Identity information checking method and device, electronic equipment and storage medium

Abstract

The application provides an identity information verification method and device, electronic equipment and storage medium, and relates to the technical field of information processing. The method integrates user-related data from multiple heterogeneous data sources, breaking the data island dilemma of fragmented system data in traditional management mode, ensuring the comprehensiveness, real-time and accuracy of the data required for identity verification. At the same time, cross verification is carried out based on multi-dimensional verification rules including at least two of household verification, kinship verification, contribution association verification and policy compliance verification, which not only avoids the limitations of single verification dimension, but also accurately adapts to various complex identity scenarios, significantly improving the accuracy of identity identification.

Description

Technical Field

[0001] This application relates to the field of information management technology, and more specifically, to an identity information verification method, device, electronic device, and storage medium. Background Technology

[0002] In the operation and management of rural collective economic organizations, member identity verification is a core prerequisite for clarifying the ownership of rights and interests and carrying out income distribution. With the increasing rural population mobility, the diversification of family structures, and the expansion of collective assets, the traditional member identity verification model has gradually become inadequate for the needs of reality.

[0003] In existing technologies, member identity verification relies on a single dimension of household registration information. The main process involves: grassroots village collective staff obtaining household registration data from the public security department, manually comparing it with the paper member roster retained by the village collective, and confirming the identity of each member one by one, in conjunction with the written application materials submitted by the members. This verification method uses the place of household registration as the core criterion, assuming that "those with household registration in the collective are qualified as members." However, for special groups such as farmers who have moved out of the collective but still hold land contract management rights, those whose household registration has not moved out but have been living away from the collective for a long time and have not participated in collective affairs, and sons-in-law or daughters who have married out, household registration information alone cannot make a reasonable determination, leading to biases in the identity determination results. Summary of the Invention

[0004] The purpose of this application is to provide an identity information verification method, device, electronic device, and storage medium to improve the problem of deviations in identity information verification in the prior art.

[0005] In a first aspect, embodiments of this application provide an identity information verification method, the method comprising: In response to a user identity verification request, retrieve the user's relevant data from multiple heterogeneous data sources; According to the multi-dimensional verification rules, the user-related data is subjected to multi-dimensional identity verification to determine whether the user meets the identity of a member of a rural collective economic organization. The multi-dimensional verification rules include at least two of the following: household registration verification rules, kinship verification rules, contribution association verification rules, and policy compliance verification rules.

[0006] In the above implementation process, by integrating user-related data from multiple heterogeneous data sources, the data silo dilemma of fragmented data between systems in the traditional management model is broken, ensuring the comprehensiveness, real-time nature and accuracy of the data required for identity verification. At the same time, cross-validation is carried out based on multi-dimensional verification rules including at least two of the following: household registration verification, kinship verification, contribution association verification and policy compliance verification. This not only avoids the limitations of a single verification dimension, but also accurately adapts to various complex identity scenarios, significantly improving the accuracy of identity recognition.

[0007] Optionally, the step of performing multi-dimensional identity verification on the user-related data according to multi-dimensional verification rules to determine whether the user meets the membership requirements of a rural collective economic organization includes: According to each of the multi-dimensional verification rules, the user-related data corresponding to the verification rule is used to verify the identity and obtain the verification result of the verification rule. Based on the verification results of multiple verification rules, it is determined whether the user meets the criteria for membership in a rural collective economic organization.

[0008] In the above implementation process, by first verifying user-related data from corresponding sources according to various dimensions such as household registration verification, kinship verification, contribution association verification, and policy compliance verification, it is ensured that each verification result is based on a precisely matched data source and rule logic adapted to the special rural scenario. This avoids the one-sidedness of traditional single-dimensional verification and can cover the core scenarios of member identity identification through the special verification of each rule. Then, by combining all verification results for comprehensive judgment, a comprehensive and objective identification of member identity is achieved, effectively avoiding the risk of misjudgment by a single rule.

[0009] Optionally, determining whether the user meets the membership criteria of a rural collective economic organization based on the verification results of multiple verification rules includes: If the verification results of multiple verification rules meet the preset conditions, then it is determined that the user is a member of a rural collective economic organization. If the verification results of multiple verification rules do not meet the preset conditions, a dispute warning result will be output and a manual review will be prompted.

[0010] In the above implementation process, the binary judgment mechanism of automatic identification based on preset conditions and manual review for dispute warnings can achieve efficient and objective identification of members' identities, avoiding the subjective bias and inefficiency of traditional manual review. Furthermore, by triggering warnings and guiding manual reviews for scenarios that do not meet preset conditions, it can accurately cover complex scenarios such as the identification of special identities in rural areas and the verification of data doubts, effectively avoiding the mechanical limitations of intelligent verification. At the same time, the manual review process can further calibrate the identification results by combining the actual situation of the village collective and supplementary supporting materials, ensuring the rigor and fairness of identity identification.

[0011] Optionally, the multi-dimensional verification rules include household registration verification rules, which include household registration in the location of the collective economic organization and meeting a preset duration. The step of performing multi-dimensional identity verification on the user-related data according to the multi-dimensional verification rules includes: Obtain the user's household registration information from the user-related data; Determine whether the household registration verification rules are met based on the household registration information; If the conditions are met, the verification is considered successful.

[0012] In the above implementation process, household registration information is accurately extracted from user-related data integrated from multiple sources, and identity verification is performed using household registration verification rules. This not only leverages the advantages of multi-source data fusion to solve the problems of traditional household registration verification relying on a single data source and lagging data updates, ensuring the real-time and accuracy of household registration information, but also adapts to policy differences in different regions through a design that allows for flexible configuration of preset durations, taking into account both the universality and local adaptability of the rules.

[0013] Optionally, the multi-dimensional verification rules include kinship verification rules, which include the existence of legal kinship with existing members of the rural collective economic organization. The step of performing multi-dimensional identity verification on the user-related data according to the multi-dimensional verification rules includes: Obtain the user's kinship information from the user-related data; Determine whether the kinship verification rules are met based on the kinship information; If the conditions are met, the verification is considered successful.

[0014] In the above implementation process, by accurately extracting kinship information from user-related data integrated from multiple sources and verifying identity using kinship verification rules, the shortcomings of traditional manual identification in kinship verification, such as one-sidedness and insufficient adaptation to special circumstances, are effectively made up for, thus improving the efficiency and rigor of kinship verification.

[0015] Optionally, the multi-dimensional verification rules include contribution association verification rules, which include records of collective labor contributions and / or asset investments within a preset time period. The step of performing multi-dimensional identity verification on the user-related data according to the multi-dimensional verification rules includes: Obtain the user's contribution information from the user-related data; Determine whether the contribution association verification rule is satisfied based on the contribution information; If the conditions are met, the verification is considered successful.

[0016] In the above implementation process, contribution information is accurately extracted from user-related data integrated from multiple sources, and identity verification is performed using contribution association verification rules. This replaces the tedious operation of manually checking paper records, greatly reducing subjective errors and time costs, and improving the efficiency and accuracy of contribution association verification.

[0017] Optionally, the multi-dimensional verification rules include policy compliance verification rules, which include compliance with relevant member identity verification policies. The step of performing multi-dimensional identity verification on the user-related data according to the multi-dimensional verification rules includes: Obtain the user's unique identity tag information from the user-related data; Determine whether the policy compliance verification rules are met based on the special identity tag information; If the conditions are met, the verification is considered successful.

[0018] In the above implementation process, special identity tag information is accurately extracted from user-related data integrated from multiple sources, and identity verification is performed using policy compliance verification rules. The dynamically updated policy library adapts to the special rules of different regions, accurately covering the identification needs of special groups. This makes up for the shortcomings of inaccurate policy application and insufficient protection of the rights and interests of special groups in traditional manual review, and improves the efficiency and rigor of policy compliance verification.

[0019] Optionally, the plurality of heterogeneous data sources include at least two of the following: basic identity data source, civil affairs marriage data source, rural land contracting data source, collective asset data source, and member contribution record data source; And / or, the data from the multiple heterogeneous data sources is stored on the corresponding blockchain nodes.

[0020] In the above implementation process, by integrating multiple heterogeneous data sources, the "data silo" dilemma of data fragmentation between systems in traditional management is broken, realizing the correlation and complementarity of key data in multiple dimensions, ensuring the comprehensiveness, relevance and timeliness of the data required for identity verification and profit distribution, and avoiding information bias or deviation caused by a single data source; at the same time, storing heterogeneous data source data on corresponding blockchain nodes, and leveraging the immutability and distributed consensus characteristics of blockchain, the authenticity and security of data are guaranteed from the source, preventing data from being maliciously or illegally tampered with, and achieving full lifecycle traceability of data.

[0021] Secondly, embodiments of this application provide an identity information verification device, the device comprising: The data acquisition module is used to retrieve user-related data from multiple heterogeneous data sources in response to user identity verification requests. The identity verification module is used to perform multi-dimensional identity verification on the user-related data according to multi-dimensional verification rules to determine whether the user meets the identity of a member of a rural collective economic organization. The multi-dimensional verification rules include at least two of the following: household registration verification rules, kinship verification rules, contribution association verification rules, and policy compliance verification rules.

[0022] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the method provided in the first aspect above are performed.

[0023] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the method provided in the first aspect above.

[0024] Fifthly, embodiments of this application provide a computer program product, including computer program instructions, which, when read and executed by a processor, perform the steps of the method provided in the first aspect above.

[0025] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating an identity information verification method provided in this application embodiment; Figure 2 A detailed flowchart of an identity information verification method provided in this application embodiment; Figure 3 A system architecture diagram of a hierarchical supervision system provided in this application embodiment; Figure 4 A structural block diagram of an identity information verification device provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device for performing an identity information verification method, provided as an embodiment of this application. Detailed Implementation

[0028] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0029] It should be noted that the terms "system" and "network" in the embodiments of this invention can be used interchangeably. "Multiple" refers to two or more; therefore, in the embodiments of this invention, "multiple" can also be understood as "at least two". "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / ", unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0030] It should also be noted that all actions involving the acquisition of signals, information, or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where the application is located, and with the authorization granted by the owner of the relevant device.

[0031] This application provides an identity information verification method. By integrating user-related data from multiple heterogeneous data sources, this method breaks the data silo dilemma of fragmented data between systems in the traditional management model, ensuring the comprehensiveness, real-time nature, and accuracy of the data required for identity verification. At the same time, it relies on multi-dimensional verification rules, including at least two of the following: household registration verification, kinship verification, contribution association verification, and policy compliance verification, to carry out cross-verification. This not only avoids the limitations of a single verification dimension but also accurately adapts to various complex identity scenarios, significantly improving the accuracy of identity recognition.

[0032] Please refer to Figure 1 , Figure 1 A flowchart of an identity information verification method provided in this application embodiment, the method including the following steps: Step S110: In response to the user identity verification request, obtain the user-related data from multiple heterogeneous data sources.

[0033] Heterogeneous data sources refer to various data sets that come from different systems, have different data results and uses, but are related to user identity, such as government system interface data, manually entered data, and IoT collected data.

[0034] In some implementations, multiple heterogeneous data sources may include at least two of the following: basic identity data source, civil affairs marriage data source, rural land contracting data source, collective asset data source, and member contribution record data source.

[0035] When a user initiates a verification request for membership in a rural collective economic organization (e.g., submitting a request online through the system's application portal, or having a village collective staff member initiate the verification on their behalf), the system enters the data collection phase. For example, the system automatically extracts relevant user data from multiple heterogeneous data sources through a pre-defined standardized interface and data collection terminal. The scope of data collection is shown in the table below:

[0036] Specifically, basic identity data, including user name, ID number, registered address, household registration transfer time, and family member relationships, can be obtained from the National Rural Collective Economic Asset Supervision and Management Platform (i.e., basic identity data source). Marital status data, such as marriage status, marriage / divorce time, and spouse information, can be collected through the Civil Affairs Marriage Registration System (i.e., Civil Affairs Marriage Data Source). Collective-related data, such as land contract management rights, collective asset shareholding, and original registration information of members, can be extracted from the Rural Land Contracting System (i.e., Rural Land Contracting Data Source) and the Collective Asset System (i.e., Collective Asset Data Source). Contribution value data, such as the user's collective labor participation time over the past three years, contributions to public welfare undertakings (e.g., participation in village road construction, flood control and disaster relief), industrial investment amount, and honorary awards records, can be obtained through village collective manual input ports or IoT device records (i.e., member contribution record data source). Simultaneously, data on special groups such as low-income status, disability level, military dependent status, and poverty alleviation household identification can be collected by connecting to the Civil Affairs Low-Income System and the Disabled Persons' Federation System.

[0037] Optionally, after data collection, the system can use ETL (Extract-Transform-Load) tools to clean and transform multi-source data, unify the data format (for example, standardize the different formats of ID numbers in different systems into a unified 18-digit format, and convert the date to the "YYYY-MM-DD" format), and use "ID number + name" as the core association key. The Levenshtein distance algorithm is used to solve the data entry error problem (for example, when the user's name "Zhang San" is mistakenly entered as "Zhang San", the algorithm can identify and correct the error), and finally form a structured user data set to provide data support for subsequent verification.

[0038] Step S120: Based on the multi-dimensional verification rules, perform multi-dimensional identity verification on the user's relevant data to determine whether the user meets the criteria for membership in a rural collective economic organization.

[0039] The multi-dimensional verification rules are a set of verification standards built from different dimensions based on the needs of rural collective economic organization member identity verification. The sub-rules complement each other, covering core identity verification scenarios. The multi-dimensional verification rules include at least two of the following: household registration verification rules, kinship verification rules, contribution association verification rules, and policy compliance verification rules. The various verification dimensions and their corresponding descriptions are shown in the table below:

[0040] The core of the household registration verification rules is to determine whether a user's household registration is located in the rural collective economic organization and whether it meets the preset number of years of household registration (the number of years can be configured by the village collective in the system according to local policies, for example, configured as "household registration for 3 years"). For example, if user Li's household registration address is in Village B (the location of the target collective economic organization), the system query shows that his household registration was transferred in 2018, and the current verification time is 2024, with a duration of 6 years, which meets the household registration verification rules. If user Wang's household registration has been moved out of Village B, but the rural land contracting system shows that he still holds land contracting and management rights in Village B, the system will, according to the special identity scenario adaptation rules for rural areas, determine that he meets the relevant requirements of the household registration verification rules and retain his identity verification eligibility.

[0041] The kinship verification rules are used to verify whether a user has a legal kinship relationship with a registered member of the collective economic organization, such as parents, spouse, or children. For example, if user Zhao's spouse, Zhang, is a registered member of Village B, the system cross-matches the spouse information obtained from the civil affairs marriage registration system with Zhang's information in the Village B member database to confirm the authenticity and validity of their marriage. Zhao passes the kinship verification rule. For Chen, a son-in-law who married into the family, although his own household registration was not directly transferred to Village B, his spouse is a member of Village B. The system recognizes him as a special case under the kinship verification rules and includes him in the verification range. Liu, an adopted child, successfully passes the rule verification by matching the kinship information of the adoptive parent (a registered member of Village B) with the adoption certificate issued by the civil affairs department.

[0042] The core of the contribution association verification rule is to check whether a user has any records of participation in collective labor, contributions to public welfare, or industrial investment in recent years. For example, user Sun has accumulated 620 hours of labor in the collective orchard of Village B in the past 3 years. The system confirms that he meets the contribution requirements through the labor record data manually entered by the village collective. User Zhou invested 80,000 yuan in the collective breeding industry of Village B. The relevant investment record is synchronized from the collective asset system to the verification engine and is verified by the contribution association verification rule. User Wu has no ability to work due to physical disability (marked as level 2 by the Disabled Persons' Federation system) and has been registered in Village B for more than 8 years. The system exempts him from the contribution verification requirement according to the special group adaptation rule. This rule determines that he passes.

[0043] The policy compliance verification rules are used to check whether a user meets the specific national or local policies regarding the identification of members of collective economic organizations. For example, if user Zheng is a person who has been lifted out of poverty (identified as such in the civil affairs minimum living allowance system), and local policies clearly state that people who have been lifted out of poverty are given priority in membership identification, the system determines that he meets the policy compliance verification rules. If a user simultaneously holds the dual identities of a person who has been lifted out of poverty and a military dependent (the military dependent identity is synchronized by the civil affairs system), the system will apply the "most favored principle," prioritizing the policy provisions that are more favorable to the identification of the user's identity.

[0044] The system can use a forward inference engine as the core verification algorithm to cross-match user-related data with the above-mentioned multi-dimensional verification rules one by one, thereby determining whether the user meets the identity of a member of a rural collective economic organization.

[0045] In the above implementation process, by integrating user-related data from multiple heterogeneous data sources, the data silo dilemma of fragmented data between systems in the traditional management model is broken, ensuring the comprehensiveness, real-time nature and accuracy of the data required for identity verification. At the same time, cross-validation is carried out based on multi-dimensional verification rules including at least two of the following: household registration verification, kinship verification, contribution association verification and policy compliance verification. This not only avoids the limitations of a single verification dimension, but also accurately adapts to various complex identity scenarios, significantly improving the accuracy of identity recognition.

[0046] Based on the above embodiments, if the multi-dimensional verification rules include household registration verification rules, which include household registration in the location of the collective economic organization and meeting a preset duration, when performing identity verification, the user's household registration information can be obtained from the user's relevant data first, and then it can be determined whether the household registration verification rules are met based on the household registration information. If they are met, the verification is confirmed to be successful.

[0047] The household registration verification rules are mainly used to verify the stability of the geographical association between users and collective economic organizations.

[0048] The preset duration refers to the threshold for the number of years of household registration that the village collective pre-configures in the system based on national policies and local conditions (such as the history of the formation of collective assets and household registration management practices). This threshold can be flexibly adjusted, such as 3 years or 5 years.

[0049] During the identity verification process, the system can accurately extract household registration information from the integrated user-related data. This information may include key details such as the registered address, the date of household registration transfer, and the current status of the household registration (active / transferred). The system can obtain the user's registered address (e.g., "XX Province, XX City, XX County, XX Township, XX Village, XX Group") and the date of household registration transfer (e.g., "2015-08-20") by connecting to the standardized interface of the National Rural Collective Economic Asset Supervision and Management Platform. Simultaneously, it synchronizes data from the public security department's household registration system to confirm whether the user's current household registration status is "active" (not having moved out of the target collective economic organization's location).

[0050] After extraction, the system preprocesses the household registration information using ETL tools: standardizing the household registration address according to the hierarchical format of "province-city-county-township-village-group" (such as correcting the wording error of "XX township XX village" to "XX township XX village", and using the Levenshtein distance algorithm to identify and correct address abbreviations and typos during entry), and uniformly converting the household registration migration time into the "YYYY-MM-DD" format to ensure that the data format is standardized and error-free.

[0051] During the verification process, the first step is to determine the household registration affiliation. The pre-processed user's household registration address is compared with the preset jurisdiction address of the target rural collective economic organization (e.g., "XX Province XX City XX County XX Township XX Village"). If the "township-village" level of the user's household registration address is completely consistent with the preset address, the household registration location is determined to meet the requirements. If the levels are inconsistent (e.g., the user's household registration address is "XX Province XX City XX County XX Township YY Village"), the preliminary judgment that the household registration verification rule fails is directly triggered.

[0052] Next, the system determines the duration of the registration: It automatically obtains the current verification time (e.g., "2024-09-10"), calculates the user's registration duration using a date calculation algorithm (current verification time - registration transfer time), and compares it with the preset duration (e.g., 3 years configured by the village collective). For example, user Zhang's registration transfer time is 2015-08-20, the current verification time is 2024-09-10, and the registration duration is 9 years and 1 month, exceeding the preset 3-year duration, thus meeting the duration requirement. User Zhao's registration transfer time is 2022-05-15, and the duration is 2 years and 4 months, which does not meet the preset 3-year duration, thus failing the duration requirement.

[0053] Meanwhile, the system adapts to special rural household registration scenarios and makes exception judgments for special cases: If a user's household registration has been moved out of the target collective economic organization's location, but the user still holds the land contract management rights of the collective through the rural land contracting system (e.g., user Chen moved out of XX village in 2020, but still contracts 5 mu of farmland in XX village, with the contract period until 2030), the system determines that the household registration relationship is valid according to the special scenario adaptation logic of the household registration verification rules, and the household registration verification rules are verified; if the user is a married woman (e.g., user Liu got married in 2021, but her household registration has not been moved out of XX village), the system retains her household registration relationship qualification by default and directly determines that the location and duration of household registration meet the requirements, without the need to submit additional supporting materials.

[0054] Finally, the system integrates the dual judgment results with the special scenario adaptation results to determine the verification conclusion of the household registration verification rules: if the household registration location is consistent and the duration meets the standard (including exceptions in special scenarios), the household registration verification rule verification passes, and the result can be synchronized to the comprehensive scoring system of multi-dimensional verification (e.g., the household registration verification rule is included in the total score with a weight of 30%); if the household registration location is inconsistent, or the household registration location is consistent but the duration does not meet the standard (and there are no exceptions in special scenarios), the household registration verification rule verification fails, and the system automatically records the reason for failure (e.g., "household registration address is not XX village" or "household registration duration is 2 years and 4 months, which does not meet the 3-year preset requirement"), and feeds it back to the subsequent dispute handling module or directly includes it in the deduction items of the comprehensive score.

[0055] In the above implementation process, household registration information is accurately extracted from user-related data integrated from multiple sources, and identity verification is performed using household registration verification rules. This not only leverages the advantages of multi-source data fusion to solve the problems of traditional household registration verification relying on a single data source and lagging data updates, ensuring the real-time and accuracy of household registration information, but also adapts to policy differences in different regions through a design that allows for flexible configuration of preset durations, taking into account both the universality and local adaptability of the rules.

[0056] Based on the above embodiments, if the multi-dimensional verification rules include kinship verification rules, which include having a legal kinship with existing members of the rural collective economic organization, when performing identity verification, the user's kinship information can be obtained from the user's relevant data first, and then the kinship information can be used to determine whether the kinship verification rules are met. If they are met, the verification is confirmed to be successful.

[0057] The core of the kinship verification rules is the criteria for determining whether a user has a legal kinship relationship with an existing registered member of the collective economic organization. Legal kinship refers to a kinship relationship established based on legal provisions or legal proof, including direct relatives such as parents, spouses, and children, as well as collateral relatives that meet the local collective economic organization's membership recognition policy.

[0058] During the identity verification process, the system can first extract the user's kinship information from the integrated user-related data. This kinship information may include key details such as family member relationships, spouse information, and adoption certificates. The system can obtain the user's registered family member relationship data (e.g., "Father: Li, ID number: XXX; Mother: Wang, ID number: XXX") through the National Rural Collective Economic Asset Supervision and Management Platform interface; it can also extract the user's marital status, spouse's name, and ID number (e.g., "Marital status: Married, Spouse: Zhang, ID number: XXX") through the Civil Affairs Marriage Registration System interface; for users with adoptions, the system simultaneously collects relevant data such as the adoption certificate number and adoptive parent information filed with the village collective (e.g., "Adoption certificate number: XXX, Adoptive parent: Zhao, ID number: XXX").

[0059] After extraction, the system can use ETL tools to preprocess the kinship information: standardize the format of key fields such as ID number and name (e.g., complete the 15-digit old ID number into the standard 18-digit format and correct name input errors), compare the consistency between kinship information and user's basic identity information using the Levenshtein distance algorithm (e.g., verify whether the registered address corresponding to the spouse's ID number has a reasonable association with the user's registered address), and remove duplicate or contradictory kinship records (e.g., when the spouse information registered by the same user in different systems is inconsistent, the data from the civil affairs marriage registration system shall prevail), ensuring the accuracy and standardization of kinship information.

[0060] The system can access the core database of existing members of rural collective economic organizations to obtain a complete list of registered members (including key identifying information such as name, ID number, and registration time). Then, it cross-references the pre-processed user kinship information with this list to determine if a matching legal kinship exists. For example, user Liu's family relationship data shows his father is Liu Moumou. The system queries the existing member list and finds that Liu Moumou is already registered as a member of the B village collective economic organization, and both their ID numbers correspond to the same registered address in B village. Therefore, it determines that Liu and the existing member have a legal father-son relationship, initially satisfying the kinship verification rules. Similarly, user Chen's marriage registration information shows his spouse is Zhang, who is a registered member of the C village collective economic organization. The system verifies the validity of their marriage (the civil affairs system shows the marriage status as "existing"), confirming that Chen and the existing member have a legal spousal relationship, meeting the verification requirements.

[0061] Meanwhile, the system is fully adapted to special kinship scenarios in rural areas and makes exception judgments: For Wu, a son-in-law who married into the family, his household registration is not directly registered in Village D, but his spouse Zhang is a registered member of Village D. According to the special adaptation logic of the kinship rules, the system includes the son-in-law in the scope of spousal relationship recognition. No additional related proof is required, and it is directly determined that he meets the kinship verification rules. For Yang, an adopted child, his household registration is registered in Village E, and the adoption certificate filed by the village collective shows that the adoptive parent Yang (a registered member of Village E) is his legal guardian. After verifying the legality and validity of the adoption certificate, the system determines that Yang passes the kinship verification according to the legal kinship relationship.

[0062] Finally, the system integrates the core judgment results with the special scenario adaptation results to determine the kinship rule verification conclusion: If the user's kinship information has a valid legal kinship with the existing registered members of the collective (including exceptions in special scenarios), the kinship rule verification passes and is included in the multi-dimensional verification comprehensive score according to a preset weight (e.g., 30%); if the user's kinship information does not have a legally eligible kinship, or if the kinship information is verified to be false or invalid (e.g., forged adoption certificate), the verification fails, and the system automatically records the reason for failure (e.g., "no legal kinship record of registered members of the collective" or "adoption certificate not filed and its validity cannot be verified"), and pushes it to the dispute handling module for further verification by village collective staff.

[0063] In the above implementation process, by accurately extracting kinship information from user-related data integrated from multiple sources and verifying identity using kinship verification rules, the shortcomings of traditional manual identification in kinship verification, such as one-sidedness and insufficient adaptation to special circumstances, are effectively made up for, thus improving the efficiency and rigor of kinship verification.

[0064] Based on the above embodiments, if the multi-dimensional verification rules include contribution association verification rules, and the contribution association verification rules include records of collective labor contributions and / or asset investment within a preset time period, during identity verification, the user's contribution information can be obtained from the user's relevant data first, and then it can be determined whether the contribution association verification rules are met based on the contribution information. If they are met, the verification is confirmed to be successful.

[0065] The criteria for determining the contribution association verification rule is that the user has a valid record of collective labor contribution or asset investment within a preset time period. This is used to verify the actual association and contribution between the user and the collective economic organization. The preset time period can be set according to factors such as the village collective's asset operation cycle and contribution accounting practices. The time threshold is pre-configured in the system (e.g., the past 3 years, which can be flexibly adjusted according to local policies).

[0066] During the identity verification process, the system accurately extracts contribution information from the integrated user data, including key information such as the duration of collective labor participation, records of contributions to public welfare undertakings, amount of industrial investment, and records of honors and awards. The system can obtain records of user participation in collective production labor (such as collective farmland cultivation and orchard management), details of participation in public welfare undertakings (such as village road construction, flood control and disaster relief, and epidemic prevention and control), and the number and content of honors and awards documents issued by the village collective or higher-level departments through the village collective's manual input portal. Real-time data collected through IoT devices (such as labor attendance terminals and public welfare activity check-in devices) further verifies the authenticity and accuracy of labor participation. Through the collective asset system interface, the system extracts data related to user investment in collective economic organization industrial projects (such as agricultural product processing plant construction and rural tourism project development), including investment amount, investment time, and shareholding ratio.

[0067] After extraction, the system uses ETL tools to preprocess the contribution information: unifying the data format (converting labor hours to "hours", investment amounts to "yuan", and time records to "YYYY-MM-DD" format), correcting input errors using the Levenshtein distance algorithm (such as text errors like mistaking "public service labor" for "justice labor" and numerical errors in investment amounts), and removing duplicate records (if a user records duplicate durations for the same public service activity through both manual entry and IoT check-in, the IoT data will be used), ensuring the accuracy and standardization of the contribution information.

[0068] The system automatically obtains the current verification time and calculates a preset time range (e.g., if the current verification time is October 2024 and the preset time range is the past 3 years, then the time range is from October 2021 to October 2024). Then, it matches the pre-processed user contribution information with this time range to determine if there are any valid contribution records. If a user has collective labor contributions: for example, user Wang participated in 720 hours of labor at the village collective vegetable base between March 2022 and August 2024, and all records are within the preset time range, the system determines that the user meets the collective labor contribution requirements. If a user has asset investment records: for example, user Zhao invested 150,000 yuan in the village collective rural tourism project in January 2022, the investment period is within the preset time range, and the collective asset system shows that the investment is in operation, the system determines that the user meets the asset investment contribution requirements. If a user has both types of contribution records (e.g., user Li participated in 300 hours of collective labor and invested 80,000 yuan in collective industries), the system automatically confirms the contribution records are valid without further judgment.

[0069] Meanwhile, the system is fully adapted to the scenarios of special groups in rural areas, and makes exception judgments: For users who are elderly, weak, sick, or disabled and have no ability to work, the system obtains special group data such as age (e.g., 70 years old or above) and disability level (e.g., level one physical disability) by connecting to the civil affairs minimum living allowance system and the disabled persons' federation system. If it is verified that the user is indeed unable to work and is registered in the location of the collective economic organization, the system exempts him from the requirements for collective labor contribution and asset investment according to the special scenario adaptation logic of the contribution association rules, and directly determines that the contribution association verification is passed. For example, if user Zhang is 75 years old or above and has been registered in the village for more than 10 years, and the disabled persons' federation system marks him as having a level one visual disability and no ability to work, the system does not need to check his labor or investment records and automatically determines that he meets the contribution association verification rules.

[0070] Finally, the system integrates the core judgment results with the special scenario adaptation results to determine the contribution association rule verification conclusion: if the user has a valid collective labor contribution record or asset investment record (including special group exemption recognition) within the preset time period, the contribution association rule verification passes and is included in the multi-dimensional verification comprehensive score according to the preset weight (e.g., 20%); if the user has no valid contribution record within the preset time period and does not belong to the special group exemption category, the verification fails, the system automatically records the reason for failure (e.g., "no collective labor contribution record and no asset investment record from October 2021 to October 2024"), and pushes it to the dispute handling module, so that the village collective staff can verify whether there is any unrecorded contribution information.

[0071] In the above implementation process, contribution information is accurately extracted from user-related data integrated from multiple sources, and identity verification is performed using contribution association verification rules. This replaces the tedious operation of manually checking paper records, greatly reducing subjective errors and time costs, and improving the efficiency and accuracy of contribution association verification.

[0072] Based on the above embodiments, if the multi-dimensional verification rules include policy compliance verification rules, and the policy compliance verification rules include compliance with relevant member identity recognition policies, when performing identity verification, the user's unique identity tag information can be obtained from the user's relevant data first, and it can be determined whether the policy compliance verification rules are met based on the special identity tag information. If they are met, the verification is determined to be successful.

[0073] The criteria for determining policy compliance are that users meet the specific policies for identifying members of rural collective economic organizations issued by the state or local governments. This aims to ensure that the identification of members is consistent with the macro policy orientation and to protect the rights and interests of special groups.

[0074] During the identity verification process, the system can accurately extract special identity tag information from the integrated user-related data, including low-income status, disability level, military dependent status, and poverty alleviation household identification. The system can obtain user identification (e.g., "yes / no") through the civil affairs low-income system interface; collect user disability level (e.g., level one, level two limb disability) and disability type-related data through the disabled persons' federation system interface; extract military dependent status identification (e.g., "active duty military dependent" or "veteran relative") through the veterans affairs department interface; and obtain poverty alleviation household identification (e.g., "households that have escaped poverty but have not fallen back into it" or "households whose poverty alleviation status is unstable") through the rural revitalization department's relevant system interface.

[0075] After extraction, the system can use ETL tools to preprocess special identity label information: unify the data identification format (e.g., standardize different expressions such as "low-income recipient" and "low-income household" into the "low-income identity" label, and uniformly record disability levels according to the "Level 1 / Level 2 / Level 3 / Level 4" standard), correct input errors through the Levenshtein distance algorithm, and eliminate contradictory data (if the same user is simultaneously marked as "lifted out of poverty household" and "not yet lifted out of poverty household", the latest synchronized data from the rural revitalization department shall prevail), to ensure the accuracy and standardization of special identity label information.

[0076] Then, the system loads a preset policy database, which contains all current national and local special policies for the identification of members of rural collective economic organizations. It also supports dynamic updates and categorized searches based on policy effective date, applicable region, and applicable group (e.g., classifying "priority identification policy for households lifted out of poverty" as a national general policy and "simplified identification policy for local military dependents" as a local special policy). Subsequently, the system precisely matches the pre-processed user-specific identity tag information with the policy application conditions in the policy database: If the user holds the "Poverty Alleviation Household" tag, and the national policy clearly states that "Poverty Alleviation Households enjoy priority in the identification of members of rural collective economic organizations, and can pass the identification if they meet the basic conditions," then the user is directly determined to meet the policy compliance verification rules; if the user holds the "Level 2 Disability" tag, and the local policy stipulates that "Residents with Level 1 or 2 disabilities registered in the village can be exempted from some identification conditions and directly pass the policy compliance verification," and the user has already met the basic requirement of household registration in the location of the collective economic organization, then the user is determined to meet the policy compliance rules; if the user is a "Family Member of Active Military Personnel," and the local special policy clearly states that "For military family members applying for membership in rural collective economic organizations, the review process is simplified, and compliance is directly recognized at the policy level," then the system automatically matches the policy and confirms that the user meets the requirements.

[0077] Meanwhile, the system is fully adaptable to scenarios with overlapping special policies, making exception judgments based on the "most favored" principle: if a user simultaneously holds multiple special identity labels (such as being both a poverty-stricken household and a person with a level-two disability), and corresponds to multiple applicable policies, the system will compare the leniency of the recognition criteria for each policy (e.g., "exemption from more recognition conditions for disabled persons" is more beneficial for users to pass the verification than "priority recognition for poverty-stricken households"), and select the policy most favorable to the user as the basis for judgment to ensure the maximization of user rights. For example, user Li is both a poverty-stricken household and a person with a level-one visual disability. The national policy for recognizing poverty-stricken households requires "meeting the household registration conditions to pass," while the local policy for recognizing people with a level-one disability requires "no contribution-related conditions to be met, and compliance verification can be passed directly." The system adapts to the local special policy for disabled persons according to the "most favored" principle and determines that he meets the policy compliance verification rules.

[0078] Finally, the system integrates the core judgment results with the special scenario adaptation results to determine the policy compliance rule verification conclusion: If the user's special identity tag information matches any policy application condition in the policy library (including the most favorable adaptation in the case of superimposed special policies), the policy compliance rule verification passes and is included in the multi-dimensional verification comprehensive score according to a preset weight (e.g., 20%); if the user has no special identity tag information, or if the tag information cannot match any policy application condition in the policy library, it is necessary to further verify whether the user meets the basic policy for ordinary member identification (e.g., users without special identities need to meet basic conditions such as household registration and kinship to pass the policy compliance verification). If they still do not meet the requirements, the verification fails, the system automatically records the reason for failure (e.g., "no special identity tag that meets the specific policy and does not meet the basic policy for ordinary member identification"), and pushes it to the dispute handling module for village collective staff to verify in combination with the actual situation.

[0079] In the above implementation process, special identity tag information is accurately extracted from user-related data integrated from multiple sources, and identity verification is performed using policy compliance verification rules. The dynamically updated policy library adapts to the special rules of different regions, accurately covering the identification needs of special groups. This makes up for the shortcomings of inaccurate policy application and insufficient protection of the rights and interests of special groups in traditional manual review, and improves the efficiency and rigor of policy compliance verification.

[0080] Based on the above embodiments, when performing identity verification, the user-related data corresponding to each verification rule in the multi-dimensional verification rules can be verified to obtain the verification result of the verification rule. Then, based on the verification results of multiple verification rules, it can be determined whether the user meets the identity of a member of a rural collective economic organization.

[0081] During identity verification, the system can first verify the user's identity using the verification methods described above, obtaining the verification result for each rule, such as verification passed or failed. Then, the system can combine the verification results from multiple rules to determine whether the user qualifies as a member of a rural collective economic organization.

[0082] In some implementations, the system can execute the verification rules for each dimension sequentially in a preset order to obtain the verification result of a single rule. The preset order could be: household registration verification rule -> kinship verification rule -> contribution relationship verification rule -> policy compliance verification rule.

[0083] When judging based on the verification results of various verification rules, if the verification results of multiple verification rules meet the preset conditions, it is determined that the user is a member of the rural collective economic organization. If the verification results of multiple verification rules do not meet the preset conditions, a dispute warning result is output and a manual review is prompted.

[0084] In some implementations, the preset condition may include multiple verification rules all passing the verification. If this preset condition is met, the user is determined to be a member of a rural collective economic organization; otherwise, they are not. The detailed implementation process of this comprehensive verification is as follows: Figure 2 As shown.

[0085] In some implementations, the preset conditions may include a comprehensive matching score calculated by a weighted scoring mechanism that is greater than or equal to 80 points.

[0086] For each verification rule, the system can output the score corresponding to the verification result of a single rule. For example, user Wang's registered address is in the target collective and has been there for 5 years (meets the household registration rule, gets 30 points), his spouse is a registered member of the collective (meets the kinship rule, gets 30 points), he has participated in collective labor for 600 hours in the past 3 years (meets the contribution association rule, gets 20 points), he has no special identity label but meets the policy requirements for ordinary members (meets the policy compliance rule, gets 20 points); user Zhao's household registration has been there for 2 years but he retains land contracting rights (special adaptation passes the household registration rule, gets 30 points), he has no legal relatives in this collective (kinship rule fails, gets 0 points), he is exempt from contribution verification due to level one disability (special adaptation passes the contribution association rule, gets 20 points), he is a military dependent and meets the special policy (meets the policy compliance rule, gets 20 points); user Sun's household registration rule passes (30 points), kinship rule fails (0 points), contribution association rule fails (0 points), policy compliance rule fails (0 points).

[0087] The system then invokes the forward inference engine to perform a weighted summation of the verification results of individual rules based on preset weights, calculating a comprehensive matching score. For example, in the above example, user Wang's comprehensive score is 30+30+20+20=100 points, user Zhao's is 30+0+20+20=70 points, and user Sun's is 30+0+0+0=30 points. Subsequently, the system applies preset conditions for judgment: if the comprehensive score is ≥80 points, it is determined that the preset conditions are met; if the comprehensive score is <80 points, it is determined that the preset conditions are not met.

[0088] Based on the judgment results, Wang in the above example meets the criteria for a member of a rural collective economic organization, while users Zhao and Sun do not. At this point, the system triggers a dispute warning mechanism, automatically generating a dispute warning result, which includes basic user information, details of each rule verification (such as user Zhao's "failed to pass the kinship rule" and user Sun's "failed to pass both the contribution association rule and the policy compliance rule"), and data doubt markers (such as user Zhao's failure to find kinship registration records and user Sun's failure to collect contribution records). The warning information is then pushed to the village collective dispute handling module, and staff are prompted to initiate a manual review via system message. During the manual review stage, staff can access complete user data and verification process logs through the system to verify any suspicious points. For example, they can contact user Zhao to supplement proof of kinship, or check whether user Sun has any unrecorded collective contribution records. If the data can be supplemented after verification, the verification result can be corrected (e.g., if user Sun supplements 3 years of public service records, the contribution association rule is changed to pass, and the comprehensive score is raised to 50 points but still does not reach 80 points, further verification of other related evidence outside of household registration is required). Finally, the staff will issue a review opinion based on the verification results to determine whether the user meets the membership requirements.

[0089] In the above implementation process, the binary judgment mechanism of automatic identification based on preset conditions and manual review for dispute warnings can achieve efficient and objective identification of members' identities, avoiding the subjective bias and inefficiency of traditional manual review. Furthermore, by triggering warnings and guiding manual reviews for scenarios that do not meet preset conditions, it can accurately cover complex scenarios such as the identification of special identities in rural areas and the verification of data doubts, effectively avoiding the mechanical limitations of intelligent verification. At the same time, the manual review process can further calibrate the identification results by combining the actual situation of the village collective and supplementary supporting materials, ensuring the rigor and fairness of identity identification.

[0090] In the above implementation, to ensure data security and reliability, data from the multiple heterogeneous data sources can be stored on corresponding blockchain nodes. However, considering cost and security, only some critical data, such as identity verification results and revenue distribution records, are stored on the blockchain nodes.

[0091] like Figure 3 The diagram shown is the system architecture of the hierarchical supervision system in this plan. The architecture covers the data layer, support layer, core business layer, application layer and supervision layer.

[0092] The data layer completes multi-source data collection through a multi-source data collection library, a core member database, a revenue distribution database, and a blockchain-based evidence repository. Its core function is to provide standardized, real-time, and secure data resources for all upper layers. The engine operation of the support layer, the business execution of the core business layer, the operational response of the application layer, and the supervision and verification of the regulatory layer all rely on the structured information output by the data layer, such as basic identity data, marital status data, and contribution value data.

[0093] The support layer relies on the data resources provided by the data layer and completes the relevant data processing through a big data processing engine (ETL / data fusion), an intelligent algorithm engine, blockchain services, and a permission management system. Core business processes such as identity verification, revenue calculation, and allocation execution require the support layer to call its verification algorithms, calculation rule configurations, and dispute resolution mechanisms.

[0094] The core business layer transforms the technical capabilities of the support layer into specific business functions, directly providing services to the application layer. Through member identity verification engines, revenue calculation and distribution engines, rule configuration modules, and dispute resolution modules, data is transformed into executable business capabilities. All user operations on the application layer's terminal side (data collection terminal, identity authentication module, revenue query module, etc.) and operational side (village collective operation terminal) must be implemented through the core business layer.

[0095] The regulatory layer (county-level regulatory end, township-level regulatory end, and audit supervision end) does not rely on a single upper level, but instead uses a three-level permission architecture to connect the data layer, support layer, core business layer, and application layer.

[0096] A blockchain node is a participating unit in a blockchain network that has the functions of data storage, verification, and synchronization. According to the system architecture of the hierarchical supervision system in this plan, it may include village collective operation nodes, township supervision nodes, county-level supervision nodes, and audit supervision nodes, etc. Each node maintains data consistency through a consensus mechanism. The distributed ledger is the core carrier of the blockchain. All nodes share the same copy of the ledger. Once data is written, it generates an immutable record, ensuring data traceability and security.

[0097] When data is written to the blockchain node, a data preprocessing process is initiated first, taking into account the characteristics of data from multiple heterogeneous data sources. Due to differences in data formats and field definitions across different data sources (e.g., the marital status field in the civil affairs marriage registration system is "married / unmarried / divorced," the date field in the rural land contracting system is "MM-DD-YYYY," and the investment amount field in the collective asset system is a dual unit of "ten thousand yuan / yuan"), the data needs to be cleaned, transformed, and standardized using ETL tools: duplicate data is removed (e.g., duplicate investment records of the same member in the collective asset system and manually entered by the village collective), input errors are corrected (e.g., the name data where "Wang Fang" was mistakenly entered as "Wang Fang" is corrected using the Levenshtein distance algorithm), and the data format is unified (the date field is unified to "YYYY-MM-DD," the amount unit is unified to "yuan," and the ID number is standardized to 18 digits). Meanwhile, sensitive data is encrypted: for privacy data such as low-income status, disability level, and spouse information, asymmetric encryption algorithms (such as RSA algorithm) can be used to generate public and private keys. The public key is used for data encryption and verification, and the private key is uniformly kept by the county-level supervision node to ensure the security of data transmission and storage.

[0098] After preprocessing, the data can proceed to the data uploading preparation stage. In the specific implementation, data can be classified and identified according to its type and sensitivity level, and a unique data source identifier can be associated with each type of data (e.g., the "National Rural Collective Economic Asset Supervision and Management Platform" is identified as "DATA-001", and the Civil Affairs Marriage Registration System is identified as "DATA-002"). A timestamp (accurate to the second) and a data verification code (generated by jointly calculating the data content, data source identifier, and timestamp) are added. For example, a member's basic identity data (name: Zhang, ID number: 1101011990XXXX1234, household registration date: 2010-05-15), after preprocessing, is identified as "DATA-001", with a timestamp of "2024-06-10 09:23:45", and the verification code is "hash value (1101011990XXXX1234+Zhang+2010-05-15+DATA-001+2024-06-10 09:23:45)". Simultaneously, according to the hierarchical supervision system of this scheme, node access permissions are configured as follows: village collective operation nodes can only access non-sensitive data such as basic identity data and contribution value data of members within their own collective; township supervision nodes can access member data of all collectives within their jurisdiction (excluding encrypted sensitive data); county-level supervision nodes have access and decryption permissions for all data, ensuring the compliance of data access.

[0099] Subsequently, data writing to the blockchain node is initiated. This solution can adopt a consortium blockchain architecture (adapting to the needs of multi-party participation and hierarchical supervision in rural collective economic management, with only authorized nodes able to join the network). Each heterogeneous data source is connected to a dedicated authorized node: data from the National Rural Collective Economic Asset Supervision and Management Platform is written by the consortium blockchain node corresponding to the provincial agricultural and rural affairs department; data from the civil affairs marriage registration system is written by the node corresponding to the municipal civil affairs department; and contribution value data manually entered by the village collective is written by the village collective's operation node.

[0100] When writing data, nodes can first verify the data's checksum and the legality of the data source (e.g., verifying whether data identified by "DATA-002" truly comes from the authorized interface of the civil affairs marriage registration system). After successful verification, a consensus request is initiated using the PBFT (Practical Byzantine Fault Tolerance) consensus mechanism: at least 2 / 3 of the authorized nodes (e.g., village collectives, townships, and county-level nodes) must confirm the data's authenticity and validity (e.g., a village collective node submitting a member's record of contributions to public welfare undertakings requires the township supervisory node to verify that the public welfare activity was indeed carried out, and the county-level node to verify that the record format is compliant). After consensus is reached, the data will be packaged into blocks, associated with the hash value of the previous block, and written into the distributed ledgers of each authorized node. For example, after Zhang's marital status data (marital status: married, spouse's ID number: 1101021991XXXX5678) is written by the civil affairs node, the ledgers of the village collective, township, and county-level nodes will all record this data synchronously, and the block will contain the hash value of the previous block, forming a chain structure.

[0101] During the data storage phase, each blockchain node synchronizes the distributed ledger in real time, ensuring that all authorized nodes hold complete and consistent data copies and avoiding data loss due to single points of failure. For example, if a village collective's operating node cannot operate due to equipment failure, township or county-level nodes can still provide complete data query and verification services. When the system needs to call heterogeneous data sources for identity verification (such as verifying Zhang's kinship verification rules), the verification engine initiates a data query request to the blockchain network, submits a node signature (proving access rights), and after receiving the request, the authorized node locates the target data through the data identifier and timestamp, extracts the data verification code and compares it with the verification code in the local ledger. If they match, it means that the data has not been tampered with, and the decrypted valid data is returned to the verification engine (sensitive data needs to be decrypted by the county-level node using its private key before being returned). For example, when querying Zhang's spouse information, the system locates the civil affairs marriage system data through the "DATA-002" identifier, compares the verification code to confirm the data completeness, returns the spouse's ID number, cross-verifies it with the spouse information in the member core database, and completes the kinship verification rule verification.

[0102] In the above implementation process, heterogeneous data source data is stored on the corresponding blockchain nodes. By leveraging the immutability and distributed consensus characteristics of blockchain, the authenticity and security of the data are guaranteed from the source, preventing the data from being maliciously or illegally tampered with, and enabling traceability of the data throughout its entire lifecycle.

[0103] Please refer to Figure 4 , Figure 4 This is a structural block diagram of an identity information verification device 200 provided in an embodiment of this application. The device 200 may be a module, program segment, or code on an electronic device. It should be understood that the device 200 corresponds to the above method embodiment and is capable of performing the various steps involved in the method embodiment. The specific functions of the device 200 can be found in the description above. To avoid repetition, detailed descriptions are appropriately omitted here.

[0104] Optionally, the device 200 includes: Data acquisition module 210 is used to acquire user-related data from multiple heterogeneous data sources in response to user identity verification requests; The identity verification module 220 is used to perform multi-dimensional identity verification on the user-related data according to multi-dimensional verification rules to determine whether the user meets the identity of a member of a rural collective economic organization. The multi-dimensional verification rules include at least two of the following: household registration verification rules, kinship verification rules, contribution association verification rules, and policy compliance verification rules.

[0105] Optionally, the identity verification module 220 is used to perform identity verification on the user-related data corresponding to each verification rule in the multi-dimensional verification rules, and obtain the verification result of the verification rule; and determine whether the user meets the identity of a member of a rural collective economic organization based on the verification results of multiple verification rules.

[0106] Optionally, the identity verification module 220 is used to determine that the user is a member of a rural collective economic organization if the verification results of multiple verification rules meet the preset conditions; and to output a dispute warning result and prompt for manual review if the verification results of multiple verification rules do not meet the preset conditions.

[0107] Optionally, the multi-dimensional verification rules include household registration verification rules, which include household registration in the location of a collective economic organization and meeting a preset duration. The identity verification module 220 is used to obtain the user's household registration information from the user-related data; determine whether the household registration verification rules are met based on the household registration information; if met, the verification is confirmed to be successful.

[0108] Optionally, the multi-dimensional verification rules include kinship verification rules, which include the existence of legal kinship with existing members of rural collective economic organizations. The identity verification module 220 is used to obtain the user's kinship information from the user-related data; determine whether the kinship verification rules are met based on the kinship information; and determine that the verification is successful if the kinship verification rules are met.

[0109] Optionally, the multi-dimensional verification rules include contribution association verification rules, which include records of collective labor contributions and / or asset investments within a preset time period. The identity verification module 220 is used to obtain the user's contribution information from the user-related data; determine whether the contribution association verification rules are met based on the contribution information; if they are met, the verification is determined to be successful.

[0110] Optionally, the multi-dimensional verification rules include policy compliance verification rules, which include compliance with relevant member identity recognition policies. The identity verification module 220 is used to obtain the user's special identity tag information from the user-related data; determine whether the policy compliance verification rules are met based on the special identity tag information; and if so, determine that the verification has passed.

[0111] Optionally, the plurality of heterogeneous data sources include at least two of the following: basic identity data source, civil affairs marriage data source, rural land contracting data source, collective asset data source, and member contribution record data source; And / or, the data from the multiple heterogeneous data sources is stored on the corresponding blockchain nodes.

[0112] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0113] Please refer to Figure 5 , Figure 5This application provides a schematic diagram of the structure of an electronic device for performing an identity verification method. The electronic device may include: at least one processor 310, such as a CPU; at least one communication interface 320; at least one memory 330; and at least one communication bus 340. The communication bus 340 is used to establish communication between these components. In this embodiment, the communication interface 320 is used for signaling or data communication with other node devices. The memory 330 may be a high-speed RAM or a non-volatile memory, such as at least one disk storage device. Optionally, the memory 330 may also be at least one storage device located remotely from the processor. The memory 330 stores computer-readable instructions; when these instructions are executed by the processor 310, the electronic device performs the aforementioned method process.

[0114] Understandable. Figure 5 The structure shown is for illustrative purposes only; the electronic device may also include components that are more advanced than those shown. Figure 5 The more or fewer components shown, or having the same Figure 5 The different configurations shown. Figure 5 The components shown can be implemented using hardware, software, or a combination thereof.

[0115] This application provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it performs the method process executed by the electronic device in the above method embodiments.

[0116] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer can perform the methods provided in the above-described method embodiments, such as including: In response to a user identity verification request, retrieve the user's relevant data from multiple heterogeneous data sources; According to the multi-dimensional verification rules, the user-related data is subjected to multi-dimensional identity verification to determine whether the user meets the identity of a member of a rural collective economic organization. The multi-dimensional verification rules include at least two of the following: household registration verification rules, kinship verification rules, contribution association verification rules, and policy compliance verification rules.

[0117] In summary, the embodiments of this application provide an identity information verification method, device, electronic device, and storage medium. This method integrates user-related data from multiple heterogeneous data sources, breaking the data silo dilemma of fragmented data between systems in the traditional management model, and ensuring the comprehensiveness, real-time nature, and accuracy of the data required for identity verification. At the same time, it relies on multi-dimensional verification rules, including at least two of household registration verification, kinship verification, contribution association verification, and policy compliance verification, to carry out cross-verification. This not only avoids the limitations of a single verification dimension, but also accurately adapts to various complex identity scenarios, significantly improving the accuracy of identity recognition.

[0118] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0119] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0120] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0121] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0122] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for verifying identity information, characterized in that, The method includes: In response to a user identity verification request, retrieve the user's relevant data from multiple heterogeneous data sources; According to the multi-dimensional verification rules, the user-related data is subjected to multi-dimensional identity verification to determine whether the user meets the identity of a member of a rural collective economic organization. The multi-dimensional verification rules include at least two of the following: household registration verification rules, kinship verification rules, contribution association verification rules, and policy compliance verification rules.

2. The method according to claim 1, characterized in that, The step of performing multi-dimensional identity verification on the user-related data according to multi-dimensional verification rules to determine whether the user meets the membership requirements of a rural collective economic organization includes: According to each of the multi-dimensional verification rules, the user-related data corresponding to the verification rule is used to verify the identity and obtain the verification result of the verification rule. Based on the verification results of multiple verification rules, it is determined whether the user meets the criteria for membership in a rural collective economic organization.

3. The method according to claim 2, characterized in that, The process of determining whether a user qualifies as a member of a rural collective economic organization based on the verification results of multiple verification rules includes: If the verification results of multiple verification rules meet the preset conditions, then it is determined that the user is a member of a rural collective economic organization. If the verification results of multiple verification rules do not meet the preset conditions, a dispute warning result will be output and a manual review will be prompted.

4. The method according to claim 1, characterized in that, The multi-dimensional verification rules include household registration verification rules, which include household registration in the location of the collective economic organization and meeting a preset duration. The step of performing multi-dimensional identity verification on the user-related data according to the multi-dimensional verification rules includes: Obtain the user's household registration information from the user-related data; Determine whether the household registration verification rules are met based on the household registration information; If the conditions are met, the verification is considered successful.

5. The method according to claim 1, characterized in that, The multi-dimensional verification rules include kinship verification rules, which include the existence of legal kinship with existing members of rural collective economic organizations. The multi-dimensional identity verification of the user-related data based on these rules includes: Obtain the user's kinship information from the user-related data; Determine whether the kinship verification rules are met based on the kinship information; If the conditions are met, the verification is considered successful.

6. The method according to claim 1, characterized in that, The multi-dimensional verification rules include contribution association verification rules, which include records of collective labor contributions and / or asset investments within a preset time period. The multi-dimensional identity verification of the user-related data based on these rules includes: Obtain the user's contribution information from the user-related data; Determine whether the contribution association verification rule is satisfied based on the contribution information; If the conditions are met, the verification is considered successful.

7. The method according to claim 1, characterized in that, The multi-dimensional verification rules include policy compliance verification rules, which include compliance with relevant member identity verification policies. The multi-dimensional identity verification of the user-related data based on these rules includes: Obtain the user's unique identity tag information from the user-related data; Determine whether the policy compliance verification rules are met based on the special identity tag information; If the conditions are met, the verification is considered successful.

8. The method according to any one of claims 1-7, characterized in that, The multiple heterogeneous data sources include at least two of the following: basic identity data source, civil affairs marriage data source, rural land contracting data source, collective asset data source, and member contribution record data source; And / or, the data from the multiple heterogeneous data sources is stored on the corresponding blockchain nodes.

9. An identity information verification device, characterized in that, The device includes: The data acquisition module is used to retrieve user-related data from multiple heterogeneous data sources in response to user identity verification requests. The identity verification module is used to perform multi-dimensional identity verification on the user-related data according to multi-dimensional verification rules to determine whether the user meets the identity of a member of a rural collective economic organization. The multi-dimensional verification rules include at least two of the following: household registration verification rules, kinship verification rules, contribution association verification rules, and policy compliance verification rules.

10. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer-readable instructions that, when executed by the processor, perform the method as described in any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it performs the method as described in any one of claims 1-8.

12. A computer program product, characterized in that, It includes computer program instructions, which, when read and executed by a processor, perform the method as described in any one of claims 1-8.

Images