Medital big data computer system based on AI matching algorithm
By constructing an AI-based marriage big data computer system, combined with multi-dimensional data verification and security protection, the problems of low matching success rate and insufficient privacy protection in existing marriage matching systems have been solved, and accurate and efficient marriage matching services have been achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NINGBO HAOGE INTERNET INFORMATION SERVICE CO LTD
- Filing Date
- 2026-01-29
- Publication Date
- 2026-05-19
AI Technical Summary
Existing marriage matching systems rely on surface information, lack in-depth analysis of potential influencing factors, have low matching success rates, difficulty in ensuring data authenticity, and insufficient privacy protection, thus failing to meet the needs for accurate matching.
A marriage big data computer system based on AI matching algorithms is constructed. Through multi-dimensional data collection and verification, combined with a three-dimensional correlation model of 'hidden number - positive number - mother and child number', reinforcement learning algorithms are used to optimize the matching logic, and multi-layer security protection mechanisms are introduced to ensure data accuracy and privacy protection.
It significantly improves the success rate of marriage registration, with a data collection accuracy rate of no less than 99%, and the matching accuracy rate dynamically improves with the duration of system use. User privacy is fully protected, and the system is convenient and highly secure.
Smart Images
Figure CN122064950A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the interdisciplinary field of marriage services and computer big data, specifically relating to a marriage big data computer system based on AI matching algorithms. Background Technology
[0002] With social development, the difficulty of marriage for eligible men and women has become a widespread social problem, affecting not only individual happiness but also the optimization of population structure and social stability. Traditional marriage introduction methods rely heavily on manual introductions, and matching criteria are often limited to superficial information such as age, education, and occupation, lacking in-depth exploration of the core factors influencing marriage matching, resulting in low matching success rates and serious waste of resources. Meanwhile, remarried individuals face more complex factors during the remarriage process, making it difficult for traditional matching methods to meet their needs for precise matching.
[0003] In existing technologies, some marriage matching systems only perform simple screening based on basic user information, without considering potential influencing factors such as family background and kinship, and lack scientific matching logic and algorithm support, resulting in highly subjective and unreliable matching results. Furthermore, existing systems generally suffer from problems such as difficulty in ensuring the authenticity of data collection, fixed matching algorithms that cannot be dynamically optimized, and insufficient protection of user privacy, thus hindering the quality and efficiency of marriage matching services.
[0004] Therefore, there is an urgent need for a marriage big data computer system that integrates multi-dimensional data, adopts advanced AI algorithms, and has comprehensive data security. By mining hidden correlations in birth years and family member-related data, a scientific matching model can be constructed to improve the success rate of marriage registration and provide efficient and accurate matching services for unmarried men and women. Summary of the Invention
[0005] The purpose of this invention is to provide a marriage big data computer system based on AI matching algorithms, which solves the problems of low success rate, unscientific matching logic, and poor data security in existing marriage matching methods, realizes intelligent, accurate and efficient marriage matching, improves the success rate of marriage registration for unmarried men and women, and meets the social demand for marriage services.
[0006] The specific technical solution adopted by this invention is as follows:
[0007] A marriage big data computer system based on AI matching algorithms includes a data acquisition module, a data preprocessing module, an AI matching algorithm module, a database module, a result output module, and a user interaction module. Each module achieves data interaction and collaborative work through a distributed network architecture. The system uses the birth years of unmarried men and women and their family members as core input, and combines AI algorithms to construct a three-dimensional correlation model of "hidden number - positive number - mother-child number." Through multi-dimensional data verification and logical matching, it outputs recommendations of suitable marriage partners and analysis of matching results. The birth years are defined as those from February 4th of the current year to February 3rd of the following year.
[0008] In a preferred embodiment, the data collection module includes a user self-reporting unit, a questionnaire survey unit, and a data verification unit. The user self-reporting unit allows users to enter their own birth year, the birth years of their family members, and basic personal information online. The questionnaire survey unit collects supplementary data through pre-set standardized questionnaires and supports online and offline multi-channel questionnaire distribution and collection. The data verification unit, with user authorization, connects to the public security household registration system interface to verify the authenticity of the core data such as the birth year and kinship, eliminating false data and invalid information to ensure that the data collection accuracy rate is not less than 99%.
[0009] In a preferred embodiment, the data preprocessing module includes a data cleaning unit, an encoding conversion unit, and a data standardization unit. The data cleaning unit uses an outlier detection algorithm to identify and process missing values, duplicate values, and logically contradictory data, and uses similarity-based interpolation to complete missing non-core data. The encoding conversion unit converts the collected birth years into corresponding "hidden numbers" according to preset year encoding rules. The "hidden numbers" are any digit from 0 to 9, and the encoding rules are based on a mapping relationship library between human birth years and hidden numbers. The data standardization unit converts data on family member relationships, birth year ranges, and hidden numbers into structured data in a unified format, generating a standardized data matrix to provide input data for the AI matching algorithm module.
[0010] In a preferred embodiment, the AI matching algorithm module includes a variable positive number calculation unit, a mother-child number derivation unit, a logical matching unit, and a dynamic optimization unit. The variable positive number calculation unit calculates the user's corresponding "variable positive number" based on the user's birth year and standardized "hidden number" data, referring to a preset "variable positive number positioning map," using a linear mapping algorithm. The mother-child number derivation unit derives the mother number in the "mother-child number" using a decision tree algorithm based on the association rule base between the "variable positive number" and the "mother-child number," and the mother number is the core indicator for achieving marriage matching. The logical matching unit follows the matching logic of "odd number of spouses, even number of spouses with odd number," constructs a bidirectional matching model, and simultaneously verifies whether the mother number required by both parties exists in the "hidden numbers" of their respective and each other's family members, generating preliminary matching results. The dynamic optimization unit introduces a reinforcement learning algorithm to continuously optimize the matching weight parameters based on historical matching success rates and user feedback data, so that the matching accuracy dynamically improves with the system's usage time.
[0011] In a preferred embodiment, the database module includes a basic database, an association rule database, a historical matching database, and a user behavior database. The basic database stores user personal information, family member information, birth year, and corresponding core data such as "hidden number," "positive number," and "mother-child number," employing a distributed storage architecture to ensure data security and access efficiency. The association rule database stores "hidden number-birth year" mapping rules, "positive number-birth year" corresponding rules, "mother-child number" association rules, and matching logic rules, supporting dynamic updates and expansions of the rules. The historical matching database records all users' matching records, matching results, and marriage certificate success rate data, providing data support for algorithm optimization. The user behavior database collects user login frequency, interaction operations, and matching result feedback behavior data, employing encrypted storage to protect user privacy.
[0012] In a preferred embodiment, the result output module includes a matching result generation unit, a visualization unit, and a report export unit. The matching result generation unit generates matching results based on the output of the AI matching algorithm module, including a list of matched objects, a matching compatibility score, and core matching criteria. The core matching criteria include the correspondence between the "hidden numbers," "positive numbers," and "mother-child numbers" of both parties. The visualization unit uses charts to display the matching logic chain, the distribution of "hidden numbers" among family members, and the composition of the compatibility score, allowing users to intuitively view the matching principles. The report export unit supports generating a PDF matching analysis report from the matching results, which users can download and save. The report includes data sources, algorithm logic, matching conclusions, and recommendations.
[0013] In a preferred embodiment, the user interaction module includes a registration and login unit, an information entry unit, a matching and query unit, a feedback interaction unit, and a privacy settings unit. The registration and login unit supports multiple registration and login methods, including mobile phone verification and facial recognition, ensuring account security. The information entry unit provides a guided entry interface, prompting users step-by-step to enter their own and their family members' information, and supports modification and supplementation of information. The matching and query unit allows users to filter matching results based on compatibility, region, and age, providing precise query functionality. The feedback interaction unit provides entry points for evaluating matching results satisfaction and providing feedback on certificate issuance results; user feedback data is synchronized to the dynamic optimization unit in real time. The privacy settings unit allows users to independently set the visibility range of information and employs anonymization processing for core privacy data to ensure that user information is not illegally disclosed.
[0014] In a preferred embodiment, the data verification unit further includes a kinship verification subunit. The kinship verification subunit analyzes the temporal logical relationship of the birth years of family members, including the requirement that the parents' birth year must be earlier than the children's preset number of years, and the grandparents' birth year must be earlier than the parents' preset number of years. Combined with the kinship information fed back by the public security household registration system, the kinship validity verification model is constructed to eliminate false kinship data and further improve data reliability.
[0015] In a preferred embodiment, the dynamic optimization unit further includes an abnormal data processing subunit. The abnormal data processing subunit uses the isolated forest algorithm to identify abnormal records in the historical matching data, including false certificate application feedback and abnormal matching results caused by data entry errors, and marks them as invalid data to avoid abnormal data interfering with algorithm optimization. At the same time, it generates abnormal data reports periodically for system administrators to check and process.
[0016] In a preferred embodiment, the system further includes a security protection module, which comprises a data encryption unit, an access control unit, a vulnerability protection unit, and a log auditing unit. The data encryption unit uses a symmetric encryption algorithm to encrypt stored data and employs a transport layer security protocol to ensure the security of data transmission. The access control unit uses a role-based access control model to assign different access permissions to system administrators and ordinary users to prevent unauthorized operations. The vulnerability protection unit regularly scans and repairs system vulnerabilities, supports emergency response mechanisms, and defends against malicious attacks. The log auditing unit records all system operation logs, with log retention time not less than 3 years.
[0017] The technical effects achieved by this invention are as follows:
[0018] This system constructs a three-dimensional correlation model of "hidden number - positive number - mother and child number", and combines AI algorithms with scientific matching logic to deeply explore the potential correlations in birth year and family member data. Compared with traditional matching methods, the matching basis is more scientific and comprehensive, significantly improving the success rate of marriage registration.
[0019] By connecting the data verification unit to the public security household registration system and combining it with the time logic verification of kinship, false data and invalid information are effectively eliminated, and the data collection accuracy rate is no less than 99%, providing solid data support for accurate matching.
[0020] By introducing reinforcement learning algorithms and anomaly data processing mechanisms, matching parameters are continuously optimized based on historical matching success rates and user feedback, and abnormal data interference is eliminated, so that the matching accuracy dynamically improves with the system usage time, thereby continuously optimizing the user experience.
[0021] It offers features such as guided information entry, multi-condition result filtering, visual result display, and report export. The operation is convenient and intuitive. It also supports user privacy settings and data anonymization to fully protect user privacy and improve user satisfaction.
[0022] Employing multiple security technologies such as AES-256 encryption algorithm, TLS1.3 transmission protocol, and RBAC access control model, a comprehensive security protection system is constructed to ensure the security of data storage and transmission. Logs are retained for no less than 3 years to facilitate security auditing and problem tracing.
[0023] In response to the national policy of encouraging marriage and allowing multiple births, this initiative provides an efficient solution to the social problems of difficulty in marriage for eligible men and women and difficulty in remarriage for those who have been married before, which helps to optimize the population structure and promote social stability and development. Attached Figure Description
[0024] Figure 1 This is a schematic diagram of a marriage big data computer system based on an AI matching algorithm according to the present invention. Detailed Implementation
[0025] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0026] Example 1
[0027] Please see Figure 1As shown, this invention provides a marriage big data computer system based on AI matching algorithms. The system includes a data acquisition module, a data preprocessing module, an AI matching algorithm module, a database module, a result output module, and a user interaction module. Each module achieves data interaction and collaborative work through a distributed network architecture. The system takes the birth year data of unmarried men and women and their family members as the core input, and combines AI algorithms to construct a three-dimensional correlation model of "hidden number - positive number - number of mothers and children". Through multi-dimensional data verification and logical matching, it outputs recommendations of objects that meet the conditions for marriage registration and analysis of matching results. The time period for birth years is defined as from February 4 of the current year to February 3 of the following year.
[0028] The data collection module includes a user self-reporting unit, a questionnaire survey unit, and a data verification unit. The user self-reporting unit allows users to enter their own birth year, the birth years of their family members, and basic personal information online. The questionnaire survey unit collects supplementary data through pre-set standardized questionnaires and supports online and offline multi-channel questionnaire distribution and collection. The data verification unit, with user authorization, connects to the public security household registration system interface to verify the authenticity of the core data such as the birth year and kinship, eliminating false data and invalid information to ensure that the data collection accuracy rate is not less than 99%.
[0029] The data preprocessing module includes a data cleaning unit, an encoding and conversion unit, and a data standardization unit. The data cleaning unit uses an outlier detection algorithm to identify and process missing values, duplicate values, and logically contradictory data, and uses similarity-based interpolation to complete missing non-core data. The encoding and conversion unit converts the collected birth years into corresponding "hidden numbers" according to preset year encoding rules. The "hidden numbers" are any digit from 0 to 9, and the encoding rules are based on a mapping relationship library between human birth years and hidden numbers. The data standardization unit converts data on family member relationships, birth year ranges, and hidden numbers into structured data in a unified format, generating a standardized data matrix to provide input data for the AI matching algorithm module.
[0030] The AI matching algorithm module includes a variable positive number calculation unit, a mother-child number derivation unit, a logical matching unit, and a dynamic optimization unit. The variable positive number calculation unit calculates the user's corresponding "variable positive number" based on the user's birth year and standardized "hidden number" data, referring to a preset "variable positive number positioning map," and using a linear mapping algorithm. The mother-child number derivation unit derives the mother number in the "mother-child number" using a decision tree algorithm based on the association rule base between the "variable positive number" and the "mother-child number," and the mother number is the core indicator for achieving marriage matching. The logical matching unit follows the matching logic of "odd number of spouses, even number of spouses with odd number," constructs a bidirectional matching model, and simultaneously verifies whether the mother number required by both parties exists in the "hidden numbers" of their respective and each other's family members, generating preliminary matching results. The dynamic optimization unit introduces a reinforcement learning algorithm to continuously optimize the matching weight parameters based on historical matching success rates and user feedback data, so that the matching accuracy dynamically improves with the system's usage time.
[0031] The database module includes a basic database, an association rule database, a historical matching database, and a user behavior database. The basic database stores core data such as user personal information, family member information, birth year, and corresponding "hidden numbers," "positive numbers," and "mother-child numbers," employing a distributed storage architecture to ensure data security and access efficiency. The association rule database stores the mapping rules between "hidden numbers and birth years," the corresponding rules between "positive numbers and birth years," the association rules between "mother-child numbers," and matching logic rules, supporting dynamic updates and expansions of the rules. The historical matching database records all users' matching records, matching results, and marriage certificate success rate data, providing data support for algorithm optimization. The user behavior database collects user login frequency, interaction operations, and matching result feedback behavior data, using encrypted storage to protect user privacy.
[0032] The results output module includes a matching result generation unit, a visualization unit, and a report export unit. The matching result generation unit generates matching results based on the output of the AI matching algorithm module, including a list of matched individuals, a compatibility score, and core matching criteria. These core criteria include the correspondence between the "hidden numbers," "positive numbers," and "mother-child numbers" of both parties. The visualization unit uses charts to display the matching logic chain, the distribution of "hidden numbers" among family members, and the composition of the compatibility score, allowing users to intuitively view the matching principles. The report export unit supports generating a PDF matching analysis report, which users can download and save. The report includes data sources, algorithm logic, matching conclusions, and recommendations.
[0033] The user interaction module includes a registration and login unit, an information entry unit, a matching and query unit, a feedback interaction unit, and a privacy settings unit. The registration and login unit supports multiple registration and login methods, including mobile phone verification and facial recognition, ensuring account security. The information entry unit provides a guided entry interface, prompting users step-by-step to enter their own and their family members' information, and supports modification and supplementation of information. The matching and query unit allows users to filter matching results based on compatibility, region, and age, providing precise search functionality. The feedback interaction unit provides entry points for evaluating matching results satisfaction and providing feedback on certificate issuance results; user feedback data is synchronized to the dynamic optimization unit in real time. The privacy settings unit allows users to independently set the visibility range of their information, and uses anonymization processing for core privacy data to ensure that user information is not illegally disclosed.
[0034] The data verification unit also includes a kinship verification subunit. This subunit analyzes the temporal logical relationship of family members' birth years, including the requirement that parents' birth years must be earlier than the children's and grandparents' birth years must be earlier than the parents'. Combined with kinship information fed back from the public security household registration system, it constructs a kinship validity verification model, eliminates false kinship data, and further improves data reliability.
[0035] The dynamic optimization unit also includes an abnormal data processing subunit. The abnormal data processing subunit uses the isolated forest algorithm to identify abnormal records in historical matching data, including false certificate application feedback and abnormal matching results caused by data entry errors, and marks them as invalid data to avoid abnormal data from interfering with algorithm optimization. At the same time, it regularly generates abnormal data reports for system administrators to check and handle.
[0036] The system also includes a security protection module, which comprises a data encryption unit, an access control unit, a vulnerability protection unit, and a log auditing unit. The data encryption unit uses a symmetric encryption algorithm to encrypt stored data and employs a transport layer security protocol to ensure the security of data transmission. The access control unit uses a role-based access control model to assign different access permissions to system administrators and ordinary users to prevent unauthorized operations. The vulnerability protection unit regularly scans and repairs system vulnerabilities, supports an emergency response mechanism, and defends against malicious attacks. The log auditing unit records all system operation logs, and the logs are retained for no less than 3 years.
[0037] Example 2
[0038] This embodiment provides a specific implementation of a marriage big data computer system based on an AI matching algorithm, as detailed below:
[0039] 1. System Deployment Environment
[0040] Hardware environment: The server adopts a distributed cluster deployment, including application server (CPU: Intel Xeon Gold 6248, memory: 64GB, hard disk: 2TB SSD), database server (CPU: Intel Xeon Gold 6254, memory: 128GB, hard disk: 4TB SSD), storage server (adopting a distributed storage architecture, with a total storage capacity of no less than 100TB), and user terminals support a variety of devices such as smartphones, tablets, and desktop computers.
[0041] Software environment: The operating system is Linux CentOS 7.9, the database is MySQL 8.0 (distributed deployment), the AI algorithm is developed based on Python 3.8, and the reinforcement learning and decision tree algorithms are implemented using the TensorFlow 2.5 framework. The front end is developed using Vue 3.0, the back end is developed using the Spring Boot 2.7 framework, and the data transmission uses the TLS 1.3 protocol.
[0042] 2. System Operation Flow
[0043] User registration and login: Users can open the system mini-program on their smartphones, select mobile phone number verification registration, enter their mobile phone number to obtain an SMS verification code, set a login password after verification, or select facial recognition registration, complete the registration after passing the liveness detection, and log in to the system after successful registration.
[0044] Information Submission and Verification: After logging in, users enter the information submission interface and follow the system prompts to enter their birth year (1990, timeframe defined as February 4, 1990 - February 3, 1991), family member information (father born in 1965, mother born in 1968, sister born in 1988), and basic personal information (region: Beijing, age: 33). After submission, the information is submitted to the system. The data verification unit connects to the public security household registration system to verify the authenticity of birth years and kinship relationships. Simultaneously, the kinship verification subunit analyzes that the father's 1965 and mother's 1968 birth years are earlier than the user's 1990, and the sister's 1988 birth year is earlier than the user's 1990, which conforms to the logical time relationship; therefore, the verification passes.
[0045] Data preprocessing: The data preprocessing module cleans the user-submitted data, removing missing values, duplicate values, and logically contradictory data. The encoding and conversion unit uses the "hidden number-birth year" mapping relationship library to convert the user's birth year of 1990 into hidden number 7, the father's birth year of 1965 into hidden number 3, the mother's birth year of 1968 into hidden number 5, and the sister's birth year of 1988 into hidden number 2. The data standardization unit converts these data into a structured data matrix.
[0046] AI matching calculation: The AI matching algorithm module calls structured data. The positive number calculation unit refers to the "positive number positioning map" and uses a linear mapping algorithm to derive the user's positive number as 6. The mother-child number derivation unit uses the association rule library and a decision tree algorithm to derive the mother number as 5. The logical matching unit uses the logic of "odd numbers match couple numbers, even numbers match odd numbers" to filter matching objects with a mother number of 5 and meet the conditions of region, age and other criteria in the system database. It finds that user B (born in 1992, hidden number 4, positive number 7, mother number 6, mother born in 1967, hidden number 5) meets the conditions and generates a preliminary matching result with a compatibility score of 92.
[0047] Results Output and Interaction: The results output module generates a list of matched partners, displaying user B's basic information, match compatibility score, and core matching criteria (user B's mother number 5 matches user B's mother's hidden number 5, and user B's mother number 6 matches user B's father's hidden number 3, conforming to the "odd number of spouses" logic). The visualization unit shows the matching process in flowchart form, allowing users to clearly see the matching principles. Users can also download a PDF matching analysis report through the report export unit. If users are satisfied with the matching results, they can submit a 5-star rating through the feedback interaction unit, and the feedback data is synchronized to the dynamic optimization unit.
[0048] Security and logging: Throughout the process, the data encryption unit encrypts and stores user information using AES-256 encryption, data transmission uses the TLS1.3 protocol, the access control unit restricts users to only viewing their own matching results, and the log auditing unit records all operation logs such as user registration, information entry, matching query, and feedback, and retains them for future reference.
[0049] 3. System optimization and maintenance
[0050] System administrators regularly review abnormal data reports through the backend management system and handle abnormal data such as reports of fraudulent certificate issuance; they also regularly update rules such as "hidden number - birth year" and "positive number - birth year" in the association rule database; the vulnerability protection unit performs a system vulnerability scan monthly, promptly fixing any vulnerabilities found to ensure stable system operation.
[0051] In this invention, during the data collection phase: users complete account registration and login through the registration and login unit of the user interaction module, and then enter core information such as their own and their family members' birth years through the information filling unit. The system supplements and collects auxiliary data through the questionnaire survey unit of the data collection module, and the data verification unit verifies the authenticity and validity of all collected data to ensure data quality.
[0052] Data preprocessing stage: The data preprocessing module cleans the raw data that has passed the verification, and handles missing values, duplicate values and logically contradictory data. Then, the birth year is converted into the corresponding "hidden number" through the encoding conversion unit. Finally, it is converted into a structured data matrix with a unified format through the data standardization unit.
[0053] AI matching stage: The AI matching algorithm module calls the standardized data, and the positive number calculation unit obtains the user's "positive number". Then, the mother-child number derivation unit derives the core mother number. The logical matching unit constructs a two-way matching model based on the logic of "odd numbers match couple numbers, even numbers match odd numbers", verifies the matching of the mother numbers of both parties and the "hidden numbers" of family members, and generates preliminary matching results. The dynamic optimization unit combines historical data and user feedback to optimize the matching parameters in real time.
[0054] Results Output and Interaction Phase: The results output module generates a list of matching objects, visualization charts, and analysis reports from the matching results, which are then presented to the user through the user interaction module. Users can filter results through the matching query unit and submit evaluations and feedback through the feedback interaction unit. Feedback data is used for algorithm optimization.
[0055] Security Protection Phase: The security protection module ensures the security of data storage, transmission, and system operation throughout the entire process. It prevents data leakage and malicious attacks through encryption, access control, vulnerability protection, and other means. The log auditing unit records all operations to ensure that the system is traceable and auditable.
[0056] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.
Claims
1. A computer system for marriage big data based on AI matching algorithms, characterized in that: The system includes a data acquisition module, a data preprocessing module, an AI matching algorithm module, a database module, a result output module, and a user interaction module. Each module achieves data interaction and collaborative work through a distributed network architecture. The system takes the birth year data of unmarried men and women and their family members as the core input, and combines AI algorithms to construct a three-dimensional correlation model of "hidden number - positive number - mother and child number". Through multi-dimensional data verification and logical matching, it outputs recommendations of objects that meet the conditions for marriage registration and analysis of matching results. The birth year is defined as from February 4 of the current year to February 3 of the following year.
2. The marriage big data computer system based on AI matching algorithm according to claim 1, characterized in that: The data collection module includes a user self-reporting unit, a questionnaire survey unit, and a data verification unit. The user self-reporting unit allows users to enter their own birth year, the birth years of their family members, and basic personal information online. The questionnaire survey unit collects supplementary data through pre-set standardized questionnaires and supports online and offline multi-channel questionnaire distribution and collection. The data verification unit, with user authorization, connects to the public security household registration system interface to verify the authenticity of the core data such as the birth year and kinship, eliminating false data and invalid information to ensure that the data collection accuracy rate is not less than 99%.
3. The marriage big data computer system based on AI matching algorithm according to claim 2, characterized in that: The data preprocessing module includes a data cleaning unit, an encoding conversion unit, and a data standardization unit. The data cleaning unit uses an outlier detection algorithm to identify and process missing values, duplicate values, and logically contradictory data, and uses similarity-based interpolation to complete missing non-core data. The encoding conversion unit converts the collected birth years into corresponding "hidden numbers" according to preset year encoding rules. The "hidden numbers" are any digit from 0 to 9, and the encoding rules are based on a mapping relationship library between human birth years and hidden numbers. The data standardization unit converts data on family member relationships, birth year ranges, and hidden numbers into structured data in a unified format, generating a standardized data matrix to provide input data for the AI matching algorithm module.
4. The marriage big data computer system based on AI matching algorithm according to claim 3, characterized in that: The AI matching algorithm module includes a variable positive number calculation unit, a mother-child number derivation unit, a logical matching unit, and a dynamic optimization unit. The variable positive number calculation unit calculates the user's corresponding "variable positive number" based on the user's birth year and standardized "hidden number" data, referring to a preset "variable positive number positioning map," using a linear mapping algorithm. The mother-child number derivation unit derives the mother number in the "mother-child number" using a decision tree algorithm based on the association rule base between the "variable positive number" and the "mother-child number," and the mother number is the core indicator for marriage matching. The logical matching unit follows the matching logic of "odd number of spouses, even number of spouses with odd number," constructs a bidirectional matching model, and simultaneously verifies whether the required mother number for both the man and woman exists in the "hidden numbers" of their respective and each other's family members, generating preliminary matching results. The dynamic optimization unit introduces a reinforcement learning algorithm to continuously optimize the matching weight parameters based on historical matching success rates and user feedback data, so that the matching accuracy dynamically improves with the system's usage time.
5. A computer system for marriage big data based on AI matching algorithm according to claim 4, characterized in that: The database module includes a basic database, an association rule database, a historical matching database, and a user behavior database. The basic database stores user personal information, family member information, birth year, and corresponding core data such as "hidden number," "positive number," and "mother-child number," employing a distributed storage architecture to ensure data security and access efficiency. The association rule database stores "hidden number-birth year" mapping rules, "positive number-birth year" corresponding rules, "mother-child number" association rules, and matching logic rules, supporting dynamic updates and expansions of the rules. The historical matching database records all users' matching records, matching results, and marriage certificate success rate data, providing data support for algorithm optimization. The user behavior database collects user login frequency, interaction operations, and matching result feedback behavior data, using encrypted storage to protect user privacy.
6. A marriage big data computer system based on an AI matching algorithm according to claim 5, characterized in that: The result output module includes a matching result generation unit, a visualization unit, and a report export unit. The matching result generation unit generates matching results based on the output of the AI matching algorithm module, including a list of matched objects, a matching compatibility score, and core matching criteria. The core matching criteria include the correspondence between the "hidden numbers," "positive numbers," and "mother-child numbers" of both parties. The visualization unit uses charts to display the matching logic chain, the distribution of "hidden numbers" among family members, and the composition of the compatibility score, allowing users to intuitively view the matching principles. The report export unit supports generating a PDF matching analysis report from the matching results, which users can download and save. The report includes data sources, algorithm logic, matching conclusions, and recommendations.
7. A computer system for marriage big data based on AI matching algorithm according to claim 6, characterized in that: The user interaction module includes a registration and login unit, an information entry unit, a matching and query unit, a feedback interaction unit, and a privacy settings unit. The registration and login unit supports multiple registration and login methods, including mobile phone verification and facial recognition, ensuring account security. The information entry unit provides a guided entry interface, prompting users step-by-step to enter their own and their family members' information, and supports modification and supplementation of information. The matching and query unit allows users to filter matching results based on compatibility, region, and age, providing precise search functionality. The feedback interaction unit provides entry points for evaluating matching results satisfaction and providing feedback on certificate issuance results; user feedback data is synchronized to the dynamic optimization unit in real time. The privacy settings unit allows users to independently set the visibility range of their information and employs anonymization processing for core privacy data to ensure that user information is not illegally disclosed.
8. A computer system for marriage big data based on AI matching algorithm according to claim 7, characterized in that: The data verification unit also includes a kinship verification subunit. This kinship verification subunit analyzes the temporal logical relationship of family members' birth years, including the requirement that parents' birth years must be earlier than the children's and grandparents' birth years must be earlier than the parents'. Combined with kinship information fed back from the public security household registration system, it constructs a kinship validity verification model, eliminates false kinship data, and further improves data reliability.
9. A marriage big data computer system based on an AI matching algorithm according to claim 8, characterized in that: The dynamic optimization unit also includes an abnormal data processing subunit. The abnormal data processing subunit uses the isolated forest algorithm to identify abnormal records in the historical matching data, including false certificate application feedback and abnormal matching results caused by data entry errors, and marks them as invalid data to avoid abnormal data from interfering with algorithm optimization. At the same time, it regularly generates abnormal data reports for system administrators to check and process.
10. A marriage big data computer system based on an AI matching algorithm according to claim 9, characterized in that: The system also includes a security protection module, which comprises a data encryption unit, an access control unit, a vulnerability protection unit, and a log auditing unit. The data encryption unit uses a symmetric encryption algorithm to encrypt stored data and employs a transport layer security protocol to ensure security during data transmission. The access control unit uses a role-based access control model to assign different access permissions to system administrators and ordinary users, preventing unauthorized operations. The vulnerability protection unit regularly scans and repairs system vulnerabilities, supports emergency response mechanisms, and defends against malicious attacks. The log auditing unit records all system operation logs, with log retention for at least three years.