervention. From the screening of candidate pairs, the determination of matching results to the final writing and merging of the database, all require manual operations. When faced with a large amount of data, the efficiency is extremely low and the decision-making criteria are vague, making it difficult to meet the needs of rapid business changes. Summary of the Invention
[0005] The purpose of the present invention is to provide an AI-driven intelligent matching and aggregation system for hotel static information to solve the problems of insufficient matching accuracy, low efficiency, and poor scalability caused by the lack of semantic understanding, the disconnection of business rules, and the low degree of process automation in the existing technology.
[0006] To solve the above technical problems, the present invention provides the following technical solutions:
[0007] An AI-driven intelligent matching and aggregation system for hotel static information, comprising:
[0008] The data access and preprocessing module receives hotel static information streams from multiple heterogeneous supplier data sources in parallel, and performs standardized cleaning and structure transformation on each piece of information to generate hotel records to be processed in a unified format.
[0009] The multi-level feature vectorization engine performs feature extraction based on a deep semantic understanding model and feature encoding based on business rules in parallel on the standardized hotel records, generating a high-dimensional composite feature vector that integrates semantic features and rule features.
[0010] The candidate pair generation and coarse screening module, based on geospatial grid index and key attribute hash index, performs fast neighborhood search on hotel records from different suppliers, generates an initial candidate matching pair set, and applies preset coarse-grained filtering rules to perform preliminary screening of the candidate pair set.
[0011] The refined matching decision module receives candidate matching pairs after coarse screening and inputs the composite feature vector corresponding to each candidate record into a cascaded matching decision pipeline. This pipeline sequentially performs semantic similarity calculation based on Siamese neural network, multi-dimensional attribute consistency verification based on configurable rule engine, and final matching probability prediction based on ensemble learning model.
[0012] The dynamic threshold and conflict resolution unit receives the matching probability output by the refined matching decision module, and dynamically calculates and applies the matching judgment threshold based on the distribution characteristics of the current batch data and the preset business confidence strategy. For boundary cases where the matching probability is close to the threshold or cases that generate logical conflicts on different verification dimensions, the conflict resolution algorithm based on evidence chain weighted voting is activated to generate a definite matching or non-matching instruction.
[0013] The automated aggregation execution and master data management module responds to confirmed matching instructions and automatically performs record merging operations. Based on preset priority strategies and data freshness rules, this module selects the optimal field values from multiple successfully matched records, constructs or updates authoritative hotel master data records, and synchronizes them to a unified master database. At the same time, this module is also responsible for maintaining a complete matching relationship graph and operation audit logs.
[0014] The multi-level feature vectorization engine includes a semantic feature extraction submodule and a rule feature encoding submodule:
[0015] The semantic feature extraction submodule has a built-in pre-trained multilingual hotel domain-specific text encoding model. The text encoding model takes a combination string of hotel name, address, brand and description text as input, and outputs a fixed-dimensional deep semantic embedding vector through its internal Transformer encoder structure.
[0016] The rule feature encoding submodule performs one-hot encoding or numerical mapping on the discrete attributes in the hotel records according to a predefined business rule dictionary; the discrete attributes include, but are not limited to, hotel star rating, chain group code, basic room type classification, and whether it contains specific facility identifiers; the output of the rule feature encoding submodule is a sparse high-dimensional rule feature vector;
[0017] Finally, the semantic embedding vector output by the semantic feature extraction submodule and the regular feature vector output by the regular feature encoding submodule are concatenated and dimensionality reduced through a feature fusion layer to generate the high-dimensional composite feature vector.
[0018] The specific workflow of the cascaded matching decision pipeline in the refined matching decision module is as follows:
[0019] The first level is the semantic similarity calculation unit, the core of which is a Siamese neural network structure with shared parameters. This unit receives a pair of composite feature vectors of candidate hotel records, processes them through the same neural network branch, and calculates the cosine similarity between the two output vectors as a preliminary semantic matching score.
[0020] The second level is a multi-dimensional attribute consistency verification unit, the core of which is a rule engine that can dynamically load business rule scripts. This engine executes multiple verification rule groups in parallel, including geographic coordinate distance verification, telephone number format and area code verification, official certification identifier comparison verification, and room type name keyword intersection verification. Each set of verification rules outputs a boolean value or consistency score.
[0021] The third level is the integrated prediction unit, the core of which is a gradient boosting decision tree model. The input features of the gradient boosting decision tree model are the semantic matching score output by the first level and the vector of all consistency verification results output by the second level after numerical transformation. The gradient boosting decision tree model is trained with massive historical matching annotation data and outputs a final matching probability value between 0 and 1.
[0022] The working mechanism of the dynamic threshold and conflict resolution unit includes a dynamic threshold calculation sub-process and a conflict resolution sub-process:
[0023] The dynamic threshold calculation sub-process first statistically analyzes the distribution of matching probability values of all candidate pairs in the current batch output by the integrated prediction unit, and calculates their mean and standard deviation. Then, according to the preset business strategy configuration file, it reads the basic threshold, strict mode offset coefficient, and lenient mode offset coefficient. Based on the recent mismatch rate fed back by the data quality monitoring module, the system automatically selects to apply strict mode or lenient mode, and obtains the dynamic judgment threshold actually applied in the current batch by weighting the basic threshold with the offset coefficient and standard deviation corresponding to the selected mode.
[0024] The conflict resolution subprocess is activated for two scenarios: the first scenario is when the matching probability value falls within a preset narrow interval centered on a dynamic threshold, defined as a boundary case; the second scenario is when contradictory conclusions appear in the multi-dimensional attribute consistency verification results of the rule engine, such as highly consistent geographical coordinates but completely different phone numbers. For these scenarios, the conflict resolution algorithm is activated. This algorithm uses semantic matching score, consistency verification scores for each item, historical accuracy weights from the supplier's data source, and the hotel's existing association strength in the master data graph as evidence items. A preset credibility weight is assigned to each piece of evidence, and a weighted sum is calculated. If the total weighted score exceeds the preset conflict resolution threshold, it is determined to be a match; otherwise, it is determined to be a mismatch.
[0025] Specifically, the priority strategy implementation method in the automated aggregation execution and master data management module involves the system maintaining a supplier weight mapping table, which dynamically updates the weight values based on the supplier's data coverage, historical update frequency, and error rate.
[0026] When performing record merging, for each field to be aggregated, the system compares the source supplier weight of the field with the timestamp of the field itself in all matching records; it prioritizes the field value in the record with the highest source supplier weight; when the highest weights are tied, it selects the field value with the latest timestamp.
[0027] For descriptive text fields, the AI-driven intelligent matching and aggregation system for hotel static information employs a text fusion algorithm based on sentence embedding to extract key information clauses from the descriptive text of multiple records, remove sentences with repetitive semantics, and merge them to generate a more comprehensive, concise, and authoritative description.
[0028] The AI-driven intelligent matching and aggregation system for hotel static information also includes a closed-loop feedback and model iteration module. This module collects correction records submitted via the manual review interface during automated aggregation execution, as well as anomalies from the system operation log, in real time to construct an incremental training dataset. At preset intervals, the closed-loop feedback and model iteration module uses the incremental training dataset to fine-tune the Siamese neural network in the refined matching decision module and the gradient boosting decision tree model in the ensemble prediction unit. Simultaneously, the closed-loop feedback and model iteration module analyzes patterns in the correction records, automatically generates new or adjusts existing business rule scripts, and pushes them to the rule engine for hot updates, thereby achieving continuous self-optimization of the system's matching performance.
[0029] Furthermore, the overall architecture of the AI-driven intelligent matching and aggregation system for hotel static information is an event-driven microservice architecture. The data access and preprocessing module, the refined matching decision module, and the automated aggregation execution and master data management module are all encapsulated as independent microservices, communicating asynchronously via message queues. The AI-driven intelligent matching and aggregation system for hotel static information defines and executes the entire process from data access to master data updates through a unified workflow orchestration engine. This workflow orchestration engine supports dynamic adjustment of process nodes and parameters through a graphical interface to adapt to different business scenarios and data source characteristics.
[0030] Compared with the prior art, the beneficial technical effects of the present invention are as follows:
[0031] This invention creatively integrates deep semantic understanding capabilities with interpretable business rule logic by designing a multi-level feature vectorization engine and a cascaded matching decision pipeline. The semantic feature extraction submodule fundamentally solves the problem that traditional string matching cannot handle cross-language and synonymous heterogeneous expressions, while rule feature encoding and verification ensure the consistency of key business attributes such as price and room type. This integrated architecture enables the system to possess both the fuzzy semantic judgment intelligence of artificial intelligence and the precise control and interpretability of the rule system in key business logic, thereby achieving an order-of-magnitude improvement in matching accuracy in complex and ever-changing real-world data scenarios.
[0032] This invention introduces a dynamic threshold and conflict resolution unit, solving the problem of poor adaptability of static thresholds when facing data distribution fluctuations. Through dynamic calculation based on data batch statistical characteristics and business strategies, the system can adaptively adjust the tightness of judgment. More importantly, the evidence chain weighted voting resolution mechanism designed for boundary cases and rule conflict cases simulates the decision-making process of human experts comprehensively weighing multiple sources of information, significantly reducing the incidence of mismatches and missed matches, improving the robustness and reliability of system decisions, achieving full-process automation under high confidence, and greatly reducing the reliance on human intervention.
[0033] This invention constructs a complete technical system from automated aggregation execution to closed-loop feedback iteration. The automated aggregation execution module intelligently synthesizes optimal authoritative data based on a dynamic weight priority strategy, ensuring the quality of master data. The closed-loop feedback and model iteration module enables the system to continuously learn and evolve, automatically extracting knowledge from manual correction and business changes to optimize model parameters and business rules. Combined with an event-driven microservice architecture and orchestratable workflows, the entire system exhibits high scalability, flexibility, and maintainability, efficiently handling massive data processing needs and quickly adapting to frequent updates to business rules in the hotel distribution field. Attached Figure Description
[0034] Figure 1 This is a schematic diagram of the overall technical solution architecture of an AI-driven intelligent matching and aggregation system for hotel static information proposed in this invention.
[0035] Figure 2 This is a schematic diagram illustrating the core principle framework of the multi-level feature vectorization engine and cascaded matching decision pipeline proposed in this invention. Detailed Implementation
[0036] The features and exemplary embodiments of various aspects of the present invention will now be described in detail. To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely intended to explain the present invention and not to limit the present invention. For those skilled in the art, the present invention can be practiced without some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present invention by illustrating examples of the invention.
[0037] Example 1
[0038] The overall technical architecture of the AI-driven intelligent matching and aggregation system for hotel static information proposed in this invention is shown in the attached figure. Figure 1 As shown. This system is based on an event-driven microservice architecture and achieves a complete automated process through modular design, from heterogeneous data source access, feature vectorization, candidate pair generation, refined matching decision-making, dynamic threshold determination, conflict resolution to master data aggregation and closed-loop feedback. The following will combine the attached... Figure 1 With appendix Figure 2 The system provides a detailed explanation of each component module and its internal working mechanism.
[0039] The system first starts with the data access and preprocessing module, deployed as an independent microservice. This module connects in parallel to multiple heterogeneous supplier data sources through configurable adapter interfaces, including but not limited to global distribution systems, online travel agency platforms, hotel group central reservation systems, and regional local supplier databases. Each data source pushes a static hotel information stream in a different data format, covering fields such as hotel name, physical address, geographic coordinates, contact number, brand logo, star rating, facility tags, room type list, and descriptive text. Upon receiving the raw data, the data access and preprocessing module immediately initiates a standardized cleaning and structured transformation process. The structured transformation process includes character encoding standardization, special symbol filtering, multilingual text normalization, address segmentation and component identification, telephone number format correction, and default value filling strategies for missing fields. After the above processing, each raw record is converted into a hotel record to be processed with a unified schema. Its field structure strictly follows the data model defined internally by the system and is tagged with metadata, including data source identifier, receiving timestamp, original format version number, and data integrity score. All pending hotel records are then encapsulated into standardized message objects and asynchronously delivered to the downstream multi-level feature vectorization engine via a high-throughput message queue.
[0040] The multi-level feature vectorization engine, as the core feature generation unit of the system, has the following internal structure: Figure 2As shown, the system consists of a semantic feature extraction submodule and a rule-based feature encoding submodule operating in parallel. The multi-level feature vectorization engine receives standardized hotel records from the data access and preprocessing module and generates a high-dimensional composite feature vector for each record independently. The semantic feature extraction submodule incorporates a pre-trained multilingual hotel domain-specific text encoding model. This model is based on the Transformer architecture and undergoes domain-adaptive pre-training on a large-scale hotel-related corpus. During feature extraction, the text encoding model concatenates the hotel name, standardized address, brand name, and descriptive text into a single input string and performs deep semantic encoding through a multi-layer self-attention mechanism, ultimately outputting a deep semantic embedding vector with a fixed dimension of 768. This deep semantic embedding vector effectively captures the semantic equivalence between cross-language synonyms, brand variations, and address differences. Simultaneously, the rule-based feature encoding submodule performs structured encoding of discrete attributes in the hotel records based on a pre-loaded business rule dictionary. This business rule dictionary, maintained by business experts, includes hotel star ratings, chain group codes, basic room type classifications, and specific facility identifiers. The output of the regular feature encoding submodule is a sparse, high-dimensional regular feature vector, typically 2048-dimensional, with non-zero elements comprising less than 5%. Subsequently, the semantic embedding vector and the regular feature vector are fed into the feature fusion layer. The feature fusion layer uses a linear projection matrix to concatenate the two vectors, reducing the original dimension to a dense vector of 1024 dimensions—a high-dimensional composite feature vector. This fusion process preserves the key discriminative features of both semantic and regular information while reducing subsequent computational overhead through dimensionality reduction. All generated composite feature vectors are bound to the original hotel record ID and stored in the feature vector database for use in subsequent matching processes.
[0041] The candidate pair generation and coarse screening module is responsible for quickly locating potential matches from a massive amount of hotel records. This module first constructs two core indexes: a geospatial grid index and a key attribute hash index. The geospatial grid index uses the Geohash algorithm to divide the Earth's surface into grid cells of different precision levels, mapping each hotel record to its corresponding grid ID based on its latitude and longitude coordinates. The key attribute hash index generates hash buckets based on a combination of the hotel's name's initials in pinyin, brand code, and city code, using a consistent hash function. During the candidate pair generation phase, the system iterates through all hotel records from different suppliers. For each record A, it first queries its geographical grid and all other supplier records within its eight adjacent grids, forming a geographical neighborhood set; simultaneously, it queries all records within its key attribute hash bucket, forming an attribute similarity set. The union of these two sets is the initial candidate matching pair set for record A. This process is executed in parallel through a distributed index service, enabling neighborhood retrieval of millions of records within seconds. Subsequently, the system applies a set of preset coarse-grained filtering rules to initially screen the candidate pair set. Coarse-grained filtering rules include: geographic distance threshold filtering, mandatory brand code consistency rules, and name keyword overlap thresholds. After coarse screening, the number of candidate pairs is typically reduced by more than 90%, significantly reducing the computational load of subsequent fine-grained matching. All candidate pairs that pass the coarse screening are encapsulated into matching task units, containing the IDs of the two records, a composite feature vector reference, and the coarse screening score, and are passed to the fine-grained matching decision module via a message queue.
[0042] The refined matching decision module is the core logical unit of the system for determining whether two hotel records point to the same physical entity. Internally, it employs a cascaded matching decision pipeline structure, such as... Figure 2 As shown, the cascaded matching decision pipeline consists of three processing stages: a semantic similarity calculation unit, a multi-dimensional attribute consistency verification unit, and an integrated prediction unit.
[0043] The semantic similarity calculation unit is implemented based on a Siamese neural network architecture. This network consists of two structurally identical, parameter-shared fully connected neural network branches. Each branch receives a 1024-dimensional composite feature vector as input and undergoes a non-linear transformation through three hidden layers, ultimately outputting a refined 128-dimensional feature representation. The cosine similarity between the two output vectors is calculated as the semantic matching score. Its value range is [-1, 1], but in practical applications, since the feature vectors are all forward encoded, therefore... This score reflects the overall semantic similarity between two records and is highly robust to noise such as spelling errors in names and differences in address representations.
[0044] The multi-dimensional attribute consistency verification unit is driven by a configurable rule engine that supports dynamically loading business rule scripts written in DSL, allowing verification logic to be updated without restarting the service. During the verification phase, the engine executes four core verification rule groups in parallel:
[0045] One method is geographic coordinate distance verification, which calculates the Haversine distance between two points. ,like If the distance is 200 meters, the consistency score is 1.0; if the distance is 200 meters, the consistency score is 1.0. Meters, output linear decay fraction ;like Meters, output 0.0;
[0046] The second part is the telephone number format and area code verification. First, it verifies whether both numbers conform to the International Telecommunication Union E.164 standard format. If both are compliant, it compares whether their country codes and area codes are consistent. If they are consistent, it outputs 1.0; otherwise, it outputs 0.0. If either of the formats is invalid, it skips the verification item and outputs an empty value.
[0047] Thirdly, there is the official certification identifier comparison and verification. If both parties provide a unique global hotel identifier, a precise string comparison is performed. If they match, the output is 1.0; otherwise, it is 0.0. If only one party provides it, the output is 0.5 as weak evidence.
[0048] Fourthly, keyword intersection verification of room type names is performed, extracting the core keywords from both parties' room type lists and calculating the Jaccard similarity coefficient. Output As a consistency score.
[0049] Each set of validation rules outputs a numerical result, which together form a 5-dimensional consistency validation vector. ,in A boolean value indicating whether the brand codes are consistent.
[0050] The ensemble prediction unit uses a gradient boosting decision tree model as the final decision maker, with semantic matching scores as the input feature. With consistency check vector The model concatenates non-empty elements, resulting in a total of 6 features. It is trained on millions of historically manually labeled matching / non-matching samples to learn the non-linear interactions between features. Its output is a final matching probability between 0 and 1. This represents the system's confidence that the current candidate pair belongs to the same hotel entity. This probability value is appended to the matching task unit and passed to the next module.
[0051] The dynamic threshold and conflict resolution unit is responsible for converting matching probabilities into deterministic matching instructions. This unit first executes the dynamic threshold calculation sub-process, and the system counts all candidate pairs in the current batch. The values are used to calculate their arithmetic mean μ and standard deviation σ. Simultaneously, the business strategy configuration file, which contains the basic thresholds, is read from the configuration center. Strict mode offset coefficient Offset coefficient with relaxed mode The system queries the data quality monitoring module in real time to obtain the false match rate ε for the past 7 days. If ε > 5%, it is determined that the data quality has deteriorated, and strict mode is activated; if ε < 2%, lenient mode is activated; otherwise, basic mode is maintained. Dynamic threshold is used for determination. Calculated according to the following logic:
[0052]
[0053] This formula ensures that the threshold can be adaptively adjusted according to fluctuations in data distribution, avoiding systematic misjudgments caused by the overall high or low quality of batch data.
[0054] Subsequently, the system executes the conflict resolution sub-process. The conflict resolution sub-process is activated for two types of scenarios: the first is boundary cases, i.e. Where δ is the preset narrow interval width; the second type is the rule conflict case, i.e., the consistency verification vector. There are logical contradictions in it, for example (Geographically highly consistent) but (The phone number is completely inconsistent) and (No common identifier). For any of the above scenarios, the system initiates the evidence chain weighted voting algorithm. The evidence chain weighted voting algorithm defines four types of evidence items and their credibility weights: semantic matching score. The weighted average of various consistency check scores, the historical accuracy weight of the data source, and the master data graph correlation strength are all factors. The historical accuracy weight of the data source is dynamically calculated based on the supplier's successful matching accuracy over the past 30 days, ranging from 0.5 to 1.0. The master data graph correlation strength refers to whether any record in the current candidate pair already exists in the master data; if it exists and there are multiple successful matches, a higher strength value is assigned. Weighted total score. The calculation is as follows:
[0055]
[0056] in, Let i be the consistency check score of the i-th item. Its confidence coefficient; if A preset conflict resolution threshold, with a default value of 0.78, is used to determine a match; otherwise, it is considered a mismatch. This mechanism effectively simulates the comprehensive expert judgment process, significantly improving the accuracy of decision-making in boundary and conflict scenarios.
[0057] The automated aggregation execution and master data management module responds to the final matching command, executing the construction and updating of authoritative master data. This module maintains a supplier weight mapping table, where each supplier ID corresponds to a dynamic weight value. , The calculation is based on a weighted average of three metrics: data coverage, historical update frequency, and inverse error rate, and is updated daily using a sliding window mechanism. During record merging, for each structured field, the system iterates through all successfully matched records and compares their source vendor weights. With the timestamp field Preferred selection The highest value in the field; if multiple records have the same highest value. Then choose The latest field values. For descriptive text fields, the system employs a text fusion algorithm based on sentence embedding: First, the Sentence-BERT model is used to segment each descriptive text into sentences and generate sentence vectors; then, a clustering algorithm is used to group semantically similar sentences into one category; within each category, sentences of moderate length and with the highest information entropy are selected as representatives; finally, the representative sentences are reassembled in logical order to generate a deduplicated, comprehensive, and fluent authoritative descriptive text. All aggregation results are written to a unified master database, triggering a master data change event. Simultaneously, the automated aggregation execution and master data management modules persist the matching relationship to the matching relationship graph database and record a complete operation audit log, including operation time, execution module, input / output snapshots, and anomaly markers.
[0058] The system also includes a closed-loop feedback and model iteration module to achieve continuous self-optimization. It monitors correction records submitted via the manual review interface and system anomaly logs. All correction records are labeled as positive or negative samples and associated with the original features and decision paths to form an incremental training dataset. Every 24 hours, the closed-loop feedback and model iteration module automatically triggers a fine-tuning training process: first, it performs backpropagation updates on the last two layers of the Siamese neural network with a small learning rate; then, it incrementally fits the GBDT model using new samples, preserving the original tree structure and adding only new trees. Simultaneously, the closed-loop feedback and model iteration module analyzes high-frequency error patterns in the correction records, automatically generates new business rule scripts, and dynamically loads them through the rule engine's hot update interface without requiring a service restart. This closed-loop mechanism ensures that the system's matching accuracy continuously improves over time.
[0059] The entire system coordinates the execution order and data flow between microservices through a unified workflow orchestration engine. Built on Apache Airflow, the engine supports defining Directed Acyclic Graphs (DAGs) via a graphical interface. Typical workflows include: data access → feature vectorization → candidate pair generation → fine-grained matching → dynamic judgment → aggregation execution. Each node can be configured with resource quotas, retry policies, and timeout thresholds. The system supports partitioning workflow instances by data source, region, or hotel type, enabling flexible business isolation and resource scheduling. All inter-module communication is asynchronously completed via Kafka message queues, ensuring high throughput and fault tolerance. The system is horizontally scalable, capable of processing over 50 million hotel records daily with a matching accuracy of 98.7% and a human intervention rate of less than 0.5%.
[0060] Example 2
[0061] In another implementation, the system is optimized for the specific characteristics of regional small-scale supplier data sources. These suppliers typically have lower data quality, higher field missing rates, and lack standardized brand and address information. To address this, an enhanced address parsing submodule is introduced into the data access and preprocessing module. This submodule integrates a third-party geocoding service to perform reverse geocoding on the original address strings, supplementing missing latitude and longitude coordinates and resolving fuzzy addresses into precise coordinate points. Simultaneously, in the multi-level feature vectorization engine, the semantic feature extraction submodule is replaced with a lightweight dual-tower model: one tower processes the name and brand text, and the other tower processes the address and description text. The outputs of the two towers are concatenated to generate 384-dimensional vectors, reducing computational overhead to adapt to resource-constrained environments.
[0062] During the candidate pair generation phase, since smaller suppliers often lack brand information, the key attribute hash index was reconstructed to be based solely on a combined hash of city code and name pinyin. The coarse-screening rules were also adjusted accordingly, eliminating the mandatory brand consistency rule and replacing it with a weak brand hint rule: if one party has a brand while the other does not, the candidate pair is retained but the coarse-screening score is reduced.
[0063] The rule engine in the refined matching decision module loads a regionalized set of verification rules. For example, in Southeast Asia, a "hotel alias database" verification is added to compare with commonly used local names; in Europe, the consistency verification between postal codes and administrative division codes is strengthened. The GBDT model with integrated prediction units is trained separately using regionally labeled data to capture local data distribution characteristics.
[0064] In the dynamic threshold calculation sub-process, the basic threshold The weighting was lowered to 0.65 to accommodate the lower data quality baseline. In the conflict resolution algorithm, the historical accuracy weight of the data source is... The value was increased to 0.35, emphasizing the accumulation of trust in reliable small suppliers.
[0065] The automated aggregation execution module assigns the lowest initial weight to small suppliers when selecting fields, but sets up a rapid boosting mechanism: if 10 consecutive matches are manually confirmed as correct, the weight increases by 0.1, with a maximum of 0.7. This strategy encourages high-quality small suppliers to participate in the ecosystem.
[0066] The closed-loop feedback module establishes an independent analysis pipeline for regional errors, generating weekly regional data quality reports to guide local operations teams in source data governance. After this implementation was deployed in a Southeast Asian country, the matching accuracy improved from 82% to 94%, significantly enhancing the data integration effect with long-tail suppliers.
[0067] Example 3
[0068] In another implementation, the system is deployed on a high-security, compliance-compliant financial-grade hotel booking platform. To comply with GDPR and local data sovereignty regulations, all personally identifiable information is anonymized during the preprocessing stage: phone numbers are replaced with irreversible hash values, retaining only country codes and area codes for verification. Geographic coordinates are perturbed by superimposing differential privacy-compliant Gaussian noise onto the original coordinates to protect the privacy of the hotel's precise location.
[0069] In the multi-level feature vectorization engine, the pre-trained model of the semantic feature extraction submodule is replaced with a version fine-tuned on de-identified data to ensure that the embedding space does not leak original sensitive information. The rule-based feature encoding submodule disables any facility labels that may be associated with personal identity.
[0070] The rules engine of the refined matching decision module loads a set of compliance verification rules, including: prohibiting the use of unauthorized data sources, prohibiting cross-jurisdictional matching, and mandating audit trails. All matching decisions must be accompanied by a compliance signature, recording the authorization status of the data sources used.
[0071] The dynamic threshold and conflict resolution unit add compliance constraint checks: if candidate pairs involve different jurisdictions, even if the matching probability is high, manual review is still required; in the conflict resolution algorithm, the master data graph association strength weight is set to 0 to avoid compliance risks caused by historical associations.
[0072] The automated aggregation execution module implements field-level encryption in the main database, and sensitive fields are only decrypted by authorized services. The matching relationship graph only stores the anonymized record IDs and matching results, and does not retain the original field values.
[0073] The manual review interface of the closed-loop feedback module adds a compliance approval node, requiring all correction records to be reviewed by the data protection officer before being included in the training set. The model iteration process is executed in an isolated, compliant training environment to ensure that the training data does not go out of bounds.
[0074] This implementation was deployed in the travel management system of a multinational bank in Europe and successfully passed the ISO 27001 and GDPR compliance audits, maintaining a matching accuracy rate of over 96% while meeting strict privacy protection requirements.
[0075] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape, and principle of the present invention should be covered within the scope of protection of the present invention.
Claims
1. An AI-driven hotel static information intelligent matching and aggregation system, characterized in that, include: The data access and preprocessing module is used to receive hotel static information streams from multiple heterogeneous supplier data sources in parallel, and perform standardized cleaning and structure transformation on each piece of information to generate hotel records to be processed in a unified format. A multi-level feature vectorization engine is used to perform feature extraction based on a deep semantic understanding model and feature encoding based on a business rule dictionary in parallel on the standardized hotel records to be processed, generating a high-dimensional composite feature vector that integrates semantic features and rule features. The candidate pair generation and coarse screening module is used to perform fast neighborhood retrieval on hotel records to be processed from different suppliers based on geospatial grid index and key attribute hash index, generate an initial candidate matching pair set, and apply preset coarse-grained filtering rules to perform preliminary screening of the initial candidate matching pair set. The refined matching decision module is used to receive candidate matching pairs after preliminary screening and input the composite feature vector corresponding to each candidate matching pair into a cascaded matching decision pipeline. The dynamic threshold and conflict resolution unit is used to receive the matching probability output by the refined matching decision module, and dynamically calculate and apply the matching judgment threshold based on the distribution characteristics of the current batch data and the preset business confidence strategy. The automated aggregation execution and master data management module is used to automatically perform record merging operations in response to defined matching instructions. 2.The AI-driven hotel static information intelligent matching and aggregation system based on AI according to claim 1, characterized in that, The multi-level feature vectorization engine includes a semantic feature extraction submodule and a rule feature encoding submodule; The semantic feature extraction submodule has a built-in pre-trained multilingual hotel domain-specific text encoding model, which takes a combination string of hotel name, address, brand and description text as input and outputs a fixed-dimensional deep semantic embedding vector. The rule feature encoding submodule performs one-hot encoding or numerical mapping on the discrete attributes in the hotel records according to the predefined business rule dictionary, and outputs a sparse high-dimensional rule feature vector. The deep semantic embedding vector output by the semantic feature extraction submodule and the regular feature vector output by the regular feature encoding submodule are concatenated and dimensionality reduced through a feature fusion layer to generate the high-dimensional composite feature vector. 3.The AI-driven hotel static information intelligent matching and aggregation system based on AI driving according to claim 1, characterized in that, The cascaded matching decision pipeline includes a semantic similarity calculation unit, a multi-dimensional attribute consistency verification unit, and an integrated prediction unit. The semantic similarity calculation unit is based on a shared parameter twin neural network structure. It receives a pair of composite feature vectors of candidate hotel records, processes them through the same neural network branch, and calculates the cosine similarity between the two output vectors as the semantic matching score. The multi-dimensional attribute consistency verification unit is a rule engine that can dynamically load business rule scripts. It is used to execute multiple verification rule groups in parallel and output Boolean values or consistency scores. The verification rule groups include geographic coordinate distance verification, telephone number format and area code verification, official certification identifier comparison verification, and room type name keyword intersection verification. The integrated prediction unit, whose core is a gradient boosting decision tree model, is used to take the semantic matching score and the vector of all consistency verification results after numerical transformation as input features, and output a final matching probability value between 0 and 1.
4. The AI-driven hotel static information intelligent matching and aggregation system based on claim 1, characterized in that, The working mechanism of the dynamic threshold and conflict resolution unit includes a dynamic threshold calculation sub-process and a conflict resolution sub-process. The dynamic threshold calculation sub-process first statistically analyzes the distribution of matching probability values of all candidate pairs in the current batch and calculates their mean and standard deviation. Subsequently, based on the preset business strategy configuration file, the system reads the basic threshold, strict mode offset coefficient, and lenient mode offset coefficient. The system automatically selects to apply strict mode or lenient mode based on the recent mismatch rate fed back by the data quality monitoring module, and calculates the dynamic judgment threshold for the current batch by summing the product of the basic threshold and the offset coefficient and standard deviation corresponding to the selected mode. The conflict resolution subprocess initiates a conflict resolution algorithm for boundary cases where the matching probability value falls within a preset narrow range centered on the dynamic judgment threshold, or for logically conflicting cases where contradictory results appear in the multi-dimensional attribute consistency verification results of the rule engine. The conflict resolution algorithm uses semantic matching score, consistency verification scores of each item, historical accuracy weights of the supplier data source, and the existing association strength of the hotel in the master data graph as evidence items, assigns a preset credibility weight to each piece of evidence, and performs a weighted summation calculation. If the weighted total score exceeds the preset conflict resolution threshold, it is determined to be a match; otherwise, it is determined to be a mismatch. 5.The AI-driven hotel static information intelligent matching and aggregation system based on AI according to claim 1, characterized in that, The priority strategy implementation method in the automated aggregation execution and master data management module is as follows: the system maintains a supplier weight mapping table, which dynamically updates the weight values according to the supplier's data coverage, historical update frequency, and error rate indicators. When performing record merging, for each field to be aggregated, the system compares the source supplier weight of that field with the timestamp of that field itself in all matching records; Prioritize the field values from the record with the highest source supplier weight; When the highest weights are tied, the field value with the latest timestamp is selected. 6.The AI-driven hotel static information intelligent matching and aggregation system based on AI according to claim 1, characterized in that, The system also includes a closed-loop feedback and model iteration module; The closed-loop feedback and model iteration module is used to collect correction records submitted by the manual review interface and abnormal cases in the system operation log during the automated aggregation execution process in real time, and construct an incremental training dataset. Every preset period, the incremental training dataset is used to fine-tune the Siamese neural network in the fine matching decision module and the gradient boosting decision tree model in the integrated prediction unit. At the same time, the patterns in the correction records are analyzed, new or existing business rule scripts are automatically generated, and pushed to the rule engine for hot updates. 7.The AI-driven hotel static information intelligent matching and aggregation system based on AI driving according to claim 1, characterized in that, The overall system architecture is an event-driven microservice architecture; The data access and preprocessing module, the fine-grained matching decision module, and the automated aggregation execution and master data management module are all encapsulated as independent microservices and communicate asynchronously through message queues; The system defines and executes the entire process from data access to master data update through a unified workflow orchestration engine, which supports dynamic adjustment of process nodes and parameters through a graphical interface.
8. The AI-driven intelligent matching and aggregation system for hotel static information according to claim 2, characterized in that, The semantic feature extraction submodule has a pre-trained multilingual hotel domain-specific text encoding model built in, which is a Transformer-based model. Its input is a concatenated string of hotel name, standardized address, brand name and descriptive text, and its output is a deep semantic embedding vector with a fixed dimension of 768.
9. The AI-driven intelligent matching and aggregation system for hotel static information according to claim 3, characterized in that, The calculation process for the consistency score output by the geographic coordinate distance verification rule group in the multi-dimensional attribute consistency verification unit is as follows: Calculate the Haversine distance between two points; if the distance is less than or equal to 200 meters, the consistency score is 1.0; if the distance is greater than 200 meters and less than or equal to 500 meters, the linear decay score is output; if the distance is greater than 500 meters, the consistency score is 0.
0. 10.The AI-driven hotel static information intelligent matching and aggregation system based on AI driving according to claim 5, characterized in that, The automated aggregation execution and master data management module uses a text fusion algorithm based on sentence embedding to merge descriptive text fields. The text fusion algorithm first uses a sentence embedding model to segment each descriptive text into sentences and generate sentence vectors. Then, it uses a clustering algorithm to group semantically similar sentences into one category. The sentence with the highest information entropy in each category is selected as the representative sentence. Finally, the representative sentences are reorganized in logical order to generate authoritative descriptive text.