Contact database construction method and system based on data integration and block chain evidence storage

Abstract

The invention provides a contact database construction method and system based on data integration and block chain evidence storage, and belongs to the field of public service data governance and block chain application. Inputting into a data integration engine, and after acquisition, cleaning conversion and association fusion processing, outputting a standardized customer data set; inputting contact information data in the standardized customer data set into a block chain data processing system to generate a customer contact information reference library; based on the customer contact information reference library, abnormal monitoring is carried out on customer contact information through an intelligent checking system, and optimal contact information recommendation is generated; and obtaining a customer archive abnormal data governance demand, inputting the demand into a governance platform, and outputting a governance effect assessment including a KPI index. According to the method, the problems of fragmentation, hysteresis and low credibility in public utility customer data management are effectively solved, and the method has relatively high practical value and popularization prospect.

Description

Technical Field

[0001] This invention belongs to the field of public utility data governance and blockchain application technology, and in particular relates to a method and system for constructing a linked database based on data integration and blockchain evidence storage. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of the digital economy and the deepening of the digital transformation of public services, customer data governance in the public utility sector (including electricity, water, gas, and other industries) is facing unprecedented challenges and opportunities. The accuracy and timeliness of customer contact information are directly related to the service quality and operational efficiency of key businesses such as electricity bill collection, power outage notifications, and emergency services.

[0004] Currently, public utility companies face the following main challenges in customer contact data governance: First, customer data is scattered across multiple independent business systems such as marketing, bill payment, and internet services, forming data silos. These data vary significantly in format, update frequency, and quality standards, making it difficult to generate a unified and accurate customer profile view. Existing technical solutions mostly employ centralized data cleaning or distributed system synchronization; however, centralized solutions struggle to handle the complex fusion and real-time requirements of multi-source heterogeneous data, while distributed solutions fall short in ensuring data consistency.

[0005] Secondly, traditional data governance solutions lack effective and reliable evidence storage and audit traceability mechanisms. Manual modifications to customer contact information or the synchronization process between systems lack immutable records, making it difficult to provide a legally valid chain of evidence in the event of data disputes or compliance audits. While blockchain technology offers a new path for data evidence storage, existing applications in this field mostly remain at the level of simply storing data hash values, failing to deeply integrate with business rules. This results in problems such as low evidence storage efficiency, insufficient smart contract performance, and inconvenient querying, failing to meet the real-time and ease-of-use requirements of business systems.

[0006] Finally, in terms of monitoring and optimizing customer contact information, current methods generally rely on manual experience for screening and judgment. This approach is not only inefficient, but also lacks intelligent analysis of multi-dimensional behavioral data (such as payment records, SMS interaction status, channel preferences, etc.), making it difficult to accurately identify abnormal contact methods (such as invalid numbers, multiple accounts for one number, etc.). Furthermore, it fails to provide frontline staff with scientific and reliable recommendations for optimal contact methods, resulting in low customer service reach and high operating costs. Summary of the Invention

[0007] To overcome the shortcomings of existing technologies, this invention provides a method and system for constructing a contact database based on data integration and blockchain notarization. By building a multi-source data fusion engine and a dual-chain collaborative notarization architecture, the real-time performance, accuracy, and tamper-proof nature of customer contact data are enhanced. Blockchain notarization ensures the traceability of data throughout its entire lifecycle, significantly improving the automation level and decision-making reliability of utility customer data governance. Adopting an integrated architecture of data acquisition, intelligent analysis, blockchain notarization, and closed-loop governance, end-to-end integration from raw data to business decisions is achieved, providing utility companies with a brand-new customer data governance solution.

[0008] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: The first aspect of this invention provides a method for constructing a connection database based on data integration and blockchain evidence storage; Methods for constructing a connection database based on data integration and blockchain notarization include: Acquire raw customer data from multiple business systems; input the raw data into a data integration engine, and after collection, cleaning, transformation and correlation fusion processing, output a standardized customer dataset; The contact information data in the standardized customer dataset is input into the blockchain data processing system. After main chain notarization, side chain integration and intelligent service processing, a customer contact information reference library is generated. Based on the aforementioned customer contact information reference library, an intelligent verification system is used to detect anomalies in customer contact information and generate optimal contact information recommendations. Obtain customer profile abnormal data governance requirements, input the requirements into the governance platform, and after monitoring and early warning, task scheduling and effect evaluation, output a governance effectiveness evaluation including KPI indicators.

[0009] As a further technical solution, the data integration engine includes a data acquisition module, a cleaning and transformation module, and a correlation and fusion module connected in sequence; The data acquisition module includes a streaming data acquisition submodule and a batch data acquisition submodule; the streaming data acquisition submodule achieves real-time data capture through message queues, stream processing engines, and real-time caching; the batch data acquisition submodule achieves periodic acquisition of historical data through a task scheduler, data extractor, and temporary storage area. The cleaning and conversion module includes a data verification unit, a data repair unit, and a standardization processing unit connected in sequence, which sequentially perform data integrity and accuracy checks, problem data repair, and data format standardization conversion. The association and fusion module includes an entity recognition unit, a relationship building unit, and a data fusion unit connected in sequence, which sequentially realize cross-system customer matching, multi-dimensional data association, data conflict handling, and customer file merging.

[0010] As a further technical solution, the blockchain data processing system includes a main chain evidence storage module, a side chain fusion module, and a smart service module connected in sequence. The main chain notarization module includes a data verification submodule and a consensus notarization submodule. The data verification submodule performs data format standardization, compliance verification, and privacy encryption processing in sequence through a format validator, a business rule generator, and a data encryptor. The consensus notarization submodule achieves data consistency notarization through a data receiving pool, a consensus engine with optimized PBFT algorithm, and a block generator with parallel computing. The sidechain fusion module includes a customer profile builder, a credibility evaluator, and an index optimizer. The customer profile builder uses graph database technology to aggregate data from all channels for the same customer. The credibility evaluator calculates contact information scores through a machine learning model. The index optimizer builds an efficient query structure. The intelligent service module includes a query analyzer, a recommendation engine, and an application interface. The query analyzer supports natural language processing and semantic understanding, the recommendation engine generates recommendation results by combining scene features and scores, and the application interface supports blockchain verification and output of evidence.

[0011] As a further technical solution, the intelligent verification system includes an anomaly monitoring module and an intelligent recommendation module connected in sequence.

[0012] As a further technical solution, the anomaly monitoring module includes a basic verification submodule and a business consistency submodule. The basic verification submodule verifies the standardization of the number format and outputs an anomaly list. The business consistency submodule labels the mobile phone number based on multiple rule tags and dynamically updates the incremental tags. The intelligent recommendation module aggregates mobile phone numbers from multiple channels such as archives and payment to establish a reference database, generates recommendation results through a confidence model, and is equipped with a visual verification interface, an automated update process, and a closed-loop feedback optimization mechanism.

[0013] As a further technical solution, the governance platform includes a monitoring and early warning module, a task scheduling module, and an effect evaluation module connected in sequence.

[0014] As a further technical solution, the real-time monitoring submodule of the monitoring and early warning module generates abnormal alarm events through a data collector, a rule engine, and an alarm processor; The task scheduling module matches an appropriate processing flow according to the warning level, drives task execution through the workflow engine, and sends back the results. The performance evaluation module provides quantitative analysis of the execution results, and the platform is equipped with a verification interface, intelligent outbound calling tools, and a closed-loop optimization mechanism.

[0015] The second aspect of this invention provides a system for constructing a connection database based on data integration and blockchain evidence storage.

[0016] A database construction system based on data integration and blockchain notarization includes: The standardized customer dataset output module is configured to: acquire raw customer data from multiple business systems; input the raw data into a data integration engine; and output a standardized customer dataset after collection, cleaning, transformation, and correlation fusion processing. The customer contact information reference library generation module is configured to: input the contact information data in the standardized customer dataset into the blockchain data processing system, and generate the customer contact information reference library after main chain notarization, side chain integration and intelligent service processing; The anomaly detection and optimal contact method recommendation module is configured to: based on the customer contact method reference library, use an intelligent verification system to detect anomalies in customer contact methods and generate optimal contact method recommendations; The governance effectiveness evaluation module is configured to: obtain the governance requirements of abnormal data in customer files, input the requirements into the governance platform, and after monitoring and early warning, task scheduling and effect evaluation, output a governance effectiveness evaluation containing KPI indicators.

[0017] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the method for constructing a linked database based on data integration and blockchain evidence storage as described in the first aspect of the present invention.

[0018] A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the method for constructing a linked database based on data integration and blockchain evidence storage as described in the first aspect of the present invention.

[0019] The above one or more technical solutions have the following beneficial effects: This invention achieves real-time, automated integration of multi-source, heterogeneous customer data by constructing a data integration engine that combines streaming and batch data acquisition. The cleaning and transformation module, along with the association and fusion module, effectively breaks down data silos between different business systems through format standardization, anomaly repair, entity recognition, and relationship building. This generates a high-quality, unified data view centered on the customer, laying a reliable data foundation for subsequent analysis and applications. The data integration efficiency is significantly improved compared to traditional methods.

[0020] By introducing a dual-chain blockchain architecture that integrates main-chain evidence storage and sidechains, this invention not only utilizes the distributed consensus and encryption technology of the main chain to ensure the immutability of data change records, but also achieves efficient indexing and aggregated querying of evidence data through the sidechain. This design, while ensuring data security and trustworthiness, effectively overcomes the shortcomings of traditional blockchain solutions, such as low evidence storage performance and inconvenient querying. It provides a complete and reliable chain of evidence for business disputes and compliance audits, reducing data verification time from hours to seconds.

[0021] The dynamic monitoring and intelligent recommendation model constructed in this invention can automatically and accurately identify abnormal contact methods by establishing a multi-dimensional, dynamically updatable rule and tag system. Furthermore, based on the constructed dynamic intelligent customer contact database, it comprehensively utilizes multi-objective optimization algorithms such as credibility assessment and business scenario matching to intelligently recommend the optimal contact method for frontline staff. This effectively solves the problems of low efficiency and poor accuracy in traditional manual screening methods, significantly improving customer service reach and success rates.

[0022] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0023] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0024] Figure 1 This is a flowchart of the method in the first embodiment.

[0025] Figure 2 This is a system structure diagram of the second embodiment. Detailed Implementation

[0026] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0027] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0028] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0029] Example 1 This embodiment discloses a method for constructing a connection database based on data integration and blockchain evidence storage; like Figure 1 As shown, the method for constructing a connection database based on data integration and blockchain notarization includes: Step S1: Obtain raw customer data from multiple business systems; input the raw data into the data integration engine, and after collection, cleaning, transformation and correlation fusion processing, output a standardized customer dataset.

[0030] Step S11: Obtain raw customer data from various business platforms, including the marketing system, payment platform, internet service platform, and SMS platform. Marketing system data includes basic customer profiles (account number, account name, contact person type, contact person's mobile phone number, etc.) and electricity usage information (electricity address, account opening date, etc.). Payment platform data covers payment records from various channels (payment time, payment amount, payment mobile phone number, etc.). Internet service platform data (account number bound to mobile phone number, authentication status) and SMS platform data include uplink SMS messages (customer-initiated feedback information) and downlink SMS messages (system-sent notification records, including receiving mobile phone number, sending time, success / failure status). To ensure the comprehensiveness of the data sources, the raw data must cover historical records for the past three years and real-time business data.

[0031] Step S12: The raw data is input into the data integration engine for processing to obtain a standardized customer dataset. The data integration engine includes a data acquisition module, a cleaning and transformation module, and a correlation and fusion module connected in sequence. The raw data is input into the data acquisition module to obtain real-time streaming data and batch data, which are then processed uniformly into the cleaning and transformation module. The cleaning and transformation module performs format validation and anomaly handling on the various types of data to obtain standardized data. Then, it establishes correlation relationships across various dimensions with customers as the main body, generates a unified data view, obtains customer master data, and outputs the final standardized customer dataset.

[0032] The data acquisition module includes sub-modules for real-time data change acquisition and batch data acquisition. In this embodiment, for data changes with high real-time requirements, such as customers changing their mobile phone numbers in the marketing system, generating new payment records, and updating SMS sending status, the system captures these dynamic data in real time to ensure timely responses to changes in customer information.

[0033] Specifically, firstly, real-time data from the marketing system (such as phone number modification operations), real-time transaction data from the payment platform, and instant sending results from the SMS platform are input into a message queue for buffering and traffic control. When there are tens of thousands of concurrent data messages per second during peak business periods (such as the end-of-month payment period), the message queue achieves horizontal scaling through a partitioning mechanism to ensure that data is transmitted in chronological order and to avoid data loss or out-of-order transmission due to traffic fluctuations.

[0034] Secondly, the ordered data stream output from the message queue is input into the stream processing engine for real-time parsing and transformation. The stream processing engine has built-in parsers for different data sources: it automatically extracts fields such as "account number," "new mobile number," and "modification time" from data from the marketing system; it parses information such as "payment order number," "associated account number," and "payment mobile number" from data from the payment platform; and it extracts content such as "receiving number," "sending status," and "timestamp" from data from the SMS platform. Finally, it converts all types of data into unified structured event data.

[0035] Finally, the structured event data output by the stream processing engine is input into a real-time cache (using a Redis in-memory database) for temporary storage. This enables millisecond-level data read and write operations, providing low-latency data support for subsequent cleaning and transformation modules, and especially meeting the need for rapid retrieval of customers' latest contact information.

[0036] The batch data acquisition submodule comprises a task scheduler, a data extractor, and a temporary storage area connected in sequence. This submodule is primarily used to acquire historical accumulated data and non-real-time updated information, such as complete customer profile data from the past three years and monthly payment record summaries. Scheduled tasks ensure data integrity. Specifically, the processing steps of the batch data acquisition submodule are as follows: First, the data extraction task is triggered periodically via the task scheduler. For historical full data, a full extraction is configured to be performed once a month at 2:00 AM on the 1st; for incremental data (such as newly added customer files each day), incremental extraction is configured to be triggered daily at 0:30 AM (filtered based on data update timestamps). Manual triggering of emergency extraction tasks is also supported.

[0037] Secondly, a data extractor connects to the source system database to perform data extraction. For full extraction, a "table-level snapshot + batch export" mode is used to export complete data such as customer profile tables and payment record tables. For incremental extraction, by comparing the last update time field of the source table, only the data updated since the last extraction is extracted, reducing the amount of data transmission.

[0038] Finally, the extracted raw data is temporarily stored in a temporary storage area to support the application of the cleaning and transformation module.

[0039] The data cleaning and transformation module comprises a data verification unit, a data repair unit, and a standardization unit, connected sequentially. This module improves data quality through multiple steps, laying the foundation for subsequent data association and fusion. Specifically, the processing steps of the cleaning and transformation module are as follows: First, the data validation unit performs integrity, accuracy, and consistency checks on the data input to the real-time cache and temporary storage area. Integrity checks verify that each data entry contains required fields such as "Account Number" and "Contact Information," marking records with missing fields as "Incomplete." Accuracy checks validate the phone number format using rules (e.g., whether it is an 11-digit number), marking incorrectly formatted phone numbers as "Format Error." Consistency checks compare the same account number across different data sources, marking inconsistent records as "Conflict."

[0040] Secondly, the data repair unit automatically repairs or marks problematic data. For phone numbers with "incorrect format," a unified repair and supplementation process is performed based on a consistent strategy. For severely erroneous data that cannot be repaired (such as non-existent account number), it is directly marked as "invalid" and the reason for the error is recorded.

[0041] Finally, the repaired data is converted into a unified format and encoding through a standardized processing unit. Mobile phone numbers are uniformly converted to "11-digit pure numeric" format (removing the "+86" prefix or separator); dates are uniformly converted to "yyyy-MM-ddHH:mm:ss" format; at the same time, "data source identifier" (such as "marketing system" or "payment platform") and "processing timestamp" are added to all data to ensure subsequent traceability of data origin, and finally output standardized data.

[0042] Step S2: Input the contact information data in the standardized customer dataset into the blockchain data processing system. After main chain notarization, side chain integration and intelligent service processing, a customer contact information reference library is generated.

[0043] Step S21 involves deploying blockchain data collection nodes to connect in real-time with marketing systems, payment platforms, and internet service platforms to obtain multi-source contact information data, such as customer file phone numbers and pre-registered payment numbers. Before entering the blockchain network, all collected data undergoes standardized preprocessing via smart contracts: standardizing phone number formats (adding a +86 prefix and removing special characters), verifying basic compliance (11-digit verification), and attaching data source tags and timestamps. Operation logs generated during preprocessing are synchronously stored on the blockchain to ensure the traceability of the data collection process. The distributed nature of blockchain effectively prevents data tampering during the collection phase, providing a reliable data source for the reference library construction. Step S22: The preprocessed multi-channel contact information data enters the dual-chain storage system. At the main chain level, an improved PBFT consensus algorithm is used to package customer contact information change transactions onto the chain. Each block contains key information such as customer ID, contact type, and source system, and sensitive data is encrypted using the national cryptographic algorithm SM4. At the side chain level, a lightweight index structure with the customer ID as the key is constructed to aggregate all contact information of the same customer across different channels, including: marketing system registration number, most recent payment usage number, APP binding number, etc. The main and side chains are periodically synchronized via Merkle tree root hash, ensuring data consistency while improving contact information query efficiency by more than 10 times.

[0044] Step S23: Construct a contact information credibility assessment model based on blockchain-stored data. The model comprehensively considers the following dimensions: storage time (numbers verified within the last 3 months receive higher scores), channel weight (pre-registered numbers for payment have higher weight than ordinary registration numbers), and usage frequency (numbers frequently used for business transactions receive bonus points). The assessment results are written to the sidechain index via a smart contract and dynamically updated. The system adopts a multi-layered index structure: L1 cache stores highly credible contact information; L2 index covers all historical records and supports complex queries. All assessment processes and data updates are traced on the blockchain, ensuring the transparency and immutability of the assessment standards.

[0045] Step S24 provides a blockchain-based intelligent query service. When the business system submits a query request via API, the system first quickly locates all customer contact information from the sidechain index, and then automatically recommends the optimal contact channel based on the credibility score (e.g., prioritizing the number used for recent electricity bill payments). The query results are accompanied by a blockchain-based notarized certificate, containing verification information such as the contact information's on-chain time and change history. A feedback mechanism is also established to send the actual contact results (e.g., whether the call was successfully connected) back to the blockchain network for continuous optimization of the credibility assessment model. All query operations and feedback data are recorded on the blockchain, forming a complete application loop.

[0046] Step S3: Based on the customer contact information reference library, an intelligent verification system is used to monitor for anomalies in customer contact information and generate optimal contact information recommendations.

[0047] In step S31, the system collects multi-dimensional customer behavior data in real time, including basic information, transaction records, modification and maintenance records, etc. The collected raw data undergoes preprocessing operations such as data cleaning, format conversion, and feature extraction to provide a complete data foundation for subsequent analysis.

[0048] In step S32, the preprocessed customer data is sent to the anomaly monitoring module for analysis. This module establishes a contact information anomaly tag management system, setting rules and tags for categories such as whether anomalies are actively reported, whether the mobile phone number is standardized, whether there is a contact person, SMS sending status, whether there are multiple accounts for one number, comparison with mobile phone numbers bound to internet service platforms, and comparison with mobile phone numbers in payment records. Based on the account number, all mobile phone numbers are tagged. A comprehensive risk value is calculated according to preset scoring rules, and finally, a list of anomaly numbers with detailed risk descriptions is output.

[0049] Step S33 involves real-time aggregation of data from multiple channels, including mobile phone numbers registered in the marketing system, contact numbers reserved in the payment system, and authentication numbers bound to the internet service platform. Each number is labeled with its business source and usage scenario. A standardized mobile phone number reference library is then established through data cleaning and intelligent deduplication. Based on this, a dynamic evaluation model is built to comprehensively analyze the activity, credibility, and business relevance of numbers from various channels, automatically generating a priority ranking. This ultimately forms a continuously updated intelligent reference library. Based on information from different channels, a user-trusted mobile phone number recommendation model is built, recommending highly trustworthy mobile phone numbers to frontline staff. This provides optimal contact information for business scenarios such as electricity bill collection and emergency notifications, achieving closed-loop management across the entire chain from data collection to business application.

[0050] In step S34, the system displays the analysis results to business personnel through a visual interactive interface, mainly showing the analysis status and risk level of various anomalies. Business personnel can view detailed information such as the modification and verification status of each risk record, and the system can record all the operation trajectory of business personnel, providing feedback data for model optimization.

[0051] Step S4: Obtain the customer file abnormal data management requirements, input the requirements into the management platform, and after monitoring and early warning, task scheduling and effect evaluation, output the management effectiveness evaluation including KPI indicators.

[0052] In step S41, the intelligent monitoring module performs multi-dimensional analysis on the collected customer data. Using pre-set anomaly identification rules (including whether there is a contact person, whether anomalies are proactively reported, whether there are multiple accounts under one account, consistency with the internet service platform, and payment data), it provides real-time statistics on the total amount, type distribution, and processing status of abnormal data. The monitoring dashboard uses dynamic visualization technology to display the governance progress by region, business line, and other dimensions, supporting managers to accurately grasp the governance situation through drill-down analysis.

[0053] In step S42, when abnormal contact information is detected, the system automatically pushes the to-do task to the frontline staff's workbench. After the staff enters the "File Correction" page, the system intelligently displays all contact information for that customer's issues and related auxiliary information. Based on different priority rule engines, the system automatically recommends the optimal contact method option, with the recommendation results accompanied by a confidence score and usage history explanation to assist staff in making quick decisions.

[0054] Step S43: The self-service correction module provides a one-stop modification interface. After the staff selects a recommended number or manually enters a new number, the system automatically triggers the customer information update process. It uses a transaction mechanism to synchronously update the customer files of all related business systems, and informs the relevant business systems to update synchronously to ensure data consistency.

[0055] In step S44, the governance tracking module records the processing trajectory of each abnormal data in real time, including the discovery time, the personnel involved, the modified content, and the verification results—the entire process information. The system automatically compares data quality indicators before and after governance, generating a governance evaluation analysis that includes remediation rate and timeliness. Key operation logs are stored using blockchain technology to ensure the process is auditable.

[0056] Step S45: Build a governance support toolset, providing verification interface services and intelligent outbound calling capabilities. The verification interface connects to the databases of the three major telecom operators in real time, supporting batch number validity checks; the intelligent outbound calling system automatically initiates confirmation calls, verifies the recipient's identity through voice recognition technology, and stores the call results in a structured format. Tool call records are automatically linked to customer profiles, forming a complete chain of verification evidence.

[0057] In step S46, the system establishes a closed-loop optimization mechanism, feeding back the decision data generated during the governance process to the recommendation model. Model parameters are continuously optimized through incremental learning, and the weight configuration of rules is continuously improved to enhance the accuracy of intelligent recommendations. Simultaneously, based on historical governance data analysis of abnormal patterns, the monitoring rule thresholds are dynamically adjusted to achieve adaptive optimization of the governance strategy.

[0058] Example 2 This embodiment discloses a system for constructing a connection database based on data integration and blockchain evidence storage; like Figure 2 As shown, a database construction system based on data integration and blockchain notarization includes: The standardized customer dataset output module is configured to: acquire raw customer data from multiple business systems; input the raw data into a data integration engine; and output a standardized customer dataset after collection, cleaning, transformation, and correlation fusion processing. The customer contact information reference library generation module is configured to: input the contact information data in the standardized customer dataset into the blockchain data processing system, and generate the customer contact information reference library after main chain notarization, side chain integration and intelligent service processing; The anomaly detection and optimal contact method recommendation module is configured to: based on the customer contact method reference library, use an intelligent verification system to detect anomalies in customer contact methods and generate optimal contact method recommendations; The governance effectiveness evaluation module is configured to: obtain the governance requirements of abnormal data in customer files, input the requirements into the governance platform, and after monitoring and early warning, task scheduling and effect evaluation, output a governance effectiveness evaluation containing KPI indicators.

[0059] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0060] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for constructing a linked database based on data integration and blockchain evidence as described in Example 1.

[0061] Example 4 The purpose of this embodiment is to provide an electronic device.

[0062] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the method for constructing a connection database based on data integration and blockchain notarization as described in Embodiment 1.

[0063] The steps and methods involved in the apparatuses of Embodiments 2, 3, and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0064] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0065] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for constructing a connection database based on data integration and blockchain notarization, characterized in that: include: Acquire raw customer data from multiple business systems; input the raw data into a data integration engine, and after collection, cleaning, transformation and correlation fusion processing, output a standardized customer dataset; The contact information data in the standardized customer dataset is input into the blockchain data processing system. After main chain notarization, side chain integration and intelligent service processing, a customer contact information reference library is generated. Based on the aforementioned customer contact information reference library, an intelligent verification system is used to detect anomalies in customer contact information and generate optimal contact information recommendations. Obtain customer profile abnormal data governance requirements, input the requirements into the governance platform, and after monitoring and early warning, task scheduling and effect evaluation, output a governance effectiveness evaluation including KPI indicators.

2. The method for constructing a connection database based on data integration and blockchain notarization as described in claim 1, characterized in that, The data integration engine includes a data acquisition module, a cleaning and transformation module, and a correlation and fusion module connected in sequence. The data acquisition module includes a streaming data acquisition submodule and a batch data acquisition submodule; the streaming data acquisition submodule achieves real-time data capture through message queues, stream processing engines, and real-time caching; the batch data acquisition submodule achieves periodic acquisition of historical data through a task scheduler, data extractor, and temporary storage area. The cleaning and conversion module includes a data verification unit, a data repair unit, and a standardization processing unit connected in sequence, which sequentially perform data integrity and accuracy checks, problem data repair, and data format standardization conversion. The association and fusion module includes an entity recognition unit, a relationship building unit, and a data fusion unit connected in sequence, which sequentially realize cross-system customer matching, multi-dimensional data association, data conflict handling, and customer file merging.

3. The method for constructing a connection database based on data integration and blockchain notarization as described in claim 1, characterized in that, The blockchain data processing system includes a main chain evidence storage module, a side chain fusion module, and a smart service module connected in sequence. The main chain notarization module includes a data verification submodule and a consensus notarization submodule. The data verification submodule performs data format standardization, compliance verification, and privacy encryption processing in sequence through a format validator, a business rule generator, and a data encryptor. The consensus notarization submodule achieves data consistency notarization through a data receiving pool, a consensus engine with optimized PBFT algorithm, and a block generator with parallel computing. The sidechain fusion module includes a customer profile builder, a credibility evaluator, and an index optimizer. The customer profile builder uses graph database technology to aggregate data from all channels for the same customer. The credibility evaluator calculates contact information scores through a machine learning model. The index optimizer builds an efficient query structure. The intelligent service module includes a query analyzer, a recommendation engine, and an application interface. The query analyzer supports natural language processing and semantic understanding, the recommendation engine generates recommendation results by combining scene features and scores, and the application interface supports blockchain verification and output of evidence.

4. The method for constructing a connection database based on data integration and blockchain notarization as described in claim 1, characterized in that, The intelligent verification system includes an anomaly monitoring module and an intelligent recommendation module connected in sequence.

5. The method for constructing a connection database based on data integration and blockchain notarization as described in claim 4, characterized in that, The anomaly monitoring module includes a basic verification submodule and a business consistency submodule. The basic verification submodule verifies the standardization of the number format and outputs an anomaly list. The business consistency submodule labels the mobile phone number based on multiple rule tags and dynamically updates the incremental tags. The intelligent recommendation module aggregates archives and mobile phone numbers from multiple payment channels to establish a reference database, generates recommendation results through a confidence model, and is equipped with a visual verification interface, an automated update process, and a closed-loop feedback optimization mechanism.

6. The method for constructing a connection database based on data integration and blockchain notarization as described in claim 1, characterized in that, The governance platform includes a monitoring and early warning module, a task scheduling module, and an effect evaluation module connected in sequence.

7. The method for constructing a connection database based on data integration and blockchain notarization as described in claim 6, characterized in that, The real-time monitoring submodule of the monitoring and early warning module generates abnormal alarm events through a data collector, a rule engine, and an alarm processor. The task scheduling module matches an appropriate processing flow according to the warning level, drives task execution through the workflow engine, and sends back the results. The performance evaluation module provides quantitative analysis of the execution results, and the platform is equipped with a verification interface, intelligent outbound calling tools, and a closed-loop optimization mechanism.

8. A database construction system based on data integration and blockchain notarization, characterized in that: include: The standardized customer dataset output module is configured to: acquire raw customer data from multiple business systems; input the raw data into a data integration engine; and output a standardized customer dataset after collection, cleaning, transformation, and correlation fusion processing. The customer contact information reference library generation module is configured to: input the contact information data in the standardized customer dataset into the blockchain data processing system, and generate the customer contact information reference library after main chain notarization, side chain integration and intelligent service processing; The anomaly detection and optimal contact method recommendation module is configured to: based on the customer contact method reference library, use an intelligent verification system to detect anomalies in customer contact methods and generate optimal contact method recommendations; The governance effectiveness evaluation module is configured to: obtain the governance requirements of abnormal data in customer files, input the requirements into the governance platform, and after monitoring and early warning, task scheduling and effect evaluation, output a governance effectiveness evaluation containing KPI indicators.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the method for constructing a linked database based on data integration and blockchain notarization as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the method for constructing a linked database based on data integration and blockchain notarization as described in any one of claims 1-7.

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