Unproven urban service management method and device, computer equipment and storage medium

Abstract

The application relates to the technical field of data management, in particular to a no-proof city data sharing method and device, computer equipment and a storage medium, wherein the no-proof city data sharing method comprises the following steps: acquiring a matter proof area, acquiring a corresponding proof matter type according to the matter proof area, and acquiring basic proof data according to the proof matter type; extracting key user information from the basic proof data, concatenating the basic proof information according to the key user information, and obtaining a user proof data chain; acquiring a user proof request, acquiring user proof matter type from the user proof request, and acquiring user identification data from the user proof matter type; extracting user proof data from the user proof data chain according to the user proof matter type and the user identification data, and responding to the user proof request according to the user proof data. The application has the effect of improving the proof efficiency in city services.

Description

Technical Field

[0001] This application relates to the technical field of data management, and in particular to a method, apparatus, computer equipment, and storage medium for managing city services without proof. Background Technology

[0002] In recent years, with the deepening of digital government reforms, various regions have actively promoted digital transformation, especially in the areas of electronic certificates and data sharing, aiming to simplify procedures, improve service efficiency, and reduce the burden on the public. Nevertheless, at the current stage, the application models of electronic certificate services are limited, the total number of certificates used is low, and the public's perception of these services is relatively low.

[0003] Traditional technologies rely heavily on offline, manual issuance of certificates. Although some regions have introduced preliminary trials of electronic certificates, the lack of a unified management platform and efficient data sharing mechanism has resulted in a much lower-than-expected adoption rate and data verification efficiency for electronic certificates. In most cases, when handling government services, citizens still need to personally bring paper certificates to relevant departments, going through cumbersome approval processes, which increases unnecessary administrative costs and wastes social resources.

[0004] Current technical solutions have failed to fully automate and intelligentize the management of supporting documents, especially in terms of timeliness in data verification. The actual application coverage of electronic certificates is low, which leads to many inconveniences for the public when handling affairs, such as repeatedly submitting the same materials, frequently traveling between different departments, and waiting for approval results for a long time. These problems greatly affect the efficiency of service data flow, thereby limiting the overall efficiency of government services and public satisfaction. Summary of the Invention

[0005] In order to improve the efficiency of data flow in urban services and thus enhance the efficiency of proof in urban services, this application provides a proof-free urban data sharing method, apparatus, computer equipment, and storage medium.

[0006] The above-mentioned objective of this application is achieved through the following technical solution:

[0007] A proof-free city data sharing method, the proof-free city data sharing method comprising:

[0008] Obtain the proof area for the matter, obtain the corresponding proof matter type based on the proof area, and obtain the basic proof data based on the proof matter type;

[0009] Key user information is extracted from the basic proof data, and the basic proof information is concatenated based on the key user information to obtain a user proof data chain.

[0010] Obtain a user authentication request, obtain the user authentication item type from the user authentication request, and obtain user identification data from the user authentication item type;

[0011] User proof data is extracted from the user proof data chain according to the user proof item type and the user identification data, and the user proof request is responded to according to the user proof data.

[0012] By adopting the above technical solutions, this method can effectively improve the efficiency of government services and user satisfaction. Specifically: First, it clarifies the specific geographical scope of the service items to be processed, ensuring that subsequent data collection and processing are targeted. By accurately dividing the proof areas, interference from invalid or redundant data can be avoided, improving the overall system efficiency. Service requirements are further refined, and different types of proof items are categorized and organized, facilitating subsequent data processing and analysis. Accurate identification of proof item types allows for quick location of relevant laws, regulations, and supporting policies, providing users with more precise service guidance. Based on this, the system automatically retrieves and integrates all raw data related to specific proof items, forming a complete basic database. This data may originate from multiple government departments or institutions; unified management and standardized processing ensure data consistency and reliability. Through data analysis algorithms, the system can filter out key information directly related to users from a large amount of basic proof data, such as name, ID number, and contact information. This process not only improves data processing speed but also reduces the need for manual intervention and the possibility of human error. By connecting user-related information scattered across various systems, a complete user proof data chain is formed. This structured data organization makes subsequent queries and verifications simpler and faster, while also helping to ensure information security and protect personal privacy. When a user submits a specific proof request, the system can receive and parse the request content through a pre-established interface, including the type of proof required and user identification data (such as mobile phone number and ID number) for identity verification. This process typically occurs when a user accesses a government service platform or uses a mobile application, and the interface is user-friendly and easy to operate. By analyzing the received user request in detail, the system clarifies the type of proof the user desires and extracts necessary personal information for subsequent verification. This helps ensure that only legally authorized individuals can obtain the corresponding proof documents, enhancing system security. Based on the received specific request parameters, the system quickly finds relevant records that meet the requirements. This process fully utilizes the advantages of big data technology and artificial intelligence algorithms, enabling complex data matching and retrieval tasks to be completed in a very short time. Finally, the system will generate corresponding electronic proof documents or message notifications based on the search results and send them to the user through specified methods (such as email, SMS, online download links, etc.). This method greatly simplifies the traditional paper-based proof application process, saves a significant amount of time and resources, and also improves the public's enthusiasm and convenience in participating in government services.

[0013] In a preferred embodiment, this application can be further configured as follows: extracting key user information from the basic proof data, and concatenating the basic proof information based on the key user information to obtain a user proof data chain, specifically includes:

[0014] After cleaning the basic proof data based on the key user information, the cleaning results are classified according to the key user information to obtain the data to be concatenated.

[0015] The association between each piece of data to be concatenated is obtained based on the key user information, and the data to be concatenated is concatenated based on the association to obtain the user authentication data chain.

[0016] By adopting the above technical solution, the system can effectively clean, classify, and chain basic proof data to form a complete user proof data chain. Specifically, when the system receives a user's proof request, it first obtains the corresponding proof item type from each proof item area, and then further obtains basic proof data based on that proof item type. During this process, the precise extraction of key user information ensures that the obtained basic proof data highly matches the user's specific needs. The basic proof data is then cleaned, removing redundant or invalid information and retaining only valid and relevant parts. This cleaning process not only improves data quality but also lays a solid foundation for subsequent data processing. Subsequently, the system classifies the cleaned results based on key user information, generating multiple data sets to be chained. This classification process is based on user identity information and other relevant factors to ensure that each data set to be chained has a clear attribution and meaning. After obtaining the data to be chained, the system further analyzes the relationships between these data. These relationships can be evaluated in various ways, such as using data analysis algorithms to determine the logical connections between data segments or using historical proof results for comparison and verification. In this way, the system can accurately capture the inherent connections between different data segments, thus providing a reliable basis for subsequent chaining operations. Once the relationships between the various data to be concatenated are determined, the system can effectively link them according to these relationships. The specific concatenation process involves combining different data fragments in a certain order and according to certain rules, ultimately forming a complete and coherent user verification data chain. This data chain not only includes the user's basic information but also the historical records and current status of various supporting documents, thus providing users with a comprehensive and systematic verification management platform. The high degree of automation and intelligence throughout the process greatly reduces the need for manual intervention and improves the efficiency and accuracy of data processing. Simultaneously, through precise data cleaning and classification, the system can generate high-quality user verification data chains in a short time, meeting diverse user needs. Furthermore, since all operations are completed internally, data security and privacy protection are enhanced, avoiding the risks of human error and leakage.

[0017] In a preferred embodiment, this application can be further configured as follows: obtaining the association relationship between each piece of data to be concatenated based on the key user information, and concatenating the data to be concatenated based on the association relationship to obtain the user authentication data chain, specifically includes:

[0018] Based on the aforementioned relationship, obtain the association proof data associated with the key user information from the data to be concatenated;

[0019] The association proof data are sorted according to the degree of association in the association relationship to obtain the association ranking result;

[0020] Historical proof results are obtained from the data to be concatenated, and the associated sorting results are concatenated based on the historical proof results to obtain the user proof data chain.

[0021] By adopting the above technical solution, the process of extracting key user information from basic proof data and cleaning, classifying, and linking the basic proof data based on this information to form a user proof data chain has been significantly optimized. Specifically: Basic proof data is comprehensively cleaned based on key user information, eliminating invalid or redundant information and retaining valid proof materials. This process not only improves data quality but also lays a solid foundation for subsequent classification. Subsequently, the system classifies the cleaned results according to key user information, generating multiple data sets to be linked. Each set of data contains all valid proof information related to a specific user, thus ensuring data consistency and integrity. In-depth analysis of the relationships between each set of data to be linked is conducted. By mining the logical connections between data items, the system can accurately identify which data are directly related and which have indirect relationships. For example, if a user's ID number appears in multiple proof materials, the system will consider it strongly related data; while some auxiliary proof materials (such as family member information) may be considered weakly related data. This refined relationship analysis helps to make subsequent sorting and linking work more accurate. After obtaining detailed relationships, the system sorts these related proof data according to their importance. The sorting algorithm considers various factors, including but not limited to data relevance, historical usage frequency, and legal validity. This ensures that the most important supporting evidence is processed first, thereby improving the overall system's response speed and accuracy. Simultaneously, the sorting result provides a clear structural framework for the final user proof data chain. To further enhance the integrity and credibility of the data chain, the system also retrieves historical proof results related to the current user from the existing database. This historical data not only effectively supplements new data but also helps verify the authenticity and validity of new supporting evidence. The system further links the sorted related proof data based on historical proof results, forming a long-chain-like user proof data chain. In this way, even if a piece of supporting evidence is missing or incomplete, it can be supplemented through historical records, ensuring the continuity and reliability of the data chain.

[0022] In a preferred embodiment, this application can be further configured as follows: the step of extracting user proof data from the user proof data chain based on the user proof matter type and the user identification data, and responding to the user proof request based on the user proof data, specifically includes:

[0023] The user verification level is obtained according to the user verification item type, and the user verification material data is obtained from the user verification data chain according to the user verification material data;

[0024] The correlation degree corresponding to the user proof materials is verified according to the user proof level, and the user proof request is responded to according to the verification result.

[0025] By adopting the above technical solution, the convenience and efficiency for users when handling government services can be significantly improved. Specifically, when a user submits a proof request, the system extracts the corresponding user proof data from a pre-established user proof data chain based on the type of proof matter and the user's identification data, and responds to the user's proof request accordingly. In this process, the system first obtains the required user proof level based on the type of proof matter provided by the user, and then retrieves the relevant user proof material data from the user proof data chain based on this level. Next, the system verifies the correlation between these user proof materials to ensure that the provided proof materials meet the user's needs. This not only greatly reduces manual intervention and improves the processing speed of proof materials, but also effectively avoids errors caused by human negligence. Traditional proof material management usually requires staff to manually review each application, which is not only time-consuming and labor-intensive, but also prone to misjudgment or omissions. Through the system's automatic processing, a large number of proof material verification tasks can be completed in a short time, thereby greatly improving work efficiency. Through accurate matching and verification of the user proof data chain, the authenticity and validity of the proof materials are guaranteed. For example, in some complex government matters, multiple departments may need to collaborate to verify the same proof material. In traditional methods, information asymmetry often exists between different parts, leading to frequent instances of repeated verification. In this technical solution, all related supporting documents are integrated into a unified data chain. The system automatically compares and verifies these documents to ensure they meet requirements, thus avoiding the hassle of repeated verification. Furthermore, the user verification data chain design considers data security and privacy protection. When processing user verification documents, the system only accesses necessary data and strictly controls access permissions to prevent the leakage of sensitive information. Simultaneously, by classifying and linking various supporting documents according to user identification data, each verification request can accurately find the corresponding supporting document, which is convenient for users and ensures data security.

[0026] In a preferred embodiment, this application can be further configured such that: the step of verifying the degree of association corresponding to the user proof materials based on the user proof level, and responding to the user proof request based on the verification result, specifically includes:

[0027] The degree of proof corresponding to the user's proof materials is determined based on the user's proof level;

[0028] If the proof level value reaches a preset value, then the user's proof request will be responded to.

[0029] By adopting the above technical solution, the relevance of user proof materials can be accurately verified based on the user's proof level, thereby ensuring that only proof requests that meet specific standards can be effectively responded to. Specifically, upon receiving a user's proof request, the system extracts the corresponding proof materials from a pre-established user proof data chain based on the specific type of the request and the user's identification data. In this process, the system not only considers the basic information provided by the user but also comprehensively analyzes multiple related historical proof results to form a complete user proof data chain. Subsequently, the required proof level is determined based on the type of proof requested by the user. This proof level is essentially a quantitative assessment of the importance and complexity of the proof required by the user. For example, some high-risk or highly sensitive proof matters may require a higher proof level, while some routine basic proof matters can accept a lower proof level. This hierarchical mechanism helps to rationally allocate system resources, avoid unnecessary over-verification, and ensure the security and accuracy of important proof matters. Next, the relevance of the extracted user proof materials is verified. Here, "relevance" refers to the strength of the logical connection between the proof materials and other relevant information. Through in-depth analysis of these relationships, the system can determine which proof materials are the most persuasive and use them as core evidence. If a piece of supporting material exhibits high relevance across multiple dimensions, its degree of relevance is considered higher, and vice versa. After calculating the degree of relevance, the system further compares the actual calculated value with a preset standard value. This preset value is typically a threshold set based on historical experience and statistical data, used to distinguish between acceptable and unacceptable supporting materials. Only when the calculated degree of relevance reaches or exceeds this preset value will the system consider the supporting material reliable and ultimately decide to respond to the user's proof request. Conversely, if the degree of relevance does not reach the preset value, the system will reject the request and prompt the user to provide more supporting materials or resubmit more detailed supporting information.

[0030] The second objective of this invention is achieved through the following technical solution:

[0031] A certificateless city data sharing device, the certificateless city data sharing device comprising:

[0032] The basic data acquisition module is used to acquire the matter proof area, acquire the corresponding proof matter type based on the matter proof area, and acquire basic proof data based on the proof matter type;

[0033] The proof data concatenation module is used to extract key user information from the basic proof data, and concatenate the basic proof information according to the key user information to obtain a user proof data chain;

[0034] The user data acquisition module is used to acquire user authentication requests, acquire user authentication item types from the user authentication requests, and acquire user identification data from the user authentication item types.

[0035] The data verification module is used to extract user verification data from the user verification data chain according to the user verification item type and the user identification data, and respond to the user verification request based on the user verification data.

[0036] By adopting the above technical solutions, the data sharing method for cities without proof can effectively improve the efficiency of government services and user satisfaction. Specifically: First, it clarifies the specific geographical scope of the service items to be processed, ensuring that subsequent data collection and processing are targeted. By accurately dividing the proof areas, interference from invalid or redundant data can be avoided, improving the overall system efficiency. Service needs are further refined, and different types of proof items are categorized and organized, facilitating subsequent data processing and analysis. Accurate identification of proof item types allows for quick location of relevant laws, regulations, and supporting policies, providing users with more precise service guidance. Based on this, the system automatically retrieves and integrates all raw data related to specific proof items, forming a complete basic database. This data may originate from multiple government departments or institutions; through unified management and standardized processing, data consistency and reliability are ensured. Through data analysis algorithms, the system can filter out key information directly related to users from a large amount of basic proof data, such as name, ID number, and contact information. This process not only improves the speed of data processing but also reduces the need for manual intervention and the possibility of human error. By connecting user-related information scattered across various systems, a complete user proof data chain is formed. This structured data organization makes subsequent queries and verifications simpler and faster, while also helping to ensure information security and protect personal privacy. When a user submits a specific proof request, the system can receive and parse the request content through a pre-established interface, including the type of proof required and user identification data (such as mobile phone number and ID number) for identity verification. This process typically occurs when a user accesses a government service platform or uses a mobile application, and the interface is user-friendly and easy to operate. By analyzing the received user request in detail, the system clarifies the type of proof the user desires and extracts necessary personal information for subsequent verification. This helps ensure that only legally authorized individuals can obtain the corresponding proof documents, enhancing system security. Based on the received specific request parameters, the system quickly finds relevant records that meet the requirements. This process fully utilizes the advantages of big data technology and artificial intelligence algorithms, enabling complex data matching and retrieval tasks to be completed in a very short time. Finally, the system will generate corresponding electronic proof documents or message notifications based on the search results and send them to the user through specified methods (such as email, SMS, online download links, etc.). This method greatly simplifies the traditional paper-based proof application process, saves a significant amount of time and resources, and also improves the public's enthusiasm and convenience in participating in government services.

[0037] The third objective of this invention is achieved through the following technical solution:

[0038] A certificate-free city application system includes a unified certificate catalog management module, a certificate item configuration management module, an investigation tool module, an investigation interface API module, a public platform integration module, and a business implementation service module.

[0039] The unified certificate catalog management module is used to periodically obtain information on government service items and application materials from all levels and departments through a preset data communication interface with the government service item management system. Based on the obtained information, it generates a list of items to be cleared and provides a graphical interface with fixed field options and interactive controls for each department to check and determine whether the application materials are certificates. At the same time, it integrates a data storage component to record structured information such as the reason for the cancellation of the certificate, the type of alternative measures, and the corresponding certificate issuing unit code.

[0040] The certificate configuration management module has a built-in demand-side certificate application flow engine. After the demand-side submits a certificate application on the platform, it triggers a message push to the target department's online certificate issuance system, which automates the entire process.

[0041] The assistance tool module is equipped with an electronic credential verification gateway, which performs online verification of the issued materials by executing an identity authentication protocol that conforms to preset security standards.

[0042] The cooperation interface API module has a strict access policy. When the corresponding role permission constraints are met, it sends a request command to the department's business system to read the supporting material data or file details required for the proof matters within the specified scope.

[0043] The public platform interface module is pre-configured with a bidirectional adapter compatible with mainstream communication protocols, and follows a predetermined schedule to batch capture the item descriptors and their associated process metadata published by external cooperative systems;

[0044] The business implementation service module initializes the initial environment setup script set, assigns values ​​to global variables, and simultaneously plans a hierarchical account architecture diagram to ensure clear and accurate attribution of responsibilities.

[0045] By adopting the above technical solutions, a certificate-free urban application system covering the entire certificate management process has been realized. The unified certificate catalog management module automatically synchronizes government service items and application material information, generates a list of items to be cleared, and supports efficient judgment and recording of certificate-related information by various departments through a graphical interface, thereby improving the efficiency and accuracy of certificate clearing work. The certificate item configuration management module realizes fully automated flow of certificate applications from demanders, reducing manual intervention and improving response speed and service quality. The assistance tool module uses an electronic credential verification gateway to complete secure online verification of issued materials, ensuring data authenticity and security. The assistance interface API module ensures that sensitive information circulates only within authorized scope through a strict access control mechanism, enhancing system security and compliance. The public platform integration module achieves seamless connection with external systems through a two-way adapter, batch captures item descriptors and their metadata, and optimizes resource allocation and collaboration efficiency. The business implementation service module ensures clear responsibilities and convenient operation and maintenance by initializing the initial environment setup script set and planning a hierarchical account architecture.

[0046] The fourth objective of this application is achieved through the following technical solution:

[0047] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described proofless city data sharing method.

[0048] The fourth objective of this application is achieved through the following technical solution:

[0049] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described proofless city data sharing method.

[0050] In summary, this application includes at least one of the following beneficial technical effects:

[0051] 1. By introducing a unified certification management module, standardized and regulated management of certification materials has been achieved, significantly reducing manual intervention and improving the efficiency of government services;

[0052] 2. The real-time response capability of the data collaboration interface has been optimized, the data sharing speed has been significantly accelerated, the processing cycle has been greatly shortened, and the overall service efficiency has been improved;

[0053] 3. The intelligent matching and usage mechanism of electronic certificates makes it more convenient for users to handle business and reduces their reliance on physical certificates. Attached Figure Description

[0054] Figure 1This is a flowchart of a proof-free city data sharing method in one embodiment of this application;

[0055] Figure 2 This is a flowchart illustrating the implementation of step S20 in the method of proofless city data sharing in one embodiment of this application;

[0056] Figure 3 This is a flowchart illustrating the implementation of step S22 in the method of proofless city data sharing in one embodiment of this application;

[0057] Figure 4 This is a flowchart illustrating the implementation of step S40 in the method of proofless city data sharing in one embodiment of this application;

[0058] Figure 5 This is a flowchart illustrating the implementation of step S42 in the method of proofless city data sharing in one embodiment of this application;

[0059] Figure 6 This is a schematic diagram of a proof-free city data sharing system according to one embodiment of this application;

[0060] Figure 7 This is a schematic diagram of a device according to one embodiment of this application. Detailed Implementation

[0061] The present application will be further described in detail below with reference to the accompanying drawings.

[0062] In one embodiment, such as Figure 1 As shown, this application discloses a proof-free urban data sharing method, which specifically includes the following steps:

[0063] S10: Obtain the proof area for the matter, obtain the corresponding proof matter type based on the proof area, and obtain the basic proof data based on the proof matter type.

[0064] In this embodiment, an actual development system is used as an example, including:

[0065] I. Project Background and Issues

[0066] 1. Core Issues

[0067] Data sharing is difficult: information exchange between departments is not smooth, and problems such as duplicate and circular proofs are prominent.

[0068] Limited variety of electronic certificates: Some frequently used certificates are not yet available or data updates are lagging (e.g., household registration certificates are incorrect and not synchronized).

[0069] Narrow coverage of certificates: Some departments still rely on paper materials due to concerns about liability risks or system limitations, resulting in a low application rate of electronic certificates.

[0070] II. Project Objectives and Significance

[0071] 1. Overall Objectives

[0072] To create a "proof-free city," we will use technological means to achieve "all requirements not stipulated by law will be cancelled, and no documents are required if stipulated by law."

[0073] We have streamlined 200 certification items, completed the management of 20 types of certification catalogs and launched 20 certification services, and achieved integration with 3 business systems.

[0074] III. Construction Plan and Implementation Content

[0075] 1. System Development Module

[0076] 1.1 Unified management of the certificate catalog

[0077] Review and compile a list of documents required for government services, creating a pending list.

[0078] Management of certification catalog (such as electronic certificate association, configuration of assisting departments);

[0079] Establish a mechanism for reviewing and dynamically revising the list of required documents.

[0080] 1.2 Management of Certification Item Configuration

[0081] Online certificate issuance (connected to departmental data interface);

[0082] Assistance configuration (set assistance time limits, such as 30-minute data query, 1 working day file assistance);

[0083] Departmental cooperation process management (application, processing, archiving and progress tracking).

[0084] 1.3. Tools for Assistance in Investigation

[0085] Electronic certificate verification and archiving (supports online verification and uploading to the business system by window staff);

[0086] The notification commitment letter is automatically generated and signed;

[0087] Data verification (retrieving information from the big data center to verify the applicant's information).

[0088] 1.4. API Interface Open

[0089] It provides five types of interfaces, including assistance application, progress query, and data sharing, supporting seamless integration with business systems.

[0090] 1.5 Public Platform Integration

[0091] Connect with government service systems, big data centers, and electronic certificate systems to achieve data interoperability.

[0092] 2. Business Implementation Services

[0093] Review of certification requirements: Completed the cleanup of 200 certification requirements and clarified the cancellation or replacement measures (such as electronic certificates and notification commitments).

[0094] Catalog and service launch: 20 new types of certificates have been added to the catalog, and 20 online processing procedures for certificates have been configured (joint testing and trial operation).

[0095] System integration: Promote the integration of the collaborative investigation function into three business systems (such as administrative approval and social security).

[0096] Training and support: Provide system operation training and technical consulting services to ensure successful implementation.

[0097] Specifically, this solution is based on explicit cancellation or replacement measures in the system architecture's review of required documentation. First, the scope of specific matters requiring processing is determined, clarifying which government affairs involve the need for supporting documents. This process ensures the accuracy and relevance of subsequent operations, avoids interference from irrelevant data, and improves the overall system's operational efficiency. Through in-depth analysis of specific matters, the system can accurately identify the various types of required supporting documents. For example, property registration may require household registration certificates, marital status certificates, etc. This classification not only facilitates subsequent data processing but also effectively prevents omissions or redundancies, further guaranteeing service quality. In this step, the system retrieves basic supporting data from the database that matches the selected type of supporting document. This data typically includes, but is not limited to, basic personal information (such as name and ID number), historical service records, and third-party verification information. By collecting and organizing this basic information in advance, the number of original documents required by users during actual applications can be significantly reduced, thereby simplifying procedures and improving efficiency.

[0098] S20: Extract key user information from the basic proof data, and concatenate the basic proof information based on the key user information to obtain the user proof data chain.

[0099] Specifically, this step involves in-depth analysis of the acquired basic supporting data to identify the most critical parts—the core information directly related to the current proof request. For example, when a user applies for housing subsidies, the system might focus on fields such as the user's income and family composition. The purpose of this work is to ensure that the generated "user proof data chain" more closely aligns with actual needs, enhancing the relevance and credibility of the data. By establishing a user-centric data chain, the system can organically integrate fragmented information from different sources, forming a complete evidence chain. Each node in this chain represents a valid proof act or an important information update event. This design not only facilitates traceability and review but, more importantly, provides users with comprehensive and authoritative proof support in a very short time, completely solving the problem of repeatedly submitting the same materials to multiple departments. When a user submits a specific proof request through various channels (such as government service websites, mobile apps, etc.), the system immediately captures and parses the request, extracting necessary parameters, especially the unique identification code or serial number used to identify the request. This allows for rapid location of relevant background information and facilitates subsequent tracking and progress feedback.

[0100] Once the user verification data chain is completed, a comprehensive review of the supporting documents required for public and business transactions will be conducted throughout the city. The review and verification of the documents submitted by administrative agencies and public service institutions will be organized. Based on the compiled list of submitted documents, i.e. the user verification data chain, a list of canceled supporting documents will be published, streamlining and optimizing various supporting documents. Service guides will also be revised both online and offline simultaneously.

[0101] S30: Obtain the user authentication request, obtain the user authentication item type from the user authentication request, and obtain the user identification data from the user authentication item type.

[0102] In this embodiment, a number of certificates are replaced by directly eliminating those that do not require proof outside of the law, promoting electronic certificates to replace a batch of certificates, promoting data connectivity to share a batch of certificates, implementing notification and commitment to reduce or exempt a batch of certificates, and implementing departmental verification to cancel a batch of certificates. These methods are applied to actual business operations.

[0103] Specifically, the system refines users' specific needs, enabling it to prepare relevant supporting documents in a targeted manner. Simultaneously, the introduction of "user identification data" ensures differentiation between individual cases even within the same type of proof, enhancing personalized service capabilities. Based on the constructed "user proof data chain," the system can filter out the most suitable content for the current request according to precise conditions. This is a highly intelligent process involving complex algorithmic models and technical means to ensure the accuracy and timeliness of the results. Once a suitable proof fragment is found, the system immediately packages it into a standard format for users to download or view.

[0104] S40: Extract user proof data from the user proof data chain based on the user proof item type and user identification data, and respond to the user proof request based on the user proof data.

[0105] Specifically, the prepared supporting documents are sent to the applicant, either electronically or in paper form. This step also includes strict measures to ensure the security of transmission, guaranteeing that personal information is not leaked while meeting legal and regulatory requirements.

[0106] In summary, this technical solution represents a fundamental shift from passively receiving supporting documentation to proactively providing certification services. It not only significantly reduces the workload of relevant departments but also brings unprecedented convenience to the public, truly embodying the concept of "letting data do the work, so people don't have to."

[0107] In one embodiment, such as Figure 2 As shown, in step S20, key user information is extracted from the basic proof data, and the basic proof information is concatenated based on the key user information to obtain the user proof data chain, specifically including:

[0108] S21: After cleaning the basic proof data based on the key user information, classify the cleaning results based on the key user information to obtain the data to be concatenated;

[0109] S22: Obtain the correlation between each piece of data to be concatenated based on key user information, and concatenate the data to be concatenated according to the correlation to obtain the user proof data chain.

[0110] Specifically, by cleaning and classifying the basic supporting data, redundant and invalid information is effectively eliminated, ensuring the accuracy and efficiency of subsequent data processing. In particular, after acquiring basic supporting data containing a large amount of original supporting materials, the system conducts a comprehensive review of this data based on key user information, removing supporting materials that do not meet the requirements or are no longer needed. This process not only reduces the need for data storage space but also avoids problems caused by erroneous data in subsequent steps.

[0111] Furthermore, the cleaned data was categorized according to key user information, forming multiple datasets to be concatenated. This classification method ensures that each data point can be accurately categorized, facilitating further correlation analysis. For example, if a user's ID number is used as key user information, then all supporting documents related to that ID number will be grouped into the same category, forming a complete user verification dataset. This approach allows for the rapid retrieval of all supporting documents associated with a specific user in subsequent steps, improving the speed and accuracy of data retrieval.

[0112] Furthermore, the relationships between each piece of data to be concatenated are obtained, and the data is concatenated based on these relationships to ultimately generate a user verification data chain. These relationships can take various forms, such as chronological order, logical causal connections, or geographical proximity. By establishing these relationships, not only can the historical trajectory of a user's verification materials be completely preserved, but more complex multi-dimensional data analysis can also be performed, thereby providing users with a more personalized service experience.

[0113] Finally, the resulting user verification data chain not only contains all the user's supporting documentation information but also possesses a high degree of coherence and consistency. This means that when a user submits a new verification request, the system can quickly locate the corresponding supporting documentation based on the existing data chain and respond promptly according to the latest information. Throughout the process, the system's intelligence level is fully demonstrated. Whether it's data cleaning, classification, or correlation analysis, all can be completed in a short time, greatly improving the efficiency of government services, reducing the cost of manual intervention, and also enhancing user experience and satisfaction.

[0114] In one embodiment, such as Figure 3 As shown, in step S22, the association relationship between each piece of data to be concatenated is obtained based on the key user information, and the data to be concatenated is concatenated according to the association relationship to obtain the user authentication data chain, specifically including:

[0115] S221: Based on the association relationship, obtain the association proof data related to the key user information from the data to be concatenated;

[0116] S222: Sort the association proof data according to the degree of association in the association relationship to obtain the association ranking result;

[0117] S223: Obtain historical proof results from the data to be concatenated, and concatenate the associated sorting results according to the historical proof results to obtain the user proof data chain.

[0118] Specifically, the relationships between each piece of data to be concatenated are obtained based on key user information, and these relationships are then used to concatenate the data, thus forming a complete user authentication data chain. The specific implementation of this process is as follows:

[0119] First, key user information is extracted from the basic proof data. This key user information may include, but is not limited to, basic information such as the user's name, ID number, and contact information. Then, the system cleans this basic proof data, removing invalid or redundant data to ensure the accuracy of subsequent processing. After cleaning, the system categorizes the cleaned results based on the key user information, generating multiple datasets to be concatenated.

[0120] Further analysis of each data item in the dataset to be concatenated is conducted to determine the relationships between them. These relationships can be direct (e.g., a causal link between one piece of evidence and another) or indirect (e.g., both pieces of evidence relate to different aspects of the same event). To accurately capture these relationships, the system employs natural language processing and machine learning algorithms to perform deep analysis of the text content, extracting key features and building association models.

[0121] Once the relationships between the data items are determined, the system will sort all the data to be concatenated according to these relationships. Specifically, the system will calculate the degree of correlation between each pair of data items and sort them according to the degree of correlation. In this process, data with high correlation will be given priority, while data with low correlation will be placed later. The purpose of this is to ensure that the final user verification data chain is logical and coherent, facilitating subsequent verification and use.

[0122] After ranking the correlation levels, the system will further retrieve historical verification results from the data to be concatenated. These historical verification results typically contain previously verified relevant information, helping the system better understand the authenticity and reliability of the current data. The system will then concatenate the already sorted related data again based on these historical verification results. The specific concatenation rules may be adjusted according to the needs of different scenarios, but the overall principle is to maintain data consistency and integrity as much as possible.

[0123] Finally, once all the data to be concatenated is successfully linked into a complete user verification data chain, the system will extract the corresponding user verification data from this data chain based on the user's verification request. This data will be used to respond to the user's actual needs and help them smoothly complete various government affairs.

[0124] The entire process not only significantly improved data processing efficiency but also effectively ensured data quality and security. Especially when dealing with large amounts of complex data, the automated methods of mining and sorting relationships simplified and streamlined the process, making manual verification much faster and more efficient, thus greatly improving the overall level of government services. Furthermore, because the system can intelligently utilize historical evidence to support decision-making, it effectively avoids redundant work, reduces error rates, and enhances user experience.

[0125] In one embodiment, such as Figure 4 As shown, in step S40, user authentication data is extracted from the user authentication data chain according to the user authentication item type and user identification data, and the user authentication request is responded to based on the user authentication data. This specifically includes:

[0126] S41: Obtain the user's proof level based on the type of user proof matter, and obtain the user's proof material data from the user proof data chain based on the user proof material data;

[0127] S42: Verify the relevance of the user's proof materials based on the user's proof level, and respond to the user's proof request based on the verification result.

[0128] Specifically, the process of extracting user authentication data from the user authentication data chain based on the type of user authentication matter and user identification data, and responding to user authentication requests based on the user authentication data, has been optimized. Specifically, this process includes the following steps:

[0129] First, the system receives the user's proof request and parses it to determine the type of proof required. This determination is fundamental to subsequent operations, ensuring the system can accurately locate all necessary information related to the user's needs.

[0130] At the same time, the system also needs to extract user identification data from user verification requests, such as unique identifiers like ID card numbers and names. This identification data is used to further filter and accurately locate the user's relevant verification information.

[0131] The system automatically matches the appropriate level of proof based on the type of proof provided by the user. The level of proof reflects the importance and complexity of the supporting documents; different levels may correspond to different verification standards and processing procedures. This step helps ensure that the system can take appropriate measures to guarantee data accuracy and security when processing proof requests of varying importance.

[0132] The system uses user identification data to search for matching supporting documentation within a pre-built user verification data chain. This user verification data chain is a database containing a large amount of historical verification records and related information. Through efficient indexing and retrieval of this data, the system can quickly locate the required supporting documentation. The key to this step lies in the design and maintenance of the data chain, ensuring its rational structure and fast query speed.

[0133] Based on the user's level of proof, the system assesses the relationship between the obtained supporting data and its relevance. Relevance refers to the strength of the logical connection between supporting materials and other relevant evidence; a high degree of relevance indicates more reliable and supportive supporting materials. The system calculates a relevance score for each piece of supporting material using an algorithm and then ranks them. If the relevance of a piece of supporting material does not meet the preset standard, the system will prompt the user to supplement relevant information or resubmit the request.

[0134] Ultimately, the system will decide whether to respond to the user's proof request based on the verification results. If the relevance of all supporting documents meets the requirements, the system will generate and return the required proof documents to the user; otherwise, the system will provide the user with specific feedback, explaining which supporting documents do not meet the requirements and guiding the user on how to improve them.

[0135] The system achieves efficient processing and accurate response to user verification requests. Throughout the process, the system's intelligence and automation levels have been significantly improved, greatly reducing the need for manual intervention, increasing the efficiency of government services, simplifying procedures for citizens, and enhancing overall service quality and user experience. Simultaneously, rigorous correlation verification ensures the authenticity and reliability of supporting documents, enhancing the system's credibility and public trust.

[0136] In one embodiment, such as Figure 5 As shown, in step S42, the relevance of the user's verification materials is verified according to the user's verification level, and the user verification request is responded to based on the verification result. This specifically includes:

[0137] S421: Determine the degree of proof corresponding to the user's proof materials based on the user's proof level;

[0138] S422: If the proof level reaches the preset value, then respond to the user's proof request.

[0139] Specifically, when a user submits a proof request, the system first extracts the corresponding user proof materials from the user proof data chain based on the type of proof matter and the user's identification data. Next, the system determines the level of proof corresponding to the proof materials based on the user's proof level. Specifically, the user proof level is determined by a comprehensive evaluation based on the information provided by the user and the system's preset rules; different proof matters may correspond to different proof level requirements. For example, in certain highly sensitive business scenarios (such as household registration transfer and real estate transactions), the system may set higher proof level requirements to ensure information security and legality.

[0140] After determining the user's level of proof, the system will proceed to the verification phase. The core of this phase is assessing whether the relevance of the user's supporting documents meets preset standards. Relevance refers to the matching degree and credibility between the user's supporting documents and the required proof; it reflects whether the supporting documents provided by the user effectively support their proof request. The system performs in-depth analysis of each piece of supporting document, calculating its relevance score to the user's proof request, thus forming a detailed relevance list. During this process, the system also considers the impact of multiple factors, such as the time validity of the supporting documents, the reliability of their source, and their consistency with other existing supporting documents.

[0141] Subsequently, all collected correlation scores are weighted and averaged or aggregated using other algorithms to arrive at an overall proof score. If this proof score meets or exceeds a pre-set threshold, the system considers the user's supporting documentation sufficient and trustworthy, and responds accordingly. This design not only effectively prevents false proofs but also significantly improves the transparency and fairness of the proof process.

[0142] Furthermore, to further enhance the system's intelligence, machine learning algorithms have been introduced for dynamic adjustments. As more and more real-world cases are incorporated into the training set, the system can continuously optimize its evaluation model, enabling it to more accurately identify real-world proof needs in various complex situations. This means that even in the face of new and unforeseen circumstances, the system can make reasonable judgments in a short time, greatly improving overall service quality and user experience. It not only achieves efficient processing of user proof requests but also ensures the security and reliability of the proof process through a rigorous proof degree verification mechanism, truly achieving both convenience and rigor. This not only improves the efficiency and quality of government services but also brings tangible benefits to the general public, effectively solving various proof difficulties they encounter in their daily lives.

[0143] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0144] In one embodiment, a certificateless city data sharing device is provided, which corresponds one-to-one with the certificateless city data sharing method described in the above embodiments. For example... Figure 6 As shown, the certificate-free city data sharing device includes a basic data acquisition module, a certificate data concatenation module, a user data acquisition module, and a data verification module. Detailed descriptions of each functional module are as follows:

[0145] The basic data acquisition module is used to acquire the proof area, acquire the corresponding proof type based on the proof area, and acquire basic proof data based on the proof type.

[0146] The proof data concatenation module is used to extract key user information from the basic proof data, and concatenate the basic proof information based on the key user information to obtain the user proof data chain.

[0147] The user data acquisition module is used to acquire user authentication requests, obtain user authentication item types from user authentication requests, and obtain user identification data from user authentication item types.

[0148] The data verification module is used to extract user verification data from the user verification data chain based on the user verification item type and user identification data, and respond to user verification requests based on the user verification data.

[0149] Optionally, the proof data concatenation module includes:

[0150] The classification and concatenation submodule is used to clean the basic proof data based on key user information, classify the cleaned results based on key user information, and obtain the data to be concatenated.

[0151] The data association submodule is used to obtain the association relationship between each piece of data to be associated based on key user information, and to associate the data to be associated based on the association relationship to obtain the user authentication data chain.

[0152] Optionally, the data association submodule includes:

[0153] The associated data acquisition unit is used to obtain associated proof data related to key user information from the data to be concatenated based on the association relationship;

[0154] The data sorting unit is used to sort the association proof data according to the degree of association in the association relationship, and obtain the association sorting result;

[0155] The data association unit is used to obtain historical proof results from the data to be concatenated, and to concatenate the association sorting results according to the historical proof results to obtain the user proof data chain.

[0156] Optionally, the data proof module includes:

[0157] The proof data acquisition submodule is used to obtain the user's proof level according to the type of user proof matter, and to obtain the user's proof material data from the user proof data chain based on the user proof material data;

[0158] The verification response submodule is used to verify the relevance of user verification materials according to the user's verification level, and respond to the user's verification request based on the verification result.

[0159] Optionally, the verification response submodule includes:

[0160] The degree value calculation unit is used to determine the degree value of the user's proof materials based on the user's proof level.

[0161] The request-response unit is used to respond to the user's proof request if the proof level value reaches a preset value.

[0162] Specific limitations regarding the certificateless city data sharing device can be found in the limitations of the certificateless city data sharing method described above, and will not be repeated here. Each module in the aforementioned certificateless city data sharing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0163] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a proof-free urban data sharing method.

[0164] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0165] Obtain the proof area, obtain the corresponding proof type based on the proof area, and obtain the basic proof data based on the proof type;

[0166] Extract key user information from the basic proof data, and concatenate the basic proof information based on the key user information to obtain the user proof data chain;

[0167] Obtain the user authentication request, obtain the user authentication item type from the user authentication request, and obtain the user identification data from the user authentication item type;

[0168] User authentication data is extracted from the user authentication data chain based on the user authentication item type and user identification data, and user authentication requests are responded to based on the user authentication data.

[0169] In one embodiment, a certificate-free urban data sharing device is provided, comprising six parts: a unified certificate catalog management module, a certificate item configuration management module, an assistance tool module, an assistance interface API module, a public platform integration module, and a business implementation service module. These modules are seamlessly connected through standardized data channels, enabling efficient resource allocation and real-time information exchange.

[0170] Specifically, the unified certification catalog management module integrates two important components: a periodic synchronization unit and an interactive interface generation submodule. The periodic synchronization unit, leveraging a precise timer control mechanism and a high-performance RESTful API client program, can stably and continuously extract updated transaction lists and material indexes from the government service management system. For example, a quartz scheduling library is used to control the time interval, ensuring an automatic synchronization request is initiated at 2 AM daily, and JSON format is used to encapsulate data packets to reduce transmission burden. Meanwhile, the interactive interface generation submodule is meticulously crafted using a modern web development framework, not only possessing the ability to dynamically add and delete table row items but also effectively shielding potential threats from improper input. For example, JavaScript is used to write front-end validation functions to check whether the length of user-entered fields is compliant, while CSS styles enhance user-friendliness. This module also embeds a structured log tracking system, ensuring that every operation can be traced back to its source, strengthening overall transparency and security.

[0171] The verification request configuration management module introduces a highly automated request forwarding engine. Upon receiving a verified request for verification from a client, it immediately activates its internal workflow orchestrator, supplemented by a message queue server array, ensuring that instructions are quickly delivered to the designated department while maintaining uninterrupted feedback. For example, a Kafka distributed event bus is used to handle a large volume of concurrent message traffic, ensuring smooth operation even during peak periods. Furthermore, to address network fluctuations caused by unforeseen circumstances, this module has specifically constructed a multi-layered failover architecture. For instance, it sets a primary / backup switchover strategy and quickly migrates to a backup node when the primary node fails, significantly reducing the risk of unexpected outages.

[0172] Moving to the investigation tool module, this device integrates a suite of encryption algorithms and combines them with digital signature verification logic to identify the authenticity of various documents. For example, it uses RSA public key infrastructure to sign certificate documents and encrypts sensitive data segments using AES-256 symmetric keys.

[0173] When investigating the API module, a basic authentication framework was built to clearly define the data boundaries and service scopes accessible to different roles. For example, administrators are only allowed to view global status reports, while ordinary employees can only access information within their assigned jurisdiction. Furthermore, by customizing and optimizing IP whitelist filtering rules and enabling HTTPS as a mandatory transmission protocol, the network security defenses were further strengthened.

[0174] The public platform integration module is characterized by its extensive adoption of cross-platform, interoperable communication protocols, such as common channel protocols like SOAP / XML-RPC, and the creation of a robust, bidirectional adapter bridge matrix. With this support, the system can easily capture fresh information snippets from third-party public service providers, then properly categorize and store them in a database for easier and smoother subsequent retrieval and referencing. For example, for captured demographic data, redundant and duplicate items are first removed through an ETL (Extract Transform Load) process before being stored in a MySQL relational database.

[0175] The business implementation service module utilizes automated script deployment tools to pre-deploy the basic environment, such as using Ansible Playbook for one-click initialization of server parameter configuration. Simultaneously, it meticulously plans the organizational hierarchy, striving to ensure that every participant clearly understands their role and responsibilities. Furthermore, it integrates an elastic load balancer component, enabling stable operation even during peak load surges.

[0176] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0177] Obtain the proof area, obtain the corresponding proof type based on the proof area, and obtain the basic proof data based on the proof type;

[0178] Extract key user information from the basic proof data, and concatenate the basic proof information based on the key user information to obtain the user proof data chain;

[0179] Obtain the user authentication request, obtain the user authentication item type from the user authentication request, and obtain the user identification data from the user authentication item type;

[0180] User authentication data is extracted from the user authentication data chain based on the user authentication item type and user identification data, and user authentication requests are responded to based on the user authentication data.

[0181] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0182] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0183] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A proof-free urban data sharing method, characterized in that, The method for sharing city data without proof includes: Obtain the proof area for the matter, obtain the corresponding proof matter type based on the proof area, and obtain the basic proof data based on the proof matter type; Key user information is extracted from the basic proof data, and the basic proof data is concatenated based on the key user information to obtain a user proof data chain, specifically including: After cleaning the basic proof data based on the key user information, the cleaning results are classified according to the key user information to obtain the data to be concatenated. Based on the key user information, the association relationship between each piece of data to be concatenated is obtained; based on the association relationship, the data to be concatenated is concatenated to obtain the user authentication data chain, specifically including: Based on the aforementioned relationship, obtain the association proof data associated with the key user information from the data to be concatenated; The association proof data are sorted according to the degree of association in the association relationship to obtain the association ranking result; Historical proof results are obtained from the data to be concatenated, and the associated sorting results are concatenated based on the historical proof results to obtain the user proof data chain. Obtain a user authentication request, obtain the user authentication item type from the user authentication request, and obtain user identification data from the user authentication item type; Extracting user authentication data from the user authentication data chain based on the user authentication item type and the user identification data, and responding to the user authentication request based on the user authentication data, specifically includes: The user's proof level is obtained based on the user proof item type, and user proof material data is obtained from the user proof data chain based on the user proof item type and the user identification data. The correlation between the user's verification materials and the verification level is verified, and the user verification request is responded to based on the verification result. Specifically, this includes: Calculate the relevance score of each of the user verification materials and the user verification request to form a relevance list. The factors considered in calculating the relevance score include the time validity, source reliability, and consistency with other existing verification materials of the user verification materials. Subsequently, a weighted average of all collected correlation values ​​is calculated to obtain the proof value corresponding to the user's proof materials; If the proof level value corresponding to the user's proof materials reaches a preset value, then the user proof request will be responded to based on the user proof data.

2. A certificateless urban data sharing device, executing the certificateless urban data sharing method according to claim 1, characterized in that, The certificate-free city data sharing device includes: The basic data acquisition module is used to acquire the matter proof area, acquire the corresponding proof matter type based on the matter proof area, and acquire basic proof data based on the proof matter type; A proof data concatenation module is used to extract key user information from the basic proof data, and concatenate the basic proof data according to the key user information to obtain a user proof data chain. The proof data concatenation module includes: The classification and concatenation submodule is used to clean the basic proof data according to the key user information, and then classify the cleaning results according to the key user information to obtain the data to be concatenated. The data association submodule is used to obtain the association relationship between each of the data to be concatenated based on the key user information, and to concatenate the data to be concatenated according to the association relationship to obtain the user authentication data chain. The data association submodule includes: The associated data acquisition unit is used to acquire associated proof data related to the key user information from the data to be concatenated based on the associated relationship; A data sorting unit is used to sort the association proof data according to the degree of association in the association relationship, and obtain an association sorting result; The data association unit is used to obtain historical proof results from the data to be concatenated, and to concatenate the association sorting results according to the historical proof results to obtain the user proof data chain. The user data acquisition module is used to acquire user authentication requests, acquire user authentication item types from the user authentication requests, and acquire user identification data from the user authentication item types. A data verification module is configured to extract user verification data from the user verification data chain based on the user verification item type and the user identification data, and respond to the user verification request based on the user verification data. The data verification module includes: The proof data acquisition submodule is used to obtain the user proof level according to the user proof matter type, and to obtain user proof material data from the user proof data chain according to the user proof matter type and the user identification data; The verification response submodule is used to verify the relevance of the user's verification materials according to the user verification level, and respond to the user verification request based on the verification result. The verification response submodule includes: The degree value calculation unit is used to calculate the relevance score of each of the user's proof materials and the user's proof request, forming a relevance score list. The factors considered in calculating the relevance score include the time validity, source reliability, and consistency with other existing proof materials of the user's proof materials. Subsequently, a weighted average of all collected relevance score values ​​is performed to obtain the proof score corresponding to the user's proof materials. The request response unit is used to respond to the user proof request based on the user proof data if the proof level value corresponding to the user proof material reaches a preset value.

3. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the proofless city data sharing method as described in claim 1.

4. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the proofless city data sharing method as described in claim 1.

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