Address matching system based on deep learning

Abstract

The invention relates to an address matching system based on deep learning, which is applied to the technical field of address matching and comprises a data preprocessing module, a data generation module, a feature extraction module, a deep learning model, an address matching module, a distributed processing module, an active learning module, an incremental learning mechanism, a performance evaluation module and an optimization module. The system improves the matching accuracy through technologies of multi-modal feature fusion, dynamic threshold adjustment, context analysis and the like; the data quality is improved by utilizing an address standardization and complementation module; an interpretability module is adopted to enhance the transparency of the system; large-scale data efficient processing is achieved through distributed processing, the method comprises the steps of data preprocessing, feature extraction, deep learning matching, dynamic threshold adjustment, active learning, incremental updating, error analysis, context optimization and the like, the accuracy, efficiency and expandability of address matching are remarkably improved, and the method has wide application prospects.

Description

Technical Field

[0001] The present invention relates to an address matching system based on deep learning, and in particular to an address matching system based on deep learning applied in the field of address matching technology. Background Art

[0002] Address matching is a key task in processing natural text data and is widely used in express delivery, finance, geography and other fields. Accurate address matching directly affects the performance and reliability of related systems. However, the diversity of address representation, string conversion errors and semantic understanding have brought great challenges to address matching. The address formats of different countries and regions are different. Even within the same country, the representation of addresses may vary due to language habits, abbreviations and other factors. During the address input or processing process, errors such as character addition, character deletion, and word replacement may be introduced. These errors need to be considered during matching. Humans can use context and semantic information when matching addresses, and traditional methods find it difficult to simulate this ability.

[0003] The prior art generally uses the following methods:

[0004] String distance measurement methods: Methods such as Levenshtein distance and Jaccard similarity are mainly based on character operations. This method is difficult to handle semantic information and common errors in addresses.

[0005] Conditional Random Field (CRF) and Blocking Technique: This approach attempts to match by segmenting the address and using blocking techniques, but its representation capacity is limited and cannot fully utilize contextual information. Moreover, the blocking module cannot provide feedback to the CRF, making it unable to enrich the representation specifically for the address matching task.

[0006] Embedding-based deep learning methods: Although some studies have tried to use word2vec embedding and deep learning models to solve the address matching problem.

[0007] However, these methods have several problems: Dataset limitations: The datasets of existing studies are usually limited to a specific area, such as a single city, and cannot handle cross-regional address matching problems. Limitations of embedding representation: word2vec embedding can only capture semantic information at the word level and cannot handle subtle differences at the character level. Simplified generation of unmatched address pairs: The unmatched address pairs generated by existing methods are often too simple and fail to fully simulate the complex situations in practical applications.

[0008] In the prior art, Chinese invention patent CN114969237B discloses an automatic address analysis and matching method for a geographic information system, which realizes automatic address analysis and matching through an MMAS algorithm model and a batch matching algorithm.

[0009] Chinese invention patent CN114676353B proposes an address matching method based on segmented inference, which uses the ESIM inference model and pointer network to match address sub-elements.

[0010] These designs have improved the accuracy and efficiency of address matching to a certain extent by introducing machine learning and deep learning technologies, but they still have certain limitations, such as: lack of comprehensive utilization of multimodal data (such as text and geographic coordinates), failure to fully consider the dynamics and diversity of address data, lack of adaptive learning and continuous optimization mechanisms, the efficiency of large-scale data processing needs to be improved, and the system's interpretability and transparency are insufficient. Summary of the invention

[0011] In view of the above-mentioned prior art, the technical problem to be solved by the present invention is to address the deficiencies of the existing address matching technology in processing multimodal data, adapting to diversified address formats, realizing adaptive learning, improving the efficiency of large-scale data processing, enhancing system interpretability, and coping with dynamically changing address rules. At the same time, it is necessary to maximize the use of address information while protecting data privacy, and provide a comprehensive solution that is easy to integrate and has broad application prospects, so as to significantly improve the accuracy, efficiency, robustness and scalability of address matching, and meet the address matching needs of various industries in complex and changing environments.

[0012] To solve the above problems, the present invention provides an address matching system based on deep learning, including a data preprocessing module, a data generation module, a feature extraction module, a deep learning model, an address matching module, a distributed processing module, an active learning module, an incremental learning mechanism, a performance evaluation module and an optimization module;

[0013] Data preprocessing module, used to clean and standardize input address data;

[0014] A data generation module, used to generate an enhanced address dataset based on string conversion operations;

[0015] Feature extraction module, used to extract character-level and word-level features from address data. This module supports multi-language processing and extracts geographic features through a geographic information system interface.

[0016] The deep learning model includes text encoder, geoencoder, attention mechanism, multimodal fusion network and classifier;

[0017] Text encoder, used to encode text features;

[0018] Geocoder, used to encode geographic features;

[0019] Attention mechanism, used to capture important relationships between features;

[0020] Multimodal fusion network, used to fuse text features and geographic features;

[0021] Classifier, used to output matching prediction results;

[0022] An address matching module is used to perform an address matching operation and output a matching result;

[0023] The address matching module includes a dynamic threshold adjustment mechanism and an error analysis submodule;

[0024] Dynamic threshold adjustment mechanism, used to automatically adjust and optimize matching thresholds based on different types of addresses, application scenarios, and historical matching results;

[0025] The error analysis submodule is used to identify common types of address errors and provide automatic correction suggestions;

[0026] Distributed processing module, used to process large-scale address data in parallel on multiple computing nodes;

[0027] An active learning module to identify hard-to-match address pairs and request manual annotation;

[0028] Incremental learning mechanism for continuous learning from new data without retraining the entire model;

[0029] Performance evaluation module, used to continuously monitor system performance and conduct A / B testing;

[0030] Optimization module, used to automatically adjust system parameters based on test results.

[0031] As a further improvement of the present application, the deep learning model also includes a pre-trained language model interface and a transfer learning module;

[0032] Pre-trained language model interface, used to extract features using large-scale pre-trained language models;

[0033] The transfer learning module is used to transfer the knowledge of the pre-trained model to the address matching task.

[0034] As a further improvement of the present application, it also includes an address normalization module and an address completion module;

[0035] Address normalization module, used to convert non-standard addresses into standard format;

[0036] The address completion module is used to complete the missing address information.

[0037] As a further improvement of the present application, the address matching module also includes a context analysis submodule for optimizing the matching strategy by utilizing the regional information when processing addresses in batches.

[0038] As another improvement of the present application, an explainability module is also included to explain the reasons for the matching decision. The explainability module includes a visualization and decision path tracing mechanism based on attention weights.

[0039] As another improved supplement of the present application, the string conversion operations adopted by the data generation module include word replacement, word deletion, character addition, character deletion, arrangement, word insertion, character replacement, word abbreviation, word repetition and homophone replacement.

[0040] As another improved supplement of the present application, the feature extraction module uses character embedding and word embedding methods to convert the address into a vector representation, character embedding is used to capture character-level information; word embedding is used to capture word-level semantic information.

[0041] As another improvement of the present application, the distributed processing module includes a task scheduler, a parallel processing engine and a result aggregator;

[0042] Task scheduler, used to distribute large-scale address data to multiple computing nodes;

[0043] A parallel processing engine for performing address matching tasks simultaneously on each computing node;

[0044] The result aggregator is used to collect and integrate the matching results of each node.

[0045] The system is implemented through a cloud computing platform and provides an API interface for external systems to call address matching services. The API interface supports single address matching requests, batch address matching requests, address normalization requests, and address completion requests.

[0046] An address matching system based on deep learning, and an address matching method thereof, comprising the following steps:

[0047] S1. Preprocess and standardize the input address data;

[0048] S2. Generate an enhanced address dataset using a data generation module;

[0049] S3. Extract text features and geographic features of the address and support multi-language processing;

[0050] S4. Use the deep learning model to process the features and output matching prediction results;

[0051] S5. Determine the final matching result according to a predefined threshold or a dynamically adjusted threshold;

[0052] S6. Using an active learning module to identify hard-to-match address pairs;

[0053] S7. Request manual annotation of address pairs that are difficult to match;

[0054] S8. Update the deep learning model using the newly annotated data;

[0055] S9. Standardize non-standard addresses;

[0056] S10. Complete the address of the missing information;

[0057] S11. Analyze matching errors and provide automatic correction suggestions;

[0058] S12. Optimizing the matching strategy of batch addresses using context information;

[0059] S13. Processing large-scale address data in parallel on multiple computing nodes;

[0060] S14. Continuously monitor system performance and conduct A / B testing;

[0061] S15. Automatically adjust system parameters to optimize performance based on test results;

[0062] S16. Detect and adapt to the new address format and naming rules, and update the processing logic of the data generation module and feature extraction module.

[0063] In summary, this application has the following beneficial effects:

[0064] 1. High accuracy and robustness. Through deep learning models and multimodal feature fusion (text, geographic information), the accuracy of address matching is significantly improved. The enhanced dataset created by the data generation module improves the model's adaptability to various address variants and errors and enhances the robustness of the system. Multilingual support enables the system to handle addresses in different languages ​​and formats, and has wide applicability.

[0065] 2. Intelligent and adaptive capabilities. The dynamic threshold adjustment mechanism can automatically optimize the matching strategy according to different types of addresses, application scenarios and historical matching results. The context analysis submodule uses the regional information when processing addresses in batches to optimize the matching strategy, which improves the intelligence level of the system. The incremental learning mechanism enables the system to continuously learn from new data without complete retraining, thus maintaining the timeliness of the model.

[0066] 3. Improved data quality: The address normalization module converts non-standard addresses into standard format, improving data consistency. The address completion module can intelligently complete missing address information, enhancing data integrity. The error analysis submodule can identify common address error types and provide automatic correction suggestions, improving data accuracy.

[0067] 4. High efficiency and scalability. The distributed processing module enables the system to process large-scale address data in parallel on multiple computing nodes, significantly improving processing efficiency. The cloud computing platform implementation and API interface design make the system highly scalable and flexible, and can quickly adjust resource allocation according to demand. The collaborative work of the task scheduler, parallel processing engine and result aggregator ensures the efficiency and reliability of large-scale data processing.

[0068] 5. Explainability and transparency. The explainability module makes the matching process more transparent through attention weight-based visualization and decision path tracking mechanism. This explainability enhances users' trust in the system, especially in fields such as finance and government that have strict requirements on the decision-making process.

[0069] 6. Continuous optimization and self-improvement. The active learning module can identify difficult-to-match address pairs and request manual labeling to continuously improve system performance. The performance evaluation module and the optimization module form a closed loop. Through continuous monitoring and A / B testing, the system parameters are automatically adjusted to optimize performance. This self-improvement mechanism enables the system to adapt to the ever-changing address formats and naming rules.

[0070] 7. Comprehensive address processing capabilities. From data preprocessing, feature extraction to matching prediction, and then to post-processing and error analysis, the system provides a comprehensive address processing solution, supporting single address matching, batch address matching, address normalization, address completion and other functions to meet the needs of different scenarios.

[0071] 8. Easy to integrate and apply. The standardized API interface design makes the system easy to integrate with other business systems, improving the practicality and applicability of the system. Cloud platform deployment and containerization technology make the deployment and maintenance of the system simple and efficient.

[0072] 9. Security and reliability. Multi-layer security measures such as API key authentication, HTTPS encryption and Web application firewall are adopted to ensure the security of address data. The distributed architecture and breakpoint resumption mechanism improve the reliability of the system and can effectively handle potential problems in large-scale data processing.

[0073] 10. Broad application prospects. The system can be applied to e-commerce, logistics distribution, financial services, government management and other fields. It has huge commercial value and social benefits. Its flexibility and scalability enable it to quickly adapt to new application scenarios and needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is the overall interactive diagram of this application;

[0075] Figure 2 A partial interactive diagram of this application;

[0076] Figure 3 This is the workflow diagram of this application. DETAILED DESCRIPTION

[0077] Three embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0078] Example 1

[0079] like Figures 1 to 3 As shown, the deep learning-based address matching system proposed in this embodiment includes the following core modules: data preprocessing module, data generation module, feature extraction module, deep learning model, address matching module, distributed processing module, active learning module, incremental learning mechanism, performance evaluation module and optimization module. These modules work together to form a complete address matching solution.

[0080] The data preprocessing module is responsible for cleaning and standardizing the input address data. The specific steps include:

[0081] Remove special characters and extra spaces;

[0082] Standardize address formats (e.g., unify the representation of "street", "road", etc.);

[0083] Correct common spelling errors;

[0084] Split addresses into standardized components (such as province, city, district, street, etc.);

[0085] The data generation module generates an enhanced address data set based on string conversion operations. The specific conversion operations include:

[0086] Word replacement: replace certain words in the address with synonyms (such as "road" with "street");

[0087] Word removal: randomly remove non-keywords in the address;

[0088] Character addition: Add characters at random positions in the address to simulate input errors;

[0089] Character deletion: Randomly delete characters in the address to simulate missing information;

[0090] Arrangement: Change the order of address components, such as changing "province, city, district" to "city, province";

[0091] Word insertion: insert common modifiers into addresses, such as "new", "old", etc.;

[0092] Character replacement: replace certain characters with similar characters to simulate errors caused by visual similarity;

[0093] Abbreviation of words: Abbreviate common words, e.g., abbreviate "square" as "sq".

[0094] Repetition of words: Repeat certain words in the address to simulate repeated input.

[0095] Homophone substitution: Replace some characters with homophones to simulate errors caused by similar pronunciations.

[0096] The feature extraction module extracts character-level and word-level features from address data, supports multi-language processing, and extracts geographical features through a Geographic Information System (GIS) interface. The specific implementation is as follows:

[0097] Character embedding: Use a trainable embedding layer to map each character to a vector space of a fixed dimension (e.g., 64 dimensions), which helps to capture subtle differences and similarities at the character level.

[0098] Word embedding: Initialize an embedding layer using a pre-trained word vector model (such as Word2Vec or GloVe) to map each word to a high-dimensional vector space (e.g., 300 dimensions), which helps to capture semantic information at the word level.

[0099] Geographical feature extraction: Through the GIS API, obtain information such as the longitude and latitude coordinates and administrative division codes of the address, and convert this information into numerical features.

[0100] The deep learning model is the core of the system and includes the following components:

[0101] Text encoder: Use a bidirectional long short-term memory network (Bi-LSTM) or a Transformer encoder to encode the character-level and word-level feature sequences into a fixed-length vector representation.

[0102] Geographical encoder: Use a multi-layer perceptron (MLP) to encode geographical features into a vector of a fixed dimension.

[0103] Attention mechanism: Implement a self-attention mechanism that allows the model to focus on important parts when processing long addresses.

[0104] Multi-modal fusion network: Use a gated fusion mechanism to fuse text features and geographical features into a unified representation.

[0105] Classifier: Use a fully connected layer and a softmax activation function to output the probability of whether the address pair matches.

[0106] Pre-trained language model interface: Integrate pre-trained language models such as BERT or XLNet to initialize the text encoder or directly extract context-related word representations.

[0107] Transfer learning module: implements fine-tuning strategies that allow the model to quickly adapt to new address matching tasks on a small amount of annotated data.

[0108] The address matching module performs the actual matching operation and outputs the results, which includes:

[0109] Dynamic threshold adjustment mechanism: Dynamically adjust the matching threshold according to different types of addresses (such as urban addresses, rural addresses), application scenarios (such as express delivery, bank verification) and historical matching results. The specific implementation can use the Bayesian optimization algorithm.

[0110] Error analysis submodule: Uses rule engines and machine learning algorithms to identify common types of address errors (such as missing house numbers, misspellings of street names, etc.) and provide automatic correction suggestions.

[0111] Working process:

[0112] The system first receives an input address pair.

[0113] The data preprocessing module cleans and standardizes the input addresses.

[0114] The data generation module generates augmented training data using various string transformation operations.

[0115] The feature extraction module extracts character-level, word-level, and geographic features from the preprocessed addresses.

[0116] The deep learning model processes these features: the text encoder and geo encoder encode text and geographic features respectively, the attention mechanism captures the important relationships between features, the multimodal fusion network fuses different types of features, and the classifier outputs matching prediction results.

[0117] The address matching module determines the final matching result based on the prediction result and the dynamically adjusted threshold.

[0118] The error analysis submodule analyzes the mismatched addresses and provides correction suggestions.

[0119] The system continuously learns and optimizes. The active learning module identifies hard-to-match address pairs and requests manual annotation. The incremental learning mechanism updates the model with newly annotated data without the need for complete retraining. The performance evaluation module continuously monitors system performance and conducts A / B testing. The optimization module automatically adjusts system parameters based on test results.

[0120] High accuracy: Through deep learning models and multimodal feature fusion, the system can handle complex and ambiguous address matching situations and significantly improve the matching accuracy.

[0121] Robustness: The augmented dataset created by the data generation module improves the model’s adaptability to various address variations and errors.

[0122] Multi-language support: The design of the feature extraction module enables the system to process addresses in different languages ​​and has wide applicability.

[0123] Adaptive capability: Dynamic threshold adjustment and incremental learning mechanisms enable the system to adapt to different scenarios and continuously optimize performance.

[0124] Explainability: The error analysis submodule provides explanations and correction suggestions for matching results, increasing the credibility and practicality of the system.

[0125] Scalability: The distributed processing module enables the system to handle large-scale address data and meet high concurrency requirements.

[0126] Sustainable optimization: Active learning, performance evaluation, and optimization modules form a closed loop that enables the system to continuously improve performance.

[0127] It provides a comprehensive, efficient and reliable solution to the address matching problem. It can not only handle the current address matching needs, but also has the ability to continuously learn and optimize, preparing for future application scenarios.

[0128] Example 2

[0129] like Figures 1 to 3 As shown, based on Example 1, the address normalization module converts the non-standard address into a standard format, which is specifically implemented as follows:

[0130] Rule engine: Based on the predefined rule base, it processes common non-standard expressions. For example, it converts "No. 1 Zhongguancun Street, Haidian District, Beijing" into "No. 1 Zhongguancun Street, Haidian District, Beijing".

[0131] Machine Learning Model: Use a sequence labeling model (such as Conditional Random Fields (CRF) or BiLSTM-CRF) to identify the components of the address.

[0132] Address component reassembly: Reassemble the identified address components into a standard format according to a standardized template.

[0133] The address completion module is used to complete the missing address information. The implementation method is as follows:

[0134] Contextual reasoning: Use existing address information to infer missing parts, for example, infer the district or county based on the street name.

[0135] Geocoding reverse query: Use the Geographic Information System API to query complete address data through known address information.

[0136] Historical data matching: Use the historical address database to find the most similar complete address for completion.

[0137] Deep learning prediction: Train a sequence-to-sequence (Seq2Seq) model to learn the mapping from incomplete addresses to complete addresses.

[0138] The context analysis submodule uses the regional information when processing addresses in batches to optimize the matching strategy, as follows:

[0139] Regional clustering: Use a clustering algorithm (such as K-means or DBSCAN) to perform regional clustering on batch addresses.

[0140] Context feature extraction: Extract regional features for each address, such as the center point of the cluster to which it belongs, the address density of the area, etc.

[0141] Dynamic weight adjustment: Dynamically adjust the weights of each component of the address during the matching process based on contextual features. For example, in an area with dense addresses, more emphasis may be placed on matching house numbers.

[0142] Matching threshold adaptation: Automatically adjust the matching threshold according to regional characteristics to balance precision and recall.

[0143] The explainability module is used to explain the reasons for matching decisions and increase the transparency of the system:

[0144] Attention weight-based visualization visualizes the attention weights in the deep learning model, showing the parts of the address that the model focuses on during the matching process, and using a heat map to show the importance of address characters or words.

[0145] The decision path tracking mechanism records the activation values ​​and weights of each layer within the model, builds a decision tree, and provides an interactive interface that allows users to explore the decision path from input to output.

[0146] Feature contribution analysis uses SHAP (SHapley Additive exPlanations) values ​​to calculate the contribution of each feature to the final decision and generate a horizontal bar chart to intuitively display the impact of each feature.

[0147] The distributed processing module is used to process large-scale address data and includes the following components:

[0148] The task scheduler implements load balancing algorithms, such as Round Robin or Least Connection, and dynamically allocates address matching tasks based on the processing capacity and current load of each computing node.

[0149] The parallel processing engine deploys an independent address matching processing unit on each computing node and uses a message queue (such as Apache Kafka) to achieve inter-node communication and task distribution.

[0150] The result aggregator implements the Map-Reduce mode, collects the matching results of each node, uses the consistent hashing algorithm to ensure the consistency and integrity of the results, implements the breakpoint resume mechanism, and ensures the reliability of large-scale processing.

[0151] Cloud computing platform implementation and API interface;

[0152] The system is implemented through a cloud computing platform and provides an API interface:

[0153] Cloud platform deployment uses container technology (such as Docker) to encapsulate various system modules, and uses Kubernetes for container orchestration and management to achieve automatic expansion and reduction.

[0154] API interface, implements RESTful API, supports the following functions: single address matching request, batch address matching request, address normalization request, address completion request and use API gateway (such as Nginx) to manage request routing and load balancing.

[0155] Security assurance: implement API key authentication and HTTPS encryption, and deploy WAF (Web Application Firewall) to defend against common Web attacks.

[0156] Performance optimization, implement caching mechanism (such as Redis), improve the matching speed of popular addresses, and use CDN to accelerate API access speed.

[0157] Working process:

[0158] When the system receives an address matching request, it first authenticates and distributes the request through the API gateway.

[0159] For non-standard addresses, the address normalization module performs preprocessing as follows:

[0160] The rule engine is applied to process common non-standard expressions, using machine learning models to identify address components, reorganize address components, and generate standardized formats.

[0161] If the address information is detected to be incomplete, the address completion module processes it as follows:

[0162] Try to complete the missing information through contextual reasoning. If reasoning is not possible, use the geocoding API or historical data matching to complete it. For complex cases, use deep learning models to predict the missing parts.

[0163] For batch address matching requests, the context analysis submodule plays a role as follows:

[0164] Perform regional clustering on addresses, extract context features, and dynamically adjust matching strategies and thresholds based on the context.

[0165] The task scheduler distributes processing tasks to multiple computing nodes.

[0166] The parallel processing engine performs the address matching operation on each node.

[0167] The result aggregator collects and integrates the matching results of each node.

[0168] The explainability module generates explanations for matching decisions as follows:

[0169] Generate visual representations of attention weights, construct decision paths, and calculate feature contributions.

[0170] The system returns a matching result, including a matching score, a normalized address, completion information (if any), and a decision explanation.

[0171] Improve data quality: The address normalization and completion module significantly improves the quality and consistency of input data, laying the foundation for the subsequent matching process.

[0172] Enhanced matching accuracy: The context analysis submodule improves the matching accuracy and adaptability by utilizing the regional information of batch addresses.

[0173] Increased transparency: The explainability module makes the system's decision-making process more transparent and enhances users' trust in the system.

[0174] Improve processing capabilities: The distributed processing module greatly improves the system's ability to process large-scale address data and meet high concurrency requirements.

[0175] Flexible deployment: The cloud computing platform makes the system highly scalable and flexible, and can quickly adjust resource configuration according to demand.

[0176] Easy to integrate: The standardized API interface makes the system easy to integrate with other business systems, improving the practicality and applicability of the system.

[0177] Performance optimization: Through caching mechanism and CDN acceleration, the system can respond to frequent address matching requests more quickly.

[0178] Safe and reliable: Multi-layer security measures are adopted to ensure the security of address data and the reliability of the system.

[0179] The functions and performance of the deep learning-based address matching system have been enhanced, which not only improves the accuracy and efficiency of address matching, but also increases the interpretability, scalability and ease of use of the system, which enables the system to adapt to various complex practical application scenarios and provide users with comprehensive, efficient and reliable address matching solutions.

[0180] Example 3

[0181] like Figures 1 to 3 As shown, based on Examples 1 and 2, this example describes in detail the complete workflow of an address matching system based on deep learning. The method includes steps such as data preprocessing, feature extraction, deep learning matching, active learning, address normalization and completion, error analysis, context optimization, distributed processing, and continuous optimization.

[0182] S1. Preprocess and standardize the input address data

[0183] Use regular expressions to remove special characters and extra spaces; expand common address abbreviations (such as converting "st." to "street"); use dictionaries to convert addresses to a unified format (such as converting "avenue", "ave", and "av." to "av"); use word segmentation algorithms (such as the Jieba word segmenter) to split addresses into words or characters;

[0184] S2. Generate an enhanced address dataset using the data generation module

[0185] Apply random character replacement: replace a character in the address with a probability of 5%; Apply random word removal: remove non-keywords in the address with a probability of 10%; Apply homophone replacement: use a predefined homophone dictionary to replace a word in the address with a probability of 15%; Apply address component rearrangement: change the order of address components with a probability of 20%; Generate 5-10 variants for each original address to expand the training data set;

[0186] S3. Extract text features and geographic features of the address and support multi-language processing

[0187] Text feature extraction: Use the pre-trained Word2Vec model to convert each word into a 300-dimensional vector; use character-level CNN to extract character-level features, with convolution kernel sizes of 3, 4, and 5, 128 for each type;

[0188] Geographic feature extraction: Use the geocoding API to obtain the longitude and latitude coordinates of the address; calculate the postal code and administrative division code of the address

[0189] Multi-language support: Use language detection libraries (such as langdetect) to identify the address language; select the corresponding pre-trained word vector model and word segmenter according to the identified language;

[0190] S4. Use deep learning models to process features and output matching prediction results

[0191] Model structure: text features are processed by a bidirectional LSTM layer (128 hidden units); geographic features are processed by two fully connected layers (64 and 32 neurons); text and geographic features are fused using an attention mechanism; finally, matching probabilities are output through two fully connected layers (64 and 2 neurons)

[0192] Training process: Use cross entropy loss function; use Adam optimizer, learning rate set to 0.001; batch size set to 64, training 50 rounds;

[0193] S5. Determine the final matching result based on the predefined threshold or dynamically adjusted threshold

[0194] The initial matching threshold is set to 0.5;

[0195] Dynamic threshold adjustment: Use Bayesian optimization algorithm to adjust the threshold based on historical matching results; the optimization target is F1 score; the threshold is updated every 1,000 matches

[0196] S6. Use the active learning module to identify hard-to-match address pairs. Calculate the closeness between the matching probability output by the model and the threshold, and select the top 10% address pairs with the probability closest to the threshold as hard-to-match samples.

[0197] S7. Request manual annotation of address pairs that are difficult to match; design a manual annotation interface that displays the address pairs and the model's preliminary judgment; allow the annotator to select "match", "do not match" or "cannot determine"; record the annotation time for evaluating the annotation quality.

[0198] S8. Update the deep learning model using newly annotated data

[0199] Add the newly annotated data to the training set;

[0200] Update the model using incremental learning methods: fix the parameters of the first few layers of the model; only fine-tune the last two fully connected layers; use a smaller learning rate (such as 0.0001) for 5 epochs of training;

[0201] S9. Normalize non-standard addresses

[0202] Use rule engines to handle common non-standard expressions; apply sequence labeling models (such as BiLSTM-CRF) to identify the components of the address; reorganize address components according to standard templates;

[0203] S10. Complete the address of missing information

[0204] Use the Conditional Random Field (CRF) model to predict missing address components; use the Geocoding API to reverse query the complete address information; use the nearest neighbor algorithm to find the most similar complete address from historical data for completion;

[0205] S11. Analyze matching errors and provide automatic correction suggestions

[0206] Build an error type classifier to classify errors into categories such as spelling errors, missing information, outdated information, etc.

[0207] Provides corresponding correction suggestions based on the error type:

[0208] Spelling errors: Use the edit distance algorithm to provide the closest correct spelling;

[0209] Missing information: prompts users to fill in specific missing fields;

[0210] Outdated information: Provides the latest address information update suggestions;

[0211] S12. Optimizing the matching strategy of batch addresses using context information

[0212] Use the DBSCAN algorithm to cluster batch addresses; calculate the center point and address density of each cluster;

[0213] Dynamically adjust the matching weight according to the clustering characteristics: in high-density areas, increase the matching weight of house numbers; in low-density areas, increase the matching weight of street names.

[0214] S13. Parallel processing of large-scale address data on multiple computing nodes

[0215] Use Apache Spark as a distributed computing framework; divide address data into multiple partitions, and assign each partition to a computing node; perform address matching tasks in parallel on each node; use Spark's reduceByKey operation to aggregate the matching results of each node;

[0216] S14. Continuously monitor system performance and conduct A / B testing

[0217] Real-time monitoring of key performance indicators: accuracy, recall, F1 score, processing speed; set performance alert thresholds and automatically notify when indicators drop;

[0218] Conduct A / B testing:

[0219] Randomly select 5% of the traffic to use the new version of the algorithm; compare the performance indicators of the new and old versions; if the new version performs significantly better, expand the test scale;

[0220] S15. Automatically adjust system parameters to optimize performance based on test results

[0221] Automatically tune system parameters using Bayesian optimization algorithms:

[0222] Optimization goal: Comprehensively consider accuracy, recall rate and processing speed;

[0223] Adjustable parameters include: model structure, learning rate, batch size, matching threshold, etc.

[0224] Perform comprehensive parameter optimization regularly (e.g. weekly);

[0225] Monitor performance in real time and trigger immediate optimization when performance degradation is detected;

[0226] Working process:

[0227] After receiving the address matching request, the system first executes S1 and S3 to preprocess and extract features of the address.

[0228] For the training phase, S2 is performed to generate enhanced training data.

[0229] Then execute S4 to process the features using the deep learning model and output preliminary matching results.

[0230] The system executes S5 to determine the final matching result according to the current threshold.

[0231] For address pairs that are difficult to match, the system executes S6 and S7 and requests manual labeling.

[0232] Using the newly labeled data, the system executes S8 to update the deep learning model.

[0233] If a non-standard address or missing information is encountered, the system executes S9 and S10 respectively for processing.

[0234] For matching errors, the system executes S11 to analyze and provide correction suggestions.

[0235] When processing batch addresses, the system executes S12 and optimizes the matching strategy using context information.

[0236] For large-scale data processing, the system executes S13 and processes in parallel on multiple nodes.

[0237] The system continuously executes S14 and S15, monitors performance, conducts A / B testing, and automatically optimizes parameters.

[0238] High accuracy: The accuracy of address matching is significantly improved through deep learning models and multi-feature fusion.

[0239] Strong robustness: Data augmentation and multi-language support enable the system to handle various complex and variant address forms.

[0240] Adaptability: Dynamic threshold adjustment and context optimization enable the system to adapt to different matching scenarios.

[0241] Continuous optimization: Active learning and incremental update mechanisms enable the system to continuously improve performance.

[0242] Intelligent error correction: Error analysis and automatic correction suggestion functions improve the practicality of the system.

[0243] High efficiency: The distributed processing architecture enables the system to efficiently process large-scale address data.

[0244] Scalability: Modular design and automatic parameter optimization make the system easy to expand and maintain.

[0245] Comprehensiveness: The complete process from data pre-processing to post-processing ensures the quality and reliability of the matching results.

[0246] In short, this deep learning-based address matching method provides a comprehensive, efficient and intelligent solution. It can not only accurately handle various complex address matching situations, but also has the ability of self-optimization and continuous learning, providing strong support for various application scenarios.

[0247] In view of current practical needs, the above-mentioned implementation mode adopted in this application is not limited to the scope of protection. Various changes made within the knowledge scope of technical personnel in this field without departing from the concept of this application still fall within the scope of protection of the present invention.

Claims

1. An address matching system based on deep learning, characterized in that: It includes data preprocessing module, data generation module, feature extraction module, deep learning model, address matching module, distributed processing module, active learning module, incremental learning mechanism, performance evaluation module and optimization module; The data preprocessing module is used to clean and standardize the input address data; The data generation module is used to generate an enhanced address data set based on a string conversion operation; The feature extraction module is used to extract character-level and word-level features from address data, and the module supports multi-language processing and extracts geographic features through a geographic information system interface; The deep learning model includes a text encoder, a geo encoder, an attention mechanism, a multimodal fusion network and a classifier; The text encoder is used to encode text features; The geocoder is used to encode geographic features; The attention mechanism is used to capture important relationships between features; The multimodal fusion network is used to fuse text features and geographic features; The classifier is used to output a matching prediction result; The address matching module is used to perform an address matching operation and output a matching result; The address matching module includes a dynamic threshold adjustment mechanism and an error analysis submodule; The dynamic threshold adjustment mechanism is used to automatically adjust and optimize the matching threshold according to different types of addresses, application scenarios and historical matching results; The error analysis submodule is used to identify common address error types and provide automatic correction suggestions; The distributed processing module is used to process large-scale address data in parallel on multiple computing nodes; The active learning module is used to identify difficult-to-match address pairs and request manual labeling; The incremental learning mechanism is used to continuously learn from new data without retraining the entire model; The performance evaluation module is used to continuously monitor system performance and perform A / B testing; The optimization module is used to automatically adjust system parameters according to the test results.

2. The address matching system based on deep learning according to claim 1, characterized in that: The deep learning model also includes a pre-trained language model interface and a transfer learning module; The pre-trained language model interface is used to perform feature extraction using a large-scale pre-trained language model; The transfer learning module is used to transfer the knowledge of the pre-trained model to the address matching task.

3. The address matching system based on deep learning according to claim 1, characterized in that: It also includes an address normalization module and an address completion module; The address normalization module is used to convert non-standard addresses into a standard format; The address completion module is used to complete the missing address information.

4. The address matching system based on deep learning according to claim 1, characterized in that: The address matching module also includes a context analysis submodule for optimizing the matching strategy by utilizing the regional information when processing addresses in batches.

5. The address matching system based on deep learning according to claim 1, characterized in that: An explainability module is also included for explaining the reasons for the matching decisions, and the explainability module includes a visualization and decision path tracing mechanism based on attention weights.

6. The address matching system based on deep learning according to claim 1, characterized in that: The string conversion operations adopted by the data generation module include word replacement, word deletion, character addition, character deletion, arrangement, word insertion, character replacement, word abbreviation, word repetition and homophone replacement.

7. The address matching system based on deep learning according to claim 1, characterized in that: The feature extraction module converts the address into a vector representation using character embedding and word embedding methods, wherein the character embedding is used to capture character-level information; and the word embedding is used to capture word-level semantic information.

8. The address matching system based on deep learning according to claim 1, characterized in that: The distributed processing module includes a task scheduler, a parallel processing engine and a result aggregator; The task scheduler is used to distribute large-scale address data to multiple computing nodes; The parallel processing engine is used to simultaneously execute address matching tasks on each computing node; The result aggregator is used to collect and integrate the matching results of each node.

9. The address matching system based on deep learning according to claim 1, characterized in that: The system is implemented through a cloud computing platform and provides an API interface for external systems to call address matching services. The API interface supports single address matching requests, batch address matching requests, address normalization requests, and address completion requests.

10. An address matching system based on deep learning according to any one of claims 1 to 9, characterized in that: The address matching method includes the following steps: S1. Preprocess and standardize the input address data; S2. Generate an enhanced address dataset using a data generation module; S3. Extract text features and geographic features of the address and support multi-language processing; S4. Use the deep learning model to process the features and output matching prediction results; S5. Determine the final matching result according to a predefined threshold or a dynamically adjusted threshold; S6. Using an active learning module to identify hard-to-match address pairs; S7. Request manual annotation of address pairs that are difficult to match; S8. Update the deep learning model using the newly annotated data; S9. Standardize non-standard addresses; S10. Complete the address of the missing information; S11. Analyze matching errors and provide automatic correction suggestions; S12. Optimizing the matching strategy of batch addresses using context information; S13. Processing large-scale address data in parallel on multiple computing nodes; S14. Continuously monitor system performance and conduct A / B testing; S15. Automatically adjust system parameters to optimize performance based on test results; S16. Detect and adapt to the new address format and naming rules, and update the processing logic of the data generation module and feature extraction module.

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